| 1. |
- Fan, Jieyu, et al.
(författare)
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Emission impacts of left-turn lane on light-heavy-duty mixed traffic in signalized intersections: Optimization and empirical analysis
- 2023
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Ingår i: Heliyon. - 2405-8440. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- Reducing emissions from the transport sector is one of the crucial countermeasures for climate action. This study focuses on the optimization and emission analysis regarding the impacts of left-turn lanes on the emissions of mixed traffic flow (CO, HC, and NOx) with both heavy-duty vehicles (HDV) and light-duty vehicles (LDV) at urban intersections, combining high-resolution field emission data and simulation tools. Based on high-precision field emission data collected by Portable OBEAS-3000, this study first develops instantaneous emission models for HDV and LDV under various operating conditions. Then, a tailored model is formulated to determine the optimal left-lane length for mixed traffic. Afterward, we empirically validate the model and analyze the effect of the left-turn lane (before and after optimization) on the emissions at the intersections using the established emission models and VISSIM simulations. The proposed method can reduce CO, HC, and NOx emissions crossing intersections by around 30% compared to the original scenario. The proposed method significantly reduces average traffic delays after optimization by 16.67% (North), 21.09% (South), 14.61% (West), and 2.68% (East) in different entrance directions. The maximum queue lengths decrease by 79.42%, 39.09%, and 37.02% in different directions. Even though HDVs account for only a minor traffic volume, they contribute the most to CO, HC, and NOx emissions at the intersection. The optimality of the proposed method is validated through an enumeration process. Overall, the method provides useful guidance and design methods for traffic designers to alleviate traffic congestion and emissions at urban intersections by strengthening left-turn lanes and improving traffic efficiency.
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| 2. |
- Gao, Jianqiang, et al.
(författare)
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An ADAS with better driver satisfaction under rear-end near-crash scenarios: A spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk
- 2024
-
Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 159
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Tidskriftsartikel (refereegranskat)abstract
- Current advanced driver assistance systems (ADASs) do not consider drivers’ preferences of evasive behavior types and risk levels under rear-end near-crash scenarios, which undermines driver satisfaction, trust, and use of ADASs. Additionally, spatio-temporal interactions between vehicles are not fully involved in current evasive behavior prediction models, and the influence of evasive behavior is ignored while predicting collision risk. To address these issues, this study aims to propose an ADAS with better driver satisfaction under rear-end near-crash scenarios by establishing a spatio-temporal graph transformer-based prediction framework of evasive behavior and collision risk. A total of 822 evasive events are extracted from 108,000 real vehicle trajectories on highways, and variables from three sources (i.e., road environment features, evading vehicle features, and interactive behavior features) are used to construct rear-end near-crash scenario knowledge graphs (RNSKGs). By utilizing RNSKGs embedding and multi-head self-attention mechanism, spatio-temporal graph transformer networks can effectively capture the spatio-temporal interactions between vehicles. The results show that the prediction accuracy of evasive behavior (i.e., braking-only or braking and steering) and collision risk (lower, medium, or higher risk) is 96.34% and 92.12%, respectively, superior to other commonly-used methods. After including the selected evasive behavior in predicting collision risk, the overall accuracy increases by 10.91%. Then, an autonomous evasive takeover system (AET) based on the prediction framework is developed, and its effectiveness and satisfaction are verified by driving simulation experiments. According to the self-reported data of participants, the safety, comfort, usability, and acceptability of AET proposed in this study all significantly outperform existing autonomous takeover systems (i.e., autonomous emergency braking and autonomous emergency steering). The findings of this study might contribute to the optimization of ADASs, the enhancement of mutual understanding between ADASs and human drivers, and the improvement of active driving safety.
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| 3. |
- Gao, Kun, 1993, et al.
(författare)
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Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
- 2021
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 218
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Tidskriftsartikel (refereegranskat)abstract
- Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods.
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| 4. |
- Gao, Kun, 1993, et al.
(författare)
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Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis
- 2021
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Ingår i: Transportation Research Part A: Policy and Practice. - : Elsevier BV. - 0965-8564. ; 144, s. 89-103
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Tidskriftsartikel (refereegranskat)abstract
- Despite many qualitative discussions about the benefits of free-floating bike-sharing systems (FFBS), high-resolution and quantitative assessments about the economic benefits of FFBS for users are absent. This study proposes an innovative trip-level inference approach for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling. The proposed approach is able to analyze the economic benefit for every single bike-sharing trip and investigate the spatiotemporal heterogeneity in the economic benefits from FFBS. An empirical analysis in Shanghai is conducted using the proposed approach. The estimated saved travel time, cost, and economic benefit due to using FFBS per trip are estimated to be 9.95 min, 3.64 CNY, and 8.68 CNY-eq, respectively. The annual saved travel time, cost, and economic benefits from FFBS in Shanghai are estimated to be 17.665 billion min, 6.463 billion CNY, and 15.410 billion CNY-eq, respectively. The relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined using Multiple Linear Regression to explain the spatial heterogeneity in the economic benefits of FFBS. The outcomes provide a useful tool for evaluating the benefits of shared mobility systems, insights into the users’ economic benefit from using FFBS from per-trip, aggregated and spatial perspective, as well as its influencing factors. The results could efficiently support the scientific planning, operation and policy making concerning FFBS in different urban contexts.
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| 5. |
- Gao, Kun, 1993, et al.
(författare)
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Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai
- 2021
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 94
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Tidskriftsartikel (refereegranskat)abstract
- Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
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| 6. |
- Gao, Kun, 1993, et al.
(författare)
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Unraveling the mode substitution of dockless bike-sharing systems and its determinants: A trip level data-driven interpretation
- 2023
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Ingår i: Sustainable Cities and Society. - 2210-6707. ; 98
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the mode substitution of shared micro-mobility systems is essential for assessing their societal and environmental impact and developing improvement planning instruments. This study carries out a fine-grained analysis of the mode substitution of dockless bike sharing (DLBS) in relation to other transport modes at the trip level, leveraging multi-modal route planning techniques, transaction data of bike-sharing, and travel behavior modeling. More importantly, the study leverages interpretable machine learning to reveal the complex effects of built environment factors on the mode substitution patterns of DLBS based on multiple data sources. The results indicate that the probabilities of DLBS replacing other transport modes present large heterogeneity among different trips and in different urban contexts, which can be successfully quantified by the proposed approach at the trip level. The average substitution rates of bike-sharing to bus, metro, walking and ride-hailing in Shanghai are estimated to be 0.356, 0.116, 0.347 and 0.181, respectively. Built environment factors such as presence of transit systems can explain the variations in the substitution rates of DLBS to a certain transport mode in different urban contexts. Especially, the effects of some built environment factors show complex nonlinear and threshold patterns revealed by the data-driven method. The effects of key built environment factors are quantitatively interpreted and their practical implications discussed.
