☆ 4.7 Article

Managing connected and automated vehicles at isolated intersections: From reservation- to optimization-based methods

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2019)

获取全文
添加到收藏夹

期刊

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
卷 122, 期 -, 页码 416-435

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2019.03.002

关键词

Connected and automated vehicle; Isolated intersection; Reservation; Optimization

类别

Economics Engineering, Civil Operations Research & Management Science Transportation Transportation Science & Technology

资金

  1. US Department of Energy (EERE Award) [DE-EE0007212]
  2. Shanghai Yangfan Program [19YF1451600]
  3. National Natural Science Foundation of China [61773293]

向作者/读者索取更多资源

Protocol

社区支持

Reagent

社区支持

Reservation-based methods with simple policies such as first-come-first-service (FCFS) have been proposed in the literature to manage connected and automated vehicles (CAVs) at isolated intersections. However, a comprehensive analysis of intersection capacity and vehicle delay under FCFS-based control is missing, especially under high traffic demand. To address this problem, this study adopts queueing theory and analytically shows that such method is incapable of handling high demand with multiple conflicting traffic streams. Furthermore, an optimization model is proposed to optimally serve CAVs arriving at an intersection for delay minimization. This study then compares the performance of the proposed optimization-based control with reservation-based control as well as conventional vehicle-actuated control at different demand levels. Simulation results show that the proposed optimization-based control performs best and it has noticeable advantages over the other two control methods. The advantages of reservation-based control are insignificant compared with vehicle-actuated control under high demand. (C) 2019 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Engineering, Civil

Learning-Based Stochastic Driving Model for Autonomous Vehicle Testing

Lin Liu, Shuo Feng, Yiheng Feng, Xichan Zhu, Henry X. Liu

Summary: This paper proposes a learning-based stochastic driving model for AV testing, which can reproduce trajectories more similar to human drivers and generate a naturalistic driving environment, showing significant importance for AV testing and evaluation.

TRANSPORTATION RESEARCH RECORD (2022)

添加到收藏夹
Article Engineering, Civil

Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework

Shuo Feng, Yiheng Feng, Haowei Sun, Yi Zhang, Henry X. Liu

Summary: A customized testing scenario library for a specific CAV model is generated through an adaptive process to compensate for the performance dissimilarities and leverage each test of the CAV.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

添加到收藏夹
Article Engineering, Civil

Optimization of Bus Scheduling and Bus-Berth Matching at Curbside Stops under Connected Vehicle Environment

Shiqi Ou, Wanjing Ma, Chunhui Yu

Summary: A mixed-integer linear programming model is proposed in this study to optimize the scheduling of bus arrivals and the matching of bus berths at a curbside stop under the connected vehicle environment. The objective is to minimize bus delays weighted by the number of passengers, taking into consideration bus punctuality. Numerical studies validate the advantages of the proposed model in terms of weighted bus delays and bus punctuality.

JOURNAL OF ADVANCED TRANSPORTATION (2022)

添加到收藏夹
Article Transportation Science & Technology

Learning the max pressure control for urban traffic networks considering the phase switching loss

Xingmin Wang, Yafeng Yin, Yiheng Feng, Henry X. Liu

Summary: This study proposes a new framework for max pressure control using reinforcement learning algorithms, considering phase switching loss and optimizing parameters. Simulation results show that the proposed control method outperforms traditional max pressure control. This research is of great significance for real-world implementations.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2022)

添加到收藏夹
Article Engineering, Civil

Design, Implementation, and Evaluation of a Roadside Cooperative Perception System

Rusheng Zhang, Zhengxia Zou, Shengyin Shen, Henry X. Liu

Summary: This paper introduces a newly developed and deployed roadside cooperative perception system with an edge-cloud structure and multiple kinds of sensors. The performance of the system is analyzed using data collected from the field, and its potential in applications such as traffic volume monitoring and road safety warning is demonstrated.

TRANSPORTATION RESEARCH RECORD (2022)

添加到收藏夹
Article Transportation Science & Technology

Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification

Jun Hua, Guangquan Lu, Henry X. Liu

Summary: This study establishes a driving behavior model framework to explain drivers' approaching behaviors to signalized intersections, and obtains probabilities and distributions through simulations. The results demonstrate the validity of the proposed model and its applicability to drivers with different desired risks.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2022)

添加到收藏夹
Article Engineering, Civil

Trajectory Data Processing and Mobility Performance Evaluation for Urban Traffic Networks

Xingmin Wang, Zachary Jerome, Chenhao Zhang, Shengyin Shen, Vivek Vijaya Kumar, Henry X. Liu

Summary: This paper proposes a trajectory data processing pipeline for urban traffic network applications, which includes matching, splitting, and distance extraction steps, as well as smoothing and filtering algorithms to reduce noise and errors. The processed data is used to calculate various mobility performance indices for comprehensive evaluations. The proposed methods are efficient, robust, and scalable, and can be applied to large-scale urban traffic networks.

TRANSPORTATION RESEARCH RECORD (2023)

添加到收藏夹
Article Computer Science, Interdisciplinary Applications

Signal timing at an isolated intersection under mixed traffic environment with self-organizing connected and automated vehicles

Chengyuan Ma, Chunhui Yu, Cheng Zhang, Xiaoguang Yang

Summary: This study presents a signal timing model for isolated intersections in mixed traffic environments. The model considers both connected and human-driven vehicles (CHVs) and connected and automated vehicles (CAVs). Unlike previous studies, CAVs in this model perform self-organizing trajectory planning without external control. The proposed model is shown to effectively reduce vehicle delay and improve traffic throughput compared to benchmark and traditional signal control methods.

