Discovering Intra-Urban Population Movement Pattern Using Taxis’ Origin and Destination Data and Modeling the Parameters Affecting Population Distribution

Abstract

GPS-equipped vehicles are an effective approach for acquiring urban population movement patterns. Attempts have been made in the present study in order to identify the population displacement pattern of the study region using taxis’ origin and destination data, and then model the parameters affecting the population displacement pattern and provide an ultimate model in order to predict pick-up and drop-off locations. In this way, the passenger pick-up and drop-off locations have been identified in order to obtain the population movement pattern. In this study, Moran’s I index was used to measure the spatial autocorrelation, and hot spot analysis was used to analyze spatial patterns of pick-up and drop-off locations. Effective parameters modeling was performed using the Poisson regression. The results of the spatiotemporal distribution map for pick-up and drop-off locations indicated a similarity in patterns and equal results for some locations. Results also indicated different features of spatial distribution during different hours of the day. Spatial autocorrelation analysis results indicated a low probability of randomness in the general spatial distribution of the locations. The result of modeling the parameters shows the positive effect of the parameters on the pattern of population movement, and according to the p-value of 0.000, Poisson regression is significant for the pick-up and drop-off locations. The modeling results also highlighted the importance of movement patterns in recognizing urban hot spots, which is valuable for policymakers and urban planners.

Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis

“…Point-based flow data contain many potential locations, such as those from GPS trajectory data, while area-based flow data include migration between OD locations within a predefined area [10]. The OD distribution is correlated with the population distribution, land use, and socioeconomic factors [11][12][13]. Therefore, point-of-interest (POI) data play an important role in inferences of trip purpose and OD information [14,15].…”