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| 7. |
- Jia, Ruo, 1993, et al.
(författare)
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A spatio-temporal deep learning model for short-term bike-sharing demand prediction
- 2023
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Ingår i: Electronic Research Archive. - : American Institute of Mathematical Sciences (AIMS). - 2688-1594. ; 31:2, s. 1031-1047
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Tidskriftsartikel (refereegranskat)abstract
- Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems.
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| 8. |
- Li, Aoyong, 1993, et al.
(författare)
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An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China
- 2020
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Ingår i: Journal of Transport Geography. - : Elsevier BV. - 0966-6923. ; 88
-
Tidskriftsartikel (refereegranskat)abstract
- Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R2 = 0.774) than the OLS regression model (Adjusted R2 = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.
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| 9. |
- Li, Aoyong, 1993, et al.
(författare)
-
High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data
- 2021
-
Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 296
-
Tidskriftsartikel (refereegranskat)abstract
- Dockless bike-sharing systems (DLBS) have gained much popularity due to their environmentally friendly features. This study puts forward a distinctive framework for assessing the environmental influences of DLBS in high resolution based on DLBS transaction data. The proposed framework firstly estimates the transport mode substituted by DLBS for each recorded bike-sharing trip by utilizing the route planning techniques of online maps and a well-calibrated discrete choice model. Afterward, greenhouse gases (GHG) emission reductions in every recorded DLBS trip are quantified using Life Cycle Analysis. The proposed framework is applied to an empirical dataset from Shanghai, China. The empirical results reveal that the substitution rates of DLBS to different transport modes have substantial spatiotemporal variances and depend strongly on travel contexts, highlighting the necessity of analyzing the environmental impacts of DLBS at the trip level. Moreover, each DLBS trip is estimated to save an average 80.77 g CO2-eq GHG emissions versus than the situations without DLBS in Shanghai. The annual reduced GHG emissions from DLBS are estimated to be 117 kt CO2-eq, which is substantial and equals to the yearly GHG emissions of over 25,000 typical gasoline passenger vehicles. Additionally, the associations among built environments and GHG emission reductions from DLBS are quantitatively investigated to shed light on the spatial variances in the environmental impacts of DLBS. The results can efficiently support the benefit-cost analysis, planning, and management of DLBS.
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| 10. |
- Li, Aoyong, 1993, et al.
(författare)
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Integrating shared e-scooters as the feeder to public transit: A comparative analysis of 124 European cities
- 2024
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Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 160
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Tidskriftsartikel (refereegranskat)abstract
- E-scooter sharing is a potential feeder to complement public transit for alleviating the first-and-last-mile problem. This study investigates the integration between shared e-scooters and public transit by conducting a comparative analysis in 124 European cities based on vehicle availability data. Results suggest that the integration ratios of e-scooter sharing in different cities show significant variations and range from 5.59% to 51.40% with a mean value of 31.58% and a standard deviation of 8.47%. The temporal patterns of integration ratio for first- and last-mile trips present an opposite trend. An increase in the integration ratio for first-mile trips is related to a decrease in the integration ratio for last mile in the time series. Additionally, these cities can be divided into four clusters according to their temporal variations of the integration ratios by a bottom-up hierarchical clustering method. Meanwhile, we explore the nonlinear effects of city-level factors on the integration ratio using explainable machine learning. Several factors are found to have noticeable and nonlinear influences. For example, the density of public transit stations and a higher ratio of the young are positively associated with the integration ratio to a certain extent. The results potentially support transport planners to collectively optimize and manage e-scooter sharing and public transport to facilitate multi-modal transport systems.
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| 11. |
- Zhang, Zhe, et al.
(författare)
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How do travel characteristics of ridesplitting affect its benefits in emission reduction? evidence from Chengdu
- 2023
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Ingår i: Transportation Research Part D: Transport and Environment. - 1361-9209. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- Ridesplitting, a shared mobility service, has the potential to reduce traffic-related air pollution. This study evaluates the impacts of ridesplitting on reducing different types of emissions and investigate how travel characteristics of ridesplitting affect emission reduction based on a ridesourcing dataset in Chengdu, China. First, this study quantifies the influence of ridesplitting on emissions reduction compared to single ride (i.e., non-ridesplitting) for each trip. The results indicate that a ridesplitting trip averagely reduce CO2, CO, NOx, and HC emissions by 34.52%, 5.98%, 33.10%, and 13.42%, respectively. Subsequently, using explainable machine learning, we quantitatively analyze how the travel characteristics of ridesplitting affect emission reduction at two levels. At the trip level, shared travel distance, shared travel time, delay, and detour are important factors for emission reduction. At the grid level, the number of orders that match co-riders within the same spatial community is more important than the total number of orders.
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| 12. |
- Cheng, Huailei, et al.
(författare)
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Truck platooning reshapes greenhouse gas emissions of the integrated vehicle-road infrastructure system
- 2023
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Ingår i: Nature Communications. - 2041-1723 .- 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Reducing greenhouse gas emissions has turned into a pillar of climate change mitigation. Truck platooning is proposed as a strategy to lower emissions from vehicles on roads. However, the potential interactive impacts of this technology on road infrastructure emissions remain unclear. Here, we evaluate the decarbonization effects of truck platooning on the integrated vehicle-road system at a large-scale road network level, spanning 1457 road sections across North America. We show that truck platooning decreases emissions induced by truck operations, but it degrades faster the durability of road infrastructure and leads to a 27.9% rise in road emissions due to more frequent maintenance work. Overall, truck platooning results in a 5.1% emission reduction of the integrated vehicle-road system. In contrast to the benefits of emission reduction, truck platooning leads to additional financial burdens on car users and transportation agencies, calling for the consideration of tradeoffs between emissions and costs and between agencies and users. Our research provides insights into the potential applications of truck platooning to mitigate climate change.
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| 13. |
- Cui, Shaohua, et al.
(författare)
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Adaptive Collision-Free Trajectory Tracking Control for String Stable Bidirectional Platoons
- 2023
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 24:11, s. 12141-12153
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Tidskriftsartikel (refereegranskat)abstract
- Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.
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| 14. |
- Cui, Shaohua, 1995, et al.