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING (2023)

添加到收藏夹
Article Multidisciplinary Sciences

Dense reinforcement learning for safety validation of autonomous vehicles

Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou, Shengyin Shen, Henry X. Liu

Summary: A critical bottleneck for autonomous vehicle development and deployment is the high costs required to validate safety in real-world driving. Researchers have developed an intelligent testing environment using AI-based agents to accelerate the safety validation process without bias. Their approach reduces testing time by orders of magnitude and can also be applied to other safety-critical autonomous systems.

NATURE (2023)

添加到收藏夹
Article Engineering, Civil

A Hierarchical Vehicle Behavior Prediction Framework With Traffic Signals and Interactive Agents

Zhen Yang, Rusheng Zhang, Gaurav Pandey, Neda Masoud, Henry X. Liu

Summary: This work proposes a hierarchical vehicle behavior prediction framework that incorporates traffic signal information and models the interaction between vehicles. The framework predicts vehicle behaviors in two stages: discrete intention prediction and continuous trajectory prediction. It is designed to capture the difference among human drivers with parameterized driver characteristics.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

添加到收藏夹
Article Engineering, Civil

Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration

Zhen Yang, Jun Ying, Junjie Shen, Yiheng Feng, Qi Alfred Chen, Z. Morley Mao, Henry X. Liu

Summary: This paper proposes a method to detect GPS spoofing attacks on connected vehicles (CVs) and autonomous vehicles (AVs) using domain knowledge in transportation and vehicle engineering. A computational-efficient driving model is constructed by learning from historical trajectories, and a statistical method is developed to measure the dissimilarities between observed and predicted trajectories for anomaly detection. The proposed method is validated on real-world datasets and shown to detect almost all attacks with low false positive and false negative rates.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

添加到收藏夹
Article Transportation Science & Technology

Managing connected and automated vehicles with flexible routing at lane-allocation-freeintersections

Ruochen Hao, Yuxiao Zhang, Wanjing Ma, Chunhui Yu, Tuo Sun, Bart van Arem

Summary: With the development of internet of vehicles and automated driving, individual-based trajectory control at intersections becomes possible. This study proposes a mixed-integer linear programming (MILP) model to optimize vehicle trajectories at an isolated signal-free intersection without lane allocation, denoted as lane-allocation-free (LAF) control. Vehicle routes and trajectories at the intersection are optimized in one unified framework for system optimality in terms of total vehicle delay.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2023)

添加到收藏夹
Article Computer Science, Information Systems

Spatial-Attention and Demographic-Augmented Generative Adversarial Imputation Network for Population Health Data Reconstruction

Yujie Feng, Jiangtao Wang, Yasha Wang, Xu Chu

Summary: As a crucial part of public health system, population health monitoring plays a significant role in shaping health policies. However, the high cost of traditional data collection methods has led to the proposal of sparse-sampling-completion algorithms. Existing data-completion methods primarily focus on adjacent-spatial correlations, which may not accurately infer missing prevalence data in neighboring areas due to cost constraints. To address this problem, we propose a novel deep-learning-based prevalence inference model, SDA-GAIN (Spatial-attention and Demographic-augmented Generative Adversarial Imputation Network), which improves accuracy by learning health semantic space similarities between cross-space areas. SDA-GAIN utilizes a Transformer-based model to learn healthy semantic similarities and a GAN-based model for high-accuracy completion, with the addition of demographic data to enhance the model's ability in learning better health semantic representation through CNN. Extensive experiments demonstrate that SDA-GAIN outperforms other state-of-the-art approaches at low sampling rates (<30%), leading to significant cost savings. Moreover, the visualization of health semantic similarity learned by SDA-GAIN closely resembles real-life situations.

IEEE TRANSACTIONS ON BIG DATA (2023)

添加到收藏夹
Article Engineering, Civil

Joint Optimization of Longitudinal and Lateral Locations of Autonomous-Vehicle-Dedicated Lanes on Expressways

Congjian Liu, Cheng Zhang, Chunhui Yu, Ke Chen, Zehao Jiang

Summary: An analysis framework was developed to optimize the longitudinal and lateral locations of autonomous-vehicle-dedicated lanes (AVDLs) on urban expressways. The framework includes a lane-level multiclass equilibrium assignment model and a 0-1 integer programming model, which determine the optimal deployment plan of AVDLs with minimal total travel time (TTT). Experimental results demonstrate that the proposed framework outperforms existing methods in terms of TTT reduction. Sensitivity analysis also confirms the robustness of the developed method in reducing TTT under different conditions.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

添加到收藏夹
Proceedings Paper Computer Science, Artificial Intelligence

Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios

Jingxuan Yang, Honglin He, Yi Zhang, Shuo Feng, Henry X. Liu

Summary: This paper proposes an adaptive testing method using sparse control variates, which evaluates the performance of CAVs by adaptively utilizing testing results. It reduces estimation variance by adjusting testing results based on multiple linear regression techniques and optimizes regression coefficients for the CAV under test. The method applies sparse control variates to critical variables of testing scenarios and has been validated in high-dimensional overtaking scenarios, achieving a 30 times acceleration in the evaluation process.

2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) (2022)

添加到收藏夹
Article Economics

Maximum capture problem based on paired combinatorial weibit model to determine park-and-ride facility locations

Songyot Kitthamkesorn, Anthony Chen, Seungkyu Ryu, Sathaporn Opasanon

Summary: The study introduces a new mathematical model to determine the optimal location of park-and-ride facilities, addressing the limitations of traditional models and considering factors such as route similarity and user heterogeneity.

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2024)

添加到收藏夹
研讨会 海报 问题 讨论圈 基金 期刊 论文 科研社交 资讯集成 审稿人 关于我们 常见问题 移动App 隐私条款 使用条款
© Peeref 2019-2024. All rights reserved.