Understanding the correlation between destination distribution and urban built environment from taxi GPS data

Abstract

Exploring the relationship between travel destinations and urban built environments can help to conduct reasonable measurements in urban transportation planning. Destination distribution is affected by various factors, and quantitative description of the spatial effects is a challenge in factor correlation analysis. In this study, inhomogeneous Poisson point processes are proposed to model the correlation between destination distribution and related categories of variables, including land use, trip origin distribution, demographic information, and transport networks. First, five models based on inhomogeneous Poisson point processes are constructed, considering different combinations of variables. Second, diagnostic methods, including the G-function, smoothed residuals, and parameter influence function, are applied to evaluate the models' performance. Finally, in experiments, the taxi GPS trajectory data collected in Shenzhen City, China, are used to validate the effectiveness of the proposed method. Moreover, in the discussion, the variations of variable coefficients in different time periods (workdays, weekends, and holidays) are analyzed and explained. The results of this study may enhance understanding of the correlation between travel patterns and urban structure.

Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit

“…The size and shape of the spatial analysis units determine the amount of data to be included, resulting in differences in the data analysis results [ 13 , 14 ]. Previously, when analyzing the influence of the built environment on taxi travel demand, the research area was often divided into a single space type using the crowd sampling method of “people for their use.” For example, the study area would be divided into a traffic analysis zone (TAZ) [ 15 ], census area [ 16 ], and grid [ 17 ], neglecting the impact of the MAUP on the data aggregation and modeling results. However, scholars have recently begun to focus on this issue.…”

Impact of built environment on residential online car-hailing trips: Based on MGWR model

“…The findings of these studies are expected to serve as a foundation for making decisions about efficient, healthy and green urban development [ 5 – 7 ]. At the same time, with advances in global positioning technology, information science, and communication technology, the generation of location based service(LBS) data has led scholars to pay more attention to the spatial and temporal characteristics of residential trips [ 8 , 9 ].…”