IEEE Transactions on Intelligent Transportation Systems最新文献_第7页

An Indirect-Effect-Incorporated Linguistic Z-Number Petri Nets and Its Application to Evaluate Generalized Eco-Driving Behaviors 包含间接效应的语言z数Petri网及其在广义生态驾驶行为评价中的应用

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-23 DOI: 10.1109/TITS.2025.3558901

Yanni Rao;Xuan Wu;Guoquan Xie;Kui Wang;Yong Peng;Guangdong Tian;Honghao Zhang

{"title":"An Indirect-Effect-Incorporated Linguistic Z-Number Petri Nets and Its Application to Evaluate Generalized Eco-Driving Behaviors","authors":"Yanni Rao;Xuan Wu;Guoquan Xie;Kui Wang;Yong Peng;Guangdong Tian;Honghao Zhang","doi":"10.1109/TITS.2025.3558901","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558901","url":null,"abstract":"As a valuable tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) have obtained widespread application in many fields and achieved ideal results to some degree. However, current methods ignore the reliability of expert evaluations and the indirect effects among propositions, which may lead to decision-making errors or inaccurate evaluations. Thus, the indirect-effect-incorporated linguistic Z-number Petri nets (ILZPNs) are proposed in this paper. The linguistic Z-number is presented to capture knowledge information more comprehensively with its related fuzzy rules. The concepts of indirect effects and its aggression operators are designed to enhance the knowledge reasoning capability of ILZPN. Besides, the formal definition and the corresponding simulation algorithm of ILZPN are presented to conduct knowledge representation and reasoning. Subsequently, an empirical case, i.e., generalized eco-driving behavior evaluation, is applied to verify the proposed approach. In addition, comparison and sensitivity analysis are performed to monitor the robustness of the results. The results prove that this study offers a significant reference for the research into similar issues.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8541-8557"},"PeriodicalIF":7.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Multi-Perspective Semantic Segmentation of Ground Penetrating Radar Images for Pavement Subsurface Objects 路面地下目标探地雷达图像多视角语义分割

IF 8.4 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-23 DOI: 10.1109/TITS.2025.3559498

Sibo Huang;Guijie Zhu;Jiaming Tang;Weixiong Li;Zhun Fan

{"title":"Multi-Perspective Semantic Segmentation of Ground Penetrating Radar Images for Pavement Subsurface Objects","authors":"Sibo Huang;Guijie Zhu;Jiaming Tang;Weixiong Li;Zhun Fan","doi":"10.1109/TITS.2025.3559498","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559498","url":null,"abstract":"Effective infrastructure health monitoring is crucial within transportation cyber-physical systems, where accurate road health detection is vital for ensuring road safety and the stability of intelligent transportation systems. To address the challenges of identifying pavement subsurface objects using 3D ground penetrating radar (GPR) data, we propose a multi-perspective cascading recognition method that integrates B-scan and C-scan images. This method is built on a lightweight dual-stream semantic segmentation model called AttnGPRNet, developed in this work to improve feature extraction through attention mechanisms and enhance subsurface object recognition. Initially, the model segments B-scan images to identify potential target regions, followed by more precise segmentation of 3L-C-scan images based on preliminary results. Additionally, we constructed a multi-view dataset using 3D GPR scans from over 100 kilometers of urban roads and evaluated the effectiveness of the proposed method through experiments. Experimental results show that our model outperforms existing advanced methods, achieving mIoU of 78.80% and 83.96% on B-scan and 3L-C-scan, and F1 scores of 87.65% and 91.07%, respectively. Moreover, the method has been deployed in Xiaoning Road GPR image intelligent recognition system and verified through on-site drilling, demonstrating its practical potential for road health monitoring.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"14339-14352"},"PeriodicalIF":8.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Integrated Freeway Traffic Control Using Q-Learning With Adjacent Arterial Traffic Considerations 考虑相邻干线交通的q -学习综合高速公路交通控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-22 DOI: 10.1109/TITS.2025.3559893

