| 1. |
- Liang, Jinpeng, et al.
(author)
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Robust bus bridging service design under rail transit system disruptions
- 2019
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In: Transportation Research Part E: Logistics and Transportation Review. - : Elsevier BV. - 1366-5545. ; 132, s. 97-116
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Journal article (peer-reviewed)abstract
- This paper focuses on designing robust bus bridging service in response to the rail transit system disruptions. We firstly develop a path-based multi-commodity flow formulation to bus bridging service design. Then its robust counterpart is formulated to incorporate bus travel time uncertainty. The column generation procedure is devised to solve this problem efficiently. At last, we carry out case studies to demonstrate its applicability and promising effects. The results reveal that our approach can significantly reduce the total cost and number of stranded passengers in disruption events. Besides, the rise of bus travel time variation will deteriorate the performance of bus bridging service.
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| 2. |
- Agriesti, Serio Angelo Maria, et al.
(author)
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Roadworks warning-closure of a lane, the Impact of C-ITS messages
- 2020
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In: Infrastructures. - : MDPI AG. - 2412-3811. ; 5:3
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Journal article (peer-reviewed)abstract
- By now, it is widely acknowledged among stakeholders and academia that infrastructures will have to be composed both by a physical component and a digital one. The deployment of technologies exploiting dedicated short-range communications is viewed as the most cost-effective solution to face the foreseen growth of mobility. Still, little has been done to define the best implementation logic of DSRC. Aim of this paper is to frame the possible impacts arising by the implementation of a cooperative intelligent transport system (C-ITS)-use case: roadworks warning.closure of a lane, and, in order to achieve this result, microsimulations are exploited. The results are intended to support both road operators and car-makers in defining the best operational logics and the possible benefits achievable by presenting the cooperative message at a certain distance for certain market penetrations. Moreover, if the C-ITS message actually entails benefits or simply disrupts the upstream traffic should be assessed in advance, before implementing the system. The obtained results show that the risk of disruption and of reduction in traffic efficiency arises at lower market penetration levels. Nevertheless, a consistent trend in delay reduction is recorded upstream the roadworks, the highest reduction being equal to 8.66%. Moreover, the average speed at the roadworks entrance on the closing lane increases by a difference equal to around 10 km/h, while the average time in the queue at the highest market penetration reduces by 60 s on the open lane and 25 s on the closing one. These presented results reflect the way the traffic shifts from the slow to the fast lane thanks to the C-ITS system and effectively frames both the potentialities and the risks of the system.
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| 3. |
- Basso, Rafael, 1979, et al.
(author)
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Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning
- 2022
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In: Transportation Research Part E: Logistics and Transportation Review. - : Elsevier BV. - 1366-5545. ; 157:157
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Journal article (peer-reviewed)abstract
- Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefits
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| 4. |
- Bie, Yiming, et al.
(author)
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Dynamic headway control for high-frequency bus line based on speed guidance and intersection signal adjustment
- 2020
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In: Computer-Aided Civil and Infrastructure Engineering. - : Wiley. - 1093-9687 .- 1467-8667. ; 35:1, s. 4-25
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Journal article (peer-reviewed)abstract
- Computer-Aided Civil and Infrastructure Engineering To prevent bus bunching, a dynamic headway control method in the V2I (vehicle to infrastructure) environment for a high-frequency route with bus lane is developed. Bus operating speed guidance on the mid-blocks and intersection signal adjustment are two main strategies in the proposed method. A forecasting model of bus travel time under the dynamic control method is developed. The objective function is set up by taking into account differences between actual bus headways and dispatching headways, and the scaling ratios of intersection cycle lengths. The optimization model is solved using genetic algorithm. The proposed method is applied to a real bus route in Meihekou city, China, and compared with the current control plan as well as holding strategy. Results show that the proposed method can reduce bus headway deviations in all investigating periods; negative impacts on cars can be limited by setting reasonable values for the parameters.
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| 5. |
- Bie, Yiming, et al.
(author)
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Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption
- 2021
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In: Computer-Aided Civil and Infrastructure Engineering. - : Wiley. - 1093-9687 .- 1467-8667. ; 36:12, s. 1530-1548
-
Journal article (peer-reviewed)abstract
- This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption. First, a model for estimating the trip energy consumption is proposed based on field-collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs.
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| 6. |
- Cao, Danni, et al.
