Assessing pedestrian impacts of future land use and transportation scenarios


  • Qin Zhang Technical University of Munich
  • Rolf Moeckel Technical University of Munich
  • Kelly Clifton Portland State University



pedestrian travel demand model, pedestrian accessibility, land use and transportation policy


Portland Central City has experienced growth in population and employment over the last decades, which leads to an increase in travel demand. One of the visions of the Central City 2035 plan is to encourage walking. This paper presents a model of pedestrian travel demand to help assess the impact of land use and transportation policies in the Central City area. The model is an enhanced version of the Model of Pedestrian Demand (MoPeD). Realistic scenarios and the projected population and employment are incorporated in this study. Four future scenarios for 2035 are tested and compared to 2010 base conditions. The results suggest that demographic growth and job increases can help to encourage a large share of walk trips. Pedestrian behavior is also sensitive to network connectivity, but the influence is not as impactful compared to population and job growth. Furthermore, model results show that a good street network and a dense and diverse land-use plan can maximize the effects of promoting walk trips. This paper presents the capability of the pedestrian planning tool MoPeD. It is sensitive to the small-scale variations in local land use and transport development, which can help policymakers better understand the effects of various demographic policies and infrastructure planning on the walk share.


Borrmann, A., Kneidl, A., Köster, G., Ruzika, S., & Thiemann, M. (2012). Bidirectional coupling of macroscopic and microscopic pedestrian evacuation models. Safety Science, 50(8), 1695–1703.

City of Portland. (2018). Central city 2035 goals and policies (Vol. 1). Portland, OR: City of Portland. Retrieved from

Clifton, K. J., Orrego-Oñate, J., Singleton, P., & Schneider, R. (2019). Transferability and forecasting of the pedestrian index of the environment (PIE) for modeling applications. Portland, OR: Transportation Research and Education Center.

Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016a). Development of destination choice models for pedestrian travel. Transportation Research Part A: Policy and Practice, 94, 255–265.

Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016b). Representing pedestrian activity in travel demand models: Framework and application. Journal of Transport Geography, 52, 111–122.

Clifton, K. J., Singleton, P., Muhs, C., & Schneider, R. (2015). Development of a pedestrian demand estimation tool. Retrieved from

Erdmann, J., & Krajzewicz, D. (2015). Modelling pedestrian dynamics in SUMO. SUMO User Conference 2015, pp. 103–118. Retrieved from

Ewing, R., & Cervero, R. (2010). Travel and the built environment. Journal of the American Planning Association, 76(3), 265–294.

Khan, M., Kockelman, K., & Xiong, X. (2014). Models for anticipating non-motorized travel choices, and the role of the built environment. Transport Policy, 35, 117–126.

Kielar, P. M., & Borrmann, A. (2016). Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice. Simulation Modelling Practice and Theory, 61, 47–62.

Kuzmyak, J. R., Walters, J., Bradley, M., & Kockelman, K. M. (2014). Estimating bicycling and walking for planning and project development: A guidebook. Washington, DC: Transportation Research Board.

Oregon Metro. (2015). 2015 trip-based travel demand model methodology report. Retrieved from

Portland Bureau of Transportation. (2019). Congressman Earl Blumenauer Bicycle and Pedestrian Bridge. Retrieved from

Sallis, J. F., Bull, F., Burdett, R., Frank, L. D., Griffiths, P., Giles-Corti, B., & Stevenson, M. (2016). Use of science to guide city planning policy and practice: How to achieve healthy and sustainable future cities. The Lancet, 388(10062), 2936–2947.

Saunders, L. E., Green, J. M., Petticrew, M. P., Steinbach, R., & Roberts, H. (2013). What are the health benefits of active travel? A systematic review of trials and cohort studies. PLoS ONE, 8(8), e69912.

Singleton, P. A., Schneider, R. J., Muhs, C., & Clifton, K. J. (2014). The pedestrian index of the environment: Representing the walking environment in planning applications. Paper presented at the Transportation Research Board 93rd Annual Meeting, January 12–14, Washington DC.

Singleton, P. A., Totten, J. C., Orrego-Oñate, J. P., Schneider, R. J., & Clifton, K. J. (2018). Making strides: State of the practice of pedestrian forecasting in regional travel models. Transportation Research Record: Journal of the Transportation Research Board, 2672(35), 58–68.

Zhang, Q., Clifton, K. J., & Moeckel, R. (2019). Investigate an appropriate spatial resolution for large-scaled pedestrian travel demand model. Transportation Research Procedia, 41, 324–327.

Zhang, Q., Clifton, K. J., Moeckel, R., & Orrego-Oñate, J. (2019). Household trip generation and the built environment: Does more density mean more trips? Transportation Research Record: Journal of the Transportation Research Board, 2673(5), 596–606.




How to Cite

Zhang, Q., Moeckel, R., & Clifton, K. (2022). Assessing pedestrian impacts of future land use and transportation scenarios. Journal of Transport and Land Use, 15(1), 547–566.