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Dynamic Traffic Assignment and Sustainable Transport Systems

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 8646

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Special Issue Editors

Dr. Zhiheng Li

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Logistics and Transportation Department, Tsinghua University, Shenzhen 518055, China
Interests: intelligent transportation systems; traffic control systems; traffic planning and management; traffic big data
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Dr. Jiyuan Tan

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School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Interests: intelligent transportation systems; traffic big data; traffic flow theory
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Dr. Kai Zhang

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Guest Editor

Logistics and Transportation Department, Tsinghua University, Shenzhen 518055, China
Interests: intelligent transportation systems; intelligent driving; smart city
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Dr. Yang Zhou

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Guest Editor

Zhejiang Provincial Party School of the Communist Party of China, Hangzhou 311122, China
Interests: low-carbon traffic
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Special Issue Information

Dear Colleagues,

Dynamic traffic assignment (DTA) is one of the most important foundational theories in intelligent transportation systems (ITSs). DTA models and technologies could be used in the field of traffic planning, traffic control and management, transportation policy evaluation and online transportation systems. In recent years, technological advances have paved the way for the development of transportation systems, and have had a huge impact on the research of dynamic traffic assignment. Advanced technologies such as artificial intelligence, autonomous driving, wireless communication, and electric vehicles provide users with real-time information about traffic conditions and allow travelers to choose different travel modes, travel routes and real-time decisions. Such advanced technologies may have made the basis of DTA models’ change, such as the travel choice principle, travel demand evaluation, and traffic behaviors. Further, the application scenarios and effects of DTA models will also change greatly. All of this will have enormous potential for enhancing the sustainability of transportation systems.

In this Special Issue, we invite the submission of research papers that specifically address the potential related advanced technologies with dynamic traffic assignment models for enhancing the sustainability of transportation systems. The scope of this Special Issue is to cover DTA model and theory, DTA with autonomous driving, electric vehicles, shared traffic and other related advanced technologies. Topics of interest with a general focus on dynamic traffic assignment and sustainable transport systems include but are not limited to:

Dynamic traffic assignment model and theory;
Travel choice principle and traffic flow propagation models;
Effective algorithms for solving DTA problems;
Online DTA model and efficiency;
Application of DTA models for traffic management and control;
DTA model and application under autonomous driving environment;
DTA model and application under electric vehicle environment;
DTA model and application under shared traffic environment;
DTA model and application under other advanced technologies environment;
DTA and sustainable transport systems.

Dr. Zhiheng Li
Dr. Jiyuan Tan
Dr. Kai Zhang
Dr. Yang Zhou
Guest Editors

Manuscript Submission Information

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Keywords

dynamic traffic assignment
sustainable transport systems
advanced technology

Published Papers (5 papers)

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Research

14 pages, 1834 KiB

Open AccessArticle

Convolutional Long Short-Term Memory Two-Dimensional Bidirectional Graph Convolutional Network for Taxi Demand Prediction

by Yibo Cao, Lu Liu and Yuhan Dong

Sustainability 2023, 15(10), 7903; https://doi.org/10.3390/su15107903 - 11 May 2023

Cited by 5 | Viewed by 1216

Abstract

With the rise of the online ride-hailing market, taxi demand prediction has received more and more research interest in intelligent transportation. However, most traditional research methods mainly focused on the demand based on the original point and ignored the intention of the passenger’s destination. At the same time, many forecasting methods need sufficient investigation and data processing, which undoubtedly increases the complexity and operability of forecasting problems. Therefore, we regard the current taxi demand prediction as an origin–destination problem in order to provide more accurate predictions for the taxi demand problem. By combining a spatial network based on graph convolutional network (GCN) and a temporal network of convolutional long short-term memory (Conv-LSTM), we propose a new spatial-temporal model of Conv-LSTM two-dimensional bidirectional GCN (CTBGCN) to uncover the potential correlation between origin and destination. We utilize the temporal network for effective temporal information and the spatial network of multi-layers to get the implicit origin–destination correlation. Numerical results suggest that the proposed method outperforms the state-of-the-art baseline and other traditional methods. Full article

(This article belongs to the Special Issue Dynamic Traffic Assignment and Sustainable Transport Systems)

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12 pages, 1379 KiB

Open AccessArticle

Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting

by Lu Liu, Yibo Cao and Yuhan Dong

Sustainability 2023, 15(6), 4697; https://doi.org/10.3390/su15064697 - 07 Mar 2023

Cited by 1 | Viewed by 1531

Abstract

Traffic forecasting is essential in the development of intelligent transportation systems, as it enables the formulation of effective traffic dispatching strategies and contributes to the reduction of traffic congestion. The abundance of research focused on modeling complex spatiotemporal correlations for accurate traffic prediction, however many of these prior works perform feature extraction based solely on prior graph structures, thereby overlooking the latent graph connectivity inherent in the data and degrading a decline in prediction accuracy. In this study, we present a novel Attention-based Multiple Graph Convolutional Recurrent Network (AMGCRN) to capture dynamic and latent spatiotemporal correlations in traffic data. The proposed model comprises two spatial feature extraction modules. Firstly, a dot product attention mechanism is utilized to construct an adaptive graph to extract the similarity of road structure. Secondly, the graph attention mechanism is leveraged to enhance the extraction of local traffic flow features. The outputs of these two spatial feature extraction modules are integrated through a gating mechanism and fed into a Gated Recurrent Unit (GRU) to make spatiotemporal interaction predictions. Experimental results on two real-world traffic datasets demonstrate the superiority of the proposed AMGCRN over state-of-the-art baselines. The results suggest that the proposed model is effective in capturing complex spatiotemporal correlations and achieving about 1% improvements in traffic forecasting. Full article

