Published

Abstract

This paper presents a comprehensive review and bibliometric analysis of Large Language Models (LLMs)intransportation, exploring emerging trends, challenges and future research. Understanding their evolution and impact in transportation research is essential. The study used Scopus as the primary data source, applying Bibliometrix, VOSviewer, and Python for performance analysis and science mapping. This study analyzes 161 peer-reviewed articles and reveals a 25.74% annual growth in scholarly output. IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Intelligent Vehicles emerge as the most influential journals by publication volume and impact on LLM research. The findings highlight global disparities in research contributions, with China and the United States dominating by publication volume, followed by Germany and Canada, while developing regions exhibit lower scientific productivity. In addition, the study provides qualitative insights by reviewing recent LLM applications in transportation, examining their key contributions, methodological approaches, inherent limitations, and domain-specific challenges. Key research themes focus on autonomous mobility, traffic optimization, and sustainable transportation networks. Despite significant progress, several challenges remain, including decision-making uncertainties, computational scalability, and high energy consumption. Overcoming these challenges requires greater transparency through causal learning, enhanced reasoning via hybrid AI models, and inclusive frameworks that address algorithmic bias and ensure equitable adoption. 

M. Hassan, M. E. Kabir, M. Jusoh, H. K. An, M. Negnevitsky, and C. Li, "Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges and Future Research," IEEE Access, early access, pp. 1-1, 2025, doi:https://doi.org/10.1109/ACCESS.2025.3589319  

Highlights

• An integrated GTFS–GIS approach identifies structural inefficiencies and spatial inequities in the urban bus networks of Johor Bahru and Penang, Malaysia.

• Larkin Sentral exhibits the highest centrality; Terminal Komtar presents strong nodal connectivity.

• Network metrics indicate Johor Bahru’s polycentric and dispersed configuration, contrasted with Penang’s corridor-focused monocentric structure.

• Accessibility analysis highlights limited first-mile connectivity in peripheral areas and overlapping transit services in central corridors.

• The study recommends GTFS-RT for real-time modeling and proposes future research on adaptive planning aligned with SDG 11.2.

Mahbub Hassan, Hridoy Deb Mahin, Ferdoushi Ahmed, Md. Maruf Hassan, Atikur Rahaman, Masuk Abdullah, Assessing Public Transit Network Efficiency and Accessibility in Johor Bahru and Penang, Malaysia: A Data-Driven Approach,Results in Engineering,Volume 27,2025,106126,ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2025.106126. 

Highlights

• A bibliometric review of 283 studies (2014–2024) explores XR in transportation.

• Key themes: AV validation, pedestrian safety, AR navigation, traffic simulation.

• USA, Germany, and China dominate XR outputs with growing interdisciplinary work.

• Major gaps: ecological validity, demographics, cybersecurity, real-world trials.

• Future XR research must stress scalability, cognition, ethics, and ecosystems.

Hassan, Mahbub, Saikat Sarkar Shraban, Md Ashequl Islam, Khairul Salleh Basaruddin, Muhammad Farzik Ijaz, Nur Saifullah Bin Kamarrudin, and Hiroshi Takemura. "Integration of Extended Reality Technologies in Transportation Systems: A Bibliometric Analysis and Review of Emerging Trends, Challenges, and Future Research." Results in Engineering (2025): 105334.https://doi.org/10.1016/j.rineng.2025.105334 

Highlights

• Provides a comprehensive review of machine learning applications in Intelligent Transportation Systems (ITS).

• Conducts a bibliometric analysis to uncover trends, methodologies, and emerging research areas in ITS.

• Highlights key applications of machine learning in traffic prediction, route optimization, and safety management.

• Identifies future research directions, including the integration of deep learning and AI in ITS.

Hassan, M., Al Nafees, A., Shraban, S.S. et al. Application of machine learning in intelligent transport systems: a comprehensive review and bibliometric analysis. Discov Civ Eng 2, 98 (2025). https://doi.org/10.1007/s44290-025-00256-2 

Highlights

• High-Accuracy Forecasting– Random Forest achieved superior performance with R² = 0.9656, accurately capturing over 96% of ridership variance.

• Multifactor Analysis– Key predictors included day type (weekday/weekend), temperature, and public holidays, emphasizing the role of temporal and exogenous factors.

• Comparative Modeling– Machine learning models (Random Forest, XGBoost) significantly outperformed traditional approaches (Linear Regression, ARIMA) in predictive accuracy.

• Operational Planning Insights– Findings support the use of data-driven models for real-time transit planning, especially to manage peak demand during festivals and adverse weather.

• Future Research Pathways– The study advocates for enhanced accuracy through data integration (e.g., passenger flow, ticketing data) and model refinement across transit systems.

