In order to better dispatch taxis′ transportation capacity so as to alleviate the imbalance between taxi supply and demand in high passenger demand areas, it is important to accurately explore the spatiotemporal distribution characteristics of taxi travel demand and its influencing factors. Based on the multi-source heterogeneous data including taxi GPS data, taximeter data, public transportation transactions data and Point of Interesting(POI) data, correlation analysis methods were used to screen the influencing factors of taxi travel demand and a multi-dimensional factor set was established. Then the influencing model was put forward based on Geographical Weighted Regression(GWR). Taking the data of 1 398 traffic districts in Beijing as an example, the impact of various factors on taxi travel demand under different space-time conditions was explored. The results show that the spatial distribution of taxi travel demand has spatial agglomeration effect, and the influence degree of the variables has spatial nonstationary characteristic; the residential density in the central areas, the office density in the surrounding and company-intensive areas and the entertainment service density in the peripheral areas of the city have a strong positive impact on taxi travel demand; the residential land density in the peripheral areas and the office density in the central areas are negatively correlated with taxi travel demand; the promotion effect of entertainment service density on taxi travel demand in nonworking days is significantly greater than that in working days; the impact of regional public transportation generation volume on taxi travel demand shows significant difference between early peak and late peak. The comparative analysis of the models shows that the proposed model has high accuracy and is suitable for explaining the spatial and temporal differences of the influencing factors on taxi travel demand.
Weng Jian-cheng，He Han-mei，Wang Yuan，Zhang Ke，Qian Hui-min
. Regional Taxi Travel Demand Influencing Model Based on Geographical Weighted Regression[J]. Transport Research, 2020
, 6(6)
: 28
-38
.
DOI: 10.16503/j.cnki.2095-9931.2020.06.004
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