Abstract
随着高铁快速发展，旅客城际铁路出行具有更多类型客运列车可供选择。基于铁路客票数据，以成渝交通廊道为例，应用离散选择模型研究城际铁路出行中以高铁和普速列车作为选择对象的出行方式选择行为。根据客票出行大数据构建人口统计学特征、购票渠道、社会阶层与地位、出发日期与时段、发车频率、距离等特征变量并融合百度指数数据，以一种新视角建立出行方式选择定量分析模型。结果显示：人口统计学特征、购票渠道、社会阶层与地位、发车频率、出发日期与时段、出行目的、距离等变量显著影响旅客选择行为，能够对旅客城际铁路出行方式选择进行有效预测。研究设计为出行方式选择行为分析提供新思路，丰富了数据驱动下的交通出行选择研究。
With the rapid development of China's high-speed railway, passengers have more choice among a set of transport modes when travelling between cities by rail. Taking Chengdu-Chongqing transport corridor as an empirical case study, in which both high-speed trains and conventional trains are available, we investigated individuals' choice behaviour for intercity travelling based on a large ticket data set. We constructed various independent variables using passengers' trip records, including travel distance, socio-demographics, ticket purchasing methods, social status, train frequency, train date and train time, trip purpose and distance to railway stations and took Baidu index from Baidu search engine into account, and then developed a binary logit model to quantify the influence of the variables on individuals' choice behavior. Results show that all the variables exhibit significant effects on individuals' choice behavior, which confirm that the ticket data is useful for predicting individuals' choice behavior for intercity travelling. Our research provides a new approach to study travel mode choice in the era of big data.
With the rapid development of China's high-speed railway, passengers have more choice among a set of transport modes when travelling between cities by rail. Taking Chengdu-Chongqing transport corridor as an empirical case study, in which both high-speed trains and conventional trains are available, we investigated individuals' choice behaviour for intercity travelling based on a large ticket data set. We constructed various independent variables using passengers' trip records, including travel distance, socio-demographics, ticket purchasing methods, social status, train frequency, train date and train time, trip purpose and distance to railway stations and took Baidu index from Baidu search engine into account, and then developed a binary logit model to quantify the influence of the variables on individuals' choice behavior. Results show that all the variables exhibit significant effects on individuals' choice behavior, which confirm that the ticket data is useful for predicting individuals' choice behavior for intercity travelling. Our research provides a new approach to study travel mode choice in the era of big data.
| Translated title of the contribution | Investigating passengers' choice behavior of intercity rails with large-scale ticketing data |
|---|---|
| Original language | Chinese (Simplified) |
| Pages (from-to) | 989-1000 |
| Number of pages | 12 |
| Journal | 系统工程理论与实践 |
| Volume | 40 |
| Issue number | 4 |
| Early online date | 30 Oct 2019 |
| DOIs | |
| Publication status | Published - 1 Apr 2020 |
Bibliographical note
基金项目: 国家自然科学基金 (71490722, 71802166)Foundation item: National Natural Science Foundation of China (71490722, 71802166)
Funding
National Natural Science Foundation of China (71490722, 71802166); Major Frontier Project of Science & Technology Department of Sichuan Province (2017JY0225)
Keywords
- 客票数据
- 交通方式
- 高铁
- 出行行为
- 离散选择模型
- transport mode
- high-speed rail
- travel behavior
- ticket data
- discrete choice model