(författare)
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Delay-throughput tradeoffs for signalized networks with finite queue capacity
- 2024
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Ingår i: Transportation Research Part B: Methodological. - 0191-2615. ; 180
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Tidskriftsartikel (refereegranskat)abstract
- Network-level adaptive signal control is an effective way to reduce delay and increase network throughput. However, in the face of asymmetric exogenous demand, the increase of network performance via adaptive signal control alone is at the expense of service fairness (i.e., phase actuation fairness and network resource utilization fairness). In addition, for oversaturated networks, arbitrary adaptive signal control seems to have little effect on improving network performance. Therefore, under the assumption that the mean routing proportions/turn ratios of vehicles at intersections are fixed, this study investigates the problem of optimally allocating input rates to entry links and simultaneously finding a stabilizing signal control policy with phase fairness. We model the stochastic optimization problem of maximizing network throughput subject to network stability (i.e., all queue lengths have finite means) and average phase actuation constraints to bridge the gap between stochastic network stability control and convex optimization. Moreover, we further propose a micro-level joint admission and bounded signal control algorithm to achieve network stability and throughput optimization simultaneously. Joint control is implemented in a fully decomposed and distributed manner. For any arrival rate, joint control provably achieves network throughput within O(1/V) of optimality while trading off average delay with O(V), where V is an adjusted control parameter. Through a comparative simulation of a real network with 256 O-D pairs, the proposed joint control keeps network throughput at maximum, guarantees service fairness, and fully utilizes network capacity (i.e., increases network throughput by 17.54%).
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| 15. |
- Cui, Shaohua, 1995, et al.
(författare)
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Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications
- 2024
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Ingår i: Transportation Research Part A: General. - 0965-8564. ; 183
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Tidskriftsartikel (refereegranskat)abstract
- The maturation and scalability of unmanned aerial vehicle (UAV) technology offer transformative opportunities to revolutionize prompt delivery. This study explores integrating UAVs with public transportation vehicles (PTVs) to establish a novel delivery paradigm that enhances revenue for public transit operators and improves transport system efficiency without compromising passenger convenience or operational efficiency. Employing hexagonal planning technology, this study identifies and quantifies the available spatio-temporal resources of PTVs for UAV integration. This involves aligning the spatio-temporal dynamics of prompt delivery orders with PTV ridership, based on field data from Beijing's Haidian District. Utilizing these outputs, we quantitatively analyze the benefits of integrating UAVs with PTVs on increasing public transit revenue, and potentials of reducing carbon emissions and mitigating congestion. Furthermore, we quantify the long-term benefits of UAV-PTV integration by predicting future increases in delivery demand. Based on obtained quantitative results, this study discusses practical and policy implications to support the sustainable integration of UAVs with PTVs.
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| 16. |
- Cui, Shaohua, 1995, et al.
(författare)
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Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers
- 2023
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Ingår i: Transportation Research Part E. - : Elsevier BV. - 1366-5545 .- 1878-5794. ; 180
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Tidskriftsartikel (refereegranskat)abstract
- Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.
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| 17. |
- Cui, Shaohua, et al.
(författare)
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Temporal Finite-Time Adaptation in Controlling Quantized Nonlinear Systems Amidst Time-Varying Output Constraints
- 2024
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Ingår i: IEEE Transactions on Automation Science and Engineering. - 1558-3783 .- 1545-5955. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers. Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.
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| 18. |
- Dong, Haoxuan, et al.
(författare)
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Flexible Eco-cruising Strategy for Connected and Automated Vehicles with Efficient Driving Lane Planning and Speed Optimization
- 2024
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Ingår i: IEEE Transactions on Transportation Electrification. - 2332-7782. ; 10:1, s. 1530-1540
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Tidskriftsartikel (refereegranskat)abstract
- Eco-cruising control of vehicles is a potential approach for improving vehicle energy efficiency and reducing travel time. However, many eco-cruising studies merely focused on vehicle longitudinal speed optimization but overlooked the lane change maneuvers, which may impair the benefits of eco-cruising when the vehicle encounters the slowly moving preceding vehicle. This study proposes a flexible eco-cruising strategy (FECS) with efficient driving lane planning and speed optimization capabilities simultaneously for connected and automated vehicles. The FECS is designed with a hierarchical control framework, where the first layer uses the Dijkstra algorithm to plan an efficient driving lane sequence considering the long-term effect of the preceding vehicles, then guides the second layer to optimize the vehicle’s speed for saving energy using trigonometric speed profile. The optimized driving trajectory is implemented in the third layer by regulating the speed and yaw angle for guaranteeing safe inter-vehicle distance when uncertainties are present. Finally, stochastic simulation with randomized traffic flows and typical case analysis based on real-world traffic data are conducted to demonstrate the performance of the FECS. The results manifest FECS’s capability of lowering driving costs in moderate-flow and free-flow traffic. However, we note that the benefits are less pronounced in congested-flow traffic.
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| 19. |
- Gao, Kun, 1993, et al.
(författare)
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Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior
- 2021
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Ingår i: Transportation Research Part A: Policy and Practice. - : Elsevier BV. - 0965-8564. ; 148, s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- This study proposes an approach for modeling travel behavior under uncertainty coupling Cumulative Prospect Theory (CPT) with Multi-attribute Decision Making (MADM) theory. CPT is utilized to depict travelers’ evaluations of each attribute, and MADM describes the process of making tradeoffs among multiple conflicting criteria. Divergent perception principles for different attributes are considered in the proposed framework. The proposed approach is utilized for an empirical analysis concerning mode shift behavior for commuting in Shanghai of China, based on data collected by stated preference surveys. Results show that the proposed approach outperforms conventional methods in terms of model performances and behavioral revelations. Empirical results demonstrate that sensitivity to gains and losses in cost and travel time are divergent in mode shift behavior. More importantly, it is found that travelers underestimate the occurrence chances of low-probability travel time and overestimate the occurrence changes of high-probability travel time in mode shift behavior, which is contrary to the findings from economics. Travelers show substantial loss aversion features as well. The heterogeneity in the value functions of CPT is investigated to shed light on differences in the evaluation process among individuals. Results reveal quite different empirical CPT parameters and behavioral mechanisms in mode shift behavior as compared to monetary experiments in economics. It highlights the importance of empirical estimations in various travel choice contexts to essentially understand travel behavior mechanisms, rather than arbitrary usage of findings from economics.
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| 20. |
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| 21. |
- Gao, Kun, 1993, et al.
(författare)
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Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility
- 2023
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Ingår i: Journal of Transport Geography. - 0966-6923. ; 110
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS.
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| 22. |
- Gao, Kun, 1993, et al.
