Article
Transportation
Ruiya Chen, Xiangdong Xu, Anthony Chen, Chao Yang
Summary: Travel time variability poses challenges to reporting travel time information. This paper proposes a conservative expected travel time approach to enhance information reliability and simplicity.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Nikolaos Tsanakas, David Gundlegard, Clas Rydergren
Summary: This paper proposes a Data-Driven Network Assignment (DDNA) mechanism for estimating time-dependent Origin-Destination (OD) matrices using Floating-Car Data (FCD). The results of synthetic-data experiments indicate that the computationally expensive Dynamic Traffic Assignment (DTA) models may not be necessary for solving the OD matrix estimation problem if FCD is available.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Fushata A. Mohammed, Mahyar Amirgholy
Summary: This research explores the tradeoff between the operational capacity and battery loss of CAVs at signal-free intersections. By developing a stochastic model, the study takes into account the probability distribution of operational error in synchronizing CAV platoons at intersections. It proposes optimizing platoon size, traffic speed, and marginal gap length to balance the tradeoff between battery capacity loss and intersection capacity.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Iman Taheri Sarteshnizi, Majid Sarvi, Saeed Asadi Bagloee, Neema Nassir
Summary: Multiple pattern analyses of traffic data have been conducted previously, but this study introduces a hybrid method that considers temporal factors to measure the intensity of differences among various temporal factors' data. The proposed method can efficiently process historical data, denoise it with basis splines, reshape it into a 2-D latent space using PCA, and then apply pairwise K-means clustering and DBSCAN for anomaly elimination. By using Adjusted Rand Index matrices, similar patterns of different temporal perspectives can be systematically identified. Real data from Melbourne, Australia were used for multiple analyses, detecting dissimilarities with intensities of up to 80% that cannot be detected using general clustering approaches.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Ning Guo, Fei-Hong Jiang, Kong-Jin Zhu, Chao-Yun Wu, Qing-Yi Hao
Summary: When on-ramp bottlenecks are activated, road capacity on the mainline drops sharply. This paper presents a method of adding white solid lines between lanes of the mainline in the on-ramp area to prohibit lane-changing behavior. The interference of on-ramp vehicles on mainline vehicles can be reduced, to improve traffic efficiency.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Editorial Material
Transportation
Oded Cats, Sybil Derrible, Joseph Chow
Summary: This special issue focuses on the development of new concepts, theories, and methods related to the reliability and resilience of novel mobility systems. It consists of seven papers that cover methodological, theoretical, and advanced application developments in this domain. Some of these contributions were originally presented at INSTR2021, the 8th International Symposium on Transport Network Reliability. In this Editorial Note, the authors reflect on the contributions made in each of the articles included in this special issue.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Jiahua Zhang, Kentaro Wada, Takashi Oguchi
Summary: This paper proposes a macroscopic model to describe the equilibrium distribution of passenger arrivals for the morning commute problem in a congested urban rail transit system. The proposed model uses a macroscopic train operation sub-model to simplify the interaction between passengers and trains while maintaining their essential physical relations. The equilibrium conditions and solution method of the model are derived and the characteristics of the equilibrium are examined analytically and numerically. An application of the model is presented to analyze a time-dependent timetable optimization problem with equilibrium constraints, revealing a 'capacity increasing paradox' and evaluating the influence of timetable design on passengers' equilibrium travel costs.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Wei Huang, Jing Hu, Guoyu Huang, Hong K. Lo
Summary: This paper introduces a three-layer hierarchical model-based approach for network-wide traffic signal control, which addresses the coordination and optimization problems at different levels of the signal control system. Experimental results demonstrate that the proposed approach reduces computational complexity while maintaining overall performance of the traffic network.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Yongfu Li, Sen Zhang, Longwang Huang, Gang Huang
Summary: This article presents a new controller for heterogeneous connected vehicles (CVs) platoon under communication delay. A third-order vehicle dynamics model is used to capture the heterogeneity of vehicles. A new nonlinear controller for the CV platoon is proposed to consider car-following interactions, acceleration difference, and communication delays. The internal stability of the CV platoon and the upper bound of communication delay are deduced using the Lyapunov theorem. The string stability of the linearized CV platoon system is proved using the infinite-norm method. A hierarchical control strategy suitable for co-simulation is designed to overcome vehicle nonlinearity and achieve consistency between desired and actual acceleration. Extensive simulation and cosimulation demonstrate the superiority and effectiveness of the developed controller.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Nicolai A. Weinreich, Daniel B. van Diepen, Federico Chiariotti, Christophe Biscio
