Crowding cost estimation with large scale smart card and vehicle location data

Public transport experienced service reliability: Integrating travel time and travel conditions

“…A limitation of the proposed analysis is that not all relevant attributes can be observed from the considered automated data sources, including travellers' journey start times and ocurrences of denied boarding. In gated systems such as metro networks, AFC data collected at the ticket gates can be used to fill some of these gaps (e.g., Zhu et al, 2017;Hörcher et al, 2017). In bus networks where data are collected at boarding, these opportunities are more restricted; however, OD matrices inferred from AFC data could be used to improve, e.g., the probabilistic seat allocation model.…”

“…Seat availability at boarding depends on (i) the existing passenger load on the boarded bus, and (ii) the number of passengers boarding the bus at the same stop ahead of the probe traveller. The model makes the same seat priority assumptions as Schmöcker et al (2009) and Hörcher et al (2017). Thus, sitting passengers are guaranteed a seat until alighting.…”

“…Certain assumptions of the methodology can be refined if OD demand information based on, e.g., AFC data or assignment models is available, such as the seat allocation model (cf. Schmöcker et al, 2009;Hamdouch et al, 2011;Hörcher et al, 2017). Further, some stops are characterized by many passengers transferring from other lines, in which case the assumption of random arrivals is less realistic.…”

Congestion from a Multimodal Perspective

“…Recent contributions like Kroes et al (2014) and Hörcher et al (2017) show that the cost of crowding can be captured in a revealed preference framework as well, especially when demand patterns can be recovered using smart card and vehicle location data. In terms of the magnitude of disutilities, these empirical results reveal that the cost of crowding may be similar to the cost of uncrowded travel time.…”

On Individual Repositioning Distance along Platform during Train Waiting

“…Nevertheless, this study considered explicitly only distributions of walkingin and walking-out speeds (as normal distributions) and a two-itinerary choice scenario without transfer. To integrate transfer in crowding discomfort analysis, Hörcher et al [34] combined the probabilistic assignment model of [31][32][33] with the standing and sitting choice models [35,36] and applied to a PT network of HongKong. A group of transfers and their choices are modeled in a Bayesian framework at the journey level to model complex trips that could involve transfers hence combine legs (journeys), in order to study route choice between station pairs and reveal the influence of in-vehicle congestion on route disutility to passengers.…”