IET Intelligent Transport Systems最新文献_第5页

Dynamic Intercity Ride-Sharing Optimisation Based on Two-Stage Information Feedback 基于两阶段信息反馈的城际拼车动态优化

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-10 DOI: 10.1049/itr2.70139

Cheng Wang, Shangyu Gao, Jin Jiang

{"title":"Dynamic Intercity Ride-Sharing Optimisation Based on Two-Stage Information Feedback","authors":"Cheng Wang, Shangyu Gao, Jin Jiang","doi":"10.1049/itr2.70139","DOIUrl":"https://doi.org/10.1049/itr2.70139","url":null,"abstract":"Current approaches to intercity dynamic ride-sharing mainly adopt single-stage scheduling, where new orders are periodically batched and processed. Although effective, this strategy often causes heavy computation and delayed passenger feedback, limiting real-time applicability. To address these issues, we propose a novel two-stage information feedback framework combining coarse and fine scheduling. In the coarse stage, online scheduling (nearest insertion) promptly matches new orders with departed vehicles, while offline scheduling (best insertion) processes non-departed vehicles, thus providing passengers with staged and timely feedback. In the fine stage, assignments are further optimised through large neighbourhood search, with the triggering decision modelled as a Markov decision process and learned by deep Q-learning. This design reduces redundant computation while dynamically balancing feedback timeliness and scheduling efficiency. Unlike traditional methods, our framework is novel in integrating staged passenger feedback, hybrid heuristic optimisation and reinforcement learning-based control. Experiments on two real-world intercity carpooling datasets show that the method significantly reduces runtime and feedback delays while maintaining strong scheduling performance, demonstrating its potential as a practical solution for large-scale dynamic ride-sharing platforms.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signal Timing and CAV Trajectory Joint Control Under Mixed Vehicular Environments With Hierarchical Proximal Policy Optimisation 混合车辆环境下的信号配时与CAV轨迹联合控制

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-10 DOI: 10.1049/itr2.70147

Zongyuan Wu, Decai Wang, Mengxin Qiu, Gen Li, Wenxuan Li, Yadan Yan

{"title":"Signal Timing and CAV Trajectory Joint Control Under Mixed Vehicular Environments With Hierarchical Proximal Policy Optimisation","authors":"Zongyuan Wu, Decai Wang, Mengxin Qiu, Gen Li, Wenxuan Li, Yadan Yan","doi":"10.1049/itr2.70147","DOIUrl":"https://doi.org/10.1049/itr2.70147","url":null,"abstract":"This paper proposes a novel Signal-Vehicle Cooperative Control framework (SVCC-HPPO) based on the improved Hierarchical Proximal Policy Optimisation (H-PPO) algorithm to jointly optimise traffic signal timing and Connected and Autonomous Vehicle (CAV) trajectories under mixed vehicular environments with both CAVs and Human-Driven Vehicles (HDVs). A hierarchical hybrid action space is designed to effectively constrain CAV acceleration and signal timing adjustments while explicitly accounting for car-following dynamics near intersections, enabling flexible exploration within physical limits. The hybrid actor-critic architecture facilitates simultaneous optimisation of discrete and continuous actions through parallel actors guided by a global critic, balancing optimization effectiveness with training stability. A multi-objective reward function simultaneously minimises vehicle delay and fuel consumption and maximises ride comfort. The core improvement involves a layered entropy regularisation strategy within the H-PPO algorithm, which separately manages discrete and continuous entropy to enhance exploration efficiency and stability across hybrid action dimensions. Real-world intersections evaluation results demonstrate that SVCC-HPPO significantly outperforms benchmark methods TRANSYT and DRL-based algorithms, achieving reductions of up to 46.3% in delay, 59.5% in queue length, and 52.9% in fuel consumption, alongside a 177.4% improvement in average speed. Performance gains are further enhanced with shorter optimisation intervals and higher CAV penetration rates.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-10

