Urban Space Analysis Lab TAU - Urban movement modeling

URBAN MOVEMENT MODELING

Information on the distribution of movement volumes over the street network is essential for the planning and design of walkable and sustainable urban environments. However, as collection of pedestrian data for all city streets is unfeasible, pedestrian volume models and movement simulation are constructed in our laboratory with respect to various urban areas and different planning approaches. The model are used for planning of not-motorized movement network, creation and maintenance of livable city centers, transportation planning and for evaluating crash risk. We also developed an agent-based (AB) pedestrian volume model at the urban scale.

Pedestrian volume modeling

We construct pedestrian volume models by using street network' centrality measures and geographic information on the urban environment. The models are used for understanding the relations between movement and urban environments that were established according different planning approaches and as tools for Mobility and Transportation Planning.

Selected papers:

Wolpert L., and Omer I. (2025). Comparative analysis of pedestrian volume models: Agent-based models, machine learning methods and multiple regression analysis
Omer, I. and Kaplan, N. (2019). Structural properties of the angular and metric street network’s centralities and their implications for movement flows
Omer, I., & Goldblatt, R. (2017). Using space syntax and agent-based approaches for modeling pedestrian volume at the urban scale
Omer, I., Kaplan, N., (2016). An agent-based pedestrian model considering spatial behavior parameters

Movement simulation
We developed an Agent Based (AB) model for simulating movement in urban environment. The model was constructed by transforming the various street network centrality measures and land-use variables used in space syntax modeling into agents' spatial behavior parameters. The advantage of our AB model lies in its ability to simulate the combined effect of the street network structure and land-use pattern on movement flows in urban street networks.

Selected papers:

Ma, D., Omer, I., Osaragi, T., Sandberg, M., & Jiang, B. (2018). Why topology matters in predicting human activities.
Omer, I., & Goldblatt, R. (2016). Using space syntax and Q-analysis for investigating movement patterns in buildings: The case of shopping malls.
Omer, I., Rofè, Y., & Lerman, Y. (2015). The impact of planning on pedestrian movement: contrasting pedestrian movement models in pre-modern and modern neighborhoods in Israel
Lerman, Y., Rofè, Y. and Omer, I. (2014), Using Space Syntax to Model Pedestrian Movement in Urban Transportation Planning

Evaluating Crash Risk
We developed a method for evaluating the spatial distribution of vehicle and pedestrian trafﬁc crashes relative to the volume of vehicle and pedestrian movement in urban areas.

Selected papers:

Omer, Gitelman, Rofè, Lerman, Kaplan and Doveh (2017). Evaluating Crash Risk in Urban Areas Based onVehicle and Pedestrian Modeling