(författare)
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Diverging effects of subjective prospect values of uncertain time and money
- 2021
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Ingår i: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 1
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Tidskriftsartikel (refereegranskat)abstract
- Studies from behavioral economics show that the subjective prospect value of money has diminishing sensitivity to losses/gains, represented by an S-shape, and this has been applied in representing the subjective prospect value of time in many transportation studies such as travel behavior modeling and network equilibrium. In this study, we demonstrate that the prospect value of time has an increasing sensitivity to losses/gains and can be represented by an Ϩ-shape, which contrasts that of money. We further explain the rationality of this surprising finding based on psychological and behavioral theories and discuss extensive practical implications. The correlations between sensitivities to gains and losses in terms of magnitude are revealed as well to shed light on potential underlying correlated behavioral principles. Substantial loss-aversion features are observed in the empirical analysis, supporting endowment effects. Implications of the findings on decision-making and other areas that utilize time as a key indicator have been discussed. The findings may revolutionize many research areas that utilize time as a key indicator such as transportation engineering.
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| 23. |
- Gao, Kun, 1993, et al.
(författare)
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Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction
- 2021
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Improving passengers’ satisfaction is crucial for airline industry and requires in-depth understandings regarding the complex effects of various factors. This study investigates the importance, complex nonlinear effects and interaction effects of various factors (including passenger characteristics and service attributes) on airline travel satisfaction in data-driven manners leveraging machine-learning (ML) approaches. The results show that ML algorithms such as Random Forest have superiority in modeling airline travel satisfaction as compared to conventional logistic regressions. The quantitative importance of various factors is estimated and compared to reveal key determinants of passengers’ satisfaction using permutation-based importance and accumulated local effect analysis. More importantly, results suggest that the main effects of service attributes present piecewise nonlinear patterns. There are piecewise interaction effects between passenger characteristics and service attributes and among service attributes on airline travel satisfaction. Practical implications on efficient and cost-effective measures of promoting satisfaction are derived and discussed based on the findings.
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| 24. |
- Gao, Kun, 1993, et al.
(författare)
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How to Model the Influence of In-vehicle Crowding on Travel Behavior: A Comparison Among Moderation, Independent Variable and Interaction
- 2020
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Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 185, s. 41-52
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Konferensbidrag (refereegranskat)abstract
- Accurate modeling of travel choice behavior is crucial for effective transport demand forecasting, management and planning. This study tries to shed light on the appropriate modeling approach concerning the influences of in-vehicle crowding on mode choice behavior in the multimodal network. Stated preference surveys covering four commuting transport modes and four influencing factors are conducted to collect empirical behavior data. Three modeling methods, treating the in-vehicle crowding as a moderator of perceived travel time, as an independent variable and by incorporating interaction effect, are empirically compared. The result indicates that there is a bidirectional interaction between travel time and in-vehicle crowding. The influence of in-vehicle crowding increases with increasing travel time and vice versa. Considering crowding as an independent variable and taking the effects of travel time on the perception of in-vehicle crowding are the best ways to depict the overall influences of in-vehicle crowding. The sensitivity analysis shows that increasing the cost of using car is comparatively effective for reducing car usage. Shortening the travel time of public transit and improving service quality such as travel time reliability and in-vehicle crowding are more useful in attracting car users as compared to reduction in the cost of public transit. The results provide insights into travelers’ behavior in the multimodal network and could support scientific transport management and planning.
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| 25. |
- Gao, Kun, 1993, et al.
(författare)
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Inertia effects of past behavior in commuting modal shift behavior: interactions, variations and implications for demand estimation
- 2022
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Ingår i: Transportation. - : Springer Science and Business Media LLC. - 0049-4488 .- 1572-9435. ; 49:4, s. 1063 -1097
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Tidskriftsartikel (refereegranskat)abstract
- This paper focuses on empirically investigating the inertia effects of past behavior in commuting modal shift behavior and contributes to the current state of the art by three aspects. Firstly, this study introduces and tests the potential influences of the inertia effects of past behavior on the traveler's preferences regarding level-of-service (LOS) variables, besides the impacts of inertia effects on the preference for the frequently used transport mode in the past. Secondly, the mode-specific inertia effects are investigated to distinguish the differences in the inertia effects for different transport modes based on posterior individual-specific parameter estimations. Thirdly, the factors contributing to the heterogeneity of inertia effects including demographics and travel contexts, are quantitatively examined. A joint random parameter logit model using a revealed and stated preference survey regarding commuting behavior is employed to unravel the three aspects. The results reveal significant interactions of inertia terms with LOS variables indicating the influences of past behavior on travelers' evaluations on attributes of their previous choices. The mean values and variances of inertia effects for different transport modes are significantly and substantially distinct. For instance, the inertia effects of frequently using car are substantially positive representing strong stickiness to the car, while the inertia effects of frequently using the metro have large variances among travelers and mostly appear as dispositions to change. Besides, the effects of personal characteristics and travel contexts on the magnitude of the inertia effects of different transport modes are identified as well. A demand estimation analysis is utilized to investigate the influences of three aspects on predicting travel demands in various contexts. Incorporating the interactions and mode-specific inertia effects can remarkably improve the model performance. The demand estimation will be biased if they are neglected.
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| 26. |
- Gao, Kun, 1993, et al.
(författare)
-
Modeling Measurements Towards Effect of Past Behavior on Travel Behavior
- 2021
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Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 231, s. 141-157
-
Konferensbidrag (refereegranskat)abstract
- The inertia effect of past behavior has attracted attention in the travel behavioral literature because of its bearing on travel choice modeling. Several measurements have been proposed to model the inertia effects. However, no consensus concerning appropriate modelling methods is reached, which leads to potential biases in analysis. The study aims to conduct a comprehensive investigation of modeling measurements regarding inertia effects of past behavior from the perspectives of estimation, behavioral indications and predictions. Differing from existing literature that only focused on estimation performance, we examine the performances of different methods in predictions and behavioral interpretations. To our best knowledge, these aspects are not investigated in the literature based on empirical data. The necessary information for constructing the measurements, underlying consumption, significance in estimation, behaviorally implausible issue, performances in estimation and predictions for these measurements are all compared based on behavioral data. The results shed lights on performances and suitability of different measurements for inertia effects in terms of estimation, behavioral interpretation and prediction, which support the further investigations of past behavior on travelers’ choice behavior.
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| 27. |
- Gao, Kun, 1993, et al.