引用次数: 0

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-10

引用次数: 0

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-10

引用次数: 0

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-09

引用次数: 0

Physical Parameters Estimation Using Roadside Monocular Vision 基于路边单目视觉的物理参数估计

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-09 DOI: 10.1049/itr2.70138

Nijia Zhang, Mingfeng Lu, Shoutong Yuan, Chen Liu, Yan Wang, Zhen Yang, Canjie Zhu, Ziyi Chen, Shuai Zhang, Feng Zhang, Ran Tao, Weidong Hu, Xiongjun Fu

{"title":"Physical Parameters Estimation Using Roadside Monocular Vision","authors":"Nijia Zhang, Mingfeng Lu, Shoutong Yuan, Chen Liu, Yan Wang, Zhen Yang, Canjie Zhu, Ziyi Chen, Shuai Zhang, Feng Zhang, Ran Tao, Weidong Hu, Xiongjun Fu","doi":"10.1049/itr2.70138","DOIUrl":"https://doi.org/10.1049/itr2.70138","url":null,"abstract":"Roadside sensing is an important part of intelligent traffic management systems (ITMSs) for collecting and processing information. In order to better assess and maintain the stability and safety of objects in traffic scenes, all types of basic information are required. This paper proposes a monocular vision-based object parameter measurement and geolocation method to address the problems of high cost and limited information dimension of traditional roadside sensors. Object detection and geometric transformation mapping are combined to achieve efficient estimation of key physical parameters with input of monocular images, and global navigation satellite system (GNSS) information is further incorporated to obtain geolocation of the target. In the method, after the key target is recognized by the neural network-based object detection algorithm, the pixel-level 2D image information is mapped to a series of 3D spaces based on the construction of a geometric model, which leads to further computation of various physical parameters, realizing multi-parameter estimation under one method. The method overcomes the dependence on fixed environments or known references and is highly applicable to non-cooperative scenes. The effectiveness of the method is shown via the experiments in multiple real scenes.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-08

引用次数: 0

Safety-Critical Kinematically-Executable Overtake Planning via Contingency Path-Speed Iterative Algorithm for Automated Valet Parking* 基于应急路径-速度迭代算法的代客泊车安全临界运动可执行超车规划*

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-08 DOI: 10.1049/itr2.70140

Wei Han, Bo Leng, Peizhi Zhang, Lu Xiong

{"title":"Safety-Critical Kinematically-Executable Overtake Planning via Contingency Path-Speed Iterative Algorithm for Automated Valet Parking*","authors":"Wei Han, Bo Leng, Peizhi Zhang, Lu Xiong","doi":"10.1049/itr2.70140","DOIUrl":"https://doi.org/10.1049/itr2.70140","url":null,"abstract":"Autonomous driving has emerged as a highly topical subject within the realm of intelligent transportation systems. Automated valet parking (AVP) represents one of the initial mass-production application scenarios. However, motion planning in AVP confronts a series of formidable challenges. These challenges include a constricted movement space, vehicles parked in violation of regulations, and vehicles that intrude suddenly. In response to these issues, this article devises a safety-critical, kinematically executable overtaking planning system for AVP through a contingency path-speed iterative algorithm. A path-speed iterative optimisation framework is adopted, taking into full account both the curvature constraint and the contour constraint. The prediction probability of dynamic obstacles is incorporated into the quadratic optimisation problem, presented in the form of either soft or hard constraints. Furthermore, a contingency path-speed iterative planner is formulated to address the multi-modal predictions and the interframe probability transfer that occur during the overtaking process in parking lots. Numerical simulations (conducted on the Carla simulator with a 10 Hz planning cycle) across four complex AVP scenarios demonstrate that the proposed algorithm outperforms the baseline Baidu Apollo EM Planner. On-road experiments (deployed on a mass-produced MCU) further validate that the algorithm maintains real-time performance (average computation time < 10 ms) and reduces speed oscillation by over 50% compared to the baseline, while ensuring kinematically executable trajectories (max steering wheel angle limited to 389°). These results confirm the proposed algorithm significantly enhances overtaking safety, executability, and efficiency for AVP.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2026-01-07

引用次数: 0