Journal of Intelligent Transportation Systems最新文献

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Vehicle trajectory reconstruction for freeway traffic considering lane changing behaviors 考虑变道行为的高速公路交通车辆轨迹重构
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2307031
Cong Zhang , Yiheng Feng
{"title":"Vehicle trajectory reconstruction for freeway traffic considering lane changing behaviors","authors":"Cong Zhang ,&nbsp;Yiheng Feng","doi":"10.1080/15472450.2024.2307031","DOIUrl":"10.1080/15472450.2024.2307031","url":null,"abstract":"<div><div>Vehicle trajectory data provides critical information for many transportation applications. Due to limitations in the data collection techniques, usually, only partial trajectories can be obtained. As a result, trajectory reconstruction where the missing trajectories are inferenced by the observed data is an essential step for many downstream applications. Existing studies usually consider a connected vehicle (CV) environment for trajectory data collection and ignore the lane-changing (LC) behaviors in the reconstruction process. The deployment of connected and autonomous vehicles (CAVs) makes it possible to collect trajectory data more efficiently with much lower penetrations. This study proposes a vehicle trajectory reconstruction algorithm considering LC maneuvers in the CAV environment. The Pettit test and a rule-based optimization algorithm are designed to predict the possible LC time points. Then two car-following models are applied to reconstruct trajectories. The NGSIM US101 dataset is applied to evaluate the proposed reconstruction algorithm under varying CAV penetration rates (PRs) (e.g., 2%, 3%, 5%). The prediction of LC time points achieves high accuracy with average prediction errors less than 1 s under CAV PRs greater than 2%. Compared to the ground truth trajectories, the reconstructed trajectories have the mean absolute error (MAE) less than one vehicle length under 3% and higher CAV PRs.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 3","pages":"Pages 235-250"},"PeriodicalIF":2.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139594333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep survival analysis model for incident clearance time prediction 用于事故清理时间预测的深度生存分析模型
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2315126
Eui-Jin Kim , Min-Ji Kang , Shin Hyoung Park
{"title":"Deep survival analysis model for incident clearance time prediction","authors":"Eui-Jin Kim ,&nbsp;Min-Ji Kang ,&nbsp;Shin Hyoung Park","doi":"10.1080/15472450.2024.2315126","DOIUrl":"10.1080/15472450.2024.2315126","url":null,"abstract":"<div><div>Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers “duration dependence” of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 3","pages":"Pages 305-318"},"PeriodicalIF":2.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An anti-disturbance lane-changing trajectory tracking control method combining extended Kalman filter and robust tube-based model predictive control 结合扩展卡尔曼滤波器和鲁棒管基模型预测控制的抗干扰变道轨迹跟踪控制方法
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2315136
Fangzhi Yin , Changyin Dong , Ye Li , Yujia Chen , Hao Wang
{"title":"An anti-disturbance lane-changing trajectory tracking control method combining extended Kalman filter and robust tube-based model predictive control","authors":"Fangzhi Yin ,&nbsp;Changyin Dong ,&nbsp;Ye Li ,&nbsp;Yujia Chen ,&nbsp;Hao Wang","doi":"10.1080/15472450.2024.2315136","DOIUrl":"10.1080/15472450.2024.2315136","url":null,"abstract":"<div><div>This paper proposes a trajectory tracking control method combining extended Kalman filter (EKF) and robust tube-based model predictive control (RTMPC) methods to improve the anti-disturbance capability during lane-changing maneuver of automated vehicles. A time-based quintic polynomial function is introduced for the implementation of trajectory planning to obtain the desired reference trajectory. The planned trajectory is input to the nominal system-oriented model predictive controller (MPC) in RTMPC for optimization to obtain the optimal control of the nominal system. The EKF collects the state measurements of the current instant and the optimal state estimates of the previous instant, and performs filtering to obtain the optimal state estimates of the current instant. The optimal estimate of the current state and the current state of the nominal system are input into the auxiliary control law of RTMPC to obtain the control of the actual system. Comparative numerical simulation experiments are conducted to analyze robustness and sensitivity of the proposed method. The results show that the control method combining EKF and RTMPC can optimize the trajectory tracking performance of the vehicle system, especially in the lateral displacement and the yaw-rate control. When the amplitude of measurement noise reaches the maximum, the optimization effect of lateral control is most significant in experiments. And the optimization effect in the control of lateral displacement and yaw angle continues to enhance with the increase of measurement disturbance. Therefore, this study can provide a reference for the anti-interference lane change trajectory tracking strategy of automated vehicles in the future.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 3","pages":"Pages 319-334"},"PeriodicalIF":2.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reinforcement learning approach to develop variable speed limit strategy using vehicle data and simulations 利用车辆数据和模拟,采用强化学习方法制定变速限制策略