(författare)
-
Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips
- 2020
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Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 71, s. 272-287
-
Tidskriftsartikel (refereegranskat)abstract
- The inertia effects stemmed from repeated past behavior have been investigated by both psychology and transportation studies because of its bearing on explaining human mobility and forecasting travel demand. However, the existing literature from psychology does not strictly control potential endogeneity due to ignorance of detailed level-of-service (LOS) variables of alternatives and rational preference in the analysis. Quantitative transportation studies are insufficient in providing explicit behavior mechanisms. This paper aims to fill the gaps by empirically examining the effects of irrational psychological inertia in mode shift behavior with controlling potential endogeneity. A specific-designed comparison experiment is conducted to demonstrate the existence of psychological inertia in mode shift behavior. The effects of dominance in LOS variables and rational preference towards a certain transport mode are controlled to eliminate potential endogeneity in the analysis. The results demonstrate that after controlling the above-mentioned endogeneity, both car and metro users show significantly and substantially larger predilections to previously used transport mode in mode shift scenarios without overturning travel contexts than those in new context mode choice scenarios with noticeable changes in travel contexts. The results support that psychological inertia plays a significant role in mode shift behavior after controlling potential endogeneity. Moreover, this study utilizes hybrid choice modeling to quantitatively measure the effect of psychological inertia. The relationships between travelers’ characteristics and strength of psychological inertia are analyzed as well to shed light on heterogeneity in the strength of psychological inertia. The findings provide solid evidence of psychological inertia in mode shift behavior by a novel method and provide an approach to measure the quantitative effects of psychological inertia along with empirical studies.
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| 28. |
- He, Li, et al.
(författare)
-
An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions
- 2023
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Ingår i: Accident Analysis and Prevention. - 0001-4575. ; 186
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Tidskriftsartikel (refereegranskat)abstract
- Illegal running into the opposite lane (IROL) on curve sections of two-lane rural roads is a frequently hazardous behavior and highly prone to fatal crashes. Although driving behaviors are always determined by the information from drivers’ visual perceptions, current studies do not consider visual perceptions in predicting the occurrence of IROL. In addition, most machine learning methods belong to black-box algorithms and lack the interpretation of prediction results. Therefore, this study aims to propose an interpretable prediction model of IROL on curve sections of two-lane rural roads from drivers’ visual perceptions. A new visual road environment model, consisting of five different visual layers, was established to better quantify drivers’ visual perceptions by using deep neural networks. In this study, naturalistic driving data was collected on curve sections of typical two-lane rural roads in Tibet, China. There were 25 input variables extracted from the visual road environment, vehicle kinematics, and driver characteristics. Then, XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive exPlanation) methods were combined to build a prediction model. The results showed that our prediction model performed well, with an accuracy of 86.2% and an AUC value of 0.921. The average lead time of this prediction model was 4.4 s, sufficient for drivers to respond. Due to the advantages of SHAP, this study interpreted the impacting factors on this illegal behavior from three aspects, including relative importance, specific impacts, and variable dependency. After offering more quantitative information on the visual road environment, the findings of this study could improve the current prediction model and optimize road environment design, thereby reducing IROL on curve sections of two-lane rural roads.
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| 29. |
- Huang, Jing, et al.
(författare)
-
Differences in driver takeover performance and physiological responses in conditionally automated driving: Links to emotional instability
- 2024
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Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - 1369-8478. ; 105, s. 73-86
-
Tidskriftsartikel (refereegranskat)abstract
- When the automated driving system reaches its technical limit, drivers will have to take over control from the vehicle within a limited time. The drivers’ emotional instability may affect whether they can safely control the vehicle during takeover transitions. Accordingly, this paper explores the association between emotional instability and physiological responses and driver takeover performance. 42 drivers engaged in negative emotion induced by movie clips were involved in a sequence of takeover events in a driving simulator. Personality questionnaires were used to assess the driver's level of emotional instability. Their physiological data before the takeover request (TOR) and operational behavior data fragments for the takeover process were extracted. The results demonstrated that integration of subjective evaluations with electroencephalogram (EEG) and electrocardiogram (ECG) signals allows for more objective measurement of drivers’ level of emotional instability. The statistical analyses indicated that emotional instability has a significant effect on drivers’ heartbeat interval, heart rate, the frequency bands of θ(4–8 Hz), α(8–12 Hz), β(13–30 Hz), γ (30–50 Hz) and lateral takeover performance. Those findings of differences in driver physiological responses and takeover performance under emotional instability may provide additional support for the design of driver state monitoring and adaptive warning systems.
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| 30. |
- Huang, Jing, et al.
(författare)
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The correlation between drivers’ road familiarity and glance behavior using real vehicle experimental data and mathematical models
- 2024
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Ingår i: Traffic Injury Prevention. - 1538-957X .- 1538-9588. ; 25:5, s. 705-713
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Road familiarity is an important factor affecting drivers’ visual features. Analyzing the quantitative correlation between drivers’ road familiarity and visual features in complex environment is of great help to improve driving safety. However, there are few relevant studies. This paper takes urban plane intersection as the environmental object to explore the correlation between drivers’ glance behavior and road familiarity, and conducts research on the quantitative evaluation model of road familiarity based on this correlation. Method: First, a real vehicle experiment was carried out to record the eye movement data of 24 drivers with different road familiarity. The driver’s visual field plane was divided into 10 areas of interest (AOIs) based on the driver’s perspective. Three measures, including average glance duration, number of glances, and fixation transition probabilities between AOIs at urban plane intersections, were extracted. Finally, based on the experimental results, the driver road familiarity evaluation model was constructed using the factor analysis method. Results: There are significant differences between unfamiliar and familiar drivers regarding the average glance duration toward the forward (FW) area, the left window (LW) area, the left rearview mirror (LVM) area and the left forward (LF) area, the number of glances toward the other (OT) area, and the fixation transition probabilities of LW→RF (right forward), LF→LF, LF→FW, FW→LW, FW→FW, FW→RVM (right rearview mirror). The comprehensive evaluation results show that the accuracy rate of the driver road familiarity evaluation model reached 83%. Conclusions: This paper revealed that there is a strong correlation between drivers’ road familiarity and drivers’ glance behavior. Based on this correlation, we can include road familiarity as a part of drivers’ working status and establish a high accuracy evaluation model of driver road familiarity. The conclusion of this paper can provide some reference for the humanized design and improvement of advanced driving assistance system, which is of great significance for reducing the driving workload of drivers and improving the driving safety.
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| 31. |
- Li, Guorong, et al.
(författare)
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Deciphering spatial heterogeneity of maritime accidents considering impact scale variations
- 2024
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Ingår i: Maritime Policy and Management. - 0308-8839 .- 1464-5254. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- Ensuring maritime safety has ascended as a preeminent concern within the global maritime sector. Understanding how factors affect maritime accidents’ consequences in different water areas would be of great benefit to preventing the occurrence or reducing the consequences. This study thus employed a multi-scale geographically weighted regression (MGWR) model on the accident dataset from Fujian waters in the East China Sea, to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors on maritime accident consequence. The performances of MGWR are compared with multiple linear regression (MLR) and GWR. As expected, MGWR outperforms the other two models in terms of its ability to clearly capture the unobserved spatial heterogeneity in the effects of factors. Results reveal notably distinct influences of some factors on maritime accident consequences in different locations. An intuitive indication by MGWR is that approximately 50% of the accidents present positive coefficients of good visibility while other locations are negative, which are failed to recognize by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different water areas.
