Article
Transportation
Behzad Bamdad Mehrabani, Jakob Erdmann, Luca Sgambi, Seyedehsan Seyedabrishami, Maaike Snelder
Summary: This study proposes an open-source solution framework for the multiclass simulation-based Traffic Assignment Problem (TAP) in mixed traffic of Connected and Autonomous Vehicles (CAVs) and Human-Driven Vehicles (HDVs), aiming to better understand the impact of CAVs on mixed traffic flow.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Transportation Science & Technology
Jian Wang, Wei Wang, Gang Ren, Min Yang
Summary: This study introduces a worst-case mixed traffic assignment model to address the issue of uncertain link capacity in mixed traffic flow environments, providing an effective algorithm for solving the problem. Numerical applications demonstrate the significant impact of uncertain link capacity on network performance and flow. The results can assist traffic managers in designing robust planning strategies.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Physics, Multidisciplinary
Jialin Liu, Zheng Liu, Bin Jia, Shiteng Zheng, Hao Ji
Summary: This paper focuses on managing a multiclass traffic evacuation task of private CAVs and mass-transit CAVs. The authors propose a multiclass cell transmission model to capture the speed difference between two types of CAVs. They also formulate a system optimum collaborative evacuation model to minimize the evacuation network clearance time or minimize the total travel time of evacuees. Numerical experiments show that the proposed model can capture multiclass traffic dynamics and traffic congestion, and the fully mixed approach may have better evacuation efficiency than the lane-based approach under certain evacuation demands.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiang Zhang, Edward Robson, S. Travis Waller
Summary: This study investigates the potential positive or negative societal impacts of AVs' expected travel and parking behavior, developing an integrated transport and economic model and conducting a case study in Sydney to demonstrate the significant losses of social welfare that can result from AV parking patterns.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2021)
Article
Automation & Control Systems
Ilgin Gokasar, Alperen Timurogullari, Muhammet Deveci, Harish Garg
Summary: Traffic management methods aim to reduce congestion levels by increasing the capacity of infrastructure. Connected autonomous vehicles (CAVs) equipped with vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connectivity technologies can be used as actuators for traffic control. A CAV-based alternative approach (SWSCAV) for traffic management is proposed, where CAVs slow down to match the speed of observed shockwaves (SWS) when an incident occurs, effectively reducing traffic congestion. Simulation results show that the SWSCAV method outperforms lane control signals (LCS) and variable speed limits (VSL) systems in reducing traffic density, even at low CAV market penetration rates.
Article
Green & Sustainable Science & Technology
Mohamad Shatanawi, Ferenc Meszaros
Summary: This research investigates the impact of varying the share distribution of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) on network performance and consumer surplus in Budapest using simulation-based dynamic traffic assignment. The results suggest that the emergence of AVs and SAVs improves overall network performance, particularly with an increasing share distribution of SAVs. Similarly, consumer surplus increases in all future scenarios, especially with an increasing share distribution of AVs.