Tianchen Yuan;Petros A. Ioannou

{"title":"Integrated Freeway Traffic Control Using Q-Learning With Adjacent Arterial Traffic Considerations","authors":"Tianchen Yuan;Petros A. Ioannou","doi":"10.1109/TITS.2025.3559893","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559893","url":null,"abstract":"Numerous studies have shown the effectiveness of intelligent transportation system techniques such as variable speed limit (VSL), lane change (LC) control, and ramp metering (RM) in freeway traffic flow control. The integration of these techniques has the potential to further enhance the traffic operation efficiency of both freeway and adjacent arterial networks. In this regard, we propose a freeway traffic control (FTC) strategy that coordinates VSL, LC, RM actions using a Q-learning (QL) framework which takes into account arterial traffic characteristics. The signal timing and demands of adjacent arterial intersections are incorporated as state variables of the FTC agent. The FTC agent is initially trained offline using a single-section road network, and subsequently deployed online in a connected freeway and arterial simulation network for continuous learning. The arterial network is assumed to be regulated by a traffic-responsive signal control strategy based on a cycle length model. Microscopic simulations demonstrate that the fully-trained FTC agent provides significant reductions in freeway travel time and the number of stops in scenarios with traffic congestion. It clearly outperforms an uncoordinated FTC and a decentralized feedback control strategy. Even though the FTC agent does not control the arterial traffic signals, it leads to shorter average queue lengths at arterial intersections by taking into account the arterial traffic conditions in controlling freeway traffic. These results motivate a future research where the QL framework will also include the control of arterial traffic signals.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7655-7666"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Intelligent Eco-Driving Control for Urban CAVs Using a Model-Based Controller Assisted Deep Reinforcement Learning 基于模型控制器辅助深度强化学习的城市cav智能生态驾驶控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-22 DOI: 10.1109/TITS.2025.3559916

Jie Li;Xiaodong Wu;Xianxu Bai;Yonggang Liu;Min Xu

{"title":"Intelligent Eco-Driving Control for Urban CAVs Using a Model-Based Controller Assisted Deep Reinforcement Learning","authors":"Jie Li;Xiaodong Wu;Xianxu Bai;Yonggang Liu;Min Xu","doi":"10.1109/TITS.2025.3559916","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559916","url":null,"abstract":"Eco-driving control for connected and automated vehicles (CAVs) aims to co-optimize energy efficiency, ride comfort, and travel time while adhering to safety regulations. Model-based eco-driving strategies have proven robust and effective in simplified traffic scenarios. However, their application to complex tasks incurs high computational costs due to their reliance on precise nonlinear models that accurately reflect real-world physical systems. Model free deep reinforcement learning (DRL) methods exhibit potential in addressing challenges presented by high-dimensional state/action spaces encountered in real-time CAV control. Nevertheless, they require extensive training data and time, and are susceptible to getting stuck in suboptimal solutions, especially in complex urban traffic scenarios. To leverage the advantages of both model-based controllers and DRL algorithms, this study develops a novel model based controller online assisted-twin delayed deep deterministic policy gradient algorithm (MCOA-TD3) algorithm. The proposed algorithm integrates imitation learning into the Vanilla TD3 agent. During training, the MCOA-TD3 agent can learn from demonstrations generated by a model predictive control-based expert controller. The performance of the proposed strategy is evaluated through simulations conducted in a dynamic traffic simulation scenario replicating the testfield of Hamburg, Germany. The results show that our proposed strategy improves energy efficiency and ride comfort while maintaining comparable driving times to the Vanilla TD3 strategy. Notably, compared with the Vanilla TD3 strategy, our proposed strategy demonstrates superior adaptability and online fine-tuning ability. These improvements make it more suitable for complex and dynamic real-world scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7624-7639"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Integrated Control Policy for Heterogeneous Traffic in Container Terminals With Unsignalized Intersections 无信号交叉口集装箱码头异构交通综合控制策略

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-22 DOI: 10.1109/TITS.2025.3560067

Shuo Wang;Weimin Wu;Jiliang Luo;Jiazhong Zhou;Tao Zhang

{"title":"Integrated Control Policy for Heterogeneous Traffic in Container Terminals With Unsignalized Intersections","authors":"Shuo Wang;Weimin Wu;Jiliang Luo;Jiazhong Zhou;Tao Zhang","doi":"10.1109/TITS.2025.3560067","DOIUrl":"https://doi.org/10.1109/TITS.2025.3560067","url":null,"abstract":"This research investigates the control difficulties related to heterogeneous traffic flow in container terminals, featuring both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). The lack of signal control at intersections and the unpredictable routes taken by HDVs make the efficient transport of containers within a terminal quite challenging. To tackle this issue, we present an integrated traffic control policy aimed at enhancing transportation efficiency. For each unsignalized intersection, a virtual token ring system is introduced to manage the passage of vehicles, using a back pressure-based algorithm and specific token delivery rules to determine phase sequence and duration. Furthermore, we introduce an improved back pressure-based dynamic routing method for CAVs, which allows for the selection of roads with shorter travel time while adhering to a travel distance constraint when crossing an intersection. This approach aims to minimize disruptions from HDVs, reduce travel time, and prevent excessively long travel distances. Multiple experiments are conducted to verify the proposed method’s effectiveness.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10795-10807"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 for Robust Advisories Under Driving Compliance Errors 驱动遵从性错误下稳健咨询的强化学习

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-22 DOI: 10.1109/TITS.2025.3550418

