Non-motorised transport prioritisation model using spatial intelligence

Abstract

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.

Pedestrians and cyclists mobility models

“…While more and more models are considering active trips, a knowledge gap regarding pedestrians' and cyclists' travel quality needs remains to be plugged. Travel quality needs refer to infrastructural, personal, and social factors aimed at enhancing travelling experience which bear on the user in selecting a specific mode of transport (Ettema et al, 2016;Rojas López & Wong, 2017;Yeung et al, 2013). In many cases, walking and cycling have not been considered as actual modes of transport, which also impacted on data collection methods and hence the quality of active mobility data.…”

“…Finally, a discussion of specific impact of different factors (as considered in the different scenarios) in travel demand and usage of the network is presented. A version of this study, with main interest on demand analysis, is in preparation for submission for consideration for publication to Transportation Planning and Technology journal and it is currently under review (Rojas López et al, 2018) and some issues regarding trip distribution are discussed in a study published at Proceedings of the Institution of Civil Engineers -Transport (Poyil, Rojas López, & Wong, 2017).…”

“…The information can also be used to prioritise development or enhancement of paths/roads closer to TAZs with higher trip-rates. Trip rates can be included into spatial intelligent models to determine areas of the transport network that need prioritisation (Poyil et al, 2017). Traditionally, information from trip distribution is also utilised for the development of mode choice and as guidelines for network assignment considering the number of trips/trip-stages generated and attracted from different TAZs, however, as in the current 4SM actual trips O-D locations, rather than information obtained from TAZs' O-D matrices, are utilised.…”