IEEE Transactions on Intelligent Transportation Systems最新文献_第6页

Large Scale Pavement Crack Evaluation Through a Novel Spatial Machine Learning Approach Considering Geocomplexity 通过考虑地理复杂性的新型空间机器学习方法进行大规模路面裂缝评估

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-09 DOI: 10.1109/TITS.2024.3467257

Chunjiang Chen;Yongze Song;Ammar Shemery;Keith Hampson;Ashraf Dewan;Yun Zhong;Peng Wu

{"title":"Large Scale Pavement Crack Evaluation Through a Novel Spatial Machine Learning Approach Considering Geocomplexity","authors":"Chunjiang Chen;Yongze Song;Ammar Shemery;Keith Hampson;Ashraf Dewan;Yun Zhong;Peng Wu","doi":"10.1109/TITS.2024.3467257","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467257","url":null,"abstract":"Road transport infrastructure is a crucial component of the entire infrastructural network. Timely and efficient maintenance of roads requires accurate and effective evaluation of pavement health, of which cracking is an important aspect. However, accurately assessing pavement cracks across large-scale road networks remains challenging due to spatial variations, which diminish the effectiveness of traditional machine learning methods. This study developed a novel spatial machine learning (SML) model and employed laser scanning data and satellite remote sensing images to assess road segment-based crack severity across the state-level road network in the Wheatbelt of Western Australia. Geocomplexity is introduced to measure the complexity of local patterns and spatial dependence among neighboring road segments. Results showed that SML can accurately and effectively predict pavement cracks on a large spatial scale with an accuracy (AC) from 0.524 to 0.701. In the SMLs, laser-scanning pavement variables contributed 34.15% to 43.33% of the total explainable variations, and geocomplexity variables also contributed significantly, ranging from 27.35% to 49.92%. The SML model exhibited the highest coefficient of determination (\u0000<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>\u0000) and AC for crack prediction compared with Multiple Linear Regression (MLR), Generalized Additive Model (GAM), Bayesian Regularized Neural Network (BRNN) and Support Vector Regression (SVR). The findings provided a deep insight into large-scale crack deterioration by considering the spatial characteristics and achieved high-resolution crack assessment to support road maintenance decision-making. The spatial machine learning approach and the concept of geocomplexity can be widely applied to address large-scale spatial tasks in road engineering.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"21429-21441"},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736202","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

Resilient GNSS/INS-Based Railway Train Localization Using Odometer/Trackmap-Enabled Jamming Discrimination 利用里程表/轨迹图干扰识别技术实现基于 GNSS/INS 的铁路列车弹性定位

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-09 DOI: 10.1109/TITS.2024.3460689

Zhuo-Jian Cao;Jiang Liu;Wei Jiang;Bai-Gen Cai;Jian Wang

{"title":"Resilient GNSS/INS-Based Railway Train Localization Using Odometer/Trackmap-Enabled Jamming Discrimination","authors":"Zhuo-Jian Cao;Jiang Liu;Wei Jiang;Bai-Gen Cai;Jian Wang","doi":"10.1109/TITS.2024.3460689","DOIUrl":"https://doi.org/10.1109/TITS.2024.3460689","url":null,"abstract":"Technological advances in the Global Navigation Satellite System (GNSS) industry have brought significant advantages in enhancing the cost-efficiency of railway applications. However, GNSS vulnerability to external jamming necessitates enhanced protection ability of the GNSS-based train localization system. This paper proposes a resilient train localization solution under the tightly-coupled integration scheme. This solution maximizes the utilization of multi-source information from train-borne sensors, including INS, odometer, and the trackmap database. Based on the existing localization scheme, it achieves a compatible way to address different jamming-intrusion situations without altering the GNSS receiver structure, addressing both the GNSS failure and degradation caused by jamming. Using an odometer/trackmap-enabled equivalent measurement logic, the continuity of localization can be guaranteed against GNSS failure under strong GNSS jamming. A robust filtering algorithm enabled by a jamming discrimination mechanism is proposed for GNSS/INS integration to mitigate the negative effect from degraded GNSS measurements, reducing the hazards by jamming with an intermediate power level. Based on the field data and a jamming test platform, results under two typical jamming scenarios are evaluated to demonstrate the necessity and superiority of the proposed solution. It also emphasizes the importance of the full-life-cycle resilience of GNSS-based train localization under the railway operation environment.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19852-19872"},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736678","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

