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Data-Driven Emergency Traffic Management, Optimization and Simulation

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 13163

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Prof. Dr. Ciyun Lin

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Guest Editor

Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
Interests: traffic perception; intelligent transportation systems; emergency traffic management, optimization, and simulation; traffic data analysis and visualization
Special Issues, Collections and Topics in MDPI journals

Dr. Bowen Gong

E-Mail Website
Guest Editor

Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
Interests: intelligent traffic navigation and location; intelligent transportation systems; traffic behavior analysis; traffic safety evaluation and prevention
Special Issues, Collections and Topics in MDPI journals

Dr. Dayong (Jason) Wu

E-Mail Website
Guest Editor

Texas A&M Transportation Institute, Texas A&M University, College Station, TX 77843, USA
Interests: intelligent transportation systems; GIS in transportation and traffic safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, sudden disasters have occurred frequently and trend increasingly all over the world. Besides the conventional natural disasters (such as earthquakes, floods, and hurricanes), the accident disasters (such as fire, gas explosions in high-density urban central districts) and the new-type of disasters (such as cataphoresis, terrorist attacks, hazardous material leakage) happen occasionally and growing.

Emergency traffic management and organization are essential to ensure the emergency material and rescue workers respond on time and victims evacuate promptly. Timely and efficient emergency traffic management can lessen the loss of life and property. Many new ideas and technologies have been proposed to make emergency traffic management more efficient. These methods include traffic network reliability analysis and optimization, traffic evacuation route optimization and guidance, and emergency resource dispatch and optimization.

This special issue will highlight new opportunities and challenges for sustainable transportation, focusing on improving and evaluating emergency traffic management and control with traffic flow modeling, controlling, and simulation using multi-source data. We welcome papers on the following topics:

(1) Modeling and simulating the performance of traffic networks or traffic flow under sudden disaster.

(2) Traffic evacuation route optimization and guidance in sudden disaster.

(3) Traffic behavior modeling and characteristics analysis in sudden disaster.

(4) Emergency resource dispatch and optimization in sudden disasters.

(5) Traffic management and control in sudden disasters.

Dr. Ciyun Lin
Dr. Bowen Gong
Dr. Dayong (Jason) Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

emergency traffic management and control
traffic modeling and simulation
traffic evacuation
emergency resource dispatch and optimization
data-driven
sudden disaster

Published Papers (7 papers)

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Research

26 pages, 7744 KiB

Open AccessArticle

Simulation of Low Carbon Layout Optimization of Disassembly Line Based on SLP Method

by Jia Mao, Jinyuan Cheng, Xiangyu Li, Honggang Zhao and Dexin Yu

Sustainability 2023, 15(6), 5241; https://doi.org/10.3390/su15065241 - 15 Mar 2023

Cited by 2 | Viewed by 2172

Abstract

New concepts such as low-carbon economy, low-carbon production, low-carbon living and even low-carbon cities have become popular topics in environmental protection. The disassembly line part of reverse logistics is accompanied by high carbon emission, which is contrary to the original intention of sustainable development. In this paper, we design a systematic low-carbon layout for the disassembly line of the logistics processing center to address the problem of high carbon emissions caused by the unreasonable layout of the disassembly line. Taking the disassembly line in the logistics center of Company H as the research object, the process of the disassembly line is analyzed, and the SLP analysis method is applied to analyze the material flow and the material flow intensity level of the disassembly line layout, and three different optimization schemes are derived. Flexsim software was used to model and run the three initial layout schemes of the disassembly line, and the data related to the waiting time operation of each scheme were obtained. Finally, carbon emission and other disassembly-line-related indicators were introduced and weights were set, and the results were subjected to weighted gray correlation analysis to arrive at the optimal disassembly line layout optimization scheme. This study will provide reference for other reverse logistics processing center layout studies. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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17 pages, 2094 KiB

