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Dear Colleagues,

The demand for public and individual transport will continue to rise according to long-term economic prospects. As an obvious consequence, urban traffic congestion can be expected to become more intricate, further leading to negative environmental, social, and economic impacts. The application of intelligent transportation systems (ITS) can mitigate these impacts and improve sustainable development. Recent evolutions in information and artificial intelligence technology such as data analytics and machine learning further raise the prospects for ITS to be designed in response to specific needs. Therefore, utilizing the huge potential of big data for traffic management and ITS is a key challenge.

It is anticipated that data-driven research will create new breakthroughs for ITS and sustainability. This Special Issue aims to collect papers that reflect up-to-date findings, research achievements, and innovative ideas of data-driven design of intelligent transportation systems. Particular attention will be given to the following theme areas; however, it should be stressed that a broad range of submissions are encouraged. We welcome papers on the following topics from a data-driven perspective:

ITS element deployment and optimization such as detector location and capacity allocation, including analysis of stakeholder costs, system benefits, and so on;
Modeling the behavior and preference of travelers taking ITS modes such as shared mobility and autonomous driving;
Establishing the analytic architecture of ITS network performance, for example, to what extent ITS applications facilitate urban transportation from a sustainable development perspective;
Designing the policy and strategy for ITS operation and management, aimed at enhancing the safety and efficiency of urban transportation;
Case studies of ITS applications, such as data-enabled incident management and emergency evacuation, intelligent supply chains, intelligent parking assist system;
Future perspectives on data-driven design of ITS.

Prof. Dr. Baozhen Yao
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 papers will be 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 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

Intelligent transportation systems
Data-driven design
Sustainability

Published Papers (2 papers)

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Research

Open AccessArticle

An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

and

Sustainability 2021, 13(16), 9430; https://doi.org/10.3390/su13169430 - 22 Aug 2021

Viewed by 505

Abstract

In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW. Full article

(This article belongs to the Special Issue Towards Sustainability: Data-Driven Design of Intelligent Transportation Systems)

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Open AccessArticle

An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios

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Sustainability 2021, 13(8), 4531; https://doi.org/10.3390/su13084531 - 19 Apr 2021

Viewed by 392

Abstract

Studies on self-driving transport vehicles have focused on longitudinal and lateral driving strategies in automated structured road scenarios. In this study, a double parallel network (DP-Net) combined with longitudinal and lateral strategy networks is constructed for self-driving transport vehicles in structured road scenarios, which is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, in feature extraction and perception, a preprocessing module is introduced that can ensure the effective extraction of visual information under complex illumination. Then, a parallel CNN sub-network is designed that is based on multifeature fusion to ensure better autonomous driving strategies. Meanwhile, a parallel LSTM sub-network is designed, which uses vehicle kinematic features as physical constraints to improve the prediction accuracy for steering angle and speed. The Udacity Challenge II dataset is used as the training set with the proposed DP-Net input requirements. Finally, for the proposed DP-Net, the root mean square error (RMSE) is used as the loss function, the mean absolute error (MAE) is used as the metric, and Adam is used as the optimization method. Compared with competing models such as PilotNet, CgNet, and E2E multimodal multitask network, the proposed DP-Net is more robust in handling complex illumination. The RMSE and MAE values for predicting the steering angle of the E2E multimodal multitask network are 0.0584 and 0.0163 rad, respectively; for the proposed DP-Net, those values are 0.0107 and 0.0054 rad, i.e., 81.7% and 66.9% lower, respectively. In addition, the proposed DP-Net also has higher accuracy in speed prediction. Upon testing the collected SYSU Campus dataset, good predictions are also obtained. These results should provide significant guidance for using a DP-Net to deploy multi-axle transport vehicles. Full article

(This article belongs to the Special Issue Towards Sustainability: Data-Driven Design of Intelligent Transportation Systems)

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