Extracting potential bus lines of Customized City Bus Service based on public transport big data

Customized City Bus Service (CCBS) can reduce the traffic congestion and environmental pollution that caused by the increasing in private cars, effectively. This study aims to extract the potential bus lines and each line's passenger density of CCBS by mining the public transport big data. The datasets used in this study are mainly Smart Card Data (SCD) and bus GPS data of Qingdao, China, from October 11th and November 7th 2015. Firstly, we compute the temporal-origin-destination (TOD) of passengers by mining SCD and bus GPS data. Compared with the traditional OD, TOD not only has the spatial location, but also contains the trip's boarding time. Secondly, based on the traditional DBSCAN algorithm, we put forwards an algorithm, named TOD-DBSCAN, combined with the spatial-temporal features of TOD. TOD-DBSCAN is used to cluster the TOD trajectories in peak hours of all working days. Then, we define two variables P and N to describe the possibility and passenger destiny of a potential CCBS line. P is the probability of the CCBS line. And N represents the potential passenger destiny of the line. Lastly, we visualize the potential CCBS lines extracted by our procedure on the map and analyse relationship between potential CCBS lines and the urban spatial structure.