(author)
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A Platoon Regulation Algorithm to Improve the Traffic Performance of Highway Work Zones
- 2021
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In: Computer-Aided Civil and Infrastructure Engineering. - : Wiley. - 1093-9687 .- 1467-8667. ; 36:7, s. 941-956
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Journal article (peer-reviewed)abstract
- This paper presents a cooperative traffic control strategy to increase the capacity of non-recurrent bottlenecks such as work zones by making full use of the spatial resources upstream of work zones. The upstream area is divided into two zones: the regulation area and the merging area. The basic logic is that a large gap is more efficient in accommodating merging vehicles than several small and scattered gaps with the same total length. In the regulation area, a non-linear programming model is developed to balance both traffic capacity improvements and safety risks. A two-step solving algorithm is proposed for finding optimal solutions. In the merging area, the sorting algorithm is used to design lane changing trajectories based on the regulated platoons. A case study is conducted, and the results indicate that the proposed model is able to significantly improve work zone capacity with minor disturbances to the traffic.
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| 7. |
- Cao, Danni, et al.
(author)
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Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach
- 2021
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 157
-
Journal article (peer-reviewed)abstract
- This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.
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| 8. |
- Cao, Qi, et al.
(author)
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Jointly estimating the most likely driving paths and destination locations with incomplete vehicular trajectory data
- 2023
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In: Transportation Research, Part C: Emerging Technologies. - 0968-090X. ; 155
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Journal article (peer-reviewed)abstract
- With an ever-increasing deployment density of probe and fixed sensors, massive vehicular trajectory data is available and show a promising foundation to improve the observability of dynamic traffic demand pattern. However, due to technical and privacy issues, the raw trajectories are not always complete and the paths and destinations between discontinuous trajectory nodes are usually missing. This paper proposes a probabilistic method to jointly reconstruct the missing driving path and destination location of vehicles with incomplete trajectory data. One problem-specific HMM-structured model incorporating spatial and temporal analysis (ST-HMM) is constructed to define the matching probability between observed data and possible movement. Two algorithms, namely candidate set generation and best-match search algorithms, are developed to seek the most possible one as matching result. It can implement end-to-end processing from incomplete trajectory data to complete and connective paths and destinations for the target vehicle. The proposed method is tested based on field-test data and city-wide road network. Compared with two benchmark methods, the proposed method improved the matching accuracy in terms of both path identification and destination inference. Additionally, sensitivity analyses on the size of training dataset and candidate set were performed. We believe that experiment results of these sensitivity analyses can help to provide guidance on data sensing and candidate generation.
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| 9. |
- Chen, Jingxu, et al.
(author)
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A modelling framework of drone deployment for monitoring air pollution from ships
- 2019
-
In: Smart Innovation, Systems and Technologies. - Cham : Springer International Publishing. - 2190-3026 .- 2190-3018. ; 98, s. 281-288
-
Conference paper (peer-reviewed)abstract
- Sulphur oxide (SOx) emissions impose a serious health threat to the residents and a substantial cost to the local environment. In many countries and regions, ocean-going vessels are mandated to use low-sulphur fuel when docking at emission control areas. Recently, drones have been identified as an efficient way to detect non-compliance of ships, as they offer the advantage of covering a wide range of surveillance areas. To date, the managerial perspective of the deployment of a fleet of drones to inspect air pollution from ships has not been addressed yet. In this paper, we propose a modelling framework of drone deployment. It contains three components: drone scheduling at the operational level, drone assignment at the tactical level and drone base station location at the strategic level.
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| 10. |
- Chen, Xi, et al.
(author)
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Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis
- 2021
-
In: Expert Systems with Applications. - : Elsevier BV. - 0957-4174. ; 168
-
Journal article (peer-reviewed)abstract
- The customized bus (CB) is an emerging type of public transportation system, which not only provides a flexible and reliable demand-responsive service, but also reduces the usage of private car to alleviate traffic congestion in metropolitan cities. The customized bus route design problem (CBRDP) is a crucial procedure in the CB service system designing. In this work, we develop a new type of problem scenario: Multi-Trip Multi-Pickup and Delivery Problem with Time Windows, to describe CBRDP by simultaneously optimizing the operating cost and passenger profit, where excess travel time is introduced to estimate passenger extra cost compared with taxi service, and each vehicle is allowed to perform multiple trips for operational cost savings. To solve this problem, a constructive two-stage heuristic algorithm is presented to obtain the Pareto solution. Taking a benchmark problem and Beijing commuting corridor as case studies, we calculate and compare the monetary and travel costs of CB with other travel modes, and quantitatively confirm that the CB can be a cost-effective choice for passengers.
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| 11. |
- Chen, Zhiwei, et al.
