Relevance Of Detailed Transfer Attributes In Large-Scale Multimodal Route Choice Models For Metropolitan Public Transport Passengers

TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE(2021)

引用 16|浏览8
暂无评分
摘要
Given the aim of increasing public transport patronage, it is important to understand how passengers perceive different trip characteristics. Most of the existing studies about public transport demand and route choice assigned a higher value of time to transfers than in-vehicle time and used a general transfer penalty to capture an average increase in the travel disutility because of the amount of transfers. However, it is likely that there are nuances to the transfer behaviour depending on specific transfer conditions that existing models do not capture and hence it is difficult to evaluate measures aimed at improving transfers to make public transport more attractive.This study presents a route choice model for the large-scale multimodal public transport network in the Greater Copenhagen Region where a variety of transfer attributes were explicitly considered within a unified model framework. The model was estimated on an extensive revealed dataset of 4,810 observed routes that made it possible to evaluate the rates of substitution of transfer related attributes. The results revealed that travellers do consider attributes for transfers such as ease of wayfinding, presence of shops and escalators at stations when choosing routes in the public transport network and this influences the attractiveness of the respective routes with a quite large range of the transfer penalty from 5.4 min compared to bus in-vehicle time for the best possible transfer to 12.1 min for the worst. Furthermore, the study revealed some differences in the preferences for transfer attributes across passengers. This suggest a quite large potential for improving transfers and hence public transport patronage focusing on the attributes of the transfers.
更多
查看译文
关键词
Multimodal, Public transport, Route choice, Transfer penalty, Transfer attributes, Stations
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要