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2312808
Yunjong Kim , Kawon Kang , Nuri Park , Juneyoung Park , Cheol Oh
{"title":"Reinforcement learning approach to develop variable speed limit strategy using vehicle data and simulations","authors":"Yunjong Kim ,&nbsp;Kawon Kang ,&nbsp;Nuri Park ,&nbsp;Juneyoung Park ,&nbsp;Cheol Oh","doi":"10.1080/15472450.2024.2312808","DOIUrl":"10.1080/15472450.2024.2312808","url":null,"abstract":"<div><div>A variety of studies have been conducted to evaluate real-time crash risk using vehicle trajectory data and to establish active traffic safety management measures. Speed management is an effective way to control traffic flow on freeways and to enhance safety. Currently, the variable speed limit (VSL) system is mainly applied in a limited manner during traffic congestion or bad weather. However, it is necessary to manage traffic safety proactively to prevent crashes by providing an appropriate target safety speed to minimize the real-time crash risk. Herein, a methodology for proactive traffic safety management is developed through speed management based on the estimation of real-time crash risk. The developed methodology evaluates performance through simulations and it consists of two components. First, a crash risk analyzer evaluates freeway crash risk by developing a real-time crash risk model based on real-world vehicle trajectory data matched with crash traffic flow. Then a speed manager implements a reinforcement learning-based VSL system in the simulation environment, which includes the crash risk derived from the crash risk analyzer through VISSIM-COM interfaces. The performance of the developed methodology was evaluated through VISSIM simulation analysis, and the results demonstrated its feasibility. The real-time crash risk was reduced by approximately 55% when the target safety speed information derived from the reinforcement learning model was provided in a scenario where one lane was closed due to a crash. The findings were further applied to establish an operations strategy for VSL systems based on both crash risk and actual traffic conditions.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 3","pages":"Pages 251-268"},"PeriodicalIF":2.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data 整合交通视频和收费站数据的桥梁车辆重量时空分布识别方法
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2312810
Jianliang Zhang , Yuyao Cheng , Jian Zhang , Zhishen Wu
{"title":"A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data","authors":"Jianliang Zhang ,&nbsp;Yuyao Cheng ,&nbsp;Jian Zhang ,&nbsp;Zhishen Wu","doi":"10.1080/15472450.2024.2312810","DOIUrl":"10.1080/15472450.2024.2312810","url":null,"abstract":"<div><div>Real-time monitoring of the spatiotemporal distribution of vehicle weights on bridge decks is an important component of bridge structural health monitoring systems. However, it is still a challenge to identify the spatiotemporal distribution of vehicle weights on the whole bridge deck because the existing identification techniques are based on the theory of line of influence or need to install a weight-in-motion (WIM) system on the bridge. This paper proposes an information fusion-based identification method for the spatiotemporal distribution of vehicle weights without WIM installation, in which, (1) the traffic videos acquired by multiple cameras arranged along both sides of the bridge are used to detect the spatiotemporal distribution and license plate of the vehicles, and the weights obtained from the toll station are linked to the vehicles by matching the license plates. In addition, (2) a digital image correlation (DIC)-based vehicle tracking method is proposed to solve the problems of frame drop and missing detection and (3) a polynomial fitting-based coordinate transformation method is proposed to avoid the derivation of complicated coordinate conversion formula related to pinhole camera. The efficiency and accuracy of the proposed identification approach are verified by the field data collected from a cable-stayed bridge and nearby toll stations. The results indicate that our proposed method is a feasible and reliable solution for identifying spatiotemporal distribution of vehicle weights on bridges.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 3","pages":"Pages 287-304"},"PeriodicalIF":2.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140005231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest 通过自适应隔离林实时检测城市地区短期交通中断的异常情况
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-05-04 DOI: 10.1080/15472450.2024.2312809
Jingqin Gao , Kaan Ozbay , Yu Hu