(author)
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A Continuous Model for Designing Corridor Systems with Modular Autonomous Vehicles Enabling Station-wise Docking
- 2022
-
In: Transportation Science. - : Institute for Operations Research and the Management Sciences (INFORMS). - 0041-1655 .- 1526-5447. ; 56:1, s. 1-30
-
Journal article (peer-reviewed)abstract
- The "asymmetry" between spatiotemporally varying passenger demand and fixed-capacity transportation supply has been a long-standing problem in urban mass transportation (UMT) systems around the world. The emerging modular autonomous vehicle (MAV) technology offers us an opportunity to close the substantial gap between passenger demand and vehicle capacity through station-wise docking and undocking operations. However, there still lacks an appropriate approach that can solve the operational design problem for UMT corridor systems with MAVs efficiently. To bridge this methodological gap, this paper proposes a continuum approximation (CA) model that can offer near-optimal solutions to the operational design for MAV-based transit corridors very efficiently. We investigate the theoretical properties of the optimal solutions to the investigated problem in a certain (yet not uncommon) case. These theoretical properties allow us to estimate the seat demand of each time neighborhood with the arrival demand curves, which recover the "local impact" property of the investigated problem. With the property, a CA model is properly formulated to decompose the original problem into a finite number of subproblems that can be analytically solved. A discretization heuristic is then proposed to convert the analytical solution from the CA model to feasible solutions to the original problem. With two sets of numerical experiments, we show that the proposed CA model can achieve near-optimal solutions (with gaps less than 4% for most cases) to the investigated problem in almost no time (less than 10 ms) for large-scale instances with a wide range of parameter settings (a commercial solver may even not obtain a feasible solution in several hours). The theoretical properties are verified, and managerial insights regarding how input parameters affect system performance are provided through these numerical results. Additionally, results also reveal that, although the CA model does not incorporate vehicle repositioning decisions, the timetabling decisions obtained by solving the CA model can be easily applied to obtain near-optimal repositioning decisions (with gaps less than 5% in most instances) very efficiently (within 10 ms). Thus, the proposed CA model provides a foundation for developing solution approaches for other problems (e.g., MAV repositioning) with more complex system operation constraints whose exact optimal solution can hardly be found with discrete modeling methods.
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| 12. |
- Cui, Shaohua, 1995, et al.
(author)
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Delay-throughput tradeoffs for signalized networks with finite queue capacity
- 2024
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In: Transportation Research Part B: Methodological. - 0191-2615. ; 180
-
Journal article (peer-reviewed)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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| 13. |
- Duan, Yuzhou, et al.
(author)
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Optimising total entry delay at roundabouts with unbalanced flow: A dynamic strategy for smart metering
- 2019
-
In: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 13:3, s. 485-494
-
Journal article (peer-reviewed)abstract
- Modern roundabouts are widely used at intersections with light traffic, generally providing safety and other advantages. However, large entry delays are often observed at roundabouts with unbalanced flow patterns, even though the entry traffic flow is not high. A metering signal-based strategy is examined to mitigate the above problems. A mathematical optimisation model is formulated firstly with the objective of minimising the total entry delay, subject to the metering signal thresholds. Then a solution algorithm based on VISSIM simulation is developed. Finally, a case study is carried out to testify the feasibility and applicability of the proposed model. Extended scenarios analyses under different levels of approach volume, different demand combinations and different proportions of right-turn vehicles (left-side driving) are also conducted. Results show that the methodology can effectively improve the operational performance, and a delay reduction of up to 25.7% can.
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| 14. |
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| 15. |
- Fang, Shan, et al.
(author)
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A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation
- 2024
-
In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:1, s. 174-198
-
Journal article (peer-reviewed)abstract
- Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a balance between accuracy and efficiency. This article investigates the error variation when the traffic oscillation is simulated by the intelligent driver model (IDM). Then, it divides the traffic oscillation into four phases (coasting, deceleration, acceleration, and stationary) by using the space headway of multiple steps. To simulate traffic oscillation between multiple human-driven vehicles, a dynamic transformation CF model is proposed, which includes the long-time prediction submodel [modified sequence-to-sequence (Seq2seq)] model, short-time prediction submodel (Transformer), and their dynamic transformation strategy]. The first submodel is utilized to simulate the coasting and stationary phases, while the second submodel is utilized to simulate the acceleration and deceleration phases. The results of experiments indicated that compared to K-nearest neighbors, IDM, and Seq2seq CF models, the dynamic transformation CF model reduces the trajectory error by 60.79–66.69% in microscopic traffic flow simulations, 7.71–29.91% in mesoscopic traffic flow simulations, and 1.59–18.26% in macroscopic traffic flow simulations. Moreover, the runtime of the dynamic transformation CF model (Inference) decreased by 14.43–66.17% when simulating the large-scale traffic flow.
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| 16. |
- Gao, Kun, 1993, et al.
(author)
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Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior
- 2021
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In: Transportation Research Part A: Policy and Practice. - : Elsevier BV. - 0965-8564. ; 148, s. 1-21
-
Journal article (peer-reviewed)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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| 17. |
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| 18. |
- Gao, Kun, 1993, et al.
