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Applied Sciences
Special Issues
Applications of Big Data in Public Transportation Systems
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Applications of Big Data in Public Transportation Systems

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 903

Special Issue Editors

Dr. Ryan Cheuk Pong Wong

E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, China
Interests: public transport demand modeling/management; traffic survey; travel demand analysis; transport modeling; traffic impact assessment
Dr. Jintao Ke

E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, China
Interests: urban computing and smart cities; machine learning and data mining for intelligent transportation systems; spatio-temporal traffic pattern analysis/prediction; smart mobility services (ride sharing, ride sourcing, last-mile delivery); land use and transportation problems
Dr. Fangni Zhang

E-Mail Website
Guest Editor
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China
Interests: shared transportation and logistics systems; autonomous vehicle/UAV systems; transportation network modeling and optimization; transportation data analytics

Special Issue Information

Dear Colleagues,

Big data has played an unprecedented role in shaping the morphology of cities and urban planning processes in recent decades. With advances in technology and infrastructure, collecting big data has become more feasible than traditional data collection methods. Its availability, combined with advanced statistical techniques, has captured the attention of researchers, particularly in the field of transportation systems.

As urbanization accelerates and population density increases, public transportation will become an increasingly vital component of urban mobility. Public transportation systems offer a reliable and accessible alternative to private vehicles which not only alleviates traffic congestion but also contributes to reducing greenhouse gas emissions and improving air quality.

Big data analytics can significantly enhance public transportation systems by facilitating informed decision making and optimizing operational efficiency. The efficient collection and analysis of big data sources are essential for empowering the development of urban public transportation systems. Its potential to revolutionize transportation problem solving surpasses the capabilities of traditional data collection methods. However, it is important to address the ethical, practical, and rational concerns associated with the use of big data in public transportation systems. Despite the expansion of big data collection in the transportation domain, there is still a lack of comprehensive information on how it can be effectively utilized for analytical purposes in both research and practice.

In light of the above, it is essential to explore the application of big data in public transportation systems. This Special Issue aims to gather the latest and emerging research on the use of big data in public transportation.

Dr. Ryan Cheuk Pong Wong
Dr. Jintao Ke
Dr. Fangni Zhang
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. 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

  • smart and sustainable mobility
  • intelligent transportation systems
  • information and communication technologies in public transportation systems
  • enhancing operations and safety in public transportation systems
  • data sources and management in public transportation systems
  • smart cities and big data in transportation
  • emerging technologies in public transportation systems
  • advanced traveler information systems
  • mixed survey data in transportation research
  • risk modeling and safety in public transportation
  • data-driven approaches for managing public transportation systems
  • human factors in public transportation systems
  • public transportation network modeling and planning

Published Papers (2 papers)

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Research

22 pages, 3844 KiB  
Article
Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach
by Hantong Wang, Ziyi Shi, Yong Chen, Zheng Zhu and Xiqun Chen
Appl. Sci. 2024, 14(11), 4366; https://doi.org/10.3390/app14114366 (registering DOI) - 22 May 2024
Abstract
The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The [...] Read more.
The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively. Full article
(This article belongs to the Special Issue Applications of Big Data in Public Transportation Systems)
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20 pages, 4612 KiB  
Article
Prediction Intervals for Bus Travel Time Based on Road Segment Sharing, Multiple Routes’ Driving Style Similarity, and Bootstrap Method
by Zhenzhong Yin, Bin Wang, Bin Zhang and Xinpu Shen
Appl. Sci. 2024, 14(7), 2935; https://doi.org/10.3390/app14072935 - 30 Mar 2024
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Abstract
Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which [...] Read more.
Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes’ driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively. Full article
(This article belongs to the Special Issue Applications of Big Data in Public Transportation Systems)
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