Article
Economics
Mojtaba Abdolmaleki, Mehrdad Shahabi, Yafeng Yin, Neda Masoud
Summary: This paper focuses on scheduling travel itineraries for trucks to facilitate truck platooning for energy savings. Various solution methods are proposed and examined for their efficiency in real-world settings.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Engineering, Civil
Vasileios Liatsos, Dimitrios Giampouranis, Mihalis Golias, Sabyasachee Mishra, John Hourdos, Razi Nalim, Mark T. Frohlich, Clayton Nicholas
Summary: This paper describes a research effort to develop a model that can estimate the cost savings of truck caravanning. The results indicate that a caravan size of four trucks or greater is needed for significant cost savings to be achieved, and driver compensation is the critical factor dictating profitability.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Civil
Seolyoung Lee, Cheol Oh, Gunwoo Lee
Summary: The study explored the impact of market penetration rate on the performance of truck platooning services using microscopic traffic simulations. The results showed that increasing the MPR of truck platoons has a positive effect on longitudinal safety but a negative effect on lateral safety.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Engineering, Civil
Ardeshir Mirbakhsh, Joyoung Lee, Dejan Besenski
Summary: This paper presents a classical physics-based model for controlling platooning autonomous vehicles (AVs) in a commercial traffic simulation software. The model, based on the spring-mass-damper system, considers the coupling of each vehicle with its preceding vehicle to control spacing and speed adoption. Limitations on platooning-oriented communication range and number of vehicles are imposed to reflect real-world conditions. The model shows promising results in terms of safety and throughput improvement compared to the existing platooning module in the simulation software.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Economics
Shukai Chen, Hua Wang, Qiang Meng
Summary: This study investigates the cost allocation of cooperative autonomous truck (AT) platooning among carriers. The goal is to find a cost allocation such that the violation of efficiency and stability conditions is minimized. A cooperative AT platooning problem is proposed, and cost allocation models are formulated. An exact row-generation solution method is developed to computationally calculate the cost allocation, and extensive numerical experiments demonstrate the effectiveness of the solution methods.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Green & Sustainable Science & Technology
Vivek Sujan, Perry T. Jones, Adam Siekmann
Summary: This paper aims to analyze the payback and profitability of heavy-duty vehicle platooning in the U.S. Interstate highway system, and evaluate the factors influencing the payback for end-users and the revenue potential for suppliers. The study finds that market adoption rates, platooning velocities, platoon-able daily mileage, platooning likelihood, baseline powertrain fuel economy, fuel prices, platooning fuel economy benefits, price of added technology, and natural platooning due to traffic interactions all have an impact on the payback and profitability of the platooning system.
Article
Engineering, Electrical & Electronic
Renzong Lian, Zhiheng Li, Boxuan Wen, Junqing Wei, Jiawei Zhang, Li Li
Summary: This study proposes a predictive information multiagent soft actor-critic (PI-MASAC) control framework for improving the dynamic response and energy efficiency of human-leading automated truck platooning. Through simulation tests, the results show that the proposed method significantly enhances energy savings and demonstrates adaptability to various initial scenarios and platoon sizes.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2023)
Article
Engineering, Electrical & Electronic
Husam Altamimi, Istvan Varga, Tamas Tettamanti
Summary: Platooning is a control method for driving a group of vehicles, which can also be applied to urban roads. Combining platooning with traffic signal control can improve traffic performance and reduce congestion.
Article
Multidisciplinary Sciences
Huailei Cheng, Yuhong Wang, Dan Chong, Chao Xia, Lijun Sun, Jenny Liu, Kun Gao, Ruikang Yang, Tian Jin
Summary: In this study, the decarbonization effects of truck platooning on the vehicle-road system were evaluated, revealing a trade-off between emission reduction and cost rise. Truck platooning can lower emissions induced by truck operations, but it accelerates the degradation of road infrastructure and increases road emissions.
