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The aim of the work reported in this paper was to develop a non-motorised transport (NMT, covering walking and cycling) prioritisation model using a geographic information system that can be used for planning as well as auditing existing NMT infrastructure. ArcGIS 10.1 ModelBuilder software was used to design NMT prioritisation using the cases of Sembawang and Yishun residential townships in Singapore. The concept of spatial intelligence was used to predict the NMT prioritisation scheme; spatial intelligence is the ability to visualise spatial features and apply spatial judgement for mobility problems involving navigation or to identify fine details or patterns. The NMT prioritisation model was developed based on NMT movements in relation to spatial features such as residential buildings, community centres, schools and hospitals, recreation and sports centres, economic zones, transport hubs, bus stops and green spaces. The designed NMT prioritisation map is the weighted sum of Euclidean distance rasters of eight spatial layers. The results were subsequently used to assess the existing NMT facilities in the Sembawang and Yishun townships.

This paper analyzes the various variables affecting pedestrian road crashes, placing emphasis on the effect of daily activity patterns and the built environment, including the land use of the places. Study also develops a level of safety model in terms of number of pedestrian accidents in Chennai. First, eleven potential factors influencing pedestrian level of safety are summarized: width of road, width of sidewalk, average running speed of vehicles, vehicular volume, pedestrian volume, percentage of sidewalk area encroached, presence of crossing facilities, sight distance, pedestrian refuge and median, lightings and curb. The selected roads are typical of those prevalent in urban areas of Chennai. With the survey data, a stepwise regression analyses are carried out to develop a reliable pedestrian level of safety model for road segments, suitable for use in the vast majority of Indian urban areas. The study reveals that the factors significantly influencing pedestrian level of safety at road segments including width of sidewalk, average running speed of vehicles, percentage of sidewalk area encroached, presence of pedestrian refuge and median, lightings and curb.A model to predict the pedestrian safety level in terms of number of pedestrian accidents is developed in this study using regression analysis and integrated with GIS to produce a colour coded maps showing the predicted number of pedestrian accidents. The validation has been done by comparing the predicted number of pedestrian accidents with actual number of pedestrian accidents occurred.

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