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| 32. |
- Li, Ying, et al.
(författare)
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Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments
- 2022
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Ingår i: Journal of Intelligent and Connected Vehicles. - 2399-9802. ; 5:3, s. 270-282
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states. Design/methodology/approach: Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure. Findings: This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed. Originality/value: The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.
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| 33. |
- Lin, Hongyi, et al.
(författare)
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Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin–Destination Ride-Hailing Demand Prediction
- 2024
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:3, s. 111-124
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Tidskriftsartikel (refereegranskat)abstract
- In intelligent transportation systems, one key challenge for managing ride-hailing services is the balancing of traffic supply and demand while meeting passenger needs within vehicle availability constraints. Accurate origin–destination (OD) demand predictions can empower platforms to execute timely reallocation of cruising vehicles and improve ride-sharing services. Nonetheless, the complexity of OD-based demand prediction arises from intricate spatiotemporal dependencies and a higher need for precision compared to zone-based predictions, which leads to many unprecedented OD pairs. To tackle this issue, we design a comprehensive set of 102 features, including travel demand, passenger count, travel volume, liveliness, weather, and cross features. We also introduce an enhanced conformer model, which is composed of a single conformer block that integrates feedforward layers, multihead self-attention mechanisms, and depth-wise separable convolution layers. To address the cold-start problem and manage large values, we design a specific algorithm for OD pairs lacking training data and apply a technique to handle larger values. Our approach demonstrates a marked improvement in prediction performance, with an 18% decrease in the total travel demand error and up to a 47% reduction for certain larger values in some cases. Through extensive experiments on a dataset collected from a city, provided by a ride-hailing platform, our proposed methods significantly outperform the most advanced models.
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| 34. |
- Lin, Hongyi, et al.
(författare)
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Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models
- 2024
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- With the growing prevalence of autonomous vehicles and the integration of intelligent and connected technologies, the demand for effective and reliable vehicle speed control algorithms has become increasingly critical. Traditional car-following models, which primarily focus on individual vehicle pairs, exhibit limitations in complex traffic environments. To this end, this paper proposes an enhanced state representation for the application of multi-agent reinforcement learning (MARL) in platoon-following scenarios. Specifically, the proposed representation, influenced by feature engineering techniques in time series prediction tasks, thoroughly accounts for the intricate relative relationships between different vehicles within a platoon and can offer a distinctive perspective on traffic conditions to help improve the performance of MARL models. Experimental results show that the proposed method demonstrates superior performance in platoon-following scenarios across key metrics such as the time gap, distance gap, and speed, even reducing the time gap by 63%, compared with traditional state representation methods. These enhancements represent a significant step forward in ensuring the safety, efficiency, and reliability of platoon-following models within the context of autonomous vehicles.
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| 35. |
- Liu, Dongjie, et al.
(författare)
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Enhancing choice-set generation and route choice modeling with data- and knowledge-driven approach
- 2024
-
Ingår i: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 162
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Tidskriftsartikel (refereegranskat)abstract
- Two central and interconnected problems arise in the specification of a ‘‘complete’’ path-based route choice model: choice-set generation and choice from a choice set. Choice-set generation poses a significant challenge in personalization and the enumeration of the full choice set with large size. Despite the continued prevalence of classic econometric models for modeling choices within a given set, this requirement of knowledge-driven modeling necessitates explicit model structures and intricate domain knowledge, which may result in practical biases. In this study, a Conditional Variational AutoEncoder (CVAE)-based choice set generation model is developed, which approximates the probability distribution of the underlying choice set generation process conditional on individual and OD characteristics without relying on expert knowledge. In order to facilitate a friendly integration between knowledge-driven econometric and machine learning approaches, a neural-embedded route choice model (IAP-NERCM) with implicit availability/perception (IAP) of choice alternatives is proposed to automatically capture the heterogeneity of taste parameters without assuming any a priori relationship. Results based on synthetic data show that the proposed models are capable of reproducing the pre-defined coefficients. Field data of GPS data collected in Toyota City is used to future test the proposed models compared to classical statistical models. Results indicate that IAP-NERCM exhibits the ability to recover underlying taste function and achieves the best performance in terms of goodness-of-fit, predictability, and estimation time.
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| 36. |
- Liu, Hua, et al.
(författare)
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Optimizing the Deployment of Automated Speed Camera at the Intersections Using GPS Trajectories
- 2023
-
Ingår i: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 55-65
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Konferensbidrag (refereegranskat)abstract
- The economical and rational deployment of automated speed camera is a critical issue for traffic police department to implement speed management efficiently. Based on taxi GPS trajectories collected from Chengdu, 2016, this study optimizes the deployment interval and number of ASCs at the intersections by using K-means clustering and kernel density estimation according to the critical mixed distance halo effect and the delta speed distribution, respectively. Results illustrate that speeding is more likely to happen within the speed limit of 40 km/h rather than 60 km/h. From the whole perspective, with the growing deployment number of ASCs, the upstream distance halo effects gradually increase, while the downstream distance halo effects gradually decrease within the range of about 4500 m. Given that the interaction between two adjacent ASCs, the critical mixed distance halo effect of ASCs is about 215 m and 529 m corresponding to the smaller and larger values of critical delta speed in the northeast direction respectively, and about 315 m and 585 m in the southwest direction. Generally, one ASC should be deployed every 500 m and 600 m within the speed limit of 60 km/h, and every 200 m and 300 m within the speed limit of 40 km/h in the northeast and southwest directions, respectively.
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| 37. |
- Liu, Jiahui, et al.
(författare)
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Generative Edge Intelligence for IoT-Assisted Vehicle Accident Detection: Challenges and Prospects
- 2024
-
Ingår i: IEEE Internet of Things Magazine. - 2576-3180 .- 2576-3199. ; 7:3, s. 50-54
-
Tidskriftsartikel (refereegranskat)abstract
- With the emergence of generative intelligence at the edge of modern Internet of Things (IoT) networks, promising solutions are proposed to further improve road safety. As a crucial component of proactive traffic safety management, vehicle accident detection (VAD) encounters multiple existing challenges in terms of data accuracy, accident classification, communication latency, etc. Thus, generative edge intelligence (GEI) can be introduced to VAD systems and contribute to improving performance by augmenting data, learning underlying patterns, and so on. In this article, we investigate the integration of GEI technology in VAD systems, focusing on its applications, challenges, and prospects. We begin by reviewing conventional VAD methods and highlighting their limitations. Following this, we explore the potential of GEI in IoT-assisted VAD and then propose a novel architecture for the GEI-VAD system that is based on an end-edge-cloud framework. We delve into the details of each component and layer within the system. Finally, we conclude this article by suggesting avenues for future research.