(author)
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Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility
- 2023
-
In: Journal of Transport Geography. - 0966-6923. ; 110
-
Journal article (peer-reviewed)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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| 19. |
- Gao, Kun, 1993, et al.
(author)
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Diverging effects of subjective prospect values of uncertain time and money
- 2021
-
In: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 1
-
Journal article (peer-reviewed)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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| 20. |
- Gao, Kun, 1993, et al.
(author)
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Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction
- 2021
-
In: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 97
-
Journal article (peer-reviewed)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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| 21. |
- Gao, Kun, 1993, et al.
(author)
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Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
- 2021
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In: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 218
-
Journal article (peer-reviewed)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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| 22. |
- Gao, Kun, 1993, et al.
(author)
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Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips
- 2020
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In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 71, s. 272-287
-
Journal article (peer-reviewed)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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| 23. |
- Gao, Kun, 1993, et al.
(author)
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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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In: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 94
-
Journal article (peer-reviewed)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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| 24. |
- Ghanbarikarekani, A. Mina, et al.
(author)
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Optimization of Signalized Intersections Equipped with LRV Signal Priority Systems by Minimizing Cars' Stop Time
- 2019
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In: 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. ; , s. 4230-4235
-
Conference paper (peer-reviewed)abstract
- There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by providing them priority. In order to achieve this goal, several policies have been used such as Transit Signal Priority (TSP) system for Light Rail Vehicle (LRV). LRV signal priority is a timing strategy that gives priority to LRVs at signalized intersections. More specifically, this strategy is based on changing the sequence of phases, extending green time and reducing red time of LRV's phase. Although this method has considerable benefits for LRVs, it penalizes private vehicles by increasing their delay and stop time at intersections. This paper aims to propose a model to improve LRV signal priority systems. The modifying model for LRV signal priority systems minimizes the green extension and red reduction of LRV's phase by using linear programming (LP) method to calculate an optimal speed for LRVs reaching the stop line. Consequently, LRVs are prioritized while the performance of private vehicles would be improved.
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| 25. |
- Ghanbarikarekani, Mina, et al.
(author)
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Minimizing the Average Delay at Intersections via Presignals and Speed Control
- 2018
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In: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2018
-
Journal article (peer-reviewed)abstract
- Urban intersections have been well recognized as bottlenecks of urban transport systems. It is thus important to propose and implement strategies for increasing the efficiency of public and private transportation systems as a whole. In order to achieve this goal, an additional signal could be set up near the intersection to give priority to buses through stopping vehicles in advance of the main intersection as a presignal. It has been increasingly popular in urban cities. While presignals indeed reduce the average delay per traveler, they cause extra stops of private vehicles, which might compromise the overall efficiency, safety, and sustainability. This paper aims to propose a model to improve presignals by reducing the vehicles' number of stops behind the presignals. By applying the method, vehicles would be able to adjust their speed based on traffic conditions as well as buses' speed and approach. Numerical analyses have been conducted to determine the conditions required for implementing this method.
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| 26. |
- Ghanbarikarekani, Mina, et al.
(author)
-
Minimizing the stop time of private vehicles at intersections with LRT signal priority systems
- 2020
-
In: Transportation Research Procedia. - : Elsevier BV. - 2352-1465 .- 2352-1457. ; 48:2020, s. 939-945
-
Conference paper (peer-reviewed)abstract
- There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by prioritizing them. To this end, several methods have been used such as Transit Signal Priority (TSP) system for Light Rail transit (LRT). LRT signal priority is a timing strategy that gives priority to LRTs at signalized intersections through changing the sequence of phases, extending green time and reducing red time at LRT's phase. In this paper, we propose a model to improve LRT signal priority systems. The developed model minimizes the green extension and red reduction of LRT's phase by estimating an optimal speed for LRTs reaching the stop line. Consequently, the priority of LRTs would be maintained while the performance of private vehicles would be improved by decreasing their stop time.
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| 27. |
- Guo, Jingqiu, et al.
(author)
-
Characteristics of Mixed Traffic Flow in Two-lane Scenario Based on Cooperative Gaming Method
- 2019
-
In: Tongji Daxue Xuebao/Journal of Tongji University. - 0253-374X. ; 47:7, s. 976-983
-
Journal article (peer-reviewed)abstract
- This paper aims to explore the impacts of connected and automated vehicles (CAV) on traffic flow efficiency based on in-depth microscopic simulation studies using cooperative gaming method. First, the Gipps car-following models were integrated into an improved cellular automata model to mimic the regular vehicle's driving behavior. Then, cooperative gaming method integrated with enhanced Q-learning was employed as the modeling platform for CAV, to strengthen the capability of the simulation system in realistically reproducing CAV lane changing and car following behavior. Finally, a 2-lane freeway stretch was applied to our simulations, and with extensive simulation studies we obtained some promising results. The study results suggest that the impacts of CAV are quite positive. The inclusion of CAV considerably improves traffic flow, mean speed, and traffic capacity. Such understanding is essential for research concerning CAV as well as the CAV implication for future traffic management and control.
