| In recent years,with the acceleration of China's urbanization process,cars have become a major travel tool for people.Due to the increase of car ownership,the traffic congestion and environmental problems caused by it have become the difficult problem restricting the sustainable development of cities.Therefore,it is the general trend to guide sustainable mode of transportation,and the TOD model oriented by public transportation can better solve this problem.In the TOD area,most destinations can be reached directly by public transportation,which emphasizes the attraction of public transportation to residents' travel and reduces people's excessive dependence on private cars,thus improving urban traffic congestion.Existing studies do not have a clear conclusion on whether a transit station belongs to TOD or not.At the same time,the influence of self-selection has been ignored in most studies.Therefore,there is always a non-negligible bias in the study of the travel behavior of residents in the TOD area.Based on this,this paper mainly studies on two issues.How to distinguish whether the station belongs to TOD station? What are the differences in travel behaviors between TOD stations and non-TOD stations? Based on the above issues,the following researches have been carried out in the paper:First of all,determining the TOD evaluation index system.After determining the radius of the influence area of the rail transit station,ten indicators were selected from the three aspects of transit.The indexes within this range were calculated based on the POI data and land use data,and the TOD index evaluation system was established.The data were standardized and summed up.After the optimal cluster number was determined through 30 cluster validity indexes,k-means clustering algorithm was used to divide the stations.The stations were divided into TOD stations and non-TOD stations.Moreover,the typical stations selected from the clustering results were analyzed in detail.Secondly,the propensity score matching method was used to control the influence of residents' self-selection behavior.In this paper,it was assumed that economic attributes will cause residents' self-selection behavior,so it was necessary to get the travelers' economic attributes in the propensity score matching.The AFC data were used to identify the traveler's home,work place and activity place.Traveler's economic attributes were constructed from the living cost and consumption cost.It was worth noting that this study did not obtain the travelers' economic attributes through the questionnaire survey,but estimated the economic level of travelers through the housing price and entertainment consumption around the travelers' homes and activity places.According to the results of clustering results and traveler's economic attribute data,after verifying the existence of self-selection behavior,the propensity score matching was carried out,and the matching results were analyzed.Finally,the travel behavior of residents living around TOD station and non-TOD station was analyzed.The travel types were divided into four categories.The travel characteristics of residents on weekdays and weekends were analyzed in terms of time distribution and spatial distribution.Based on the matching results of the propensity score,the travel behaviors of residents around TOD station and non-TOD station were analyzed from the aspects of travel distance and travel frequency,so as to better study the differences between the travel behaviors of travelers in TOD and non-TOD areas.The results show that the travel behaviors of residents in TOD and non-TOD regions are significantly different. |
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