Transportmetrica A-Transport Science最新文献_第2页

A projected Newton-like inertial dynamics for modeling day-to-day traffic evolution with elastic demand 模拟具有弹性需求的日常交通演化的投影类牛顿惯性动力学

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2226245

Renxin Zhong , Xin-an Li , Qingnan Liang , Zhibin Chen , Tianlu Pan

{"title":"A projected Newton-like inertial dynamics for modeling day-to-day traffic evolution with elastic demand","authors":"Renxin Zhong , Xin-an Li , Qingnan Liang , Zhibin Chen , Tianlu Pan","doi":"10.1080/23249935.2023.2226245","DOIUrl":"10.1080/23249935.2023.2226245","url":null,"abstract":"<div><div>This paper proposes a projected Newton-like inertial dynamics for modeling second-order day-to-day (DTD) traffic evolution with elastic travel demand. The proposed DTD model describes double dynamics of traffic flow and travel cost based on a class of second-order gradient-like dissipative dynamic systems. We use the projection operator to prevent the existence of negative flow, which is regarded as a major pitfall of the existing second-order DTD traffic models. To our knowledge, this would be the first attempt to address the problem of negative flow in the second-order DTD traffic models. Meanwhile, we show that the proposed model inherits the properties of Newton-like inertial dynamics and behaves similarly to the existing second-order DTD models. The proposed model admits a Hessian-driven component, which is closely related to the congestion externality associated with the marginal link travel cost. The proposed model also extends the existing second-order DTD models from the fixed demand case to the elastic demand case. We characterize several theoretical properties of the proposed projected second-order DTD model, such as the equivalence between its fixed points and the user equilibrium with elastic demand, the convergence of the DTD traffic evolution process, and the stability analysis with different stability concepts. We show that the proposed model can be reduced to the well-known network tatonnement model. Finally, we demonstrate the properties of the projected second-order DTD model via numerical examples.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 1","pages":"Pages 101-129"},"PeriodicalIF":3.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48792035","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

Optimal public transport timetabling with autonomous-vehicle units using coupling and decoupling tactics 基于耦合和解耦策略的自动驾驶汽车单元公共交通优化时间表

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2220423

Yaoyao Wang , Avishai (Avi) Ceder , Zhichao Cao , Silin Zhang

引用次数: 0

Spatiotemporal clustering for the impact region caused by a traffic incident: an improved fuzzy C-means approach with guaranteed consistency 交通事故影响区域的时空聚类：一种保证一致性的改进模糊C均值方法

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-01-02 DOI: 10.1080/23249935.2023.2236719

Zhenjie Zheng , Zhengli Wang , Xiqun Chen , Wei Ma , Bin Ran

{"title":"Spatiotemporal clustering for the impact region caused by a traffic incident: an improved fuzzy C-means approach with guaranteed consistency","authors":"Zhenjie Zheng , Zhengli Wang , Xiqun Chen , Wei Ma , Bin Ran","doi":"10.1080/23249935.2023.2236719","DOIUrl":"10.1080/23249935.2023.2236719","url":null,"abstract":"<div><div>Traffic incidents disrupt the normal flow of vehicles and induce nonrecurrent traffic congestion. It has been well accepted that the shape of the spatiotemporal region impacted by a traffic incident should be consistent with the propagation of shockwaves. Although there has been a variety of approaches that attempt to estimate the impact region of traffic incidents, most of them are not capable of producing results with guaranteed consistency. In this research, we propose an improved fuzzy clustering approach that integrates the domain knowledge of shockwave theory for freeway incidents to address this issue, which is new to the literature. Compared to the general clustering approaches, our improved fuzzy clustering approach takes control of the clustering process by leveraging the directional propagation of shockwaves in the form of constraints, which can guarantee the consistency. In addition, unlike existing studies that employ discrete variables to distinguish traffic status in case of traffic incidents, the fuzzy clustering approach uses the continuous variable to indicate the incident impact on vehicle speed. This can help to reduce the information loss and estimate the impact region more accurately. Numerical experiments are conducted to evaluate the performance of our approach using both simulation and real data. Results show that our approach is able to guarantee that the shape of the impact region is consistent with the propagation of shockwaves and achieve higher accuracy of the estimated delay induced by the incident than the current state-of-the-art approach.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 1","pages":"Pages 358-387"},"PeriodicalIF":3.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48699623","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

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