Summary: The planning process for bike sharing systems is complex and requires knowledge of urban mobility patterns and local features. Dynamic rebalancing of bike sharing systems is expensive, making correct planning critical for economic viability. This study designs an automated planning pipeline to place stations in an area without direct knowledge of demand.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Dong Mo, Xiqun (Michael) Chen, Zheng Zhu, Chaojie Liu, Na Xie
Summary: In this paper, a stochastic evolutionary dynamic game approach is proposed to model and manage the ride-sourcing market with limited platform reputation. The analysis reveals the intrinsic mechanism of trust in ride-sourcing services and suggests adaptive pricing strategies and managerial strategies for the government to prevent the deterioration of service quality.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Zohreh Fotouhi, Hamed Narimani, Massoud Reza Hashemi
Summary: This article proposes a machine learning-based method to optimize the charging behavior model of electric vehicle drivers through parameter tuning, in order to help control congestion at charging stations and predict future demand.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Ang Ji, Mohsen Ramezani, David Levinson
Summary: This study presents a model for lane-changing events, consisting of two interconnected phases: 'stay' and 'execution'. The model incorporates stochastic duration of the 'stay' phase based on external traffic conditions, and models the 'execution' phase using longitudinal speed profiles. Bayesian survival analysis is used to predict the probability of the stay duration before a new execution phase, addressing the censoring issue. Using real-world vehicle trajectory data, the study identifies factors influencing driver behavior in lane-keeping and lane-changing execution, such as surrounding conditions, lane-changing purpose, directions, and departure lanes. The findings highlight the impact of urgency and patience on lane-changing decisions, as well as the influence of distances and relative speeds with surrounding vehicles on acceleration behavior during the execution phase.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Weijie Yu, Xuedong Hua, Wei Wang
Summary: This study analyzed traffic flow configuration by introducing IMDVs and considering multiple traffic flow operations, such as vehicle degradation and platoon formation. Multiple factors, including market penetration rate, platoon length, and information flow topology, were taken into consideration in the theoretical modeling. The high accuracy of the theoretical analysis was verified through numerical experiments, and some reasonable recommendations for traffic management were provided.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Zhao Zhang, Xiaohong Jiao
Summary: Short-term traffic flow prediction is crucial in intelligent transportation. This research paper proposes a dual-branch grammar model that extracts deep spatio-temporal features of historical traffic information, improving prediction accuracy with the use of Selu activation function and a wide attention module.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Shi-Teng Zheng, Rui Jiang, Junfang Tian, Xiaopeng Li, Bin Jia, Ziyou Gao, Shaowei Yu
Summary: This paper investigates the growth pattern of traffic oscillations in a platoon in a car-following experiment and compares it with other experiments. The study finds that there is no significant difference in the growth pattern of speed standard deviation between different experiments. However, the experiment with closer car following shows larger acceleration standard deviations, driver sensitivities, and strength of stochastic factors.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Michail A. Makridis, Aikaterini Anesiadou, Konstantinos Mattas, Georgios Fontaras, Biagio Ciuffo
Summary: Drivers' heterogeneity and vehicle characteristics contribute to the stochasticity in road traffic dynamics. This study proposes a novel framework to identify individual driver fingerprints based on their acceleration behaviors and reproduce them in microsimulation, aiming to accurately reproduce observed driving behaviors.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Ahmed Kamel, Tarek Sayed, Chuanyun Fu
Summary: This study proposes an approach for real-time road network safety analysis using data generated by autonomous vehicles (AVs). The approach utilizes a Bayesian hierarchical spatial random parameter extreme value model (BHSRP) to estimate the risk of crash (RC) and return level (RL), providing real-time safety measurements for urban corridors. The results highlight the association between high crash risk areas and severe conflict frequency.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation
Ling Wang, Lingjie Zou, Mohamed Abdel-Aty, Wanjing Ma
Summary: This study investigated the crash mechanisms in different traffic states using high-resolution trajectory data. The results showed that crash risk is mainly determined by upstream contributing factors under smooth states, and by downstream factors under congestion conditions.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Review
Transportation
Weiliang Zeng, Miaosen Wu, Peng Chen, Zhiguang Cao, Shengli Xie
Summary: With the advancement of autonomous vehicle technology, online hailing autonomous taxi system is considered to be one of the most popular public transportation services in the future. Recent studies have focused on demand forecasting, ride matching, path planning, relocation, and pricing strategy for shared online hailing and autonomous taxi services. This study conducted a survey based on 141 representative literatures from 1995 to 2022 to understand the latest developments in the key problems of operating autonomous taxi service. The study also discusses how emerging technologies such as internet of vehicles, big data, cloud and edge computing, and blockchain can enhance the autonomous taxi service, and identifies the current research challenges and public concerns or hurdles in adopting autonomous taxi services.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