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| 38. |
- Meng, Fanyu, et al.
(författare)
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Influential factors associated with consecutive crash severity: A two-level logistic modeling approach
- 2020
-
Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 17:15, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.
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| 39. |
- Najafi, Arsalan, 1987, et al.
(författare)
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Coordination of coupled electrified road systems and active power distribution networks with flexibility integration
- 2024
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Ingår i: Applied Energy. - 1872-9118 .- 0306-2619. ; 369
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Tidskriftsartikel (refereegranskat)abstract
- Electric road systems (ERS) constitute a promising technology for mobile charging and relieving mandatory stops to recharge electric vehicles. However, the ERS operation is constrained by the limitations of the Power Distribution Network (PDN) that provides electricity. This study proposes a integrated optimization of a coupled ERS-PDN system (including traffic assignment and power flow modeling), in the presence of self-interested electric vehicle drivers, diverse flexibility resources and uncertainty of energy supplies (e.g. uncertainty from renewable energy). The security of the PDN while supporting ERS can be ensured by using active and flexible energy storage and flexible power loads. A semi-dynamic model is adopted for the traffic assignment. A stochastic bi-level optimization based on Stackelberg game under uncertainty is proposed to model the joint optimization problem to minimize the general cost of coupled ERS-PDN system and maximize the profit of the energy flexibility provider. Then, the Karush Kuhn Tucker conditions are deployed to convert the bi-level model to the equivalent single level model. The results demonstrate the effectiveness and benefits of the proposed framework using numerical experiments. The results show that the proposed optimization can reduce the burden of an ERS on the underlying PDN in improving the violated voltage by 3.66%, demonstrating the effect of joint consideration of diverse sources of flexibility.
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| 40. |
- Parishwad, Omkar, 1987, et al.
(författare)
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Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models
- 2023
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Ingår i: Multimodal Transportation. - : Elsevier. - 2772-5863. ; 2:3
-
Tidskriftsartikel (refereegranskat)abstract
- Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics. Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modelling.
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| 41. |
- Poinsignon, François, et al.
(författare)
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Autonomous vehicle fleets for public transport : scenarios and comparisons
- 2022
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Ingår i: Green Energy and Intelligent Transportation. - : Elsevier BV. - 2773-1537 .- 2097-2512. ; 1:3
-
Tidskriftsartikel (refereegranskat)abstract
- Autonomous vehicles (AVs) are becoming a reality and may integrate with existing public transport systems to enable the new generation of autonomous public transport. It is vital to understand what are the alternatives for AV integration from different angles such as costs, emissions, and transport performance. With the aim to support AV integration in public transport, this paper takes a typical European city as a case study for analyzing the impacts of different AV integration alternatives. A transport planning model considering AVs is developed and implemented, with a methodology to estimate the costs of the transport network. Traffic simulations are conducted to derive key variables related to AVs. An optimization process is introduced for identifying the optimal network configuration based on a given AV integration strategy, followed by the design of different AV integration scenarios, simulation, and analyses. With the proposed method, a case study is done for the city of Uppsala with presentation of detailed cost results together with key traffic statistics such as mode share. The results show that integrating AVs into public transport does not necessarily improve the overall cost efficiency. Based on the results and considering the long transition period to fully autonomous vehicles, it is recommended that public transport should consider a gradual introduction of AVs with more detailed analysis on different combination and integration alternatives of bus services and AVs.
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| 42. |
- Qu, Xiaobo, 1983, et al.
(författare)
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Contagion spread modeling in transport networks and transport operation optimizations for containing epidemics
- 2022
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Ingår i: Transportation Amid Pandemics: Lessons Learned from COVID-19. ; , s. 349-357
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- COVID-19 has critically impacted many aspects of societies worldwide, particularly on mobility. This chapter summarizes impacts of the COVID-19 pandemic, reviews existing research, and identifies future research needs in the scope of traffic theory and modeling/optimization and traffic flow. We first review models on contagion spreading through transportation networks, including aggregated spatial metapopulation models and disaggregated individual-based models. Further research is needed to consider both intercity and intracity mobilities and leverage emerging multiple data resources for constructing individuals’ complete trip chains. Based on modeling contagion spreading, we further discuss transport operation needs in the aftermath of COVID-19. There remains a need for operating multimodal urban transport systems to satisfy basic travel demands while minimizing contagion risks. Relevant research needs are identified in optimizing transport operation via modern data acquisition technologies and advanced modeling methods. Practical intervention measures and policy implications are recommended for optimizing transport systems during the COVID-19 pandemic.
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| 43. |
- Ren, Weixi, et al.
(författare)
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An intelligent optimization method for the facility environment on rural roads
- 2024
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Ingår i: Computer-Aided Civil and Infrastructure Engineering. - 1093-9687 .- 1467-8667. ; 39:17, s. 2559-2580
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Tidskriftsartikel (refereegranskat)abstract
- This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time-consuming, this method can rapidly generate optimized visual images of the facility environment and promptly verify the optimization effects. In this study, a visual road schema model is established to quantify the facility environment from drivers’ visual perception, and an automated optimization scheme determination approach considering the original facility environment characteristics is proposed using self-explaining theory. Then, Cycle-consistent generative adversarial network is used to automatically generate optimized facility environment images. To verify the optimization effect, operation speeds of the optimized facility environments are predicted using random forest. The case study shows that this method can effectively optimize the facility environment where original operation speeds are more than 20% over the speed limits, and the whole process only takes 1 h far less than several months or years in previous ways. Overall, this study advances the intelligence level in optimizing the facility environment and enhances rural road safety.
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| 44. |
- Ren, Weixi, et al.
(författare)
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Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach
- 2022
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 19:18
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Tidskriftsartikel (refereegranskat)abstract
- Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors' flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.
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| 45. |
- Shangguan, Yidan, et al.
(författare)
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On the Fundamental Diagram for Freeway Traffic: Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models
- 2023
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Ingår i: MATHEMATICS. - 2227-7390. ; 11:16
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Tidskriftsartikel (refereegranskat)abstract
- In traffic flow, the relationship between speed and density exhibits decreasing monotonicity and continuity, which is characterized by various models such as the Greenshields and Greenberg models. However, some existing models, i.e., the Underwood and Northwestern models, introduce bias by incorrectly utilizing linear regression for parameter calibration. Furthermore, the lower bound of the fitting errors for all these models remains unknown. To address above issues, this study first proves the bias associated with using linear regression in handling the Underwood and Northwestern models and corrects it, resulting in a significantly lower mean squared error (MSE). Second, a quadratic programming model is developed to obtain the lower bound of the MSE for these existing models. The relative gaps between the MSEs of existing models and the lower bound indicate that the existing models still have a lot of potential for improvement.