NATURE COMMUNICATIONS
(2023)
Article
Economics
Limon Barua, Bo Zou, Pooria Choobchian
Summary: This research proposes a platform-based platooning system to maximize truck platooning participation and ensure stability. The two-phase algorithmic approach is found to be more efficient than the integer programming approach in forming truck platoons, and the advantages of the Maximum Stable Truck Platooning Participation problem become more prominent with a larger system.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Engineering, Civil
Wanxin Li, Collin Meese, Hao Guo, Mark Nejad
Summary: Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. To establish trust during dynamic platooning formation, ensuring vehicular data integrity and guarding against potential attackers, verifying vehicle identity information is pivotal. This study proposes an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in truck platooning, demonstrating increased security, fast performance, and programmable access control policies.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Wootaek Kim, Jongchan Noh, Jinwook Lee
Summary: This study aimed to analyze the effect of vehicle platooning between different models of trucks and SUVs, using the SolidWorks Flow Simulation tool, in order to evaluate changes in drag coefficient and their causes. The research found that the shape of the rear side of the leading vehicle can reduce the drag coefficient of the following vehicle by washing the wake, similar to a rear spoiler.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Xiaolei Ma, Enze Huo, Haiyang Yu, Honghai Li
Summary: Truck platooning involves a series of trucks driving closely through communication technologies, which can lead to significant energy savings. This study proposes data mining approaches to analyze truck platooning patterns and finds that adjusting speeds can improve platooning efficiency and fuel consumption.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Jia-You Lin, Chia-Che Tsai, Van-Linh Nguyen, Ren-Hung Hwang
Summary: This article introduces a novel decentralized coordinated platooning planning method (CPP) to address sudden traffic congestion on freeways. By using warning notifications and a maneuver control protocol, vehicles are able to quickly form platoons and safely exit the congested areas.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Analytical
Tianyang Ling, Lu Deng, Wei He, Haibing Wu, Jiayu Deng
Summary: Automated truck platooning (ATP) has great potential in the massive Chinese highway freight market due to its advantages of reducing fuel consumption and carbon emissions. This study investigates the load effects of ATP on highway bridges using typical Chinese bridges and trucks, and calculates the load effects based on Chinese design specifications. The results show that the load effect increases with gross vehicle mass and platooning size, but decreases with increasing inter-truck spacing and critical wheelbase.
Article
Computer Science, Interdisciplinary Applications
Yongqi Dong, Sandeep Patil, Bart van Arem, Haneen Farah
Summary: Accurate and reliable lane detection is crucial for the safety of lane-keeping assistance and lane departure warning systems. Existing methods mostly rely on single image detection, which may not perform well under challenging circumstances. This study proposes a hybrid spatial-temporal deep learning architecture that incorporates information from multiple continuous image frames to accurately detect lane markings in the last frame.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Transportation
Simeon C. Calvert, Bart van Arem, Jane Lappin
Summary: This paper presents the concept of Herd Immunity for Traffic Safety (HITS), which focuses on the increased safety achieved when Connected Automated Vehicles (CAV) and Human Driven Vehicles (HDV) coexist in mixed traffic. With higher levels of CAV penetration, traffic safety grows non-linearly.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Transportation
Ruochen Hao, Meiqi Liu, Wanjing Ma, Bart van Arem, Meng Wang
Summary: Due to the complexity of manoeuvre, there is a lack of models in the literature that describe the platoon formation process on urban roads. Inspired by flocking behaviours in nature, this study proposes a two-dimensional model based on the potential theory to describe the dynamics of connected automated vehicle (CAV) groups, which can also be applied to human-driven vehicles in mixed traffic environments.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Psychology, Applied
Shubham Soni, Nagarjun Reddy, Anastasia Tsapi, Bart van Arem, Haneen Farah
Summary: This study conducted a field test in the Netherlands and found that human drivers accepted smaller critical gaps and maintained shorter headway when interacting with automated vehicles (AVs). Positive information about the AV behavior resulted in closer interactions and increased trust from drivers.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2022)
Article
Computer Science, Artificial Intelligence
Filippo Santoni de Sio, Giulio Mecacci, Simeon Calvert, Daniel Heikoop, Marjan Hagenzieker, Bart van Arem
Summary: This paper presents a framework for achieving meaningful human control over Automated Driving Systems. It is based on the results of a multidisciplinary research project conducted at Delft University of Technology from 2017 to 2021. The framework emphasizes the importance of human persons and institutions being in control of the potentially dangerous operation of driving in mixed traffic. The paper proposes specific requirements for meaningful human control and discusses the implications for human actors and their skills. Future research directions and applications are also suggested.
