Article
Transportation Science & Technology
Cong Quoc Tran, Mehdi Keyvan-Ekbatani, Dong Ngoduy, David Watling
Summary: This paper discusses the possibility of wireless charging while driving and how to promote the market penetration of electric vehicles by optimizing the deployment of wireless charging lanes on the network. A multi-class dynamic system optimal model is adopted to compute an approximate representation of dynamic traffic flow, and the charging location problem is addressed through a mixed-integer linear program.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Transportation
Zhanhong Cheng, Jia Yao, Anthony Chen, Shi An
Summary: This paper investigates the conditions and characteristics of stochastic traffic assignment paradoxes in three models: multinomial logit, multinomial weibit, and multiplicative hybrid, finding that the multiplicative hybrid model fits the data the best. The study suggests that the other two models exhibit inherent tendencies, and the paradoxical links identified by the multiplicative hybrid model are a compromise of the other two models.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Lingjuan Chen, Yu Wang, Dongfang Ma
Summary: The study proposes a bounded-rational day-to-day dynamic learning and adjustment model for travellers, taking navigation information into account. By updating travellers' departure time with real-time navigation guidance and historical experience, the accuracy of route choice is improved. The model converges to a spatial-temporal oscillating equilibrium instead of a fixed-point stable status, providing insights on improving prediction accuracy of traffic state in urban street networks.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Transportation Science & Technology
Qixiu Cheng, Zhiyuan Liu, Jifu Guo, Xin Wu, Ram Pendyala, Baloka Belezamo, Xuesong (Simon) Zhou
Summary: The fluid-based queueing model is important for traffic flow modeling and state estimation. This paper proposes a spatial queue model for oversaturated traffic systems and demonstrates its effectiveness through empirical data.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiang Zhang, Steven Travis Waller, Dung-Ying Lin
Summary: This study is the first in the literature to examine the Braess paradox considering parking behavior in the autonomous vehicle (AV) environment and model the network design problem for the autonomous transportation system (NDP-ATS). It shows the existence of two distinct Braess paradoxes in AV traffic networks and develops a bi-level programming model to avoid the deterioration caused by these paradoxes. The results highlight the efficacy of the modeling framework for infrastructure development and policy assessment for AV traffic.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Economics
Michael J. Smith, Francesco Viti, Wei Huang, Richard Mounce
Summary: This paper demonstrates, using an example, that if queueing is spatial, the original policy P0 itself may not maximize network capacity, even if the queue storage capacity of each link is very large.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Physics, Multidisciplinary
Aitichya Chandra, Ashish Verma, K. P. Sooraj, Radhakant Padhi
Summary: This paper proposes a data-driven statistical framework to investigate, understand, and model the time-varying fluctuations in the aircraft arrival and departure process. The dynamic evolution and fluctuation characteristics of arrivals and departures are analyzed using Seasonality and Multifractal Detrended Fluctuation Analysis. The results suggest that arrival and departure processes exhibit weak but visible multifractal characteristics, and the fluctuations are higher at a smaller temporal scale compared to a larger scale. This framework has universal applicability and important implications for operational policies on the air transportation workforce and infrastructure management.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Transportation Science & Technology
Mohammad Noaeen, Reza Mohajerpoor, Behrouz H. Far, Mohsen Ramezani
Summary: This paper introduces a decentralized network-level traffic signal control method to handle queue spillbacks. The method strives to maximize the overall throughput of the network by estimating queue lengths with shockwave models and utilizing real-time data for control decisions.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Engineering, Civil
Hasnain Ali, Duc-Thinh Pham, Sameer Alam, Michael Schultz
Summary: This study proposes a model-free and learning-based approach to reduce airport taxi delays using Deep Reinforcement Learning (DRL). By introducing taxiway hotspot features, the convergence rate of the policy during training is significantly improved. The results show that the learnt policy achieves a reduction of approximately 44% in taxi out delays and a savings of 2 minutes in taxi-out time per aircraft in medium-density traffic scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Sadegh Motallebi, Hairuo Xie, Egemen Tanin, Jianzhong Qi, Kotagiri Ramamohanarao
Summary: One common cause of traffic congestion is the concentration of intersecting vehicle routes. The development of connected autonomous vehicles offers the opportunity to address this issue by coordinating vehicle routes globally.
Article
Green & Sustainable Science & Technology
Jiachen Li, Mengqing Ma, Xin Xia, Wenhui Ren
Summary: This study examines the spatial spillover effect of shared mobility on urban traffic congestion using spatial econometric models. The results show that bike-sharing significantly reduces congestion, while the impact of car-sharing is unclear. The influence of shared mobility enterprises varies in different regions, either alleviating or exacerbating traffic congestion.
Article
Environmental Sciences
Junqiang Wan, Honghai Zhang, Qiqian Zhang, Max Z. Li, Yan Xu
Summary: This paper proposes a deep learning-based forecasting framework for en route airspace emissions, which combines features of spatial, temporal, and global temporal trends. The experimental results show that it outperforms existing benchmark models.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Transportation
Islam Kamel, Md Sami Hasnine, Amer Shalaby, Khandker Nurul Habib, Baher Abdulhai
Summary: This paper presents a large-scale integrated modelling framework that captures the relationships between travel mode choice, departure time choice, and route choices. A case study of replacing the current flat transit fare in the City of Toronto with a time-based transit fare structure is presented, showing the effects on transit users' behavior and transit vehicles crowdedness.
CASE STUDIES ON TRANSPORT POLICY
(2021)
Article
Green & Sustainable Science & Technology
Yuli Fan, Qingming Zhan, Huizi Zhang, Zihao Mi, Kun Xiao
Summary: Detailed anticipation of highway congestion becomes more necessary with increasing regional road traffic, which puts pressure on highways and towns it passes through. This paper proposes a demand-network approach based on online route recommendations, which shows good consistency in predicting traffic volume distribution on the highway network. The study emphasizes the importance of dealing with congestion hotspots outside big cities and suggests dynamic bypassing as a potential solution.
Article
Computer Science, Hardware & Architecture
Mauro Passacantando, Giorgio Gnecco, Yuval Hadas, Marcello Sanguineti
Summary: This study introduces a new framework to investigate Braess' paradox, by utilizing cooperative games with transferable utility to evaluate the contribution of network resources to overall network performance.