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| 28. |
- He, Yixu, et al.
(author)
-
Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
- 2024
-
In: Transportation Letters. - 1942-7867 .- 1942-7875. ; In Press
-
Journal article (peer-reviewed)abstract
- The application of deep reinforcement learning (DRL) techniques in intelligent transportation systems garners significant attention. In this field, reward function design is a crucial factor for DRL performance. Current research predominantly relies on a trial-and-error approach for designing reward functions, lacking mathematical support and necessitating extensive empirical experimentation. Our research uses vehicle velocity control as a case study, build training and test sets, and develop a DRL framework for speed control. This framework examines both single-objective and multi-objective optimization in reward function designs. In single-objective optimization, we introduce “expected optimal velocity” as an optimization objective and analyze how different reward functions affect performance, providing a mathematical perspective on optimizing reward functions. In multi-objective optimization, we propose a reward function design paradigm and validate its effectiveness. Our findings offer a versatile framework and theoretical guidance for developing and optimizing reward functions in DRL, particularly for intelligent transportation systems.
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| 29. |
- Jiang, Guanying, et al.
(author)
-
A Dynamic Model Averaging for the Discovery of Time-Varying Weather-Cycling Patterns
- 2021
-
In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 22:5, s. 2786-2796
-
Journal article (peer-reviewed)abstract
- It has been well recognized that weather variations significantly impact cycling experiences of users. However, the weather-cycling dynamic relationship over time is not well studied in the literature. In this paper, in order to bridge this gap, we propose a Dynamic Model Averaging and Dynamic Model Selection (DMA and DMS) to reveal the characteristics of time-varying responses and the associated influencing factors for young people's shared bike trips. Without loss of generality, dynamic models with unknown observational variances are also proposed. We take New York City as an instance and analyze the drifts of patterns of New York CitiBike trips under six weather factors from various aspects. The results suggest that the bike trips' responses to some weather factors fluctuate dynamically while others maintain at a relatively stable level. It is concluded that a few main influencing factors are adequate to represent the travel patterns. It is observed that dynamic models, with the strength of alleviating multicollinearity, present better forecast performance than classic models. This work can facilitate the decision makers and managers to oversee and optimise travel experience of users in real time.
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| 30. |
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| 31. |
- Kong, Xiangyu, et al.
(author)
-
An Online Processing Method for the Cooperative Control of Connected and Automated Vehicle Platoons
- 2021
-
In: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 231, s. 133-139
-
Conference paper (peer-reviewed)abstract
- The recent development of connected and autonomous vehicles (CAVs) makes it increasingly realistic to develop the next generation of transportation systems with the potential to improve operational performance and flexibility. The cooperative control of CAV platoons remains one of the most crucial yet challenging problems before the CAVs can be widely implemented in practice. The present study focuses on an application of CAVs at signalized intersections to realize a well-organized CAV permutation as well as improving the performance of the intersection. An online processing A* (OPA*) algorithm is developed to improve the optimality and computation performance. A comparative analysis between the proposed OPA* algorithm and an existing A* method is made. In summary, the OPA* could result in stable and scalable results which makes it possible for widely industrial usage.
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| 32. |
- Kuang, Yan, et al.
(author)
-
Novel Crash Surrogate Measure for Freeways
- 2020
-
In: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 146:8
-
Journal article (peer-reviewed)abstract
- In this paper, a disturbance-based methodology was proposed to represent the safety level of a car-following scenario. According to the probabilistic causal model, the following vehicles always take evasive actions to avoid a collision in the crash mechanism. In this study, the probabilistic model is modified to evaluate the maximum disturbance a car-following scenario can accommodate corresponding to the maximum evasive action taken by the following vehicle. This paper aims to investigate the safety level of a car-following scenario by estimating its capability index on accommodating disturbance. The surrogate measure, Disturbance Accommodate Index (DAI), is thus proposed to represent the stability of a car-following scenario by measuring its maximum capability on accommodating disturbance. Further, a case study is conducted to evaluate the performance of DAI by using the traffic and crash data provided by the Department of Transport and Main Roads in Queensland. The results show that the DAI outperforms the other crash surrogate measures (e.g., Aggregated Crash Index, Time to Collision, and Proportion of Stopping Distance) in representing rear-end crash risk, followed by the discussion.