Lane Detection by Variational Auto-Encoder With Normalizing Flow for Autonomous Driving 利用变异自动编码器和归一化流量进行车道检测以实现自动驾驶

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-09 DOI: 10.1109/TITS.2024.3471640

Jingyue Shi;Junhui Zhao;Dongming Wang;Hong Tang

引用次数: 0

Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations 基于廉价运算的人脸和面部地标联合定位的驾驶员昏昏欲睡检测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-08 DOI: 10.1109/TITS.2024.3443832

Qingtian Wu;Nannan Li;Liming Zhang;Fei Richard Yu

{"title":"Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations","authors":"Qingtian Wu;Nannan Li;Liming Zhang;Fei Richard Yu","doi":"10.1109/TITS.2024.3443832","DOIUrl":"https://doi.org/10.1109/TITS.2024.3443832","url":null,"abstract":"Real-time detection of driver drowsiness is critical to reduce the risk of road accidents and fatalities. Current facial landmark-based methods usually use a two-stage paradigm, where faces and facial landmarks are localized separately. Additionally, most methods can be hindered by challenging conditions, such as night driving or eyes closed. To address these challenges, we present a refined YOLO network named YOLOFaceMark that can simultaneously detect faces and their facial landmarks. Furthermore, we introduce a drowsiness detection model based on facial landmarks. This model utilizes extracted eye and mouth information to identify drowsy states. We optimize the original YOLO components through structural re-parameterization, channel shuffling, and the design of a dual-branch detection head with an implicit module. These enhancements are designed to improve the accuracy while maintaining computational efficiency. We validate the real-time performance and accuracy of YOLOFaceMark on public datasets, including 300W and COFW. Additionally, we conduct further validation to demonstrate our ability to achieve effective and robust drowsiness detection solely based on the facial landmarks detected by YOLOFaceMark.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19633-19645"},"PeriodicalIF":7.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736388","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

FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-Temporal Traffic Data Imputation FastSTI：用于时空交通数据推算的快速条件伪数值扩散模型

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-08 DOI: 10.1109/TITS.2024.3469240

Shaokang Cheng;Nada Osman;Shiru Qu;Lamberto Ballan

{"title":"FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-Temporal Traffic Data Imputation","authors":"Shaokang Cheng;Nada Osman;Shiru Qu;Lamberto Ballan","doi":"10.1109/TITS.2024.3469240","DOIUrl":"https://doi.org/10.1109/TITS.2024.3469240","url":null,"abstract":"High-quality spatiotemporal traffic data is crucial for intelligent transportation systems (ITS) and their data-driven applications. Inevitably, the issue of missing data caused by various disturbances threatens the reliability of data acquisition. Recent studies of diffusion probability models have demonstrated the superiority of deep generative models in imputation tasks by precisely capturing the spatio-temporal correlation of traffic data. One drawback of diffusion models is their slow sampling/denoising process. In this work, we aim to accelerate the imputation process while retaining the performance. We propose a fast conditional diffusion model for spatiotemporal traffic data imputation (FastSTI). To speed up the process yet, obtain better performance, we propose the application of a high-order pseudo-numerical solver. Our method further revs the imputation by introducing a predefined alignment strategy of variance schedule during the sampling process. Evaluating FastSTI on two types of real-world traffic datasets (traffic speed and flow) with different missing data scenarios proves its ability to impute higher-quality samples in only six sampling steps, especially under high missing rates (60% \u0000<inline-formula> <tex-math>$sim ~90$ </tex-math></inline-formula>\u0000%). The experimental results illustrate a speed-up of \u0000<inline-formula> <tex-math>$textbf {8.3} times $ </tex-math></inline-formula>\u0000 faster than the current state-of-the-art model while achieving better performance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20547-20560"},"PeriodicalIF":7.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736389","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