Open AccessArticle

Study on the Optimization of Agricultural Production Waste Recycling Network under the Concept of Green Cycle Development

by Xi Wang, Wei Ning, Kun Wang and Dexin Yu

Sustainability 2023, 15(1), 165; https://doi.org/10.3390/su15010165 - 22 Dec 2022

Cited by 3 | Viewed by 2631

Abstract

This study is based on the concept of converting agricultural waste into green new energy, we combine the concept of green cycle development and the relevant theories in modern system engineering to optimize the study of agricultural production waste recycling network. In this paper, the optimization of the agricultural production waste recycling network is divided into two aspects—facility site selection and vehicle path planning—with the objectives of agricultural production waste green recycling and the minimization of system construction and operational costs. In this study, the site selection and path planning problems were unified and an optimization model for the agricultural production waste recycling network site-path (LRP) problem was constructed. The optimization results of agricultural production waste recycling network facility location and recycling vehicle path planning were obtained by using the simulation data in the optimization model and designing the genetic algorithm design with the relevant characteristics of agricultural production waste recycling. The feasibility and operability of the model were verified through experiments. The research related to the optimization of agricultural production waste recycling networks can be used to both reduce production costs in agricultural areas and progress the practical theory of reverse logistics in agricultural areas. Agricultural waste resource utilization provides important support for the development of an ecological agriculture cycle and helps protect the environment. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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16 pages, 719 KiB

Open AccessArticle

Public Acceptance of Last-Mile Shuttle Bus Services with Automation and Electrification in Cold-Climate Environments

by Naihui Wang, Yulong Pei and Hao Fu

Sustainability 2022, 14(21), 14383; https://doi.org/10.3390/su142114383 - 3 Nov 2022

Cited by 5 | Viewed by 1344

Abstract

The last-mile shuttle bus service with automation and electrification has emerged to fill gaps in on-demand transportation systems and its goals are to satisfy the door-to-door mobility needs of residents. It could help to enhance the happiness of public travel in cold-climate environments, which is also considered a pro-social public transportation service. Although it has the potential to promote sustainable and environmentally friendly mobility systems, the successful implementation of last-mile shuttle bus services with automation and electrification highly depends on individuals’ willingness to accept. In this paper, a theoretical acceptance model for last-mile shuttle bus services with automation and electrification is proposed. Partial least squares structural equation modeling is employed to examine research model in accordance with 986 valid questionnaires answered by public in snow and ice environments. The outcomes show that the proposed model accounts for 73.4% of the variance in behavioral intention to utilize last-mile shuttle bus services with automation and electrification. The strongest determinants of behavior intention are attitude and perceived usefulness. In addition, perceived risk negatively affects behavioral intention. We also provide theoretical findings and practical suggestions for developing last-mile shuttle bus services with automation and electrification based on the results and our analysis. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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25 pages, 11652 KiB

Open AccessArticle

Risk-Aware Travel Path Planning Algorithm Based on Reinforcement Learning during COVID-19

by Zhijian Wang, Jianpeng Yang, Qiang Zhang and Li Wang

Sustainability 2022, 14(20), 13364; https://doi.org/10.3390/su142013364 - 17 Oct 2022

Cited by 5 | Viewed by 1459

Abstract

The outbreak of COVID-19 brought great inconvenience to people’s daily travel. In order to provide people with a path planning scheme that takes into account both safety and travel distance, a risk aversion path planning model in urban traffic scenarios was established through reinforcement learning. We have designed a state and action space of agents in a “point-to-point” way. Moreover, we have extracted the road network model and impedance matrix through SUMO simulation, and have designed a Restricted Reinforcement Learning-Artificial Potential Field (RRL-APF) algorithm, which can optimize the Q-table initialization operation before the agent learning and the action selection strategy during learning. The greedy coefficient is dynamically adjusted through the improved greedy strategy. Finally, according to different scenarios, our algorithm is verified by the road network model and epidemic historical data in the surrounding areas of Xinfadi, Beijing, China, and comparisons are made with common Q-Learning, the Sarsa algorithm and the artificial potential field-based reinforcement learning (RLAFP) algorithm. The results indicate that our algorithm improves convergence speed by 35% on average and the travel distance is reduced by 4.3% on average, while avoiding risk areas, compared with the other three algorithms. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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25 pages, 2980 KiB

