Article
Computer Science, Information Systems
Sadegh Motallebi, Hairuo Xie, Egemen Tanin, Jianzhong Qi, Kotagiri Ramamohanarao
Summary: One common cause of traffic congestion is the concentration of intersecting vehicle routes. The development of connected autonomous vehicles offers the opportunity to address this issue by coordinating vehicle routes globally.
Article
Engineering, Electrical & Electronic
Huaxin Pei, Yi Zhang, Qinghua Tao, Shuo Feng, Li Li
Summary: In this paper, a distributed strategy is proposed to decompose the problem of cooperative driving in multi-intersection road networks into small-scale sub-problems and ensure appropriate coordination between adjacent areas through specially designed information exchange. Simulation results demonstrate the efficiency-complexity balanced advantage of the proposed strategy under various traffic demand settings.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Civil
Yixiao Zhang, Rui Hao, Tingting Zhang, Xiaohan Chang, Zepeng Xie, Qinyu Zhang
Summary: This paper presents a general dedicated intersection coordination framework for autonomous vehicles, which aims to improve vehicle coordination and motion planning at road intersections. The framework consists of a high-level planner and a low-level planner, which work together to generate reference trajectories, feasible tunnels, and practical trajectories. Simulations and experiments show that the proposed framework achieves significant performance advantages in various traffic metrics. Furthermore, the high-level planner effectively eliminates possible deadlocks among autonomous vehicles, which is rarely discussed in existing investigations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Tao Li, Xu Han, Jiaqi Ma
Summary: The proposed cooperative perception framework based on particle filtering provides efficient and accurate estimations and predictions of detailed microscopic traffic states by combining data from connected and automated vehicles and roadside detectors. This framework demonstrates significant improvements in accuracy and predictive performance for real-time traffic state estimation and prediction.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation Science & Technology
Handong Yao, Xiaopeng Li
Summary: This study focuses on trajectory smoothing for controlling CAVs in mixed traffic to reduce traffic oscillations, proposing a model that considers lane-change awareness and having superior performance in numerical experiments, providing additional benefits in overall system performance.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Transportation Science & Technology
Wanjing Ma, Jinjue Li, Chunhui Yu
Summary: This study proposes a shared-phase-dedicated-lane (SPDL) traffic control model for handling the driving behavior of connected and automated vehicles (CAVs) and human-driven vehicles (HVs) in a mixed traffic environment. The model optimizes signal, phase sequences, and durations, as well as conducts CAV platooning and trajectory planning to reduce vehicle delay and improve intersection capacity. Numerical studies show the advantages of this model over previous control methods.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Chemistry, Analytical
Chenghao Li, Zhiqun Hu, Zhaoming Lu, Xiangming Wen
Summary: The emerging CAV has the potential to improve traffic efficiency and safety by cooperating at intersections. However, perceptual errors may occur due to external conditions, and a trade-off between efficiency and safety needs to be considered in the presence of both CAVs and conventional vehicles. Data fusion schemes can improve overall traffic flow and energy efficiency at various CAV penetration rates.