An Efficient Rolling-Horizon Approach for Cooperative Multi-Lane Platoon Formation With Undefined Configurations 用于未定义配置的多车道排合作编队的高效滚动地平线方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-08 DOI: 10.1109/TITS.2024.3469634

Siwen Yang;Yunwen Xu;Ping Wang;Dewei Li

{"title":"An Efficient Rolling-Horizon Approach for Cooperative Multi-Lane Platoon Formation With Undefined Configurations","authors":"Siwen Yang;Yunwen Xu;Ping Wang;Dewei Li","doi":"10.1109/TITS.2024.3469634","DOIUrl":"https://doi.org/10.1109/TITS.2024.3469634","url":null,"abstract":"This study proposes a cooperative platoon formation scheme in a multi-lane traffic environment with connected and autonomous vehicles (CAVs). It coordinates the lane-changing decisions and longitudinal trajectories of CAVs to form platoons based on each vehicle’s target lane, aiming to reduce the negative impact of lane-changing maneuvers on traffic flow and improve platoon formation efficiency. Mathematically, a vehicle model for platoon formation is developed which couples the lateral lane-changing and longitudinal car-following behaviors of vehicles. A model predictive control-based mixed integer linear programming (MILP) problem is formulated, which optimizes all vehicles’ lateral and longitudinal trajectories and improves maneuverability and efficiency. In contrast to the majority of existing studies, the configurations of platoons to be formed are not predefined and can be jointly optimized to further improve flexibility. Moreover, the framework that integrates configuration decisions, trajectory planning and control is executed dynamically with a rolling horizon based on real-time traffic states to enhance reliability. To validate the proposed scheme, we conduct simulation experiments in SUMO to implement the cooperative platoon formation in a typical three-lane traffic flow with different traffic demand levels. The extensive comparison results indicate the superiority of the proposed method in improving traffic flow speed and platoon formation efficiency.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"21608-21621"},"PeriodicalIF":7.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736203","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

Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method 实时网络级交通信号控制：显式多代理协调方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3468295

Wanyuan Wang;Haipeng Zhang;Tianchi Qiao;Jinming Ma;Jiahui Jin;Zhibin Li;Weiwei Wu;Yichuan Jiang

{"title":"Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method","authors":"Wanyuan Wang;Haipeng Zhang;Tianchi Qiao;Jinming Ma;Jiahui Jin;Zhibin Li;Weiwei Wu;Yichuan Jiang","doi":"10.1109/TITS.2024.3468295","DOIUrl":"https://doi.org/10.1109/TITS.2024.3468295","url":null,"abstract":"Traffic signal control (TSC) has been one of the most useful ways for reducing urban road congestion. The challenge of TSC includes 1) real-time signal decision, 2) the complexity in traffic dynamics, and 3) the network-level coordination. Reinforcement learning (RL) methods can query policies by mapping the traffic state to the signal decision in real-time, however, are inadequate for different traffic flow environment. By observing real traffic information, online planning methods can compute the signal decisions in a responsive manner. Unfortunately, existing online planning methods either require high computation complexity or get stuck in local coordination. Against this background, we propose an explicit multiagent coordination (EMC)-based online planning methods that can satisfy adaptive, real-time and network-level TSC. By multiagent, we model each intersection as an autonomous agent, and the coordination efficiency is modeled by a cost function between neighbor intersections. By network-level coordination, each agent exchanges messages of cost function with its neighbors in a fully decentralized manner. By real-time, the message-passing procedure can interrupt at any time when the real time limit is reached and agents select the optimal signal decisions according to current message. Finally, we test our EMC method in both synthetic and real road network datasets. Experimental results are encouraging: compared to RL and conventional transportation baselines, our EMC method performs reasonably well in terms of adapting to real-time traffic dynamics, minimizing vehicle travel time and scalability to city-scale road networks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19688-19698"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736390","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

Moving Horizon Estimation With Variable Structure Interacting Multiple Model for Surrounding Vehicle States in Complex Environments 在复杂环境中利用可变结构交互多重模型对周围车辆状态进行移动地平线估计