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| 33. |
- Li, Aoyong, 1993, et al.
(author)
-
Comprehensive comparison of e-scooter sharing mobility: Evidence from 30 European cities
- 2022
-
In: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 105
-
Journal article (peer-reviewed)abstract
- Although e-scooter sharing has become increasingly attractive, little attention has been paid to a comprehensive comparison of e-scooter sharing mobility in multiple cities. To fill this gap, we conduct a comparative study to reveal the similarity and difference of e-scooter sharing mobility by collecting and analyzing vehicle availability data from 30 European cities during post COVID-19 pandemic. The comparisons are implemented from four perspectives, including temporal trip patterns, statistical characteristics (i.e., trip distance and duration), utilization efficiency, and wasted electricity during idle time. Results suggest that the similarity and difference co-exist between e-scooter sharing services in the cities, and utilization efficiency is significantly related with the number of e-scooters per person and per unit area. Surprisingly, on average nearly 33% of electricity are wasted during idle time in these cities. These research findings can be beneficial to further optimizing e-scooter sharing mobility services for transportation planners and micro-mobility operators.
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| 34. |
- Li, Aoyong, 1993, et al.
(author)
-
High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data
- 2021
-
In: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 296
-
Journal article (peer-reviewed)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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| 35. |
- Li, Guorong, et al.
(author)
-
Deciphering spatial heterogeneity of maritime accidents considering impact scale variations
- 2024
-
In: Maritime Policy and Management. - 0308-8839 .- 1464-5254. ; In Press
-
Journal article (peer-reviewed)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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| 36. |
- Li, Guofa, et al.
(author)
-
Driver Behavior in Intelligent Transportation Systems
- 2022
-
In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 14:3, s. 7-9
-
Journal article (other academic/artistic)abstract
- Drivers are the center of road/air/sea transportation systems, and they can be either human beings or artificial beings. Inconsistency between human driver behavior and artificial driver behavior will lead to accidents and congestion in intelligent transportation systems (ITSs) [1] , [2] . To make future ITSs trustworthy for traffic safety and acceptable for travel efficiency, developing industrial ITS applications based on drivers’ reliable behavioral and cognitive intelligence is essential [3] . However, there are many challenges to be addressed, including real-time behavior prediction, reliable decision making, safe interaction among human and artificial drivers, and so on.
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| 37. |
- Li, Miao, et al.
(author)
-
Unmanned aerial vehicle scheduling problem for traffic monitoring
- 2018
-
In: Computers and Industrial Engineering. - : Elsevier BV. - 0360-8352. ; 122, s. 15-23
-
Journal article (peer-reviewed)abstract
- For more accurate multiple-period real-time monitoring of road traffic, this paper investigates the unmanned aerial vehicle scheduling problem with uncertain demands. A mixed integer programming model is designed for this problem by combining the capacitated arc routing problem with the inventory routing problem. A local branching based solution method is developed to solve the model. A case study which applies this model to the road traffic in Shanghai is performed. In addition, numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.
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| 38. |
- Li, X. P., et al.
(author)
-
A piecewise trajectory optimization model for connected automated vehicles: Exact optimization algorithm and queue propagation analysis
- 2018
-
In: Transportation Research Part B: Methodological. - : Elsevier BV. - 0191-2615. ; 118, s. 429-456
-
Journal article (peer-reviewed)abstract
- This paper formulates a simplified traffic smoothing model for guiding movements of connected automated vehicles on a general one-lane highway segment. Adapted from the shooting heuristic proposed by Zhou et al. (2017) and Ma et al. (2017), this model confines each vehicle's trajectory as a piecewise quadratic function with no more than five pieces and lets all trajectories in the same platoon share identical acceleration and deceleration rates. Similar to the shooting heuristic, the proposed simplified model is able to control the overall smoothness of a platoon of connected automated vehicles and approximately optimize traffic performance in terms of fuel efficiency and driving comfort. While the shooting heuristic relies on numerical meta-heuristic algorithms that cannot ensure solution optimality, we discover a set of elegant theoretical properties for the general objective function and the associated constraints in the proposed simplified model, and consequentially propose an efficient analytical algorithm for solving this problem to the exact optimum. Interestingly, this exact algorithm has intuitive physical interpretations, i.e., stretching the transitional parts of the trajectories (i.e., parts with acceleration and deceleration adjustments) as far as they reach the upstream end of the investigated segment, and then balancing the acceleration and deceleration magnitudes as close as possible. This analytical exact model can be considered as a core module to a range of general trajectory optimization problems at various infrastructure settings. Numerical examples reveal that this exact algorithm has much more efficient computational performance and the same or better solution quality compared with the previously proposed shooting heuristic. These examples also illustrate how to apply this model to CAV control problems on signalized segments and at non-stop intersections. Further, we study a homogeneous special case of this model and analytically formulate the relationship between queue propagation and trajectory smoothing. One counter-intuitive finding is that trajectory smoothing may not always cause longer queue propagation but instead may mitigate queue propagation with appropriate settings. This theoretical finding has valuable implications to joint optimization of queuing management and traffic smoothing in complex transportation networks.
