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IJGI
Special Issues
Innovative Mobility Services for Smart Cities
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Innovative Mobility Services for Smart Cities

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3069

Editors

Dr. Agnese Pinto

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy
Interests: artificial intelligence and knowledge representation; semantic matchmaking; ubiquitous knowledge management and storage; machine learning for data mining in pervasive environments; innovative mobility service platforms
Dr. Filippo Gramegna

E-Mail Website
Guest Editor
Department of Engineering, LUM “Giuseppe Degennaro” University, Strada Statale 100 km 18, 70010 Casamassima, Italy
Interests: mobile knowledge representation and reasoning systems for ubiquitous and pervasive contexts; semantic-enhanced real-time vehicle monitoring and driving assistance
Dr. Saverio Ieva

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
Interests: decision support; ubiquitous healthcare; knowledge graph; automated reasoning; near-field communication; cloud-edge intelligence; edge AI; microservice architecture; osmotic computing; cyber-physical systems; Internet of Things
Dr. Floriano Scioscia

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy
Interests: pervasive computing and the Internet of Things; machine learning and knowledge representation systems; distributed ledger technologies; applications for wireless ad hoc networks and ubiquitous contexts
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michele Ruta

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
Interests: application of artificial intelligence and knowledge representation technologies to mobile systems; wireless networks and ubiquitous web; particularly semantic-based resource/service discovery in volatile and unpredictable contexts; technologies for the integration of knowledge representation into application-level protocols for wireless ad-hoc networks; sensor and actor networks; wireless identification and tracking systems; mobile grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities globally are facing unprecedented mobility challenges, including congestion, pollution and accessibility issues. Smart Cities offer a transformative evolution, leveraging cutting-edge technologies and data-driven approaches to create more efficient, sustainable and inclusive urban environments. At the heart of this transformation lies the development and implementation of smart mobility services.

Smart mobility refers to an integrated approach to traffic and transport management, with the potential integration of Advanced Air Mobility (AAM) / Urban Air Mobility (UAM) solutions with terrestrial Connected Autonomous Vehicles (CAVs). The integration of smart multimodal mobility services has an impact on city infrastructures as well as on real-time monitoring and predictive analytics requirements for service operations management, citizen engagement and policymaker decision support. The emerging Urban Digital Twin paradigm harnesses an encompassing digital infrastructure for data collection, analysis, model training and real-time visualization, which enables novel planning, simulation and control capabilities, essential to face the complexity of managing smart mobility service integration in the urban fabric. Urban Digital Twins leverage advanced technologies such as artificial intelligence, machine learning and the Internet of Things (IoT) and facilitate solutions to optimize traffic flows, reduce pollutant emissions and noise and enhance travel experience for citizens.

This Call for Papers invites researchers, practitioners, policymakers and industry experts to contribute their latest research, insights and case studies on  “Innovative Mobility Services for Smart Cities”. We seek original contributions that explore theoretical advancements, technological innovations, practical applications and policy implications in shaping the future of smart city models for developing, monitoring and managing innovative urban transportation services.

Submissions are welcome across a broad range of topics including, but not limited to, the following:

  • Data Analytics, AI and IoT for Smart Mobility:
    • Data Space designs for Urban Digital Twins.
    • Big data processing and analysis for urban mobility insights.
    • IoT sensor networks for real-time traffic monitoring and infrastructure management.
    • Simulation and modeling of traffic flow and mobility scenarios.
    • AI and machine-learning applications in traffic management and prediction.
    • Large Language Models and Retrieval-Augmented Generation for context-aware conversational Urban Digital Twin user interfaces.
    • Data privacy, security and governance in smart mobility systems and services.
  • Connected and Autonomous Vehicles (CAVs):
    • Integration of terrestrial and aerial CAVs into urban transport systems.
    • Advanced Air Mobility (AAM) / Urban Air Mobility (UAM) technologies, architectures and service models.
    • Safety, cybersecurity and ethical considerations of autonomous mobility.
    • Impact of multimodal CAVs on city infrastructures, urban planning and land use.
  • Mobility-as-a-Service (MaaS):
    • Design, implementation and evaluation of MaaS platforms.
    • Privacy and security in MaaS platforms and applications.
    • Regulatory frameworks and standards for new mobility services.
    • Behavioral analysis, user adoption and citizen engagement in smart mobility services.
    • Economic and environmental impact assessment of innovative mobility solutions.
    • Business models and regulatory frameworks for shared mobility.
  • Foundations and Architectures of Urban Digital Twins:
    • Conceptual frameworks and theoretical models for Urban Digital Twins.
    • Data models, ontologies and interoperability standards for urban data.
    • Simulation and modeling of traffic flow and mobility scenarios.
    • Real-time monitoring and predictive analytics using digital twins.
    • Scalable architectures for large-scale urban digital twin implementations.
    • Integration of diverse data sources (IoT, GIS, BIM, satellite imagery, social media).
  • Applications:
    • Smart public transport systems (e.g., demand-responsive transit, intelligent scheduling).
    • Autonomous connected terrestrial and aerial passenger transport services.
    • Optimizing shared vehicle fleets (e.g., car-sharing, ride-pooling, micro-mobility).
    • Sustainable last-mile logistics.
    • Aerial monitoring of urban territory for traffic and safety management.

