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Intelligent Transportation and Mobility Analytics
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Intelligent Transportation and Mobility Analytics

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

Deadline for manuscript submissions: 20 November 2026 | Viewed by 2214

Special Issue Editor

Dr. Seoungbum Kim

E-Mail Website
Guest Editor
Department of Urban Engineering, Engineering Research Institute, Gyeongsang National University, Jinju-si 52828, Republic of Korea
Interests: transportation operation and management; traffic safety and accident analysis; transportation infrastructure design; transportation planning; ITSs (intelligent transportation systems)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent transportation systems (ITS) and mobility analytics have become essential tools in addressing the pressing challenges of modern transportation, including congestion, safety, and sustainability. The emergence of connected and autonomous vehicles, mobility as a service (MaaS), and smart mobility solutions highlights the critical role of data-driven approaches in enhancing transportation planning, operation, and decision-making. Advanced technologies such as artificial intelligence, machine learning, digital twins, and big data analytics enable deeper insights into mobility patterns, supporting the design of efficient, safe, and environmentally friendly transportation systems.

This Special Issue, “Intelligent Transportation and Mobility Analytics,” invites original research and practical applications related to the collection, analysis, and utilization of transportation data. Topics of interest include traffic flow prediction, real-time traffic management, multimodal transport optimization, travel behavior analysis, safety assessment, and sustainability evaluation. Submissions that bridge theory and practice, propose innovative analytical frameworks, or demonstrate real-world case studies are especially encouraged. By bringing together contributions from academia, industry, and policy, this Special Issue aims to foster interdisciplinary dialog and advance the development of next-generation mobility systems.

Dr. Seoungbum Kim
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 250 words) can be sent to the Editorial Office for assessment.

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

  • intelligent transportation systems (ITS)
  • artificial intelligence in transportation
  • traffic flow theory
  • connected and autonomous vehicles
  • mobility as a service (MaaS)
  • data-driven transportation
  • big data analytics in mobility
  • real-time traffic monitoring
  • traffic behavior analysis
  • image processing
  • transportation safety analytics
  • digital twin for mobility

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Published Papers (3 papers)

