Assessing the Relationship between Access Travel Time Estimation and the Accessibility to High Speed Railway Station by Different Travel Modes

Abstract

This paper aims to fill the research gap of the relationship between the access travel time (ATT) estimation and the accessibility to high speed railway (HSR) station. A regression analysis was developed on the basis of risk-return model to analyze the access travel time estimation error (ATTEE). The data sources were 1595 valid interview survey data at Beijing South Railway Station (BSRS), China in October 2016. The factors and scenarios included travel mode, departure time, and travel date, etc. The coefficients of ATT estimation were obtained by different travel modes. The results showed that the expected access travel time (EATT) has positive linear correlation with the actual access travel time (AATT). Accessibility was calculated by the ratio of AATT to EATT. The accessibility coefficients ranged from 0.89 to 1.38 in different travel modes, departure time, and travel dates. A smaller coefficient indicates better travel time reliability and accessibility. This study not only provides a useful tool to estimate the travel time budget required for access to HSR station, but also establishes a connection with the accessibility and ATTEE. It offers an opportunity to estimate ATT to HSR stations by different modes of transport, which can help to better understand how the accessibility of the feeder transport changes at different time periods.

Digitalization and Sustainability in Linear Projects Trends: A Bibliometric Analysis

“…In terms of materials, the introduction of self-repairing materials and high-strength metallic alloys has increased the useful life and resistance of bridges, tunnels, and roads [12,15,16]. In the field of railways, research has focused on developing infrastructure systems that enable the operation of magnetic levitation trains and the optimization of railway networks' interconnection to improve mobility and reduce travel times [17,18]. In urban spaces, the incorporation of intelligent systems for constant infrastructure monitoring [19], using sensors and Artificial Intelligence algorithms connected to the internet and under the focus of smart cities, has enhanced various issues' improvement and maintenance schedules, ensuring the safe operation of linear projects [20,21].…”