Dr. Agnese Pinto
Dr. Filippo Gramegna
Dr. Saverio Ieva
Dr. Floriano Scioscia
Prof. Dr. Michele Ruta
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information 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 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

  • smart city
  • smart mobility
  • connected autonomous vehicles
  • mobility as a service (MaaS)
  • urban digital twin
  • urban air mobility
  • last-mile logistics
  • artificial intelligence
  • trust management

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

Published Papers (4 papers)

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Research

32 pages, 45084 KB  
Article
A Multidimensional Spatial–Temporal and Econometric Framework for Pedestrian Safety and Injury Severity Analysis in Amman, Jordan
by Haitham A. Al Hasanat, Omar Alharasees, Lafee Alshamaileh and Rana Al-Matarneh
ISPRS Int. J. Geo-Inf. 2026, 15(7), 325; https://doi.org/10.3390/ijgi15070325 - 16 Jul 2026
Viewed by 208
Abstract
This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the [...] Read more.
This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the study utilizes radar graphs, Kernel Density Estimation (KDE), and DBSCAN cluster analysis to delineate high-risk zones and temporal peaks. Temporal findings indicate that Thursdays recorded the highest accident frequency (2382 cases), with peak occurrences between 17:00 and 23:00. Spatial clustering identified five significant high-risk zones, with Central Amman emerging as the primary critical area. The study’s novelty lies in being the first in the Jordanian context to bridge accident frequency with severity mechanisms by integrating advanced spatial clustering and KDE with a robust Ordered Logit Model. Severity analysis reveals that while 59.34% of incidents resulted in minimal injuries, fatalities accounted for 5.02%. The model demonstrates that injury outcomes are systematically associated with traffic dynamics and behavior rather than environmental factors. Speed-related driver error was identified as the strongest predictor of severe outcomes (OR = 81.3). Significant dependencies were confirmed between vehicle category and road type (χ2 = 2182.20, p < 0.001), lighting and road surface (χ2 = 76.21, p < 0.001), and vehicle type and lighting (χ2 = 148.52, p < 0.001). The study proposes a multi-layered framework combining site-specific nodal improvements with corridor-level strategies to enhance urban safety in Amman City. Full article
(This article belongs to the Special Issue Innovative Mobility Services for Smart Cities)
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22 pages, 16697 KB  
Article
ASTHN: Adaptive Spatio-Temporal Hypergraph Network for Next POI Recommendation
by Fang Liu, Tianrui Li and Jiangtao Li
ISPRS Int. J. Geo-Inf. 2026, 15(6), 242; https://doi.org/10.3390/ijgi15060242 - 1 Jun 2026
Viewed by 484
Abstract
The widespread use of mobile Internet- and location-based services has generated large-scale check-in data in location-based social networks, creating opportunities for intelligent urban-mobility analysis and personalized mobility services. Making the next point-of-interest (POI) recommendation is an important task in this setting because it [...] Read more.
The widespread use of mobile Internet- and location-based services has generated large-scale check-in data in location-based social networks, creating opportunities for intelligent urban-mobility analysis and personalized mobility services. Making the next point-of-interest (POI) recommendation is an important task in this setting because it supports context-aware destination suggestion, travel assistance, and smart mobility services. However, existing methods still face challenges in jointly modeling higher-order mobility patterns, uneven time intervals, geographic reachability, and fine-grained intra-day temporal regularities. To address these issues, this paper proposes ASTHN, an Adaptive Spatio-Temporal Hypergraph Network for next POI recommendation. ASTHN constructs three fine-grained spatio-temporal context hypergraphs from minimum time interval, spatial proximity, and hourly preference, and uses hypergraph neural networks to learn view-specific POI representations. A context-adaptive fusion module then aligns and integrates multi-source spatio-temporal signals, while an ST-GRU with spatio-temporal gates captures dynamic trajectory evolution. Temperature scaling is further applied at the output layer to alleviate overly concentrated score distributions. Experiments on Foursquare-NYC and Foursquare-TKY show that ASTHN consistently outperforms representative baselines. With results reported as mean ± std over three random seeds, ASTHN improves over the strongest baseline by 3.79%, 14.62%, 2.28%, and 1.24% on NYC in Recall@5, Recall@10, NDCG@5, and NDCG@10, respectively. On TKY, the corresponding improvements are 5.83%, 37.20%, 13.86%, and 20.49%. Ablation, parameter, complexity, and application-oriented case analyses further demonstrate the effectiveness, stability, and practical usability of ASTHN for next POI recommendation in urban-mobility scenarios. Full article
