Abstract
Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.
Highlights
Statistics show that by the end of 2017, there were 584,400 public transport vehicles in China and the number of taxis was 1,395,800, which means that taxi travel accounted for a large proportion of public transport
All pick-up locations of taxi and independent variables are aggregated into corresponding grid cells, the nal shape le of the study area contains more than 1300 cells. e spatial and temporal distribution characteristics of taxi travel demand are analyzed. en hotspot areas are identi ed by kernel density estimation, and construct geographical weighted regression (GWR) model to study the relationship between taxi demand and di erent variables at di erent time
All information integrated at 500∗500 m grid cells in ArcGIS 10.2 system to explore the travel pattern of taxi demand and its related influence factors
Summary
Statistics show that by the end of 2017, there were 584,400 public transport vehicles (tram, rail transit) in China and the number of taxis was 1,395,800, which means that taxi travel accounted for a large proportion of public transport. Insu cient supply can lead to long waits for taxi passengers [2] Both cases seriously downgrade the level of service for the taxi industry, such like causing inconvenience to residents’ travel, weakening the service level of taxis, and increasing the cost of daily scheduling. Unlike other buses and light rails modes, taxis do not have xed routes and lines, so it is di cult to study taxi travel demand Under such circumstances, reducing the vacant rate and waiting time, making rational use of public resources and exploring the main factors a ecting residents’ travel demand have become one of the most important problems we need to solve urgently. Due to limited dataset and research methods, studies do not show spatial distribution differences For this reason, scholars tend to ignore the role of taxis in public transportation and the impact of socio-economic factors on travel demand. “Density” factors such as road density and residential density are used to analyze the status of taxis in residents’ travel
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