Special Issue "Traffic Flow Modelling and Simulation for Safe and Sustainable Transportation"

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

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Prof. Dr. Hao Wang
E-Mail Website
Chief Guest Editor
School of Transportation, Southeast University, Nanjing 210096, China
Interests: traffic flow theory; traffic control; traffic simulation
Dr. Xiang Zhang
E-Mail Website
Guest Editor
Research Fellow, Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, University of New South Wales, NSW 2052, Australia
Interests: transport modelling; operations research; traffic flow theory
Dr. Ye Li
E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: traffic flow theory; traffic simulation; traffic safety
Dr. Yanyan Qin
E-Mail Website
Guest Editor
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Interests: traffic flow theory; traffic simulation; connected and automated vehicles (CAVs)

Special Issue Information

Dear Colleagues,

Nowadays, the transportation system is confronted with serious challenges, such as traffic congestion, emission, and accidents. Many new ideas and technologies have been proposed to deal with the problems so as to make the transportation system more sustainable. These methods include advanced traffic control for traditional MV (manual driven vehicle) flow, CAV (connected and autonomous vehicle) technologies, and encouragement for nonmotorized travels.

This Special Issue will highlight new opportunities and challenges for sustainable transportation, focusing on how to improve and evaluate transportation system with traffic flow modelling and simulation. We welcome papers on the following topics:

  • Modelling and simulating the performance of traffic flow under various traffic control schemes or management measures, including variable speed limit control, ramp metering, traffic signal control, and application of exclusive lane/variable lane/reversible lane.
  • Modelling and simulating the CAV flow or CAV-MV mixed flow in various scenarios, including CAV platoon controlling, intersection controlling and other V2X applications.
  • Studies on nonmotorized flow modelling and simulation, including pedestrian flow and bicycle flow, especially on how to improve the capacity and safety of the nonmotorized flow by traffic designing and managements.
  • Optimizing the designs and operations of transportation facilities by traffic flow modelling and simulation. Evaluating the effectiveness of various measures on relieving traffic congestion, reducing traffic emission and improving traffic safety.

Prof. Dr. Hao Wang
Dr. Xiang Zhang
Dr. Ye Li
Dr. Yanyan Qin
Guest Editors

Manuscript Submission Information

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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 1900 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

  • Traffic flow modelling
  • Traffic flow simulation
  • Traffic congestion
  • Traffic emission
  • Traffic safety

Published Papers (4 papers)

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Research

Article
Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads
Sustainability 2021, 13(8), 4196; https://doi.org/10.3390/su13084196 - 09 Apr 2021
Viewed by 396
Abstract
There is a body of literature on the influence range and traffic risk of fixed work zones. However, relatively few studies have examined the effect of ubiquitous moving operating vehicles, such as road cleaners, on urban roads. The influence of low speed moving [...] Read more.
There is a body of literature on the influence range and traffic risk of fixed work zones. However, relatively few studies have examined the effect of ubiquitous moving operating vehicles, such as road cleaners, on urban roads. The influence of low speed moving work zones on road traffic flow and traffic risk is still unclear. In this work, we used simulations to establish an urban expressway three lanes VISSIM model, and selected the road traffic volume and speed of the moving work zone as the independent variables. We analyzed the range of influence of the moving work zone on the rear vehicles in the left, middle and right lanes of the urban expressway and the traffic risk variation law caused by the moving work zone. The results show that the left lane was indirectly affected by the moving work zone when the traffic volume reached 2000 pcu/h. The influence of the moving work zone on the middle lane was controlled by the traffic volume and the speed of the moving work zone. Both the left and middle lanes were mainly impacted by vehicles changing lane from the right lane. Regardless of the traffic volume and the speed of the moving work zone change, the vehicles 200 m behind a moving work zone will be directly affected in the right lane. Furthermore, the average traffic risk is the highest within 50 m of the moving work zone in the right lane. When the traffic volume decreases and the speed of the moving work zone increases, the average traffic risk decreases gradually. These results provide a scientific basis for the operation and management of moving working vehicles on urban roads. Full article
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Article
Evaluating Operational Features of Three Unconventional Intersections under Heavy Traffic Based on CRITIC Method
Sustainability 2021, 13(8), 4098; https://doi.org/10.3390/su13084098 - 07 Apr 2021
Cited by 1 | Viewed by 471
Abstract
Conventional four-legged intersections are inefficient under heavy traffic requirements and are prone to congestion problems. Unconventional intersections with innovative designs allow for more efficient traffic operations and can increase the capacity of the intersection, in some cases. Common unconventional designs for four-legged intersections [...] Read more.
Conventional four-legged intersections are inefficient under heavy traffic requirements and are prone to congestion problems. Unconventional intersections with innovative designs allow for more efficient traffic operations and can increase the capacity of the intersection, in some cases. Common unconventional designs for four-legged intersections include the upstream signalized crossover intersection (USC), continuous flow intersection (CFI), and parallel flow intersection (PFI). At present, an increasing number of cities are using such unconventional designs to improve the performance of their intersections. In the reconstruction of original intersections or the design of new intersections, the question of how to more reasonably select the form of unconventional intersection becomes particularly critical. Therefore, we selected a typical intersection in Xi’an for optimization and investigated traffic data for this intersection. The traffic operations, with respect to the four solutions of a conventional intersection, USC, CFI, and PFI, were evaluated using the VISSIM software. Then, we evaluated the suitability of each solution under different situations using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method, which is a multi-criteria decision-making (MCDM) method that enables a more comprehensive and integrated evaluation of the four solutions by taking into account the comparative intensities and conflicting character among the indices. The results show that the conventional intersection is only applicable to the case of very low traffic volume; PFI has the advantage in the case of moderate and high traffic volume; CFI performs better in the case of high traffic volume; and USC is generally inferior to CFI and PFI, although it has greater improvement, compared with the conventional solution, in a few cases. Full article
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Article
Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China
Sustainability 2021, 13(4), 2114; https://doi.org/10.3390/su13042114 - 16 Feb 2021
Viewed by 498
Abstract
A drive cycle describes the microscopic and macroscopic vehicle activity information that is crucial for emission quantification research, e.g., emission modeling or emission testing. Well-developed drive cycles capture the driving patterns representing the traffic conditions of the study area, which usually are employed [...] Read more.
A drive cycle describes the microscopic and macroscopic vehicle activity information that is crucial for emission quantification research, e.g., emission modeling or emission testing. Well-developed drive cycles capture the driving patterns representing the traffic conditions of the study area, which usually are employed as the input of the emission models. By considering the potential of large-scale GPS trajectory data collected by ubiquitous on-vehicle tracking equipment, the objective of this study is to demonstrate the capability of GPS-based trajectory data from rideshare passenger cars for urban drive cycle development. Large-scale GPS trajectory data and order data collected by an app-based transportation vehicle was used in this study. GPS data were filtered by thresholds of instantaneous accelerations and vehicle specific powers. The micro-trip selection-to-rebuild method with operating mode distribution was used to develop a series of speed-bin categorized representative drive cycles. Sensitivity of the time-of-day and day-of-week were analyzed on the developed drive cycles. The representativeness of the developed drive cycles was verified and significant differences exist when they are compared to the default light-duty drive cycles coded in MOVES. The findings of this study can be used for helping drive cycle development and emission modeling, further improving the understanding of localized emission levels. Full article
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Article
Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data
Sustainability 2020, 12(23), 9888; https://doi.org/10.3390/su12239888 - 26 Nov 2020
Cited by 1 | Viewed by 455
Abstract
This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a [...] Read more.
This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash. Full article
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