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Data-Driven Urban Mobility Modeling
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Data-Driven Urban Mobility Modeling

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1001

Special Issue Editors

Dr. Sailesh Acharya

E-Mail Website
Guest Editor
Center for Integrated Mobility Sciences, National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: multimodal transportation; travel behavior analysis; travel demand modeling; accessibility and mobility
Dr. Chris Hoehne

E-Mail Website
Guest Editor
Center for Integrated Mobility Sciences, National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: equitable and sustainable mobility; transportation accessibility; transportation decarbonization; travel demand management; land use and transportation; urban heat and transportation

Special Issue Information

Dear Colleagues,

Urban mobility is evolving rapidly, driven by advances in data collection, machine learning, and computational modeling. The increasing availability of real-time data from GPS, mobile devices, intelligent transportation systems, and connected infrastructure has enabled the development of more precise, dynamic, and scalable mobility models. These data-driven approaches empower policymakers, urban planners, and researchers to design transportation systems that are more efficient, equitable, and sustainable. This Special Issue, "Data-Driven Urban Mobility Modeling", invites cutting-edge research that leverages data science, artificial intelligence, and simulation techniques to advance urban mobility modeling. We welcome original research articles, case studies, and review papers covering innovative methodologies, empirical analyses, and real-world applications in the following areas:

  • Machine Learning and AI for Mobility Modeling: Applying machine learning, deep learning, reinforcement learning, and predictive modeling in transportation.
  • Multimodal Transportation Analysis: Integrating diverse transportation modes—including public transit, ride-sharing, cycling, and walking—into cohesive urban mobility systems.
  • Urban Traffic Management: Considering data-driven approaches for forecasting and mitigating congestion impacts on urban transportation networks.
  • Agent-Based and Simulation Models: Exploring large-scale simulations of urban mobility, incorporating behavioral modeling and decision-making dynamics.
  • Accessibility in Urban Mobility: Measuring and improving transportation access for all population cohorts using data-driven methods.
  • Emerging Technologies: Assessing the impact of autonomous vehicles, connected vehicles, electric mobility, and shared mobility services on urban transportation.
  • Big Data in Mobility Modeling: Leveraging large-scale datasets (e.g., GPS, mobile phones, connected vehicles), IoT, and sensor networks for mobility analysis.
  • Transportation–Energy Nexus: Examining the interplay between energy consumption and urban mobility and its implications for sustainable transportation.
  • Resilient and Adaptive Transportation Systems: Exploring data-driven strategies to enhance urban mobility resilience in the face of disruptions, climate change, and emergencies.

We encourage submissions that introduce novel methodologies, present interdisciplinary perspectives, and provide insights into real-world applications of data-driven urban mobility models. Research highlighting innovative mobility data analytics, policy implications, and frameworks for smart and sustainable cities is particularly welcome.

Dr. Sailesh Acharya
Dr. Chris Hoehne
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. Systems is an international peer-reviewed open access monthly 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

  • urban mobility
  • systems modeling
  • multimodal transportation
  • simulations
  • data-driven modeling
  • machine learning
  • big data

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Published Papers (2 papers)

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Research

27 pages, 8197 KB  
Article
Knowledge Graph-Enabled Prediction of the Elderly’s Activity Types at Metro Trip Destinations
by Jingqi Yang, Yang Zhang, Fei Song, Qifeng Tang, Tao Wang, Xiao Li, Pei Yin and Yi Zhang
Systems 2025, 13(10), 834; https://doi.org/10.3390/systems13100834 - 23 Sep 2025
Viewed by 146
Abstract
Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and [...] Read more.
Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and knowledge graph embedding to predict the elderly’s activity types at metro trip destinations, utilizing 180,143 smart card records and 885,072 points of interest (POI) records from Chongqing, China in 2019. First, an elderly metro travel profile (EMTP) tag system is developed to capture the elderly’s spatiotemporal metro travel behaviors and preferences. Subsequently, an elderly metro travel knowledge graph (EMTKG) is constructed to support semantic reasoning, transforming the activity types prediction problem into a knowledge graph completion problem. To solve the completion problem, the Temporal and Non-Temporal ComplEx (TNTComplEx) model is introduced to embed entities and relations into a complex vector space and distinguish between time-sensitive and time-insensitive behavioral patterns. Fact plausibility within the graph is evaluated by a scoring function. Numerical experiments validate that the proposed model outperforms the best-performing baselines by 13.37% higher Accuracy@1 and 52.40% faster training time per epoch, and ablation studies further confirm component effectiveness. This study provides an enlightening and scalable approach for enhancing age-friendly metro system service. Full article
(This article belongs to the Special Issue Data-Driven Urban Mobility Modeling)
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16 pages, 995 KB  
Article
An Upper Partial Moment Framework for Pathfinding Problem Under Travel Time Uncertainty
by Xu Zhang and Mei Chen
Systems 2025, 13(7), 600; https://doi.org/10.3390/systems13070600 - 17 Jul 2025
Viewed by 308
Abstract
Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark [...] Read more.
Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark travel time to measure the upper partial moment (UPM), capturing both the probability and severity of delays. By adjusting a risk parameter (θ), the model reflects different traveler risk preferences and unifies several existing reliability measures, including on-time arrival probability, late arrival penalty, and semi-variance. A bi-objective model is formulated to simultaneously minimize mean travel time and UPM. Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. Numerical experiments using GPS probe vehicle data from Louisville, Kentucky, USA, demonstrate that varying θ values lead to different non-dominated paths. Lower θ values emphasize frequent small delays but may overlook excessive delays, while higher θ values effectively capture the tail risk, aligning with the behavior of risk-averse travelers. The MUPM framework provides a flexible, behaviorally grounded, and computationally scalable approach to pathfinding under uncertainty. It holds strong potential for applications in traveler information systems, transportation planning, and network resilience analysis. Full article
(This article belongs to the Special Issue Data-Driven Urban Mobility Modeling)
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