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467042

Haikuan Lu;Ping Wang;Ting Qu;Hong Chen;Lin Zhang;Yunfeng Hu

{"title":"Moving Horizon Estimation With Variable Structure Interacting Multiple Model for Surrounding Vehicle States in Complex Environments","authors":"Haikuan Lu;Ping Wang;Ting Qu;Hong Chen;Lin Zhang;Yunfeng Hu","doi":"10.1109/TITS.2024.3467042","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467042","url":null,"abstract":"Motion prediction of surrounding vehicles in complex environments is essential for autonomous vehicle trajectory planning. Accurate motion prediction requires accurately estimating the state information of the surrounding vehicles. For this purpose, a moving horizon estimation with interacting multiple model (IMM-MHE) algorithm is first proposed here. The algorithm can match multiple vehicle maneuvers, but also fully utilizes the historical information obtained during the driving process, achieving a high estimation accuracy. Second, a moving horizon estimation with variable structure interacting multiple model (VSIMM-MHE) framework is designed. Time-domain adaptation is proposed to solve the problem that the fixed time domain of some models cannot be filled due to model activation and elimination. A new interaction method is proposed to solve the problem that models cannot interact because the starting timesteps of their time domains are different. The proposed framework reduces not only the computational burden, but also the final estimation error caused by the model not matching the current maneuver. Third, based on a model set consisting of different kinds of intention models, a VSIMM-MHE algorithm is proposed. This algorithm introduces residual information into the model classification method, reducing the dependence on the accuracy of the model probabilities. It can not only accurately estimate the state information of surrounding vehicles in a complex environment, but also identify the model that best matches the current maneuver and effectively predict the motion trajectories of surrounding vehicles through model probabilities. Finally, joint simulation with SCANeR studio, Carsim and Simulink and hardware-in-the-loop experiment demonstrate the effectiveness of not only the two proposed estimation algorithms but also the motion prediction of surrounding vehicles using the model probabilities in the VSIMM-MHE algorithm.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19943-19961"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736653","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

Smart Battery Swapping Control for an Electric Motorcycle Fleet With Peak Time Based on Deep Reinforcement Learning 基于深度强化学习的峰值时间电动摩托车队智能电池更换控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3469110

YoonShik Park;Seungdon Zu;Chi Xie;Hyunwoo Lee;Taesu Cheong;Qing-Chang Lu;Meng Xu

{"title":"Smart Battery Swapping Control for an Electric Motorcycle Fleet With Peak Time Based on Deep Reinforcement Learning","authors":"YoonShik Park;Seungdon Zu;Chi Xie;Hyunwoo Lee;Taesu Cheong;Qing-Chang Lu;Meng Xu","doi":"10.1109/TITS.2024.3469110","DOIUrl":"https://doi.org/10.1109/TITS.2024.3469110","url":null,"abstract":"This study proposes a deep Q-network (DQN) model for electric motorcycles (EMs) and a multi-agent reinforcement learning (MARL)-based central control system to support battery swapping decision-making in the delivery business. We aim to minimize expected delivery losses, especially in scenarios where delivery requests are randomly and independently generated for each EM, with fluctuating time distributions and limited BSS capacity. Our MARL benefits from a reservation mechanism and a profit-aggregated central system, which greatly reduces the complexity of MARL. Furthermore, to address the inherent non-stationary problems of MARL, we propose a decentralized agent-based MARL framework, named Decentralized Agents, Centralized Learning Deep Q Network. This framework, leveraging a tailored learning algorithm, achieves peak-averse behavior, reducing delivery losses. Additionally, we introduce a hybrid approach that combines the resulting DQN algorithm for determining when to visit the BSS, and a greedy algorithm for deciding which BSS to visit. Computational experiments using real-world delivery data are conducted to evaluate the performance of our algorithm. The results demonstrate that the hybrid approach maximizes the overall profit of the entire EM fleet in a challenging environment with limited BSS capacity.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20175-20189"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736673","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

Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic 用于高效学习和准确交通预测的动态时空直流网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3443887

Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang

{"title":"Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic","authors":"Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang","doi":"10.1109/TITS.2024.3443887","DOIUrl":"https://doi.org/10.1109/TITS.2024.3443887","url":null,"abstract":"To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18899-18912"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579171","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