Transportmetrica A-Transport Science最新文献

Timing co-evolutionary path optimisation method for emergency vehicles considering the safe passage 考虑安全通道的应急车辆时序协同进化路径优化方法

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2253477

Jiabin Wu , Yifeng Lin , Weiwei Qi

引用次数: 0

Joint operation planning of drivers and trucks for semi-autonomous truck platooning 半自动卡车队列中驾驶员和卡车的联合作业规划

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2266041

Yilang Hao , Zhibin Chen , Jiangang Jin , Xiaotong Sun

{"title":"Joint operation planning of drivers and trucks for semi-autonomous truck platooning","authors":"Yilang Hao , Zhibin Chen , Jiangang Jin , Xiaotong Sun","doi":"10.1080/23249935.2023.2266041","DOIUrl":"10.1080/23249935.2023.2266041","url":null,"abstract":"<div><div>Semi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 2","pages":"Pages 1-37"},"PeriodicalIF":3.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141513","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

Safe and efficient manoeuvring for emergency vehicles in autonomous traffic using multi-agent proximal policy optimisation 基于多智能体近端策略优化的自主交通中应急车辆安全高效机动

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2246586

Leandro Parada , Eduardo Candela , Luis Marques , Panagiotis Angeloudis

{"title":"Safe and efficient manoeuvring for emergency vehicles in autonomous traffic using multi-agent proximal policy optimisation","authors":"Leandro Parada , Eduardo Candela , Luis Marques , Panagiotis Angeloudis","doi":"10.1080/23249935.2023.2246586","DOIUrl":"10.1080/23249935.2023.2246586","url":null,"abstract":"<div><div>Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which cannot cover all possible scenarios that can take place in autonomous traffic. Multi-Agent Proximal Policy Optimisation (MAPPO) has recently emerged as a powerful method for autonomous systems because it allows for training in thousands of different situations. In this study, we present an approach based on MAPPO to guarantee the safe and efficient manoeuvring of autonomous vehicles in the presence of an emergency vehicle. We introduce a risk metric that summarises the potential risk of collision in a single index. The proposed method generates cooperative policies allowing the emergency vehicle to go at $ 15 % $ <span><math><mn>15</mn><mi>%</mi></math></span> higher average speed while maintaining high safety distances. Moreover, we conduct a comprehensive evaluation of our method in a wide range of scenarios, including assessing the trade-offs between traffic efficiency and safety, measuring the scalability of the approach with respect to the number of autonomous vehicles, analysing different distributions of mixed human and autonomous traffic, and examining the various levels of cooperation and competition among agents.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 2","pages":"Pages 1-29"},"PeriodicalIF":3.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136392616","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

Bifurcation control based on improved intelligent driver model considering stability and minimum gasoline consumption 基于考虑稳定性和最小汽油消耗的改进智能驾驶员模型的分岔控制

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2243345

Liyou Li , Weilin Ren , Rongjun Cheng

{"title":"Bifurcation control based on improved intelligent driver model considering stability and minimum gasoline consumption","authors":"Liyou Li , Weilin Ren , Rongjun Cheng","doi":"10.1080/23249935.2023.2243345","DOIUrl":"10.1080/23249935.2023.2243345","url":null,"abstract":"<div><div>Advanced driver assistance system (ADAS) plays an important role in the transition period before the full maturity of self-driving technology. In this paper, an intelligent driver model with multiple time delays (IDM-MTD) is established to describe the dynamic characteristics of vehicles equipped with ADAS. Moreover, a control term that considers the velocity difference between the current and historical moment is selected for assistance driving. A novel framework is proposed to determine control parameters in controlled IDM-MTD. The definite integral stability method (DISM) is integrated into the framework to constrain the range of parameters that make the system stable. In the control strategy design process, this paper places significant emphasis on the implementation of the DISM modification to effectively address redundant iteration steps by exploiting the characteristic of bifurcation. Through numerical simulation, a two-dimensional optimal parameter combination is found, which not only stabilizes traffic flow but also produces minimal gasoline consumption.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 2","pages":"Pages 1-22"},"PeriodicalIF":3.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44620378","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 multiclass simulation-based dynamic traffic assignment model for mixed traffic flow of connected and autonomous vehicles and human-driven vehicles 基于多类仿真的网联自动驾驶车辆与人工驾驶车辆混合交通流动态交通分配模型

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2257805

Behzad Bamdad Mehrabani , Jakob Erdmann , Luca Sgambi , Seyedehsan Seyedabrishami , Maaike Snelder