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| 39. |
- Li, X. P., et al.
(author)
-
Connected infrastructure location design under additive service utilities
- 2019
-
In: Transportation Research Part B: Methodological. - : Elsevier BV. - 0191-2615. ; 120, s. 99-124
-
Journal article (peer-reviewed)abstract
- An infrastructure system usually contains a number of inter-connected infrastructure links that connect users to services or products. Where to locate these infrastructure links is a challenging problem that largely determines the efficiency and quality of the network. This paper studies a new location design problem that aims to maximize the total weighted benefits between users and multiple services that are measured by the amount of connectivity between users and links in the network. This problem is investigated from both analytical and computational points of view. First, analytical properties of special cases of the problem are described. Next, two integer programming model formulations are presented for the general problem. We also test intuitive heuristics including greedy and interchange algorithms, and find that the interchange algorithm efficiently yields near-optimum solutions. Finally, a set of numerical examples demonstrate the proposed models and reveal interesting managerial insights. In particular, we found that a more distance-dependent utility measure and a higher concentration of users help achieve a better total utility. As the population becomes increasingly concentrated, the optimal link design evolves from a linear path to a cluster of links around the population center. As the budget level increases, the installed links gradually sprawl from the population center towards the periphery, and in the case of multiple population centers, they grow and eventually merge into one connected component.
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| 40. |
- Li, Xiaopeng (Shaw), et al.
(author)
-
Emerging Mobility Systems
- 2019
-
In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 11:3, s. 8-11
-
Journal article (other academic/artistic)
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| 41. |
- Lin, Hongyi, et al.
(author)
-
Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin–Destination Ride-Hailing Demand Prediction
- 2024
-
In: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 16:3, s. 111-124
-
Journal article (peer-reviewed)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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| 42. |
- Lin, Hongyi, et al.
(author)
-
Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models
- 2024
-
In: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; In Press
-
Journal article (peer-reviewed)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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| 43. |
- Lin, Peiqun, et al.
(author)
-
Multiple Emergency Vehicle Priority in a Connected Vehicle Environment: A Cooperative Method
- 2024
-
In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 25:1, s. 173-188
-
Journal article (peer-reviewed)abstract
- Since emergency vehicles (EMVs) in urban transit systems play a crucial role in responding to time-critical events, the quick response of EMVs is essential for improving the success rate of rescue operations and minimizing property loss. Booming connected vehicle (CV) technology provides a new perspective to further enhance the effectiveness of EMV priority. Based on this CV technology, we propose a cooperative multiple EMV priority model in which the speed, acceleration, and lane changing actions of both the EMVs and surrounding ordinary vehicles (OVs) are set as decision variables. This proposed model is rigorously formulated in integer linear programming to maximize the EMV traffic efficiency and find a trade-off between the interference with normal traffic flows and the smoothness of the EMV driving trajectories. Two customized algorithms are developed to reduce the number of decision variables and constraints to obtain the better feasible solution in an acceptable computational time. A numerical experiment based on real-world data is proposed to further verify the utility and effectiveness of the aforementioned mathematical model. The customized algorithms achieve near-exact solutions with significantly faster computation compared to the benchmark solver. The robustness of the proposed model is tested with different parameter settings in the sensitivity analysis.
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| 44. |
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| 45. |
- Liu, Xiaohan, et al.
(author)
-
Improving flex-route transit services with modular autonomous vehicles
- 2021
-
In: Transportation Research Part E: Logistics and Transportation Review. - : Elsevier BV. - 1366-5545. ; 149
-
Journal article (peer-reviewed)abstract
- With the advent of modular autonomous vehicles (MAVs), this paper presents a novel operational design for flex-route transit services to reduce operation costs of vehicles and improve the service quality of customers. The regime allows the simultaneous dispatch of a certain amount of MAVs from a bus terminal at a departure time. Each MAV is allowed to visit customers freely outside of checkpoints. Self-adaptive capacity and flexible service mode adapt time- and space-dependent demand characteristics. The presented operational design is formulated as a mixed-integer linear program that is NP-hard. A two-stage solution framework is developed to decompose the proposed mathematical programming cautiously. In the first stage, customized dynamic programming with valid cuts is designed to solve a bus scheduling problem efficiently. In the second stage, an effective and fast heuristic is proposed to solve a variant of the dial-a-ride problem and satisfy the technical requirements for developing on-line applications. Numerical examples and a case study show the effectiveness of the proposed design by comparing the flex-route transit services using traditional vehicles.