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Research

25 pages, 1844 KB  
Article
Retrieval-Augmented Large Language Model-Based Framework for Hierarchical Classification of Public Feedback on Transportation Infrastructure
by Milan Knezevic, Trevor Neece, Marko Vukojevic, Lev Khazanovich and Aleksandar Stevanovic
Appl. Sci. 2026, 16(8), 3663; https://doi.org/10.3390/app16083663 - 9 Apr 2026
Viewed by 457
Abstract
Transportation agencies receive large volumes of free-form public comments describing infrastructure conditions, safety concerns, and service issues. These comments are often processed manually for downstream operational actions, which is time-consuming, inconsistent across reviewers, and difficult to scale, thereby limiting their value for operational [...] Read more.
Transportation agencies receive large volumes of free-form public comments describing infrastructure conditions, safety concerns, and service issues. These comments are often processed manually for downstream operational actions, which is time-consuming, inconsistent across reviewers, and difficult to scale, thereby limiting their value for operational decision-making. This study presents a machine learning and Large Language Model (LLM) framework for automated triage of free-form public comments, assigning each report to a three-level hierarchical taxonomy consisting of Category, Subcategory, and Final Decision. The proposed framework uses agency historical data together with retrieval-based evidence, where semantically similar past comments are provided to the LLM as contextual support to better align predictions with agency-specific labeling practices. The framework was evaluated using TF-IDF with Logistic Regression, TF-IDF with Linear SVM, embedding-based kNN with cosine similarity, few-shot LLM prompting, and retrieval-based LLM prompting. Results show that retrieval-based prompting achieved the best overall performance, with the highest accuracy at both the Category and Subcategory levels. At the Final Decision level, retrieval-based prompting slightly outperformed kNN, while few-shot prompting performed worse. Error analysis showed that many misclassifications were semantically plausible alternatives, reflecting the overlap across infrastructure-related complaint categories. When a second candidate label was allowed, further improving performance. Latency analysis also indicated that the framework can process more than 2000 comments in under 30 min, supporting faster and more consistent agency workflows. Full article
(This article belongs to the Special Issue Intelligent Transportation and Mobility Analytics)
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19 pages, 3006 KB  
Article
An Integrated Automated Driving Risk Indicator in Urban Mixed Traffic Environments
by Sangjae Lee, Minkyung Kim, Juneyoung Park and Cheol Oh
Appl. Sci. 2025, 15(23), 12646; https://doi.org/10.3390/app152312646 - 28 Nov 2025
Viewed by 647
Abstract
In this study, a novel methodology is proposed to evaluate automated driving safety in mixed traffic environments, including autonomous vehicles (AVs) and manually driven vehicles (MVs). An open-source AV dataset obtained from a real-world autonomous mobility testbed in Korea was used for methodology [...] Read more.
In this study, a novel methodology is proposed to evaluate automated driving safety in mixed traffic environments, including autonomous vehicles (AVs) and manually driven vehicles (MVs). An open-source AV dataset obtained from a real-world autonomous mobility testbed in Korea was used for methodology development and evaluations. The driving behavior was evaluated using well-known promising indicators, including the standard deviation of the vehicle speed, acceleration noise, standard deviation of the lane offset, time to collision (TTC), and deceleration to avoid a crash (DRAC). Min-max and max-min normalization was performed to unify the units of the evaluation indicators. The importance of each driving safety indicator was derived through the Analytical Hierarchy Process (AHP) performed by traffic experts, and the weights were estimated based on the average of the collected importance. The normalized indicators were integrated to obtain the automated driving risk score (ADRS), which is regarded as a measure of automated driving safety. The automated driving safety degraded considerably in road sections where right turns were made at intersections and that had a bus stop. Hazardous driving events of AVs were visualized, which is useful for monitoring mixed traffic safety and developing effective countermeasures for proactive road safety management. Full article
(This article belongs to the Special Issue Intelligent Transportation and Mobility Analytics)
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17 pages, 3269 KB  
Article
Evaluation of Take-Over Request Lead Time Based on Driving Behavioral Interaction Between Autonomous Vehicles and Manual Vehicles
by Jieun Ko, Cheol Oh, Hoseon Kim, Kyeongpyo Kang and Seoungbum Kim
Appl. Sci. 2025, 15(23), 12512; https://doi.org/10.3390/app152312512 - 25 Nov 2025
Viewed by 678
Abstract
Autonomous vehicles (AVs) at SAE Levels 3 require a take-over request to switch from autonomous to manual mode when leaving the operational design domain (ODD). An appropriate take-over request lead time (TORlt) is necessary for safe interaction between AVs and non-AVs. This study [...] Read more.
Autonomous vehicles (AVs) at SAE Levels 3 require a take-over request to switch from autonomous to manual mode when leaving the operational design domain (ODD). An appropriate take-over request lead time (TORlt) is necessary for safe interaction between AVs and non-AVs. This study developed a methodology to derive the optimal TORlt for AVs entering the area out of the ODD using a multi-agent driving simulator experiment. The multi-criteria decision-making method was adopted to integrate evaluation indicators to derive an optimal TORlt. The TORlt was defined as 3, 6, 9, 12, and 15 s in the driving simulation experiment scenario. The driving simulation experiment was conducted with a total of 60 participants. The simulation network was a two-lane urban road in each direction with a total length of 1.7 km, including a school zone where the autonomous driving mode is prohibited. Three requirements were established to determine the optimal TORlt: minimizing the take-over time, maximizing the success rate of take-over, and minimizing the potential of rear-end collisions due to vehicle interactions. After conducting comparative analyses of individual evaluation indicators for each scenario, a multi-criteria decision-making method was used for integrated evaluation to determine the optimal TORlt. It was found that the optimal TORlt for AVs on urban roads is 9 s. The results of this study can be used as valuable fundamentals in determining take-over requests for AVs toward safer vehicle interactions in the traffic stream. Full article
(This article belongs to the Special Issue Intelligent Transportation and Mobility Analytics)
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