IHPC recently published the article titled "A spatiotemporal dynamic analyses approach for dockless bike-share system" in Computers, Environment and Urban Systems (impact factor of 4.655). The IHPC researchers involved in this work are Dr Song Jie, Dr Zhang Liye, Dr Qin Zheng, and Dr Muhamad Azfar Bin Ramli.
Using actual GPS data from all active bike-share providers in Singapore over eight consecutive days, this work demonstrated how spatiotemporal analyses could be used to better support the use of shared mobility for a more sustainable transportation system. The key technical contribution is the proposal of a novel framework that is (a) capable of quantifying spatial and temporal variations in the usage of shared bikes; (b) able to identify whether observed temporal variations are system inherent or statistically significant deviations; and (c) sufficiently robust to be applied to trips in a dockless bike-share system.
Existing approaches in the literature could only be applied to station-based systems; dockless systems are more challenging to study than station-based systems due to greater unpredictability in the determination of actual origins and destinations of these trips. The spatiotemporal analyses of shared bike trips demonstrated in the paper has the capability to identify "hotspot" regions where high numbers of bike trips occur, which period of the day do such hotspots form, and the average length of these bike trips. Such a data-informed approach could thereafter be used by bike-share operators to make better decisions on where to locate more bikes for enhanced service levels, or by urban planners to optimise the design and provisioning of cycling networks.
This work is part of a three-year ongoing project funded by the Urban Mobility Grand Challenge (UMGC) Programme run by the Land Transport Authority of Singapore (LTA). The overall objective of the project is for IHPC researchers and LTA staff to co-develop a comprehensive modelling and simulation platform of island-wide travel in Singapore incorporating multiple public transportation modes. The project utilises various advanced modelling techniques, and developed models have been closely calibrated to realistic conditions using high granularity empirical datasets available to LTA. This includes models derived from spatiotemporal analyses of shared mobility. Additionally, the IHPC team has also started the last phase of the work which involves the incorporation of behavioural modelling into the overall platform. The fully developed modelling platform and its associated applications will assist different LTA divisions by improving their existing transport planning and management processes.
Read the full article published on ScienceDirect