Determinants of passengers' metro car choice revealed through automated data sources: a Stockholm case study

Abstract

We propose a methodology based on multiple automated data sources for evaluating the effects of station layout, arriving traveler flows, and platform and on-board crowding on the distribution of boarding passengers among individual cars of metro trains. The methodology is applied to a case study for a sequence of stations in the Stockholm metro network. The findings suggest that passengers opt for less crowded train cars in crowded situations, trading-off walking and in-vehicle crowding while waiting and riding. We find that the boarding car distribution is also affected by the locations of platform access points and the distribution of entering traveler flows. These insights may be used by transit planners and operators to increase the understanding of how passengers behave under varying crowding conditions, identify the factors that affect travelers' choice of metro car and eventually reduce experienced on-board crowding and increase the capacity utilization of the trains through investments in infrastructure or operational interventions.

Modelling the effects of real-time crowding information in urban public transport systems

“…PT users may respond to crowding experience by adjusting their travel strategies (Tirachini, Hensher, and Rose 2013). These pertain to, among others, boarding a different train carriage (Peftitsi, Jenelius, and Cats 2020b), shifts in departure time choice, mode choice and route choice (Tirachini, Hensher, and Rose 2013), potentially leading to reduced trip frequency or even resignation from travelling altogether (Szarata 2014).…”

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

“…For instance, travel diary-based approaches have been transformed into electronic travel diaries with global positioning systems (GPS), thereby speeding up data transfer from users to research groups and providing details for understanding more in-depth travel patterns [26][27][28][29][30]. Innovation has also driven transport system modelling: reverse assignment procedures have been developed to update network and demand model parameters [11,18]; In particular, in transit modelling, smart-card data were used to estimate the origin-destination matrix as well as calibrate and validate assignment models ( [17,[31][32][33][34][35].…”