{"title":"Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest","authors":"Jingqin Gao ,&nbsp;Kaan Ozbay ,&nbsp;Yu Hu","doi":"10.1080/15472450.2024.2312809","DOIUrl":"10.1080/15472450.2024.2312809","url":null,"abstract":"<div><div>The escalating congestion impacts of short-term traffic disruptions, such as double parking or short-duration work zones, are gaining increased attention. This study introduces an enhanced isolation forest-based unsupervised anomaly detection algorithm to detect short-term traffic disruptions in urban areas. To enable the detection in real-time, a sliding window approach is introduced to allow the streaming data to be read and processed according to the window size. An adaptive threshold that automatically adjusts itself to overcome the limitations of the miss rate on local anomalies is shown to further enhance the model predictions. The proposed algorithm is empirically validated on four study sites in Manhattan, New York City. The experimental results demonstrate that the proposed unsupervised algorithm can effectively detect different types of traffic anomalies including accidents, work zones/road closures, traffic jams, double parking events and police activities. On all sites, the average detection rate is 81.1% for traffic jams and 89.6% for police activities, respectively. For three out of the four sites, the detection rate ranges from 71.4% to 100% for accidents, work zones and double parking. An optimizer using high-pass filter is also presented to further improve off-line detection. The primary advantages of this proposed computationally-efficient method are that its only required data input is travel time information and that it does not need labeled data for training, which make it highly deployable for real-time operational applications and can be easily adopted by other cities.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 3","pages":"Pages 269-286"},"PeriodicalIF":2.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploring traffic breakdown with vehicle-level data 利用车辆级数据探索交通细分
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2023.2301710
Youngjun Han , Jinhak Lee
{"title":"Exploring traffic breakdown with vehicle-level data","authors":"Youngjun Han ,&nbsp;Jinhak Lee","doi":"10.1080/15472450.2023.2301710","DOIUrl":"10.1080/15472450.2023.2301710","url":null,"abstract":"<div><div>Traffic breakdown involves complicated vehicle behavior, and is regarded as a probabilistic event with macroscopic traffic data from fixed detectors. However, with the advent of connected vehicle technologies, traffic data will develop to the vehicle-level, such as trajectory data, and provide unprecedented opportunities to better understand various traffic phenomena. Using novel vehicle-level data from drone videos, this research explores the traffic breakdown by interactions between vehicles. Specifically, this paper categorizes the typical behavior of individual vehicles that causes or resolves traffic congestion. Based on the behavior that occurred in the extensive time–space domain, this research develops a novel measurement method to quantify the behavior as temporal <em>delay</em> or spatial <em>residual.</em> With real-world data, this research verifies the vehicle-level congestion can be estimated from specific vehicle behavior, and their aggregation could describe the change in flow speed or traffic breakdown. The proposed framework can address the traffic phenomenon better when more extensive data is available.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 153-169"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139396228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reliability-based journey time prediction via two-stream deep learning with multi-source data 通过多源数据的双流深度学习进行基于可靠性的行程时间预测
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2023.2301707
Li Zhuang , Xinyue Wu , Andy H. F. Chow , Wei Ma , William H.K Lam , S. C. Wong
{"title":"Reliability-based journey time prediction via two-stream deep learning with multi-source data","authors":"Li Zhuang ,&nbsp;Xinyue Wu ,&nbsp;Andy H. F. Chow ,&nbsp;Wei Ma ,&nbsp;William H.K Lam ,&nbsp;S. C. Wong","doi":"10.1080/15472450.2023.2301707","DOIUrl":"10.1080/15472450.2023.2301707","url":null,"abstract":"<div><div>This paper presents a distribution-free reliability-based prediction approach for estimating journey time intervals with multi-source data using a two-stream deep learning framework. The prediction framework consists of a long short-term memory (LSTM) module for extracting temporal features and a convolutional neural network (CNN) module for extracting spatial-temporal features from the heterogeneous data. The precision and reliability of the prediction are assessed respectively by the Mean Prediction Interval Width (MPIW) and Prediction Interval Coverage Probability (PICP) metrics. For computational effectiveness, a Gaussian approximation is adopted to formulate a smooth and differentiable loss function for training the prediction framework. The computational experiments are conducted based on a real-world Hong Kong corridor, where multi-source data including traffic and weather conditions are collected. The proposed framework shows significant improvements over existing methods in terms of both precision and reliability over a range of traffic and weather conditions. This study contributes to the development of reliability-based intelligent transportation systems with advanced deep learning techniques.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 134-152"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139463577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Unified strategy for cooperative optimization of pedestrian control patterns and signal timing plans at intersections 交叉口行人控制模式和信号配时计划合作优化的统一策略
