Bus dwell time: the effect of different fare collection systems, bus floor level and age of passengers

Pricing public transport services

“…Alighting times vary between 0,4 seconds when using six door buses (TRB, 2000) up to 4,9 seconds (Li et.al., 2006). A concise overview of previous results is provided by Tirachini (2013).…”

“…Usually regression analysis is applied in order to explain variation in data collected by manual counts and in some cases (e.g. 16,22 seconds in cases where the passenger pays in cash and the driver has to return change (Tirachini, 2013). Alighting times vary between 0,4 seconds when using six door buses (TRB, 2000) up to 4,9 seconds (Li et.al., 2006).…”

Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

“…In particular, average boarding and alighting times are lower than 1.9 s/pax for densities lower than 4 pax/m 2 , however with a density of 6 pax/m 2 average boarding time is 2 s/pax but average alighting time escalates to 5.9 s/pax, explained by the difficulties of alighting passengers walking among too many standees. On the other hand, the study of Tirachini (2013) uses empirical data from buses in Sydney and estimates thataverage boarding and alighting times increase 0.34 and 0.56 seconds per passenger, respectively when there are passengers standing in the bus aisle relative to uncrowded conditions. On the engineering side, Katz and Garrow (2012) find that bus design factors (e.g., front seating area, placement of doors, fare collection system) influence the amount of people that stand near doors, which has a larger impact on increasing dwell times than the number of passengers standing in aisles.…”

“…In the case of buses, models that show how crowding levels increase boarding and alighting times have been empirically estimated using data from several cities around the world, including Santiago de Chile (Gibson et al, 1997), Chicago (Milkovits, 2008), Dhaka (Katz and Garrow, 2012), Vancouver (Fletcher and El-Geneidy, 2013) and Sydney (Tirachini, 2013). Milkovits (2008) finds that dwell time increases with the square of the number of standees inside a bus, multiplied by the total number of passengers boarding and alighting at a bus stop.…”

Dwell Time Modelling and Optimized Simulations for Crowded Rail Transit Lines Based on Train Capacity

“…The data for their research was provided by the Community of Metros (CoMET). Tirachini [7] estimated multiple regression models to analyse the influence of different payment methods, the existence of steps at doors, the age of passengers and the possible friction between users boarding, alighting and standing, on explaining the observed variation in dwell times. Qiang Meng and Xiaobo Qu [8] proposed a probabilistic approach to estimate dwell times of buses in a bus bay by incorporating the randomness.…”