(This article belongs to the Special Issue Dynamic Traffic Assignment and Sustainable Transport Systems)

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18 pages, 3097 KiB

Open AccessArticle

Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

by Mohandu Anjaneyulu and Mohan Kubendiran

Sustainability 2023, 15(1), 74; https://doi.org/10.3390/su15010074 - 21 Dec 2022

Cited by 4 | Viewed by 2059

Abstract

A vital problem faced by urban areas, traffic congestion impacts wealth, climate, and air pollution in cities. Sustainable transportation systems (STSs) play a crucial role in traffic congestion prediction for adopting transportation networks to improve the efficiency and capacity of traffic management. In STSs, one of the essential functional areas is the advanced traffic management system, which alleviates traffic congestion by locating traffic bottlenecks to intensify the interpretation of the traffic network. Furthermore, in urban areas, accurate short-term traffic congestion forecasting is critical for designing transport infrastructure and for the real-time optimization of traffic. The main objective of this paper was to devise a method to predict short-term traffic congestion (STTC) every 5 min over 1 h. This paper proposes a hybrid Xception support vector machine (XPSVM) classifier model to predict STTC. Primarily, the Xception classifier uses separable convolution, ReLU, and convolution techniques to predict the feature detection in the dataset. Secondarily, the support vector machine (SVM) classifier operates maximum marginal separations to predict the output more accurately using the weight regularization technique and a fine-tuned binary hyperplane mechanism. The dataset used in this work was taken from Google Maps and comprised snapshots of Bangalore, Karnataka, taken using the Selenium automation tool. The experimental outcome showed that the proposed model forecasted traffic congestion with an accuracy of 97.16%. Full article

(This article belongs to the Special Issue Dynamic Traffic Assignment and Sustainable Transport Systems)

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19 pages, 1613 KiB

Open AccessArticle

Dynamic Pricing Strategy of Charging Station Based on Traffic Assignment Simulation

by Jiyuan Tan, Fuyu Liu, Na Xie, Weiwei Guo and Wenxiang Wu

Sustainability 2022, 14(21), 14476; https://doi.org/10.3390/su142114476 - 04 Nov 2022

Cited by 4 | Viewed by 1697

Abstract

The number of electric vehicles is increasing rapidly worldwide, leading to increasing demand for charging. This will negatively impact the grid. Therefore, it is essential to relieve the power grid operation pressure by changing the charging behaviour of users. In this paper, the charging behaviour of electric vehicles was guided by price instruments to maintain grid balance This paper uses travel simulation to establish the relationship between travel demand and electricity prices. The results were evaluated through the amount of grid voltage drop and network loss. Furthermore, we used the differential evolutionary algorithm to calculate the optimal operation status of the grid, which contains the minimal network loss and maximal voltage drop at different charging stations and the charging price. Finally, the effectiveness of the mechanism proposed in this paper was compared with other simulation examples. The results showed that the pricing strategy could guide users’ charging choices and maintain the grid load balancing. The simulation results show that the average bus voltage increases by 1.26% and 6.59%, respectively, under different requirements. Full article

(This article belongs to the Special Issue Dynamic Traffic Assignment and Sustainable Transport Systems)

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12 pages, 1430 KiB

Open AccessArticle

Fair Assignment for Reserved Nucleic Acid Testing

by Na Xie, Zhidong Liu, Xiqun (Michael) Chen and Shen Li

Sustainability 2022, 14(18), 11752; https://doi.org/10.3390/su141811752 - 19 Sep 2022

Cited by 1 | Viewed by 1165

Abstract

Corona Virus Disease 2019 (COVID-19) is now treating the health of millions of people worldwide. The Chinese government now applies nucleic acid testing as a tool to detect patients from healthy people to control the spread of COVID-19. However, people may come to the nucleic acid testing stations simultaneously, leading to long queues and wasting time. In this paper, we proposed the reserved nucleic acid testing method, which could be easily implemented via Web applications associated with nucleic acid testing. Its key idea is to assign people to different pre-scheduled time slots so that the number of people arriving at a certain time slot can be controlled under the capacity, and thus congestion can be relieved. The key question is how to assign people in a fair manner. We propose a concise model to formalize and analyze the minimum total envy and pairwise fairness assignment problem for a variety of reservation-based applications, including nuclear acid testing. Its objective is to maximize the sum of each person’s utility under the capacity constraints of time slots. The decision variables are the time slot assignment of each person. We show that the envy-freeness solution is usually unavailable. However, we can minimize the total envy through appropriate arrangements and realize pairwise fairness with equal-chance shuffling. Full article

(This article belongs to the Special Issue Dynamic Traffic Assignment and Sustainable Transport Systems)

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