The Travel Characteristics By Bus Of The Elder People Based On Big Data:A Case Study Of Harbin

In the case of the aging trend is gradually spreading all around the world, as a developing country, China is entering a stage of accelerated incensement of aging. Senile in space travel environment and other issues are attached great importance. With the integration of big data and urban planning, personal data based on the public "micro-participation" provide data support and research method for the analysis of residents travel behavior and urban spatial.In this paper, we focus on bus travel issues of the aged who are older than 70. By using of C++ programming software, we studied the characters of spatial, temporal, bus lines and other aspects of bus travel, based on the Harbin bus IC card data and GPS record and other transport big data. We elaborated and defined the basic conception of the big data, the elderly, data mining and traffic analysis zoo, based on the quantitative research status of bus card at home and abroad, and the research status of elderly travel characteristics. In this pater, we evolved literature theory research method, quantitative statistical analysis, qualitative experience cognitive law and spatial overlay analysis method, and the age stratification theory, activity theory, space and time restrictions apply theory and the theory of hierarchy of needs are used. We summarized the characteristics of aging population, and predicted the development of aging population of Harbin. In addition, we analyzed the characteristics and the purposes of the bus travel of elders by the traditional research method.By using data cleanup method, 4,830,238 bus card data was cleaned, compared with non-elderly, the temporal characteristics of the elderly bus travel was deeply analyzed from the aspects of the number of trips, departure time, travel and other aspects of strength; we studied the features of bus travel of elderly with all lines, and studied the line 68, which is the most amount bus lines as an example of the single line features. Further use GPS data, the SCD time attributes to match the continuous-time GPS to calculate the position where the elderly get on board. From the distribution of full-day passenger, the distribution of scheduled passenger, the traffic density and other aspects of bus travel for the elderly temporal dynamics of longitudinal summary. Finally, according to the characteristics of the elderly bus travel, from the aspects of spatial planning, transport planning and urban management of the elderly bus travel, we proposed some "soft" optimization propose appropriate recommendations and strategies.