Review
Chemistry, Multidisciplinary
Fayez Alanazi
Summary: The emergence of autonomous vehicles and advancements in technology have increased the demand for intelligent intersection management systems. This study conducted a literature analysis to explore how autonomous vehicles manage traffic at junctions. Through analyzing peer-reviewed publications, four primary categories of approaches were identified: rule-based, optimization, hybrid, and machine learning procedures. The analysis provides insights into the attributes, limitations, and future directions of these solutions, contributing to the understanding of this research area.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Civil
Mohit Garg, Melanie Bouroche
Summary: CAVs have potential to enhance traffic safety and efficiency, but they face challenges in mixed-traffic scenarios due to uncertainty in human driving behavior and unreliable communication networks. This study investigates the impact of CAVs on traffic safety and efficiency in realistic scenarios with imperfect communication, large-reaction time, vehicle modeling, and traffic scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Fatemeh Fakhrmoosavi, Ehsan Kamjoo, Ali Zockaie, Archak Mittal, James Fishelson
Summary: This study explores the impacts of AV dedicated lanes on traffic performance of large-scale networks. It finds that these impacts depend on AV market penetration rate, demand level, and AV dedicated lane implementation approach. The study shows that dedicated lanes for AVs are beneficial for high demand levels, but only justified for low AV market penetration rates in the base demand scenario. The impacts of AV dedicated lanes on traffic at the network level are different from those on single segments.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Transportation Science & Technology
Hari Hara Sharan Nagalur Subraveti, Anupam Srivastava, Soyoung Ahn, Victor L. Knoop, Bart van Arem
Summary: This paper proposes a novel approach to improve traffic throughput near bottlenecks by strategically assigning connected automated vehicles (CAVs) across lanes. The research uses macroscopic analytical approach to formulate lane assignment strategies and numerical simulations to quantify the improvements in throughput. Different strategies are considered for various operational conditions and the compensatory behavior of human-driven vehicles (HDVs) in response to CAV lane assignment is explicitly accounted for.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Interdisciplinary Applications
Mariano Gallo
Summary: This paper proposes models and algorithms for estimating traffic flows generated by the centralised management of autonomous vehicles in both exclusive and mixed traffic conditions. The research demonstrates the impact of autonomous vehicles on traffic flows and provides a method for evaluating the inclusion of autonomous vehicles in traffic planning, design or policy interventions.
SIMULATION MODELLING PRACTICE AND THEORY
(2023)
Article
Green & Sustainable Science & Technology
Roxanne Neufville, Hassan Abdalla, Ali Abbas
Summary: This research investigates the potential of autonomous vehicles in reducing traffic congestion and greenhouse gas emissions. By simulating traffic networks and varying the number of autonomous vehicles, the study finds that introducing autonomous vehicles can significantly reduce delays and greenhouse gas emissions.
Article
Computer Science, Interdisciplinary Applications
Shiyao Yang, Mengxiao Du, Qun Chen
Summary: This paper investigates the impacts of connected and autonomous vehicles (CAVs) on traffic efficiency and safety. The results show that with the increase in CAV penetration rate, outflow increases, average speed distribution improves, and traffic efficiency is enhanced, while promoting traffic safety.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Computer Science, Information Systems
Moahd K. Alghuson, Khaled Abdelghany, Ahmed Hassan
Summary: SLEM is a telematics-based traffic law enforcement and management system that assigns real-time scores to drivers to measure their driving performance, adopting personalized routing strategy to guide high-performing drivers to less congested routes. Experimental results showed that SLEM's routing strategy reduced travel time during congestion situations by about 5%.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Analytical
Nikhil Kamath, Roshan Fernandes, Anisha P. Rodrigues, Mufti Mahmud, P. Vijaya, Thippa Reddy Gadekallu, M. Shamim Kaiser
Summary: This paper proposes a navigation method based on traffic knowledge to enable precise navigation of connected and autonomous vehicles (CAVs) in urban traffic environments. By analyzing sensor-oriented traffic data, a precise navigation path is generated, and traffic knowledge is shared with other vehicles to enable CAVs navigation. Experimental results confirm the benefits of this method in accurately navigating CAVs in urban traffic environments.
Article
Transportation
Lili Lu, Zhengbing He, Jian Wang, Jufeng Chen, Wei Wang
Summary: The study investigates lane-level travel time distributions for signalized arterial roads using a simulation testbed based on VISSIM and Java plugin. The results show that KDE can effectively capture travel time reliability metrics for all road segments. This finding will help improve traffic management and control on arterial roads.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Economics
Chaojie Wang, Srinivas Peeta, Jian Wang
Summary: A decentralized routing strategy based on incentives is proposed in this study to optimize network performance for all connected and autonomous vehicles (CAVs). The strategy consists of three stages and aims to push network performance closer to the system optimum.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Green & Sustainable Science & Technology
Xingliang Liu, Jian Wang, Tangzhi Liu, Jin Xu
Summary: This study develops a new method for predicting the spatiotemporal boundary propagation of traffic congestion caused by emergency events, which is more practical and applicable. By dividing the expressway network into different sections and utilizing kinetic wave theory and volume-density relationship, the velocity of congestion boundary movement is characterized effectively.
