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Data and Network Analytics in Transportation Systems
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Data and Network Analytics in Transportation Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1708

Special Issue Editors

Dr. Boyue Wang

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: traffic flow prediction; knowledge graphs
Prof. Dr. Zhiyong Cui

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: traffic forecasting; large language model
Dr. Guangyu Huo

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Interests: traffic flow prediction; traffic status generation

Special Issue Information

Dear Colleagues,

In today's transportation environment, integrating data analytics and network science is not just a trend but a necessity. With urban centers expanding and transportation networks becoming more intricate, the complexity of managing these systems has reached unprecedented levels.

The capacity to gather, analyze, and make sense of extensive data is now crucial for refining transportation networks, boosting transportation safety, and elevating transportation efficiency. By focusing on data-driven insights and network analysis, we are better equipped to tackle the various challenges that transportation systems confront. The emergence of autonomous vehicles, the growth of shared mobility options, and the heightened significance of smart city initiatives all underscore the need for data-based decisions.

This Special Issue will delve into how sophisticated analytics can be leveraged to manage traffic data, gauge the robustness of transportation networks against disruptions, and merge different forms of transit. Moreover, this Special Issue still focuses on data privacy and security within the transportation sector. By encouraging contributions from a wide spectrum of fields, including transportation engineering, urban planning, computer science, and data analytics, this Special Issue aims to promote a vibrant, interdisciplinary conversation. Topics may include but are not limited to:

  • Intelligent Transportation Systems and Smart Cities;
  • Multi-modal Analysis in Traffic Management;
  • Data-driven Traffic Prediction and Congestion Management;
  • Connected and Autonomous Vehicles;
  • Mobility as a Service;
  • Public Transportation Optimization;
  • Transportation Network Resilience;
  • Urban Planning and Transportation Networks;
  • Privacy and Security in Transportation Data.

Dr. Boyue Wang
Prof. Dr. Zhiyong Cui
Dr. Guangyu Huo
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. Sensors 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 2600 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
  • traffic prediction
  • transportation networks
  • autonomous vehicles
  • mobility as a service
  • urban planning
  • smart cities

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

19 pages, 5224 KiB  
Article
A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas
by Yun Xiao, Rongqiao Li and Jinyan Li
Sensors 2024, 24(24), 8206; https://doi.org/10.3390/s24248206 - 23 Dec 2024
Viewed by 268
Abstract
Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis [...] Read more.
Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer–Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin–destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures. Full article
(This article belongs to the Special Issue Data and Network Analytics in Transportation Systems)
►▼ Show Figures

Figure 1

Figure 1
<p>Calculation process of vehicle operational characteristic indicators.</p> Full article ">Figure 2
<p>Distribution characteristics of the training sample.</p> Full article ">Figure 3
<p>ROC curve of unlicensed-taxi-identification model.</p> Full article ">Figure 4
<p>Probability distribution of vehicles engaging in unlicensed-taxi activities.</p> Full article ">Figure 5
<p>Distribution of average daily mileage for three types of vehicles.</p> Full article ">Figure 6
<p>Operating-time-characteristic distribution: (<b>a</b>) operating days; (<b>b</b>) average daily operating time.</p> Full article ">Figure 7
<p>Distribution characteristics of operating time periods within a day.</p> Full article ">Figure 8
<p>Main operating areas of suspected unlicensed taxis: (<b>a</b>) overall distribution; (<b>b</b>) operating hotspot areas.</p> Full article ">Figure 9
<p>Distribution of traffic surveillance bayonets passed by the suspected unlicensed taxi each day: (<b>a</b>) first pass; (<b>b</b>) last pass.</p> Full article ">Figure 10
<p>Temporal distribution of traffic surveillance bayonets passed by the suspected unlicensed taxi during the statistical period: (<b>a</b>) start time; (<b>b</b>) end time.</p> Full article ">
13 pages, 380 KiB  
Article
TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting
by Xiaxia He, Wenhui Zhang, Xiaoyu Li and Xiaodan Zhang
Sensors 2024, 24(21), 7086; https://doi.org/10.3390/s24217086 - 4 Nov 2024
Viewed by 1091
Abstract
Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account [...] Read more.
Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time. To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. Experimental results on two public traffic datasets demonstrate the effectiveness of the proposed model compared to other state-of-the-art traffic flow prediction methods. Full article
(This article belongs to the Special Issue Data and Network Analytics in Transportation Systems)
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<p>The brief illustration of the proposed model.</p> Full article ">
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