(This article belongs to the Special Issue Innovative Mobility Services for Smart Cities)
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19 pages, 17745 KB  
Article
A Study on the Nonlinear Influence of Urban Environment on Outdoor Jogging: Based on an Interpretable GW-RF Hybrid Model
by Dong Li, Mengmeng Liu, Houzeng Han, Jian Wang and Lei Wang
ISPRS Int. J. Geo-Inf. 2026, 15(5), 202; https://doi.org/10.3390/ijgi15050202 - 7 May 2026
Viewed by 390
Abstract
Outdoor jogging is a significant component of daily physical activities that benefit public health and urban living environments. However, it is still challenging to untangle the intricate associations between environmental variables and jogging paces, due to nonlinear interactions, spatial heterogeneity, and inadequacy in [...] Read more.
Outdoor jogging is a significant component of daily physical activities that benefit public health and urban living environments. However, it is still challenging to untangle the intricate associations between environmental variables and jogging paces, due to nonlinear interactions, spatial heterogeneity, and inadequacy in model interpretability. To this end, an interpretable spatial machine learning framework based on the integration of the Geographically Weighted Random Forest (GW-RF) model and SHapley Additive exPlanations (SHAP) is proposed. Drawing on multi-source urban datasets and Beijing’s large-scale jogging trajectory data, this model allows for global and local interpretation of environmental effects on the built, natural, and visual dimensions. The findings are as follows: (1) Built environment variables demonstrate the greatest explanatory power, with street network configuration (GAC, GAI) and population density identified as the dominant predictors of jogging intensity; (2) All environmental variables exhibit nonlinear threshold effects, with SHAP analysis revealing sign-switching points and optimal ranges—moderate NDVI and sky openness promote jogging while extreme values suppress it; (3) Natural and visual variables operate within distinct comfort thresholds, where moderate annual mean temperature, green view index, and sky openness are consistently associated with higher jogging intensity; and (4) The GW-RF model achieves superior predictive performance (R2 = 0.7939, RMSE = 8.54, MAE = 5.72) over five benchmark models, confirming the necessity of spatial weighting in nonlinear ensemble learning. By revealing nonlinear response patterns and effective environmental ranges, the study presents quantitative evidence for the understanding urban physical activities and providing methodological guidance for fostering healthier and more activity-supportive urban environments. Full article
(This article belongs to the Special Issue Innovative Mobility Services for Smart Cities)
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28 pages, 6613 KB  
Article
Same Streets, Different Contexts: Personality-Based Differences in Cycling Willingness Revealed from Objective and Subjective Perspectives
by Chenfeng Xu, Yihan Li, Zibo Zhu, Zhengyang Zou, Xing Geng and Yike Hu
ISPRS Int. J. Geo-Inf. 2026, 15(4), 179; https://doi.org/10.3390/ijgi15040179 - 16 Apr 2026
Cited by 3 | Viewed by 1036
Abstract
Against the backdrop of rising psychological stress and declining physical fitness in cities, how streetscape characteristics and Myers–Briggs Type Indicator (MBTI) personality traits jointly influence cycling willingness across different contexts remains underexplored. Using Shenzhen, China, as a case study, we integrated objective bicycle-sharing [...] Read more.
Against the backdrop of rising psychological stress and declining physical fitness in cities, how streetscape characteristics and Myers–Briggs Type Indicator (MBTI) personality traits jointly influence cycling willingness across different contexts remains underexplored. Using Shenzhen, China, as a case study, we integrated objective bicycle-sharing travel records from 2021 and subjective pairwise ratings of 1000 street-view images from 960 participants. Cycling willingness was extrapolated through the TrueSkill algorithm and a ResNet50-based model, while street view elements were extracted via DeepLabV3+ and summarized into five indicators. Multivariate regression and multifactor ANOVA were used to test main and moderating effects across six cycling contexts. Results show that (1) Objective cycling indicators and subjective willingness exhibit a pattern of lower values in the center and higher values in the periphery. (2) The Spatial Green Index, Sky Openness Index, Path Freedom Index, and Facility Accessibility Index are the main influencing factors, while the Interface Enclosure Index has the weakest and most context-dependent effect. (3) Intuition/Feeling traits are more salient in leisure and exploration, Judging/Thinking in fitness and transport, and Extraversion/Feeling in social and companion contexts. These findings provide evidence for optimizing urban street cycling spaces in a multi-context and personality-informed manner. Full article
(This article belongs to the Special Issue Innovative Mobility Services for Smart Cities)
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