Article
Engineering, Civil
Wei Wu, Yang Liu, Wei Liu, Fangni Zhang, Vinayak Dixit, S. Travis Waller
Summary: Most existing studies on AIM focus on algorithms for resolving conflicts among vehicles, while this paper proposes optimizing entrance and exit lanes to improve traffic efficiency. Two methods are developed for optimizing entering time and route choices, and a heuristic approach is adopted for real-time applicability.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Wenbo Sun, Fangni Zhang, Wei Liu, Qingying He
Summary: This paper investigates the potential of improving overall traffic and energy efficiency by controlling a proportion of connected and autonomous vehicles (CAVs) in a mixed traffic corridor. The proposed control framework shows promising results in numerical studies, demonstrating its effectiveness in improving road throughput.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Zhiyun Deng, Kaidi Yang, Weiming Shen, Yanjun Shi
Summary: This paper proposes a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The algorithm aims to balance traffic performance and computational efficiency by incorporating the platoon formation scheme into the upper-level traffic scheduling model and jointly optimizing the passing sequence and timeslots of vehicles with the platoon configuration scheme based on real-time traffic states. A lower-level trajectory planning model is also utilized to generate dynamically-feasible and energy-efficient trajectories, improving space utilization and preventing spillbacks. Numerical experiments demonstrate the superiority of the proposed approach in optimality and stability for real-life applications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Transportation Science & Technology
Yunyi Liang, Shen Zhang, Yinhai Wang
Summary: This study addresses the optimization of road side unit (RSU) location at a single intersection to achieve low vehicle-to-road-side-unit (V2R) communication delay for connected-autonomous-vehicle-based (CAV-based) intersection control strategies. A two-stage stochastic mixed-integer nonlinear program is developed to minimize cost associated with RSU investment and V2R communication delay penalty, providing a cost-effective solution for low V2R communication delay in CAV environment. The proposed model outperforms a deterministic model, demonstrating its effectiveness and cost efficiency.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Engineering, Civil
Ruiyi Wu, Hongfei Jia, Qiuyang Huang, Jingjing Tian, Heyao Gao, Guanfeng Wang
Summary: This paper proposes a two-stage multi-lane unsignalized intersection cooperative control strategy based on mixed platoons (MICS-PF) in the mixed traffic environment of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). The strategy optimizes the throughput of intersections by organizing vehicles into mixed platoons and coordinating the passing order. It improves the capacity of approach roads and prevents traffic congestion and disorderly lane changes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Aaron Parks-Young, Guni Sharon
Summary: This paper presents a novel embedding protocol that allows the Hybrid Autonomous Intersection Management (H-AIM) protocol to operate concurrently with actuated and adaptive signal controllers. The proposed protocol extends H-AIM to handle operational uncertainty and ensures safety through a new method of computing safety bounds on signal timing. Experimental results show the feasibility and effectiveness of combining H-AIM with actuated controllers for various levels of connected and autonomous vehicle (CAV) market penetration and different combinations of common signal control schemes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Sikai Chen, Jiqian Dong, Paul (Young Joun) Ha, Yujie Li, Samuel Labi
Summary: A connected autonomous vehicle (CAV) network is a group of vehicles operating within a specific spatial scope, sharing traffic information and coordinating movements for safety and mobility. The complex nature of CAV networks poses challenges for cooperative control, which can be addressed using a novel deep reinforcement learning-based algorithm that combines graphic convolution neural network with deep Q-network. The proposed algorithm enhances CAV operations by efficiently aggregating information from sensing and connectivity sources to make operative decisions that improve safety and mobility.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2021)
Article
Mathematics
Anton Agafonov, Alexander Yumaganov, Vladislav Myasnikov
Summary: Cooperative control of vehicle trajectories and traffic signal phases is proposed to improve transportation system efficiency and safety. A cooperative control method is proposed, which combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. Numerical experiments show that the proposed method can reduce fuel consumption by 1% to 4.2%, travel time by 1% to 5.3%, and stop delays by 27%, compared to baseline methods, in different simulation scenarios.
Article
Transportation Science & Technology
Pinlong Cai, Yunpeng Wang, Guangquan Lu, Peng Chen, Chuan Ding, Jianping Sun
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2016)
Article
Engineering, Civil
Pinlong Cai, Yunpeng Wang, Guangquan Lu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2019)
Article
Physics, Multidisciplinary
Daocheng Fu, Pinlong Cai, Yilun Lin, Song Mao, Licheng Wen, Yikang Li
Summary: This paper introduces a new path planning method called Incremental Path Planning (IPP), which considers traffic flow as a superposition of spatiotemporal paths. Paths are planned incrementally based on the remaining spatiotemporal resources and travel demands, leading to improved traffic efficiency and driving experience.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Proceedings Paper
Automation & Control Systems
Jingxin Zhang, Hao Chen, Pinlong Cai
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Pinlong Cai, Hao Chen, Jingxin Zhang
2017 6TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS (DDCLS)
(2017)
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)