[2603.01325] From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction

Physics > Physics and Society arXiv:2603.01325 (physics) [Submitted on 1 Mar 2026] Title:From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction Authors:Rulla Al-Haideri, Bilal Farooq View a PDF of the paper titled From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction, by Rulla Al-Haideri and Bilal Farooq View PDF HTML (experimental) Abstract:High frequency pedestrian motion forecasting when interacting with autonomous vehicles (AVs) can be enhanced through the use of behavioural frameworks, such as discrete choice models, that can explicitly account for correlation among similar movement alternatives. We formulate the pedestrian next step choice as a spatial discrete choice defined by a grid of speed adjustment and heading change. Using naturalistic pedestrian-AV encounters from nuScenes and Argoverse 2 (1 sec decision interval), we estimate a multinomial logit baseline and four spatial generalized extreme value (GEV) specifications (SCL, GSCL, SCNL, and GSCNL). We then compare them to a residual neural network logit (ResLogit) model that learns cross alternative effects while retaining an interpretable linear utility component. Across the evaluated data, spatial GEV structures yield only marginal improvements over multinomial logit, whereas ResLogit achieves a substantially better fit and produces behaviourally coherent errors concentrated among neighbouring grid cells. The result...