Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31106
Analysis of Residents’ Travel Characteristics and Policy Improving Strategies

Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

Abstract:

To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network model, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 175

References:


[1] Ming He, Xiucheng Guo, Jiangyu Ran, Cairui Wu, Wei Zhu, Chaoping Liu. Forecasting Rail Transit Split with Disaggregated MNL Model (J). Journal of Transportation Systems Engineering and Information Technology, 2010, 10(2).
[2] Eran Ben-Elia, Yoram Shiftan. Which road do I take? A learning-based model of route-choice behavior with real-time information (J). Transportation Research Part A, 2010,44(4).
[3] Yang Liya, Shao Chunfu, Haghani A. Nested Logit Model of combined selection for travel mode and departure time (J). Journal of Traffic and Transportation Engineering, 2012,12(02):76-83.
[4] Vo Van Can. Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model (J). Transportation Research Part A, 2013,49.
[5] Cheng Long. Mode choice of low income commuters and the evaluation of their improvement measures in big cities (D). Southeast University, 2016.
[6] Ge Dapeng. Study on the traffic mode split of Metropolitan intercity passenger transport based on travel behavior analysis (D). Chang’an University, 2017.
[7] Jiang Wei. Construction and application of resident’s car rental trip choice model based on dynamic factors (D). Chongqing Jiaotong University, 2017.
[8] Lin Xiaomei, Shao Chunfu, Dong Chunjiao, Wang Shengyou. The behavior characteristics of Inter-city travel under the implication of expressway Toll-free policy during holiday (J). Journal of Transportation Systems Engineering and Information Technology, 2019, 19(02):247-254.
[9] Ransford A. Acheampong. Spatial structure, intra-urban commuting patterns and travel mode choice: Analyses of relationships in the Kumasi Metropolis, Ghana (J). Cities, 2020,96.
[10] Xu Bing. Research on the ICLV model of intercity travel mode selection (D). Beijing Jiaotong University, 2018.
[11] Liu Yufeng, Qian Yizhi, Hu Dawei, Wang Laijun, Li Lu, Ma Zhuanglin. Correlation analysis of travel mode choice for urban residents in different urban size based on structural equation model (J). Journal of Chang'an University (Social Science Edition), 2018, 38(05):87-95.
[12] Xiugang Li, Dominique Lord, Yunlong Zhang, Yuanchang Xie. Predicting motor vehicle crashes using Support Vector Machine models (J). Accident Analysis and Prevention, 2008, 40(4).
[13] Xing Wang, Xin Wang, Zhaonan Sun. Comparison on Confidence Bands of Decision Boundary between SVM and Logistic Regression (P). INC, IMS and IDC, 2009. NCM '09. Fifth International Joint Conference on, 2009.
[14] Wang Shengyou. Analysis of trip characteristics and optimal design of traffic resources on Beijing Metropolitan Area (D). Beijing Jiaotong University, 2018.
[15] Yan Jialin, Xiang Longgang, Wu Huayi, Sun Shangyu. Urban road traffic speed prediction based on LSTM (J). Geomatics World, 2019, 26(05):79-85.
[16] Cao Yu, Wang Cheng, Wang Xin, Gao Yueer. Urban road short-term traffic flow prediction based on Spatio-temporal node selection and deep learning (J/OL).Journal of Computer Applications: 1-10(2020-01-13).