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2024.2307026
Jiawen Wang , Yudi Zhang , Jing Zhao , Chunjian Shang , Xinwei Wang
{"title":"Unified strategy for cooperative optimization of pedestrian control patterns and signal timing plans at intersections","authors":"Jiawen Wang ,&nbsp;Yudi Zhang ,&nbsp;Jing Zhao ,&nbsp;Chunjian Shang ,&nbsp;Xinwei Wang","doi":"10.1080/15472450.2024.2307026","DOIUrl":"10.1080/15472450.2024.2307026","url":null,"abstract":"<div><div>Pedestrian traffic management and control at intersections is crucial for ensuring traffic safety and efficiency while promoting green transportation development. Numerous studies have been conducted on optimizing signal timing of various pedestrian control patterns, such as exclusive pedestrian phases (EPPs) and leading pedestrian intervals (LPIs). However, cooperative optimization of these patterns and the corresponding signal timing is lacking. Hence, this study proposes a unified strategy for the cooperative optimization of pedestrian control patterns and signal timing plans to improve the efficiency and safety of pedestrian–vehicle mixed traffic flow. The existing control patterns, such as EPPs, LPIs, and two-way crossing (TWC), are unified. The safety and efficiency costs are monetized, and the minimization of average costs per traffic participant is taken as the optimization objective. Additionally, decision variables for diagonal crossing at intersections and pedestrian–vehicle priority are introduced to achieve cooperative optimization of the pedestrian control patterns and signal timing plans. The proposed model parameters were calibrated and validated using a real-world case study, and the applicable boundaries of different pedestrian control patterns under different pedestrian and vehicle flow scenarios were identified based on cost difference analysis. The results indicate that the vehicle turn ratio, average vehicle carrying rate, and unit cost ratio dynamically change the applicable boundaries. On average, the proposed method reduced the cost by 2.62% compared with separately optimized EPPs, LPIs, and TWC across various scenarios.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 170-196"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139561759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles 自动驾驶车辆与普通车辆混合共用的最佳车道分配策略
IF 2.8 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2024.2307027
Yangbeibei Ji , Jingwen Liu , Hanwan Jiang , Xinru Xing , Wurong Fu , Xueqing Lu
{"title":"Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles","authors":"Yangbeibei Ji ,&nbsp;Jingwen Liu ,&nbsp;Hanwan Jiang ,&nbsp;Xinru Xing ,&nbsp;Wurong Fu ,&nbsp;Xueqing Lu","doi":"10.1080/15472450.2024.2307027","DOIUrl":"10.1080/15472450.2024.2307027","url":null,"abstract":"<div><div>Autonomous driving technology has the potential to alter the way we travel and is rapidly evolving. Sharing rides in autonomous vehicles may become a popular mode of public transportation in the future. One strategy to enhance operational efficiency is to deploy designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs). This study investigated optimal allocation for dedicated AV lanes considering mixed SAVs and regular vehicles (RVs) flows. First, the standard bottleneck model for morning rush hour was extended to account for both ridesharing and automation, and user equilibrium (UE) solutions were obtained by assuming dedicated AV lanes co-exist with general-purpose (GP) lanes. The equilibrium capacity allocation for dedicated AV lanes was determined for multi-modal traffic scenarios. The study found that commuters may prefer to use SAVs because SAVs were more cost-effective and time-saving. Furthermore, analysis of the fixed payment for ridesharing in SAVs revealed that the system performance could be negatively impacted by high fixed payments. The study proposed an enumeration algorithm for developing lane strategies for discrete lane settings. It was found that the deployment of dedicated AV lanes should be matched to the penetration rate of SAVs and different management objectives, following a gradual rather than a radical process. Finally, a sensitivity analysis was performed considering the value of travel time for SAVs and the bottleneck capacity of a dedicated AV lane. This work provides a new solution to AV lane capacity allocation and offers theoretical support for managers to deploy dedicated AV lane allocation.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 197-217"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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