Moving Towards Sustainable Urban Mobility Patterns: Addressing Barriers and Leveraging Technology in Islamabad and Rawalpindi, Pakistan

Abstract

The rapid urban growth and proliferation of private vehicles in Pakistan have intensified challenges, such as traffic congestion, longer travel times, environmental harm, road safety risks, and adverse public health outcomes. Despite global emphasis on sustainable modes of transportation, these options remain underutilized and receive limited policy attention in Pakistan. This study investigates the barriers hindering the adoption of active and public transport in Islamabad and Rawalpindi and evaluates the role of technological factors in influencing commuters’ willingness to use public transit. Data were collected through a structured questionnaire survey and analyzed using descriptive statistics, exploratory and confirmatory factor analyses, and structural equation modeling. The findings reveal varying commuter preferences across different modes and demonstrate a higher willingness to use active modes of travel when favorable conditions are available. The dominant barriers to active travel include long travel distances and durations, insufficient infrastructure, social stigma, and a lack of cycle storage facilities. For public transport, the major obstacles identified are overcrowding during peak hours, poor accessibility, excessive travel times, and a lack of comfort and convenience. The study also highlights the potential technological interventions, such as real-time travel planning apps, secure parking space provision, and smart ticketing systems, to improve the attractiveness and usability of public transport. Overall, the study provides valuable insights for policymakers seeking to develop evidence-based strategies that encourage the use of sustainable transport options. By addressing both infrastructural and perceptual barriers, such interventions can foster a transition towards more sustainable urban mobility systems in Pakistan.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 2 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.