Transportmetrica A-Transport Science最新文献_第4页

Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning 利用车辆连接性和自主性，利用强化学习缓解公路瓶颈拥堵

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2215338

Paul (Young Joun) Ha , Sikai Chen , Jiqian Dong , Samuel Labi

{"title":"Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning","authors":"Paul (Young Joun) Ha , Sikai Chen , Jiqian Dong , Samuel Labi","doi":"10.1080/23249935.2023.2215338","DOIUrl":"10.1080/23249935.2023.2215338","url":null,"abstract":"<div><div>Automation and connectivity based platforms have great potential for managing highway traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is an Active Traffic Management (ATM) strategy that addresses flow breakdown in real-time by adjusting upstream traffic speeds. However, SH has limitations including the need for supporting roadway infrastructure that is immovable and has limited coverage; the inability to enact control beyond its range; and the dependence on human driver compliance. These issues could be addressed by leveraging connected and automated vehicles (CAVs), which can collect information and execute control along their trajectories, irrespective of drivers’ awareness or compliance. In addressing this objective, this study utilises reinforcement learning to present a CAV control model to achieve efficient speed harmonisation. The results suggest that even at low market penetration, CAVs can significantly mitigate traffic congestion bottlenecks to a greater extent compared to traditional SH approaches.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 1","pages":"Pages 1-26"},"PeriodicalIF":3.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59991171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Graph convolutional networks with learnable spatial weightings for traffic forecasting applications 具有可学习空间权重的图卷积网络在交通预测应用中的应用

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2239377

Bi Yu Chen , Yaohong Ma , Jiale Wang , Tao Jia , Xianglong Liu , William H. K. Lam

{"title":"Graph convolutional networks with learnable spatial weightings for traffic forecasting applications","authors":"Bi Yu Chen , Yaohong Ma , Jiale Wang , Tao Jia , Xianglong Liu , William H. K. Lam","doi":"10.1080/23249935.2023.2239377","DOIUrl":"10.1080/23249935.2023.2239377","url":null,"abstract":"<div><div>How to select a suitable spatial weighting scheme for convolutional graph neural networks (ConvGNNs) is challenging. In this study, we propose a ConvGNN, termed learnable graph convolutional (LGC) network, which learns spatial weightings between a road and its k-hop neighbours as learnable parameters in the spatial convolutional operator. A dynamic LGC (DLGC) network is further proposed to learn the dynamics of spatial weightings by explicitly considering the temporal correlations of spatial weightings at different times of the day. A multi-temporal DLGC (MTDLGC) network is developed for forecasting traffic variables in road networks. Results of case study suggest that the MT-DLGC network can achieve higher prediction accuracy than other state-of-the-art baselines. Both LGC and DLGC networks can be used as general spatial weighting schemes for baselines with better forecasting performance than existing spatial weighting schemes, e.g., graph attention. The source code of this study is available publicly at <span>https://github.com/Mayaohong/MTDLGC</span>.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 1","pages":"Pages 436-465"},"PeriodicalIF":3.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49559216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 novel hybrid deep learning model with ARIMA Conv-LSTM networks and shuffle attention layer for short-term traffic flow prediction 基于ARIMA卷积- lstm网络和洗刷注意层的混合深度学习模型

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2236724

Ali Reza Sattarzadeh , Ronny J. Kutadinata , Pubudu N. Pathirana , Van Thanh Huynh

{"title":"A novel hybrid deep learning model with ARIMA Conv-LSTM networks and shuffle attention layer for short-term traffic flow prediction","authors":"Ali Reza Sattarzadeh , Ronny J. Kutadinata , Pubudu N. Pathirana , Van Thanh Huynh","doi":"10.1080/23249935.2023.2236724","DOIUrl":"10.1080/23249935.2023.2236724","url":null,"abstract":"<div><div>Traffic flow prediction requires learning of nonlinear spatio-temporal dynamics which becomes challenging due to its inherent nonlinearity and stochasticity. Addressing this shortfall, we propose a new hybrid deep learning model based on an attention mechanism that uses multi-layered hybrid architectures to extract spatial–temporal, nonlinear characteristics. Firstly, by designing the autoregressive integral moving average (ARIMA) model, trends and linear regression are extracted; then, integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks leads to better understanding of the model's correlations, serving for more accurate traffic prediction. Secondly, we develop a shuffle attention-based (SA) Conv-LSTM module to determine significance of flow sequences by allocating various weights. Thirdly, to effectively analyse short-term temporal dependencies, we utilise bidirectional LSTM (Bi-LSTM) components to capture periodic features. Experimental results illustrate that our Shuffle Attention ARIMA Conv-LSTM (SAACL) model provides better prediction than other comparable methods, particularly for short-term forecasting, using PeMS datasets.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 1","pages":"Pages 388-410"},"PeriodicalIF":3.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43019537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Quality of service measurement for electric vehicle fast charging stations: a new evaluation model under uncertainties 电动汽车快速充电站服务质量评价:一种新的不确定评价模型