Open AccessArticle

Optimization of Cargo Shipping Adaptability Modeling Evaluation Based on Bayesian Network Algorithm

by Siyuan Gao, Fengrong Zhang, Wei Ning and Dayong Wu

Sustainability 2022, 14(19), 12856; https://doi.org/10.3390/su141912856 - 9 Oct 2022

Cited by 1 | Viewed by 1398

Abstract

Through shipping service adaptability measurement, selecting shipping services that are more adaptable to preferences such as low cost, high efficiency, safety, and obvious emission reduction can achieve synergistic optimization of green shipping management. The study takes green shipping service adaptability as the research theme; explores three aspects, i.e., shipping safety, shipping rate and shipping choice preference, related to the evaluation and selection of a green shipping service; constructs the green shipping service adaptability evaluation index system including safety index, freight rate index and choice preference index; and applies fuzzy-exact by processing the historical data from H shipping company in Hainan Province, China. Bayesian net is applied to calculate the shipping safety adaptation degree of the transportation object. The theory of shipping service adaptability proposed in the paper can be applied to the fields of shipping supplier selection and shipping company’s detection of shipping object status. The fuzzy-exact Bayesian network method chosen in the paper can solve the problem of incomplete state coverage of the Bayesian network and correct the situation that some edge probabilities are unreasonable. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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19 pages, 2947 KiB

Open AccessArticle

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

by Yanting Zhang, Zhe Zhu, Wei Ning and Amir M. Fathollahi-Fard

Sustainability 2022, 14(17), 10822; https://doi.org/10.3390/su141710822 - 30 Aug 2022

Cited by 4 | Viewed by 1261

Abstract

This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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17 pages, 5926 KiB

Open AccessArticle

Optimal Evacuation Route Planning of Urban Personnel at Different Risk Levels of Flood Disasters Based on the Improved 3D Dijkstra’s Algorithm

by Yang Zhu, Hong Li, Zhenhao Wang, Qihang Li, Zhan Dou, Wei Xie, Zhongrong Zhang, Renjie Wang and Wen Nie

Sustainability 2022, 14(16), 10250; https://doi.org/10.3390/su141610250 - 18 Aug 2022

Cited by 13 | Viewed by 2226

Abstract

In the event of a flood, the choice of evacuation routes is vital for personnel security. This is particularly true when road factors play an important role in evacuation time. In this study, the traditional Dijkstra algorithm for route planning is improved, and the evacuation model is improved from 2D to 3D. At the same time, the Lasso regression method is adopted to take the road factors into account in the pedestrian speed, and the location of shelter is selected and optimized through the calculation results, and then based on the improved 3D Dijkstra’s algorithm, an optimal evacuation route method in different flood disasters risk levels is proposed, which can make pedestrians reach the shelters within the shortest time. After taking into account road factors (road width, slope, non-motorized lane width, and pedestrian density), through the calculation of the pedestrian speed formula, the estimated evacuation time of pedestrians is obtained. By combining available shelters with evacuation routes, the optimized algorithm improves the evacuation efficiency facing different risk levels of flood disasters. The results show that when residents are confronted with flood disasters of once-in-20-year, once-in-50-year, and once-in-100-year, the proposed optimization algorithm can save 7.59%, 11.78%, and 17.78% of the evacuation time. Finally, according to the verification of the actual effect in Meishan Town, the proposed method of optimal evacuation route planning can effectively reduce the evacuation time of pedestrians, evaluate, and optimize the location of existing shelter, and provide suggestions for urban road reconstruction. Full article

(This article belongs to the Special Issue Data-Driven Emergency Traffic Management, Optimization and Simulation)

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