| 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
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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
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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)
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High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data
- 2021
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Ingår i: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 296
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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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