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| 46. |
- Liu, Xiaohan, et al.
(author)
-
Optimizing electric bus charging infrastructure considering power matching and seasonality
- 2021
-
In: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 100
-
Journal article (peer-reviewed)abstract
- In this research, a novel optimization model for electric bus charging station location, charger configuration, charging time and vehicle flow is developed considering power matching and seasonality. The seasonality highlights the effect of air temperature on the battery performances of electric buses. Power matching between batteries and chargers jointly determines the maximum battery acceptance rates of electric buses, and this consideration results in nonlinear constraints. A surrogate-based optimization approach is proposed to solve the mixed integer nonlinear program efficiently. The optimization model is demonstrated on a sub-transit network including 17 bus lines in Beijing. The results reveal significant performance differences regarding vehicle scheduling and charging among different bus fleets in the BEB-based transit system. The interesting findings on the distribution of vehicle flows for charging provide strong evidence to consider powering match in the bus charging infrastructure layout.
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| 47. |
- Liu, Yang, 1991, et al.
(author)
-
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
- 2022
-
In: Transportation Research Part E: Logistics and Transportation Review. - : Elsevier BV. - 1366-5545. ; 161
-
Journal article (peer-reviewed)abstract
- The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020.
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| 48. |
- Liu, Yang, 1991, et al.
(author)
-
DeepTSP: Deep traffic state prediction model based on large-scale empirical data
- 2021
-
In: Communications in Transportation Research. - : Elsevier BV. - 2772-4247. ; 1
-
Journal article (peer-reviewed)abstract
- Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatio-temporal data to predict the traffic state of large-scale traffic systems. In this study, we first summarize the three challenges faced by large-scale traffic state prediction, i.e., scale, granularity, and sparsity. Based on the domain knowledge of traffic engineering, the propagation of traffic states along the road network is theoretically analyzed, which are elaborated in aspects of the temporal and spatial propagation of traffic state, traffic state experience replay, and multi-source data fusion. A deep learning architecture, termed as Deep Traffic State Prediction (DeepTSP), is therefore proposed to address the current challenges in traffic state prediction. Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.
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| 49. |
- Lu, Chaoru, et al.
(author)
-
The role of alternative fuel buses in the transition period of public transport electrification in Europe: a lifecycle perspective
- 2023
-
In: International Journal of Sustainable Transportation. - : Informa UK Limited. - 1556-8318 .- 1556-8334. ; 17:6, s. 626-638
-
Journal article (peer-reviewed)abstract
- In alignment with climate change, the European Union endeavors to accelerate the electrification progress of the public transit system. In particular, Copenhagen in Denmark and Oslo in Norway develop a blueprint to have 100% public transit electrification by 2030 and 2028, respectively. In this study, the lifecycle approach is applied to explore the role of electric buses in the electrification progress of the public transport system in different European countries. To better model the energy/fuel consumption, we integrate the theoretical model of human thermal comfortable temperature into our proposed framework. We take into account the effects of weather, the daily operation characteristics, and the energy mix of different European counties, and evaluate the lifecycle environmental and economic performance of electric buses. The result shows that the public transportation system with both hybrid and electric buses can be good compensation between financial and environmental needs instead of using electric buses to replace all the conventional buses. Moreover, the operational plan of the public transportation system mixed with electric and hybrid buses may be adjusted according to the seasonal temperature variation so as to maximize the environmental benefits. Considering the different economic and environmental scenarios of energy sources, some EU countries would be able to reduce or remove the incentives for electric buses.
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| 50. |
- Lyu, Cheng, et al.
(author)
-
Personalized Modeling of Travel Behaviors and Traffic Dynamics
- 2022
-
In: Journal of Transportation Engineering Part A: Systems. - 2473-2893 .- 2473-2907. ; 148:10
-
Journal article (peer-reviewed)abstract
- Emerging mobile Internet applications have become valuable data sources for fine-grained transportation analysis, which allows the introduction of the concept of Personalization in both microscopic and macroscopic modeling of travel behaviors and traffic dynamics. Inspired by personalized recommendation systems, the personalized transportation models emphasize the importance of individual and local information. Two representative cases are presented in this study and two architectures, namely the travel behavior modeling architecture and the geoinformation modeling architecture, are proposed to address the problems of bike-sharing destination prediction and ensemble of ride-hailing demand predictors, respectively. Their performance has been verified by two case studies using the Mobike bike-sharing data and the DiDi ride-hailing demand data.
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