MINDS AND MACHINES
(2022)
Article
Engineering, Civil
Maria Oskina, Haneen Farah, Peter Morsink, Riender Happee, Bart van Arem
Summary: This study investigates the safety of cyclists when interacting with automated vehicles compared to conventional vehicles. The results show that automated following has similar risks to manual following, while automated overtaking has higher risks compared to manual overtaking. Furthermore, longer interaction time leads to an increase in cycling speed and a decrease in lateral distance to the curb.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Civil
Na Chen, Bart van Arem, Meng Wang
Summary: This study proposes a hierarchical cooperative merging control approach that ensures collision-free and traffic-efficient merging through the interaction of a maneuver planner and an operational trajectory controller. Compared to traditional merging methods, the proposed approach consistently results in less disturbances during merging.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Maryna Ozturker, Goncalo Homem de Almeida Correia, Arthur Scheltes, Marie-Jose Olde Kalter, Bart van Arem
Summary: This study investigates the deployment potential of automated minibuses (AmBs) in the first-mile part of public transport trips. The results show that current public transport users prefer flexible service types, while car and active mode users do not show a preference for either service type. A positive attitude towards riding in AmBs is an important factor for potential users in all segments.
JOURNAL OF ADVANCED TRANSPORTATION
(2022)
Article
Economics
Pieter Jacob Storm, Michel Mandjes, Bart van Arem
Summary: This paper studies a Gaussian process approximation for a class of stochastic traffic flow models and demonstrates its effectiveness in evaluating joint vehicle-density distributions in road traffic networks. It discusses the computational complexity and impact of the assumption regarding vehicles' headways on the approximation accuracy.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Transportation Science & Technology
Peyman Ashkrof, Goncalo Homem de Almeida Correia, Oded Cats, Bart van Arem
Summary: This study examines the determinants of ride acceptance behavior among ride-sourcing drivers and proposes customized matching and pricing strategies to improve system efficiency. The results reveal that employment status, experience level, and working shift are key factors influencing the decision to accept ride requests. The study also finds that pickup time has a negative impact on ride acceptance, while a guaranteed tip and additional income from surge pricing are valued higher than trip fare.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Civil
Alphonse Vial, Gustaf Hendeby, Winnie Daamen, Bart van Arem, Serge Hoogendoorn
Summary: The increase in perception capabilities of connected mobile sensor platforms enables the collection of extensive sensed features at different temporal and spatial scales. This paper proposes a new method for tracking moving targets, such as pedestrians or cyclists, constrained by a road network, using spatially distributed sensor platforms. The key contribution is the introduction of network bound targets into the multi-target tracking problem, resulting in a network-constrained multi-hypotheses tracker (NC-MHT) that utilizes road information. Simulation results demonstrate that the method performs well and highlights the advantages of considering network constraints.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Yiyun Wang, Haneen Farah, Rongjie Yu, Shuhan Qiu, Bart van Arem
Summary: Autonomous vehicles (AVs) are being introduced to the traffic system, but empirical data shows conflicting effects. This study analyzes the driving behavior of AVs and human-driven vehicles (HDVs) at signalized intersections using the Waymo open dataset. Significant differences in driving behavior between AVs and HDVs were found in three situations: vehicle approaching the red light/queue, vehicle responding to the green light (as the first vehicle), and vehicle responding to its preceding vehicle (in the queue). Behavioral adaptations of HDV drivers were also revealed.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Civil
Alphonse Vial, Maria Salomons, Winnie Daamen, Bart van Arem, Sascha Hoogendoorn-Lanser, Serge Hoogendoorn
Summary: The main objective of this study is to enhance the situational awareness of traffic signal controllers by using observations from moving sensor platforms, in order to prioritize cyclists and reduce their idle time at signalized intersections.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Transportation
Peyman Ashkrof, Goncalo Homem de Almeida Correia, Oded Cats, Bart van Arem
Summary: As individual service suppliers, ride-sourcing drivers have the freedom to choose their own relocation strategies, such as waiting, cruising freely, or following platform recommendations. These decisions have significant impacts on supply and demand balance and overall system performance. Through a stated choice experiment using a unique dataset of 576 ride-sourcing drivers in the US, we examined the searching behavior of drivers and assessed the effects of different attributes on their decisions. The findings suggest that drivers' relocation strategies vary among different driver groups and are influenced by factors such as surge pricing and distance from high-demand areas.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Transportation Science & Technology
Ruochen Hao, Yuxiao Zhang, Wanjing Ma, Chunhui Yu, Tuo Sun, Bart van Arem
Summary: With the development of internet of vehicles and automated driving, individual-based trajectory control at intersections becomes possible. This study proposes a mixed-integer linear programming (MILP) model to optimize vehicle trajectories at an isolated signal-free intersection without lane allocation, denoted as lane-allocation-free (LAF) control. Vehicle routes and trajectories at the intersection are optimized in one unified framework for system optimality in terms of total vehicle delay.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)