Analysis on the Influencing Factors of People'S Bicycle Purchase based on Logistic Regression

Jingwen Wu

《International Core Journal of Engineering》 8卷10期 (2022/10) Pp. 192-200

https://doi.org/10.6919/ICJE.202210_8(10).0028

摘要

With the aggravation of environmental pollution and urban traffic congestion, building a ＂green, livable, low-carbon, people-oriented＂ traffic environment has become a new task of urban traffic development, and ＂slow traffic＂ has become a research hotspot. Based on binary logistic regression, this paper analyzes the influencing factors of people's bicycle purchase in slow traffic, and finds out what has a significant impact on people's bicycle purchase from eight factors: gender, income, number of family children, education level, whether there is a room, number of cars, commuting distance and living area. According to the experimental results, the corresponding countermeasures and suggestions are put forward, in order to better promote the slow traffic-- bicycle travel, and establish a more low-carbon and environmental protection travel system.

關鍵字

Slow Traffic ； Binary Logistic Regression ； Influencing Factors ； Bicycle Purchase

參考文獻

Lian Sheng, Li Xiaoda, Huang Shuqing. Research on factors influencing bicycle purchase behavior based on hierarchical Logit model [J]. Science and Technology and Innovation,2017, (23): 8-10.

Li Pengjiang, Jiang Shuping. A Study on Influencing Factors of Residents' Bicycle Travel in Low-Carbon Pilot Cities--Taking Baoding City as an Example [J]. Research World 2017,(12): 23-27.

Yang Chen, Wang Wei, Lu Jian. Study on the influence of individual factors on the choice of bicycle commuting [J]. Journal of Wuhan University of Technology (Transportation Science and Engineering), 2010,34(03): 611-615.

Yang Ning, Hun Li Kang, Zhou Jianbo. Research on Influencing Factors of Public Bicycle Travel Choice Based on Binary Logistic Model [J]. Journal of Yancheng Institute of Technology (Natural Science Edition), 2019,32(04): 63-69.

Lin Yuping, Fang Jie. Analysis of public bicycle travel characteristics based on Logistic regression: Taking Fuzhou City as an example [J]. Transportation Research, 2017, 3(03): 14-19.