On Individual Repositioning Distance along Platform during Train Waiting

Abstract

Out of waiting times spent in rail stations on boarding platforms, some part can be reinvested by the trip-makers to optimize their positions of boarding and save on travel time for the rest of their trips. This paper provides a stochastic model, in which user's journey is decomposed into phases of, successively, walking in the access station, platform positioning, waiting for boarding, train riding, and walking in the egress station. Walking speed and target position are modeled as individual factors, and in-station distances as random variables. Service timetable is exogenous. This makes egress times and exit instants random variables that are characterized by distribution and mass probability functions under closed-forms, for both single and distributed walking speeds. Specific statistical distributions are shown to ease computation. The resulting PDF formulae make likelihood functions of the model parameters. Maximum likelihood estimation is proposed and applied to a case study of commuter rail line in Paris: journeys between stations Vincennes and La Défense along line A of the Regional Express Railways. Based on data from Automated Fare Collection and Automatic Vehicle Location systems and pertaining to an individual user, satisfactory results were obtained.

The boundary between random and non-random passenger arrivals: Robust empirical evidence and economic implications

“…Another passenger-specific behavioural characteristic is the paired egress time for each trip. As mentioned previously, there may be a propensity for passengers to minimise their egress times at the destination station by trading off their access time at the origin station, thereby inducing some dependence between the two components (Leurent and Xie, 2018). The results show that there is evidence of an inverse interaction between the components; access times decrease monotonically as egress times increase with an average elasticity of -0.20.…”

“…Berggren et al (2019) perform linear regression and ANOVA tests on wait times for bus and rail modes in Sweden and observe that individual-specific traits including gender, access mode, trip purpose influence passenger arrival times. Another passenger-specific arrival preference is explored by Leurent and Xie (2018). It is asserted that passengers opt to have longer wait times at the origin station in order to optimally position themselves on the train such that their walking distance at the destination platform is minimised.…”

“…Paired time component -In the literature it is hypothesised that passengers may deliberately position themselves at the origin platform such that they reduce their egress distance and walk time to the exit of the platform at the destination station (Leurent and Xie, 2018). To account for the potential dependency between access and egress times, the paired egress time component for each trip is included in model.…”

Bayesian Analysis for Metro Passenger Flows Using Automated Data

“…for j � 1, 2, 3, k � 1, 2, 3 based on our estimators and MPTAM (15). Figure 5 shows their trajectories.…”

“…In practice, this assumption may not hold, especially during peak hours. Leurent and Xie [15] proposed a stochastic model to reposition the distances along platforms during train waiting.…”