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2232044

Zhonghao Zhao , Carman K.M. Lee , Jingzheng Ren , Yungpo Tsang

{"title":"Quality of service measurement for electric vehicle fast charging stations: a new evaluation model under uncertainties","authors":"Zhonghao Zhao , Carman K.M. Lee , Jingzheng Ren , Yungpo Tsang","doi":"10.1080/23249935.2023.2232044","DOIUrl":"10.1080/23249935.2023.2232044","url":null,"abstract":"<div><div>This study addresses the quality of service (QoS) evaluation problem for electric vehicle (EV) fast charging stations (FCSs). With the increasing market penetration of EVs, effective service quality evaluation under different charging scenarios is a pressing and open issue for planning FCSs to accommodate non-stationary customer charging demand. Unlike previous studies, we make the first attempt to define the connotation of QoS from the EV customers' standpoint based on an extended universal generating function (EUGF). First, we formulate the charging behaviour as a fuzzy queuing process, where the arrival rate and service rate are modelled as fuzzy numbers. Second, the QoS requirement level is taken into account to better reflect the real charging environment. The model is then extended to incorporate the charging station structure by introducing the concept of the composition operator. Finally, numerical experiments are conducted to examine the performance of the EUGF-based model. The results demonstrate that the proposed approach is able to obtain a realistic and precise QoS evaluation, and can serve as an effective indicator for FCS planning and operation problems.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 1","pages":"Pages 227-246"},"PeriodicalIF":3.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41326973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Accounting for continuous correlations among alternatives in the context of spatial choice modelling using high resolution mobility data 利用高分辨率流动数据建立空间选择模型时考虑替代品之间的连续相关性

IF 3.3 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2024-09-11 DOI: 10.1080/23249935.2024.2401425

Panagiotis Tsoleridis, Stephane Hess, Charisma F. Choudhury

引用次数: 0

Platoon control and external human–machine interfaces: innovations in pedestrian–autonomous vehicle interactions 排队控制和外部人机界面：行人与自动驾驶汽车互动的创新

IF 3.3 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2024-09-03 DOI: 10.1080/23249935.2024.2398594

Xu Chen, Changyin Dong, Wenzhang Yang, Yuxuan Hou, Hao Wang

引用次数: 0

Predicting metro incident duration using structured data and unstructured text logs 利用结构化数据和非结构化文本日志预测地铁事故持续时间

IF 3.3 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2024-08-29 DOI: 10.1080/23249935.2024.2396951

Yangyang Zhao, Zhenliang Ma, Hui Peng, Zhanhong Cheng

引用次数: 0

Capturing impacts of travel preference on connected autonomous vehicle adoption of risk-averse travellers in multi-modal transportation networks 捕捉旅行偏好对多模式交通网络中规避风险的旅客采用联网自动驾驶汽车的影响

IF 3.3 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2024-08-28 DOI: 10.1080/23249935.2024.2396921

Qi Zhong, Lixin Miao

引用次数: 0

Estimating traffic demand of different transportation modes using floating smartphone data 利用浮动智能手机数据估算不同交通方式的交通需求

IF 3.3 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2024-08-28 DOI: 10.1080/23249935.2024.2396935

Hekmat Dabbas, Bernhard Friedrich

引用次数: 0

Real-time traffic condition uncertainty quantification using adaptive grey prediction interval model 利用自适应灰色预测区间模型量化实时交通状况的不确定性

IF 3.3 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2024-08-26 DOI: 10.1080/23249935.2024.2394522

Zhanguo Song, Xinran Wang, Wei Huang, Meiye Li, Xiaobin Zhong, Jianhua Guo

引用次数: 0