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| 46. |
- Teng, Yang, et al.
(författare)
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Modelling the Relationships Between Headway and Speed in Saturation Flow of Signalised Intersections
- 2020
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Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 185, s. 179-188
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Konferensbidrag (refereegranskat)abstract
- The headways between vehicles in the traffic flow of intersections are one of the crucial variables for reasonable signal timing setting and intersection configuration design. Many studies apply constant discharge headways to calculate the saturation flow rate, and scarce studies quantitatively investigate the relationship of headway and speed in the saturation flow. This study endeavours to model the headway–speed relationships of saturation traffic flow at the signalised intersection. Five typical intersections with large traffic demand in Golden Coast City are surveyed to collect data regarding vehicles’ discharging speed and headways. The least squared method and the fitting degree test are applied to model the headway–speed relationships at the signalised intersections and compare the models’ fitting performance. The results indicate that the headway is significantly associated with speed. The headway increases with decreasing speed crossing the intersections. The empirically and quantitatively calibrated relationships between speed and headway can be used to calculate the saturation flow rate in the intersections with different discharging speeds and further support the design of intersections with large traffic demand.
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| 47. |
- Wang, Shuli, 1996, et al.
(författare)
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Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
- 2024
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Ingår i: Accident Analysis and Prevention. - 0001-4575. ; 199
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Tidskriftsartikel (refereegranskat)abstract
- Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.
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| 48. |
- Wang, Shuli, 1996, et al.
(författare)
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Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity With Interpretability
- 2024
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- To promise a high degree of safety in complex mixed-traffic scenarios alongside human-driven vehicles, accurately predicting the maneuvers of surrounding vehicles and their future positions is a critical task and attracts much attention. However, most existing studies focus on reasoning about positional information based on objective historical trajectories without fully considering the heterogeneity of driving behaviors. Besides, previous works have focused more on improving models’ accuracy than investigating their interpretability to explore the extent to which a cause and effect can be observed within a system. Therefore, this article proposes a personalized trajectory prediction framework that integrates driving behavior feature representation to account for driver heterogeneity. Specifically, based on a certain length of historical trajectory data, the situation-specific driving preferences of each driver are identified, where key driving behavior feature vectors are extracted to characterize heterogeneity in driving behavior among different drivers. The proposed LSTMMD-DBV (long short-term memory and mixture density networks with driving behavior vectors) framework integrates driving behavior feature representations into a long short-term memory encoder–decoder network to investigate its feasibility and validate its effectiveness in enhancing predictive model performance. Finally, the Shapley Additive Explanations method interprets the trained model for predictions. After experimental analysis, the results indicate that the proposed model can generate probabilistic future trajectories with remarkably improved predictions compared to existing benchmark models. Moreover, the results confirm that the additional input of driving behavior feature vectors representing the heterogeneity of driving behavior could provide more information and, thus, contribute to improving prediction accuracy.
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| 49. |
- Wu, Pan, et al.
(författare)
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Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations
- 2022
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Ingår i: Journal of Transport Geography. - : Elsevier BV. - 0966-6923. ; 104
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Tidskriftsartikel (refereegranskat)abstract
- Buses and metros are two main public transit modes, and these modes are crucial components of sustainable transportation systems. Promoting reciprocal integration between bus and metro systems requires a deep understanding of the effects of multiple factors on transfers among integrated public transportation transfer modes, i.e., metro-to-bus and bus-to-metro. This study aims to reveal the determinants of the transfer ratio between bus and metro systems and quantify the associated impacts. The transfer ratio between buses and metros is identified based on large-scale transaction data from automated fare collection systems. Meanwhile, various influencing factors, including weather, socioeconomic, the intensity of business activities, and built environment factors, are obtained from multivariate sources. A multivariate regression model is used to investigate the associations between the transfer ratio and multiple factors. The results show that the transfer ratio of the two modes significantly increases under high temperature, strong wind, rainfall, and low visibility. The morning peak hours attract a transfer ratio of up to 57.95%, and the average hourly transfer volume is 0.94 to 1.38 times higher at this time than in other periods. The intensity of business activities has the most significant impact on the transfer ratio, which is approximately 1.5 to 15 times that of the other independent variables. Moreover, an adaptative geographically weighted regression is utilized to investigate the spatial divergences of the influences of critical factors on the transfer ratio. The results indicate that the impact of a factor presents spatial heterogeneity and even shows opposite effects (in terms of positive and negative) on the transfer ratio in different urban contexts. For example, among the related socioeconomic variables, the impact of the housing price on the downtown transfer ratio is larger than that in the suburbs. Crowd density positively influences the transfer ratio at most stations in the northern region, whereas it shows negative results in the southern region. These findings provide valuable insights for public transportation management and promote the effective integration of bus and metro systems to provide enhanced transfer services.
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| 50. |
- Xu, Yueru, et al.
(författare)
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Modeling Commercial Vehicle Drivers’ Acceptance of Forward Collision Warning System
- 2021
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Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 231, s. 167-180
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Konferensbidrag (refereegranskat)abstract
- With the development of computer science, Forward Collision Warning (FCW) systems have been installed in various vehicles in order to improve road safety. Previous studies have been conducted to evaluate the acceptance of FCW systems and explore the contributing factors affecting drivers’ attitudes. However, few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. This paper tries to examine the attitudes of commercial vehicle drivers toward FCW systems and identify the contributing factors by using a random forests algorithm. FCW data of 24 commercial vehicles were recorded from November 1st to December 21st, 2018 in Jiangsu province. The acceptance rate (FCW records with response) of commercial vehicle drivers for FCW systems is 69.52%. (Acceptance was measured by identifying drivers who reduced their speed in response to a warning from the FCW system.) The accuracy of random forests model is 0.816 after tuning the parameter. In addition, the most important influence variable in this model is vehicle speed with an importance of 0.37. Duration time and warning hour also have significant influence on driver reaction, with values of 0.20 and 0.17, respectively. The results showed that commercial vehicle drivers’ acceptance of an FCW system decreases with the increase of vehicle speed. The response time for most cases is timely, usually within 2 s. And the response percentage is higher during daytime than at night. These regularities may be attributable to the larger size and heavier weight of commercial vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and to develop more effective FCW systems for commercial vehicles.
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