Advancing Pedestrian Models: A Comparative Review and Vision for the Future
Name
RJPA_A_2618643_O.pdf
Size
2.62 MB
Format
Adobe PDF
Checksum (MD5)
3532d42f893d9b8be28bcdc51b2b85e2
Author(s)
Zafri, Niaz Mahmud
Sevtsuk, Andres
Date Issued
February 23, 2026
Journal
Journal of the American Planning Association
Publisher
Taylor & Francis
Citation
Zafri, N. M., & Sevtsuk, A. (2026). Advancing Pedestrian Models: A Comparative Review and Vision for the Future. Journal of the American Planning Association, 1–18.
Version
Final published version
Abstract
Problem, research strategy, and findings
Pedestrian mobility is essential for creating sustainable, healthy, and equitable cities, yet pedestrian modeling remains underdeveloped compared with vehicle-centric approaches. To advance the state of the art, we critically review four available pedestrian modeling frameworks—urban network analysis (UNA), multi-agent transport simulation (MATSim), model of pedestrian demand (MoPeD), and spatial design network analysis (sDNA)—through the lens of the traditional four-step transportation modeling framework. We assess their methodological foundations, capabilities, practical applications, and limitations and outline future directions for improving modeling practice. UNA and sDNA offer high-resolution, trip-based network analyses; MATSim supports agent- and activity-based multimodal simulations; and MoPeD estimates grid-level pedestrian demand. Despite these strengths, several key gaps remain: Most models focus predominantly on utilitarian walking, neglect leisure and social activities, typically assume homogeneous pedestrian behavior by overlooking sociodemographic variations, face shortcomings with mode choice estimation, and are rarely applied in informal urban contexts. Furthermore, limited availability of pedestrian count data continues to constrain effective model calibration and validation.
Takeaway for practice
We propose that researchers and planners adopt a human-centered, inclusive, and policy-aligned modeling agenda, emphasizing simple yet intuitive metrics that capture the full spectrum of walking benefits, supporting early-stage planning even in data-scarce contexts, fostering stronger collaboration with practitioners, and promoting a modular, adaptable modeling ecosystem. Ultimately, reorienting pedestrian models as flexible decision support tools—rather than narrowly focused forecasting instruments—can meaningfully support the development of more walkable cities.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/01944363.2026.2618643