Jeongyun Kim;Jung-Hoon Cho;Cathy Wu

{"title":"Reinforcement Learning for Robust Advisories Under Driving Compliance Errors","authors":"Jeongyun Kim;Jung-Hoon Cho;Cathy Wu","doi":"10.1109/TITS.2025.3550418","DOIUrl":"https://doi.org/10.1109/TITS.2025.3550418","url":null,"abstract":"There has been considerable interest in recent years regarding how a small fraction of autonomous vehicles (AVs) can mitigate traffic congestion. However, the reality of vehicle-based congestion mitigation remains elusive, due to challenges of cost, technology maturity, and regulation. As a result, recent works have investigated the necessity of autonomy by exploring driving advisory systems. Such early works have made simplifying assumptions such as perfect driver compliance. This work relaxes this assumption, focusing on compliance errors caused by physical limitations of human drivers, in particular, response delay and speed deviation. These compliance errors introduce significant unpredictability into traffic systems, complicating the design of real-time driving advisories aimed at stabilizing traffic flow. Our analysis reveals that performance degradation increases sharply under compliance errors, highlighting the associated difficulties. To address this challenge, we develop a reinforcement learning (RL) framework based on an action-persistent Markov decision process (MDP) combined with domain randomization, designed for robust coarse-grained driving policies. This approach allows driving policies to effectively manage the cumulative impacts of compliance errors by generating various scenarios and corresponding traffic conditions during training. We show that in comparison to prior RL-based work which did not consider compliance errors, our policies achieve up to 2.2 times improvement in average speed over non-robust training. In addition, analytical results validate the experiment results, highlighting the benefits of the proposed framework. Overall, this paper advocates the necessity of incorporating human driver compliance errors in the development of RL-based advisory systems, achieving more effective and resilient traffic management solutions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7780-7791"},"PeriodicalIF":7.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Cross-Scale Overlapping Patch-Based Attention Network for Road Crack Detection 基于交叉尺度重叠斑块的道路裂缝检测关注网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-21 DOI: 10.1109/TITS.2025.3558279

Po-Hao Chen;Jun-Wei Hsieh;Yi-Kuan Hsieh;Chuan-Wang Chang;Deng-Yuan Huang

{"title":"Cross-Scale Overlapping Patch-Based Attention Network for Road Crack Detection","authors":"Po-Hao Chen;Jun-Wei Hsieh;Yi-Kuan Hsieh;Chuan-Wang Chang;Deng-Yuan Huang","doi":"10.1109/TITS.2025.3558279","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558279","url":null,"abstract":"Cracks on road surfaces pose serious risks to both pedestrians and drivers. Traditional manual crack detection methods are not only slow but also pose safety risks. Automating this process has the potential to greatly enhance detection efficiency and consequently improve driving safety. Although previous methods have shown promise in road crack detection, they often neglect interactions between multiple scales, causing smaller cracks to be overlooked in later stages of detection. This paper introduces the Cross-scale Overlapping Patch-based attention Network (COP-Net), which incorporates two critical components: the Scale-aware Channel Attention (SCA) module and the Patch-based Cross-scale Attention (PCA) module for crack detection. These innovations enable dynamic inference on multiple scales, resulting in a significant improvement in crack detection and segmentation. Notably, our approach excels at detecting both small and large cracks simultaneously. To validate the effectiveness of our approach, we conducted evaluations on three open datasets: CRACK500, CFD, and AEL. These evaluation results demonstrate that COP-Net surpasses eleven comparison methods, including HED, DeepCrack, UHDN, SSGNet, MFANet, FPHBN, DeepCrack, PBNet, PAFNet, CarNet, and SegFormer. Our model achieves new State-of-The-Art (SoTA) performance levels in terms of segmentation metrics such as AIU, ODS, and OIS.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7587-7599"},"PeriodicalIF":7.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Multimodal Driver Drowsiness Detection Using Facial Expressions and Ear-EEGs With a Lightweight Auto-Denoising Network 基于面部表情和耳-脑电图的多模态驾驶员困倦检测及轻量自动去噪网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-21 DOI: 10.1109/TITS.2025.3559098

