LAUSR

As an important transport tool, taxi plays a significant role to meet travel demand in urban city. Understanding the travel patterns of taxis is important for addressing many urban sustainability challenges. Previous research has primarily focused on examining the statistical properties of taxi trips to characterize travel patterns, while it may be more appropriate to explore taxi service strategies on seasonal, weekly or daily time scale. Therefore, intra-urban taxi mobility is investigated by examining taxi trajectory data that were collected in Harbin, China, while 12-week corresponding to 12-month is chosen as the sampling period in our study. The multivariate spatial point pattern analysis is firstly adopted to characterize and model the spatial dependence, and infer significant positive spatial relationships between the picked up points (PUPs) and the dropped off points (DOPs). Secondly, the points of interest (POIs) are identified from DOPs using the emerging hot spot detection technique, then the taxi services and movement patterns surrounding POIs are further examined in details. Moreover, our study builds on and extends the existing work to examine the statistical regularities of trip distances, and we also validate and quantify the impacts posed by airport trips on the distance distributions. Finally, the movement-based kernel density estimation (MKDE) method is proposed to estimate taxis' service ranges within three isopleth levels (50, 75 and 95%) between summer/weekday and winter/weekend from taxi driver's perspective, and season as well as temperature factors are identified as the significant effect within certain service range levels. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.