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  • Research Article
  • 10.1016/j.multra.2025.100253
Simulation-based optimization for transportation system analysis: State-of-the-art research and future endeavors
  • Jul 1, 2025
  • Multimodal Transportation
  • Ziyuan Gu + 6 more

  • Research Article
  • 10.1016/j.multra.2025.100198
Mind the perception gap: Identifying differences in views among stakeholder groups of shared mobility services through bayesian best-worst method
  • Jun 1, 2025
  • Multimodal Transportation
  • Ehsan Amirnazmiafshar + 1 more

  • Open Access Icon
  • Research Article
  • Cite Count Icon 1
  • 10.1016/j.multra.2025.100199
Integrated planning, operation and optimization of coupled transportation and energy systems
  • Jun 1, 2025
  • Multimodal Transportation
  • Arsalan Najafi + 5 more

  • Open Access Icon
  • Research Article
  • Cite Count Icon 2
  • 10.1016/j.multra.2025.100205
Exploring shared e-scooter trip patterns and links to public transport service level
  • Jun 1, 2025
  • Multimodal Transportation
  • Graham Currie + 4 more

This paper explores how public transport and shared e-scooter travel interact. Trip end travel patterns of shared e-scooter users are explored in relation to Public Transport service levels. An index measuring transit service level is developed. This is compared to spatial and temporal patterns of e-scooter trip ends to explore the hypothesis that e-scooter use is stronger in areas where inner area transit offers a poorer quality service i.e. are e-scooters acting as a ‘gap filler’ to transit providing first-last mile access to transit?Analysis methodologies including comparative spatial and temporal mapping of service level and trip end concentrations supported by statistical tests. A novel approach is also adopted to compare PT service level at each e-scooter trip end which identifies potential first-last mile and gap filling e-scooter trips from a large trip end database.Results show e-scooter trip ends are concentrated in areas and at times when transit service levels are highest. This suggests that shared e-scooters may be competing with transit service rather than filling service gaps. We therefore conclude that the hypothesis that e-scooters act as a ‘gap filler’ for areas of low transit use is not supported.Nevertheless, we have found limited and specific evidence of times and areas where ‘gap filling’ and first-last mile trips are apparent. Night time, early morning and weekend e-scooter travel volume is high when transit service levels are low. We also found limited evidence of spatial gaps in transit where first-last mile rail access was occurring and some evidence that rail-linked e-scooter travel was from lower service level trip ends and that these patterns increased with e-scooter trip distance.

  • Open Access Icon
  • Research Article
  • 10.1016/j.multra.2025.100202
Traffic flow theory-based modeling of bike-vehicle interactions for enhanced safety and mobility
  • Jun 1, 2025
  • Multimodal Transportation
  • Mustafa Gadah + 3 more

  • Open Access Icon
  • Research Article
  • 10.1016/j.multra.2025.100203
Analyzing feature importance for older pedestrian crash severity: A comparative study of DNN models, emphasizing road and vehicle types with SHAP interpretation
  • Jun 1, 2025
  • Multimodal Transportation
  • Rocksana Akter + 3 more

  • Open Access Icon
  • Research Article
  • Cite Count Icon 3
  • 10.1016/j.multra.2025.100200
Modeling the adoption of urban air mobility based on technology acceptance and risk perception theories: A case study on flying cars
  • Jun 1, 2025
  • Multimodal Transportation
  • Sangen Hu + 4 more

  • Research Article
  • Cite Count Icon 8
  • 10.1016/j.multra.2025.100210
Multimodal integration in India: Opportunities, challenges, and strategies for sustainable urban mobility
  • Jun 1, 2025
  • Multimodal Transportation
  • Rahul Tanwar + 1 more

  • Research Article
  • Cite Count Icon 10
  • 10.1016/j.multra.2025.100207
Urban intersection traffic flow prediction: A physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms
  • Jun 1, 2025
  • Multimodal Transportation
  • Yuyan Annie Pan + 4 more

  • Research Article
  • Cite Count Icon 3
  • 10.1016/j.multra.2025.100209
Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations
  • Jun 1, 2025
  • Multimodal Transportation
  • Khaled Hamad + 8 more