A. Al Nafees, M. Hassan, A. Paul, S. S. Shraban and H. Deb Mahin, "Public Transport Ridership Forecasting Using Machine Learning: A Case Study of Rapid Bus KL in Malaysia," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 3366-3371, https://doi.org/10.1109/ICCIT64611.2024.11021879   

Highlights

• Gap parameters like critical gaps and follow-up times are essential for optimizing traffic flow and safety across roundabout types.

• The study evaluates single-lane, multi-lane, and turbo roundabouts, highlighting the unique dynamics of each configuration.

• Incorporates computer simulations, empirical observations, and mathematical models to analyze gap parameters effectively.

• Traces the evolution of roundabout designs and emphasizes the importance of region-specific adaptations based on traffic conditions and driver behavior.

Hassan, M., Al Nafees, A., Shraban, S. S., Mahin, H. D., Paul, A., & Ki, A. H. (2024). A comprehensive review on the impact of gap parameters for multiple types of roundabouts in different traffic conditions. International Journal of Integrated Engineering (IJIE), 16(9), 1-20. https://doi.org/10.30880/ijie.2024.16.09.001 

Highlights

• Over 50% of users, primarily from lower and middle socioeconomic classes, find ticket prices reasonable, ensuring accessibility.

• Daily and weekly commuters rely on the metro for business or school, with shorter trip durations being a major benefit.

• Peak-hour congestion and cleanliness issues remain key concerns despite overall positive perceptions.

• The metro system improves traffic flow, reduces travel times, and is favorably viewed for safety, accessibility, and environmental benefits.

Hassan, M., Mehjabin, F., & Al Nafees, A. (2024). A study on the impact of the newly constructed metro rail in Dhaka on travel behavior. In Lecture Notes in Civil Engineering. 13th Asia-Pacific Conference on Transportation and the Environment (APTE), National University of Singapore, Singapore, July 8–10, 2024. https://link.springer.com/book/9789819696413 

Highlights

• Rising Research Trend: Scholarly output in ITS big data has grown rapidly over the past decade, signaling strong academic and industrial engagement.

• Emerging Focus Areas: Key themes include traffic prediction, anomaly detection, safety analytics, and IoT integration.

• Global Contributions: China, the U.S., and Canada lead in publications, reflecting strategic investments in smart mobility.

• Impact-Citation Gap: Rapid publication growth contrasts with slower citation rates—pointing to a need for deeper, high-impact studies.

• Neglected Ethical Issues: Privacy, security, and data ethics remain underexplored, underscoring a critical gap in current research.

Hassan, M., Mahin, H.D., Al Nafees, A. et al. Big data applications in intelligent transport systems: a bibliometric analysis and review. Discov Civ Eng 2, 49 (2025). https://doi.org/10.1007/s44290-025-00205-z 

Highlights

• Developed a robust traffic monitoring system for real-time vehicle detection and classification.

• Utilized YOLOv8 with OpenCV for enhanced precision and computational efficiency.

• Achieved remarkable accuracy in real-world highway traffic conditions.

• Proposed a solution to improve road safety and prevent accidents.

Hassan, M., Mahin, H. D., Jusoh, M., Al Nafees, A., & Paul, A. (2024). Vehicle class detection and counting on a Malaysian road using YOLOv8 and OpenCV. IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Sabah, Malaysia, August 26–28, 2024. https://doi.org/10.1109/IICAIET62352.2024.10729957

Highlights

• Effectiveness of Metering Signals: The study demonstrated that metering signals significantly improved traffic flow at unbalanced roundabouts. During the 4:30 PM scenario, the West Lane improved from LOS F to LOS D, and the North Lane from LOS F to LOS B when signals were placed at the South Lane with detectors at 240 meters.

• Optimal Detector Placement: The simulation identified that the optimal detector placement for the 12:00 PM scenario was 240 meters from the stop line, while for the 4:30 PM scenario, the best performance was achieved with detectors at 350 meters from the stop line. These placements helped to minimize queue lengths and vehicle delays.

• Reduction in Emissions and Fuel Consumption: After implementing signal control, emissions (CO, NOx, VOC) and fuel consumption were significantly reduced, particularly on the South and East Lanes. For example, CO emissions on the South Lane dropped from 92.9 g to 53.6 g during the 12:00 PM scenario.

• Statistical Validation: The VISSIM model was statistically validated using RMSE and paired t-tests. The results showed a notable reduction in vehicle delay and emissions, with p-values < 0.05, confirming the significant impact of the metering signals.

Hassan, M., An, H. K., Mahin, H. D., Al Nafees, A., Shraban, S. S., & Paul, A. (2025). Investigating the performance of metering methods in managing unbalanced roundabouts using VISSIM. ASEAN Journal of Scientific and Technological Reports, 28(2), e255674-e255674.