Traffic Planning and Control at Urban Intersections

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (25 December 2022) | Viewed by 4046

Special Issue Editor


E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University Bourgogne Franche-Comté, UTBM, 90010 Belfort, France
Interests: autonomous intersections; transportation systems; traffic control; urban mobility; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban traffic planning is a fertile area of smart cities to improve efficiency, environmental care, and safety because traffic jams and congestion are one of the biggest sources of pollution and noise. Enhancing the vehicular traffic flow becomes a mandatory task to minimize the impact of pollutant emissions and unsustainable fuel consumption in cities. Smart mobility optimization therefore emerges, with the goal of improving traffic management in the city.

Traffic lights and autonomous intersections play an important role in solving these problems because they control the flow of the vehicular network. However, the increasing number of vehicles makes it necessary to go from local control at a single intersection to a holistic approach considering a large urban area and a network of several intersections; this is only possible using advanced computational resources and techniques.

Moreover, the arrival of autonomous and connected vehicles (ACVs) that share space with other road users further complicates the urban traffic management problem. It would therefore be interesting to consider that the conflicting space is negotiated through complex interactions involving signaling, 5G/G5 communication, and communicative behavior. Indeed, autonomous vehicles communicate with each other and with the road infrastructure to jointly decide how best to traverse the conflicting space to generate time and energy savings. Similarly, these autonomous vehicles and pedestrians optimize their respective trajectories thanks to communicative behavior.

This Special Issue will be dedicated to new perspectives in control, optimization and decision-support techniques for conflict situations, especially at signalized intersections or at autonomous and connected intersections. It will cover a selection of recent research articles, short communications, reviews, as well as perspectives in the area of intelligent transportation systems and connected and autonomous driving, especially linked to traffic signals and intelligent intersection control.

Dr. Mahjoub Dridi
Guest Editor

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. Applied Sciences 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

  • cooperative driving
  • traffic control
  • urban mobility
  • combinatorial optimization

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 1059 KiB  
Article
Co-Optimization of Eco-Driving and Energy Management for Connected HEV/PHEVs near Signalized Intersections: A Review
by Ziqing Wang, Mahjoub Dridi and Abdellah El Moudni
Appl. Sci. 2023, 13(8), 5035; https://doi.org/10.3390/app13085035 - 17 Apr 2023
Cited by 4 | Viewed by 1446
Abstract
Currently, road transport constitutes a considerable proportion of global fossil fuel consumption, as well as CO2 and pollutant emissions. To mitigate transportation energy consumption, two primary approaches have emerged: the large-scale adoption of Hybrid Electric Vehicles (HEVs) and Plug-In Electric Vehicles (PHEVs), [...] Read more.
Currently, road transport constitutes a considerable proportion of global fossil fuel consumption, as well as CO2 and pollutant emissions. To mitigate transportation energy consumption, two primary approaches have emerged: the large-scale adoption of Hybrid Electric Vehicles (HEVs) and Plug-In Electric Vehicles (PHEVs), as well as the implementation of eco-driving strategies, which present an immediate and low-cost solution. In this context, this paper provides a comprehensive review of these two technologies and their integration for connected HEV/PHEVs. We summarize the framework of recent approaches to incorporate fusion road information in single-vehicle and multi-vehicle scenarios, respectively, wherein we compare their advantages, their disadvantages, and their effectiveness in reducing energy consumption. Additionally, we reflect on the future development directions of cooperative optimization in EMS and eco-driving strategies from various perspectives. This comprehensive review underscores the importance and potential impact of these approaches in addressing environmental challenges in transportation systems, thereby offering useful insights for new researchers and practitioners in this area. Full article
(This article belongs to the Special Issue Traffic Planning and Control at Urban Intersections)
Show Figures

Figure 1

23 pages, 8058 KiB  
Article
Coordinated Control Method for Passive Bus Priority Arterials Considering Multi-Conversion Standard and Bus Stopping Time
by Liang Zou, Zhifan Li, Lingxiang Zhu and Zhitian Yu
Appl. Sci. 2023, 13(6), 3634; https://doi.org/10.3390/app13063634 - 12 Mar 2023
Viewed by 950
Abstract
Public transport priority is the development trend in public transport, and signal priority is its main means. In order to further improve the accuracy of delay calculation and realize the priority of bus signals, this paper proposes the idea of multiple conversion criteria [...] Read more.
Public transport priority is the development trend in public transport, and signal priority is its main means. In order to further improve the accuracy of delay calculation and realize the priority of bus signals, this paper proposes the idea of multiple conversion criteria and consideration of stop time for the coordination and control of bus and car mixed traffic flow trunk roads. First of all, on the basis of in-depth analysis of the differences in the characteristics of bus and car models, a multi-conversion standard delay calculation method is proposed, and its effectiveness is verified by simulation. The results show that compared with the single conversion standard delay calculation method, the average delay error of cars and buses calculated by this method is reduced by 22.54% and 82.21%, respectively. Then, the influence of bus stops on bus speed and delay is further analyzed, and the coordinated control model of bus priority trunk roads considering bus stops is constructed with the passenger capacity of each bus line and the per capita delay as the goal, and the solution is given. Finally, 178 randomly generated examples are used to verify and analyze the effectiveness and sensitivity of this model. Full article
(This article belongs to the Special Issue Traffic Planning and Control at Urban Intersections)
Show Figures

Figure 1

28 pages, 1968 KiB  
Article
A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies
by Min Xiang and Yulin An
Appl. Sci. 2023, 13(3), 1311; https://doi.org/10.3390/app13031311 - 18 Jan 2023
Cited by 2 | Viewed by 1105
Abstract
The complex and diverse urban road traffic environments make it difficult to accurately assess road traffic situations. This paper proposes a collaborative monitoring method for urban road traffic situational assessment during emergency events. This method is applied to a monitoring network mapped by [...] Read more.
The complex and diverse urban road traffic environments make it difficult to accurately assess road traffic situations. This paper proposes a collaborative monitoring method for urban road traffic situational assessment during emergency events. This method is applied to a monitoring network mapped by road geographic relations. When an emergency event is captured by a monitoring node in the network, road traffic situational awareness is completed by an activation function. Then, the Incidence matrix of the emergency event is constructed based on the node degree of this monitoring node. The collaborative node set and collaborative monitoring area are formed dynamically from this Incidence matrix. Finally, the AHP and EM combination weight calculation method based on Game Theory (GT-AHP-EM) is used to fuse the data of various information in the collaborative monitoring area to predict the current road traffic trend. The experiments show that the collaborative monitoring method can effectively assess road traffic conditions and enhance the accuracy of road traffic trend prediction. Full article
(This article belongs to the Special Issue Traffic Planning and Control at Urban Intersections)
Show Figures

Figure 1

Back to TopTop