{"title":"A multiclass simulation-based dynamic traffic assignment model for mixed traffic flow of connected and autonomous vehicles and human-driven vehicles","authors":"Behzad Bamdad Mehrabani , Jakob Erdmann , Luca Sgambi , Seyedehsan Seyedabrishami , Maaike Snelder","doi":"10.1080/23249935.2023.2257805","DOIUrl":"10.1080/23249935.2023.2257805","url":null,"abstract":"<div><div>Connected and Autonomous Vehicles (CAVs) may exhibit different driving and route choice behaviours compared to Human-Driven Vehicles (HDVs), which can result in a mixed traffic flow with multiple classes of route choice behaviour. Therefore, it is necessary to solve the Multiclass Traffic Assignment Problem (TAP) for mixed traffic flow. However, most existing studies have relied on analytical solutions. Furthermore, simulation-based methods have not fully considered all of CAVs’ potential capabilities. This study presents an open-source solution framework for the multiclass simulation-based TAP in mixed traffic of CAVs and HDVs. The proposed model assumes that CAVs follow system optimal with rerouting capabilities, while HDVs follow user equilibrium. It also considers the impact of CAVs on road capacity at both micro and meso scales. The proposed model is demonstrated through three case studies. This study provides a valuable tool that can consider several assumptions for better understanding the impact of CAVs on mixed traffic flow.</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 2","pages":"Pages 1-32"},"PeriodicalIF":3.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910622","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

The optimal solution to the energy-efficient train control in a multi-trains system-part 1: the algorithm design 多列车系统中节能列车控制的最优解——算法设计

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2267685

Yu Rao , Xiaoyun Feng , Qingyuan Wang , Pengfei Sun

引用次数: 0

Convexity and global optimisation of lane-based fixed-time signal model for delay minimisation at an isolated intersection 基于车道的隔离交叉口延迟最小化固定时间信号模型的凸性与全局优化

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2259014

Haiming Hao , Hui Jin

引用次数: 0

Mapping the spatial organisation of air transport network by WENA-MLST analysis 基于WENA-MLST分析的航空运输网络空间组织映射

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2261044

Chengliang Liu , Bangjuan Wang , Hong Zhang

引用次数: 0

Empirical study of the effects of physics-guided machine learning on freeway traffic flow modelling: model comparisons using field data 物理引导机器学习对高速公路交通流建模影响的实证研究：使用现场数据的模型比较

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2264949

Zhao Zhang , Yun Yuan , Mingchen Li , Pan Lu , Xianfeng Terry Yang

{"title":"Empirical study of the effects of physics-guided machine learning on freeway traffic flow modelling: model comparisons using field data","authors":"Zhao Zhang , Yun Yuan , Mingchen Li , Pan Lu , Xianfeng Terry Yang","doi":"10.1080/23249935.2023.2264949","DOIUrl":"10.1080/23249935.2023.2264949","url":null,"abstract":"<div><div>Recent studies have shown the successful implementation of classical model-based approaches (e.g. macroscopic traffic flow modelling) and data-driven approaches (e.g. machine learning – ML) to model freeway traffic patterns, while both have their limitations. Even though model-based approaches could depict real-world traffic dynamics, they could potentially lead to inaccurate estimations due to traffic fluctuations and uncertainties. In data-driven models, the acquisition of sufficient high-quality data is required to ensure the model performance. However, many transportation applications often suffer from data shortage and noises. To overcome those limitations, this study aims to introduce and evaluate a new model, named as physics-guided machine learning (PGML), that integrates the classical traffic flow model (TFM) with the machine learning technique. This PGML model leverages the output of a traffic flow model along with observational features to generate estimations using a neural network framework. More specifically, it applies physics-guided loss functions in the learning objective of neural networks to ensure that the model not only consists with the training set but also shows lower errors on the known physics of the unlabelled set. To illustrate the effectiveness of the PGML, this study implements empirical studies with a real-world dataset collected from a stretch of I-15 freeway in Utah. Experimental study results show that the proposed PGML model could outperform the other compatible methods, including calibrated traffic flow models, pure machine learning methods, and physics unguided machine learning (PUML).</div></div>","PeriodicalId":48871,"journal":{"name":"Transportmetrica A-Transport Science","volume":"21 2","pages":"Pages 1-28"},"PeriodicalIF":3.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569703","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

Coordinated flow model for strategic planning of autonomous mobility-on-demand systems 自主移动按需系统战略规划的协调流模型

IF 3.6 2区工程技术

Transportmetrica A-Transport Science Pub Date : 2025-05-04 DOI: 10.1080/23249935.2023.2253474

Jiangbo (Gabe) Yu , Michael F. Hyland

引用次数: 0