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Walking and cycling, as active modes of transportation, play a vital role in advancing sustainable urban mobility by reducing emissions and improving public health. However, widespread adoption faces challenges such as inadequate infrastructure, safety concerns, socio-cultural barriers, and policy limitations. This study systematically reviewed 56 peer-reviewed articles from 2004 to 2024, across 30 countries across five continents, employing the Capability, Opportunity and Motivation-Behaviour (COM-B) framework to identify the main drivers of walking and cycling behaviours. Findings highlight that the lack of dedicated infrastructure, inadequate enforcement of road safety measures, personal and traffic safety concerns, and social stigmas collectively hinder active mobility. Strategic interventions such as developing integrated cycling networks, financial incentives, urban planning initiatives, and behavioural change programs have promoted increased engagement in walking and cycling. Enhancing urban mobility further requires investment in pedestrian and cycling infrastructure, improved integration with public transportation, the implementation of traffic-calming measures, and public education campaigns. Post-pandemic initiatives to establish new pedestrian and cycling spaces offer a unique opportunity to establish enduring changes that support active transportation. The study suggests expanding protected cycling lanes and integrating pedestrian pathways with public transit systems to strengthen safety and accessibility. Additionally, leveraging digital tools can enhance mobility planning and coordination. Future research is needed to explore the potential of artificial intelligence in enhancing mobility analysis, supporting the development of climate-resilient infrastructure, and informing transport policies that integrate gender perspectives to better understand long-term behavioural changes. Coordinated policy efforts and targeted investments can lead to more equitable transportation access, support sustainability goals, and alleviate urban traffic congestion.
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This content will become publicly available on February 1, 2026
Interpretable State-Space Model of Urban Dynamics for Human-Machine Collaborative Transportation Planning
Strategic Long-Range Transportation Planning (SLRTP) is pivotal in shaping prosperous, sustainable, and resilient urban futures. Existing SLRTP decision support tools predominantly serve forecasting and evaluative functions, leaving a gap in directly recommending optimal planning decisions. To bridge this gap, we propose an Interpretable State-Space Model (ISSM) that considers the dynamic interactions between transportation infrastructure and the broader urban system. The ISSM directly facilitates the development of optimal controllers and reinforcement learning (RL) agents for optimizing infrastructure investments and urban policies while still allowing human-user comprehension. We carefully examine the mathematical properties of our ISSM; specifically, we present the conditions under which our proposed ISSM is Markovian, and a unique and stable solution exists. Then, we apply an ISSM instance to a case study of the San Diego region of California, where a partially observable ISSM represents the urban environment. We also propose and train a Deep RL agent using the ISSM instance representing San Diego. The results show that the proposed ISSM approach, along with the well-trained RL agent, captures the impacts of coordinating the timing of infrastructure investments, environmental impact fees for new land development, and congestion pricing fees. The results also show that the proposed approach facilitates the development of prescriptive capabilities in SLRTP to foster economic growth and limit induced vehicle travel. We view the proposed ISSM approach as a substantial contribution that supports the use of artificial intelligence in urban planning, a domain where planning agencies need rigorous, transparent, and explainable models to justify their actions.
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- Award ID(s):
- 2125560
- PAR ID:
- 10618796
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Transportation Research Part B: Methodological
- Volume:
- 192
- Issue:
- C
- ISSN:
- 0191-2615
- Page Range / eLocation ID:
- 103134
- Subject(s) / Keyword(s):
- Smart Planning Human-AI Teaming Markov Decision Process Sketch Planning Machine Learning Multicriteria Land Use Travel Demand Regional Growth Reinforcement Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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