Estimating walking modal share: a novel approach based on spatial regression models and GIS

Abstract

Please click here to download the map associated with this article. Conventional transport modelling tools are relatively poor at forecasting pedestrian trips, partly because walking is only a realistic option within the immediate vicinity of an individual's origin. While it is possible to take this factor into account through some form of intervening opportunities models, this paper explores the potential for simpler spatial regression techniques and GIS to be used in tackling this problem. The variables used in explaining the proportion of walking trips to work include the characteristics of the local road network, car ownership, average age and population and jobs within a zone's catchment area. The results presented include plots of spatial correlation for proportion of walking trips, and plots of error terms for simple linear regression models and the Spatial Durbin Model, using Census data for the city of Leeds, UK. This work provides an unprecedented insight into the effect of spatial patterns on walking and it is shown that spatial regression models can produce improved modal split estimates at the trip generation stage by taking into account detailed spatial characteristics of surrounding zones. The results from this work are relevant to accessibility, land use and transport planning policies as well as to the wider application of GIS and spatial regression techniques in transport modelling.