Article
Transportation
Anye Zhou, Jian Wang, Srinivas Peeta
Summary: This study proposes a robust platoon control strategy for Connected and Autonomous Vehicles (CAVs) to mitigate the impacts of falsified information and ensure safe operation.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Transportation Science & Technology
Anye Zhou, Srinivas Peeta, Menglin Yang, Jian Wang
Summary: This study proposes a hybrid cooperative intersection control framework combining virtual platooning control and traffic flow regulation for traffic environments with connected autonomous vehicles. Through coordination, vehicles in a virtual platoon are grouped into compatible passing sets to maintain desired safe spacing through the intersection. The effectiveness of the framework is evaluated through numerical experiments.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Electrical & Electronic
Wei Wang, Jian Wang, De Zhao, Kun Jin
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Operations Research & Management Science
Xiaozheng He, Jian Wang, Srinivas Peeta, Henry X. Liu
Summary: This paper presents a discrete day-to-day signal retiming problem to fine-tune the green splits in a traffic network and reduce congestion and travel time.
NETWORKS & SPATIAL ECONOMICS
(2022)
Article
Transportation Science & Technology
Jian Wang, Wei Wang, Gang Ren, Min Yang
Summary: This study introduces a worst-case mixed traffic assignment model to address the issue of uncertain link capacity in mixed traffic flow environments, providing an effective algorithm for solving the problem. Numerical applications demonstrate the significant impact of uncertain link capacity on network performance and flow. The results can assist traffic managers in designing robust planning strategies.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Economics
Jian Wang, Lili Lu, Srinivas Peeta
Summary: This study proposes a robust cooperative control strategy for ensuring the safe and efficient maneuvering of a platoon of connected and autonomous vehicles (CAVs) in real-world driving environments. Results show that this strategy substantially improves platoon performance under disturbances compared to strategies that ignore uncertainties in vehicle dynamics.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Economics
Jian Wang, Xiaozheng He, Srinivas Peeta, Wei Wang
Summary: This study proposes a NRMFD algorithm integrated with the EBA method to solve the CNDP under UE. The algorithm determines a feasible descent direction in each iteration and computes a feasible step size using the EBA method to improve convergence speed.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Engineering, Electrical & Electronic
Lili Lu, Jian Wang, Yukou Wu, Xu Chen, Ching-Yao Chan
Summary: The article introduces an analytical model to accurately predict each vehicle's travel time on a signalized arterial roadway under nonsaturated traffic conditions, based on three different situations a vehicle may encounter when passing through a signalized intersection. The model considers the correlation between a vehicle's entry time to the segment and the signal-phase liming, and predicts the intersection delay of each vehicle based on queue shockwave speed and discharge shockwave speed, leading to an accurate characterization of travel time and potential application in a connected environment to improve traffic mobility.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Kai Huang, Ming Chen, Zihan Zhou, Xu Han, Jian Wang, Zhiyuan Liu
Summary: This article studies the parameter calibration of widely used SUE models and proposes a bilevel model and solution approach. The upper level minimizes the error between real and model-based traffic flow patterns using a Bayesian optimization method, while the lower level copes with SUE traffic assignment using dedicated algorithms. The proposed model and solution approach are validated based on a case study of Yuyao, China.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2023)
Article
Transportation
Yuntao Guo, Jian Wang, Srinivas Peeta, Panagiotis Ch Anastasopoulos
TRAVEL BEHAVIOUR AND SOCIETY
(2020)
Article
Engineering, Electrical & Electronic
Lili Lu, Jian Wang, Yukou Wu, Xu Chen, Ching-Yao Chan
Summary: This article proposes an analytical model to predict the travel time of vehicles on a signalized arterial roadway by considering three potential situations vehicles may encounter when passing through intersections. The model is developed based on analyzing the correlation between a vehicle's entry time to a segment and the signal-phase timing using kinematic wave theory. By predicting intersection delay using queue shockwave speed and discharge shockwave speed, the model accurately characterizes a vehicle's travel time on a road segment.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