Ngoc-Dau Mai;Ha-Trung Nguyen;Wan-Young Chung

{"title":"Multimodal Driver Drowsiness Detection Using Facial Expressions and Ear-EEGs With a Lightweight Auto-Denoising Network","authors":"Ngoc-Dau Mai;Ha-Trung Nguyen;Wan-Young Chung","doi":"10.1109/TITS.2025.3559098","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559098","url":null,"abstract":"Integrating computer vision and physiological analysis in driver drowsiness detection (DDD) is a promising technology for accurately identifying drowsy states while driving, thereby preventing potentially dangerous accidents. This study proposes a multimodal DDD system with a deep neural network that combines computer vision-based face expression analysis and electroencephalogram (EEG) data analysis. Key contributions include: 1) providing a comprehensive hardware, firmware, and software design for the DDD system to acquire behind-the-ear (BTE) EEG signals, rather than conventional scalp EEGs, due to their convenience and practicality; 2) proposing a powerful and lightweight GAN-based auto-denoising method to eliminate artifacts from EEG signals during signal acquisition, significantly influencing the quality of the obtained result; 3) developing a multimodal DDD network by combining EEG analysis and computer vision-based face expression identification to improve performance in monitoring and early detection of the driver’s drowsiness while engaging in traffic. The study employs the relative root mean squared error (RRMSE) in both temporal and spectral domains to quantitatively assess the performance of the proposed approaches in artifact removal. The proposed GAN-based auto-denoising network outperforms other comparable approaches, with an RRMSE (temporal) of 0.210 and RRMSE (spectral) of 0.161. The proposed trained multimodal model with GAN-based auto-denoising is superior to other models with different denoising approaches in driver drowsiness detection across all five-evaluation metrics, with an accuracy of 95.33%, specificity of 95.48%, sensitivity of 95.17%, precision of 95.47%, and an F1-score of 95.32%. The experimental results demonstrate the practicality and feasibility of our proposed DDD system.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7819-7832"},"PeriodicalIF":7.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

MorFormer: Morphology-Aware Transformer for Generalized Pavement Crack Segmentation MorFormer：用于路面裂缝分割的形态感知变压器

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-21 DOI: 10.1109/TITS.2025.3558782

Xin Guo;Wenzhong Tang;Haoran Wang;Jiale Wang;Shuai Wang;Xiaolei Qu;Xun Lin

{"title":"MorFormer: Morphology-Aware Transformer for Generalized Pavement Crack Segmentation","authors":"Xin Guo;Wenzhong Tang;Haoran Wang;Jiale Wang;Shuai Wang;Xiaolei Qu;Xun Lin","doi":"10.1109/TITS.2025.3558782","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558782","url":null,"abstract":"Cracks are common on pavements. Accurate crack detection plays a vital role in pavement maintenance. However, cracks have rich and varied morphological features and fine edges, making this task challenging. Additionally, noise factors such as stains, scratches, and complex textures in the pavement background can easily be confused with cracks, increasing the risk of false prediction in the segmentation process. Therefore, we propose Background Morphology Learning (BML) to reconstruct morphological features of the pavement background noise, extract background morphological dissimilarity maps to suppress interference and reduce false alarms. In addition, we propose Crack Morphology-aware Attention (CMA), which adaptively learns the morphological shape of cracks and dynamically adjusts the shape of the attention receptive field to the topological features of the cracks. This significantly improves the completeness of segmentation. Our method mitigates the problems of false alarms and incomplete segmentation results in the crack segmentation task. Therefore, we propose a Morphology-Aware Transformer (MorFormer) that achieves state-of-the-art results on five public datasets. Moreover, we propose a large-scale cross-domain benchmark for crack segmentation, where MorFormer exhibits excellent domain generalization.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8219-8232"},"PeriodicalIF":7.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Rapid Solution for Flexible Pickup and Delivery Services Problem Based on Improved Actor-Critic Deep Reinforcement Learning 基于改进Actor-Critic深度强化学习的灵活取货服务快速解决方案

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-21 DOI: 10.1109/TITS.2025.3559941

Ran Tian;Zhihui Sun;Longlong Chang;Jiarui Wu;Xin Lu

{"title":"Rapid Solution for Flexible Pickup and Delivery Services Problem Based on Improved Actor-Critic Deep Reinforcement Learning","authors":"Ran Tian;Zhihui Sun;Longlong Chang;Jiarui Wu;Xin Lu","doi":"10.1109/TITS.2025.3559941","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559941","url":null,"abstract":"The problem of the Flexible Pickup and Delivery Services Problem (FPDSP) arises from the actual needs of multi-warehouse management strategies and is one of the key challenges in the current urban distribution logistics industry. The problem aims to quickly calculate the route planning in complex scenarios to ensure that the total traveling time of the vehicle is minimized while meeting the time window requirements. To address this problem, we propose a deep reinforcement learning method based on the Actor-Critic algorithm to quickly calculate the approximate optimal solution of FPDSP. Specifically, we propose a Transformer Model with Parallel Encoders (TMPE). The model efficiently extracts order features through parallel encoders and then uses serial decoders to optimize the fusion of feature information to optimize the order selection process. In addition, we designed a reward function to reduce the number of repeated pickups made by the vehicle at the same consignor’s location between different orders, thereby effectively reducing the vehicle’s total travel time. Experimental results show that our method can quickly find feasible solutions to the problem compared with heuristic methods on seven different datasets. At the same time, compared with all baseline methods, the number of optimal solutions of our method reaches 14, which significantly improves the problem-solving ability. This result provides a new solution for optimizing pickup and delivery logistics in multiple warehouses in cities in the future.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7640-7654"},"PeriodicalIF":7.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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