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Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System

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Dear Colleagues,

The transportation sector plays a pivotal role in the economic development of society. The planning and management of this sector are significant to the government as well as different companies. Urbanization and economic development have led to traffic congestion, increased travel time, fuel consumption, as well as the emission of greenhouse gases. Although the growing use of electric vehicles (EVs) has the potential to mitigate some of these problems, wide-scale charging infrastructure is yet to be developed to enable their use. To meet many of these challenges, smart as well as sustainable transportation infrastructure is required to enable the best use of the transportation sector and enable economic growth. The objective of this special issue is to employ machine learning (ML)- and artificial intelligence (AI)-based knowledge to better plan and model future smart transportation systems. The data generated from the wide-scale sensor network in the smart city can be efficiently used for developing several applications for smart transportation. We invite manuscripts including review papers that critically examine how ML and technological innovations can the sustainable smart transportation planning. These topics include but are not limited to:

Prediction, control, and management of pollutants;
Electric vehicle routing;
Charging infrastructure development for modern electric vehicles;
Management of traffic flow and route optimization;
Management of the traffic congestion and reducing travel time;
Vehicle-to-grid (V2G), vehicle-to-infrastructure (V2I), and vehicle-to-home (V2H) technologies;
Integrating renewable energy in the transportation sector;
Vehicle energy management.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Future Transportation.

Dr. Raj Mani Shukla
Dr. Lakshmi Babu-Saheer
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.

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Keywords

smart transportation
sustainability
artificial intelligence

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21 pages, 2322 KiB

Open AccessArticle

Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information

by Abdullah Alshehri, Mahmoud Owais, Jayadev Gyani, Mishal H. Aljarbou and Saleh Alsulamy

Sustainability 2023, 15(13), 9881; https://doi.org/10.3390/su15139881 - 21 Jun 2023

Cited by 14 | Viewed by 1523

Abstract

Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors. Full article

(This article belongs to the Special Issue Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System)

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20 pages, 3649 KiB

Open AccessArticle

Mathematical Models for the Vehicle Routing Problem by Considering Balancing Load and Customer Compactness

by Rodrigo Linfati, Fernando Yáñez-Concha and John Willmer Escobar

Sustainability 2022, 14(19), 12937; https://doi.org/10.3390/su141912937 - 10 Oct 2022

Cited by 3 | Viewed by 2763

Abstract

The vehicle routing problem seeking to minimize the traveled distance and the deviation of the total workload is known as the vehicle routing problem with workload balance (WBVRP). In the WBVRP, several elements are considered: (i) the total distance or driving time, (ii) the number of customers to be visited, and (iii) the total weight or amount of delivered goods. We have considered the WBVRP by adding a concept called customer compactness and the visual attractiveness of the routes. The WBVRP allows a similar workload for drivers to improve their well-being and social development. Unbalanced routes could generate high costs due to potential strikes by drivers seeking an equitable workload. We have proposed three mathematical formulations for solving the WBVRP by minimizing the customer compactness and the distance with and without considering workload balancing. The workload balancing is based on the deviation concerning the average load of the routes and considering waiting and driving time. We have tested the efficiency of the proposed models on a synthetic set of instances, analyzing different aspects such as depot location, customer location, and demand. The analysis of the results has been performed considering customer compactness and the visual attractiveness of the obtained solution. Computational experiments on generated random instances show the efficiency of the proposed approaches. Full article

(This article belongs to the Special Issue Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System)

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Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System

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