A Stochastic Model of Passenger Generalized Time Along a Transit Line (original) (raw)
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Transportation research procedia, 2017
Traffic along a transit line involves two kinds of mobile entities: passengers versus vehicles. The paper develops a stochastic model to deal with: headways between successive runs serving stations, wait times at boarding stations, passenger flows per vehicle and by leg (i.e. pair of entry-exit stations), in-vehicle comfort differentiating between seated and standing places. While previous static models of transit assignment consider vehicle passenger flows that are averaged over the vehicle runs, we model distributed headways, thus taking into account the issues of regularity and reliability. The "Rank conservation" postulate of Leurent et al. (2012a,b) is used to establish analytical formulae for the expectation and variance of every traffic variable of interest: leg flows, link flows, wait times, leg physical times, generalized times at waiting and in-vehicle. The linkage between waiting prior to boarding and in-vehicle crowding is modeled: each user is concerned individually, conditionally to the headway during which he waits for the vehicle. A computation scheme is provided to deliver the statistical summaries of the array of traffic variables.
Passenger arrival rates at public transport stations
2006
The amount of time spent waiting at a public transport station is a key element in a passenger's assessment of service quality and in mode choice decisions. Many transport models estimate the average wait time is half the headway for small headways and use a maximum waiting time for headways over a given value. The assumption is that at small headways passengers do not bother to consult schedules since vehicles arrive frequently; therefore these passengers arrive regularly at the station. In contrast, at longer headways passengers do consult schedules to reduce their waiting time; these passengers arrive clustered around the departure time. This research evaluated the influence of headway and other factors on passenger arrival rates at public transport stations based on data collected at 28 stations in Zurich's public transport network. It found that even at 5-minute headways, some passengers consulted schedules and did not arrive randomly at the station. This finding is interesting since 5-minutes is much lower than many models assume, therefore these models may be overstating passenger wait time. The research also found time-of-day and reliability had an important influence on passenger arrival rates. The research proposes a model for passenger arrival rates at stations that combines a uniform distribution with a shifted Johnson S B distribution.
Transit Vehicles' Headway Distribution and Service Irregularity
Public Transport
Pairing, or bunching, of vehicles on a public transportation line influences the adaptive choice at stops due to the random headways and waiting times it determines. In order to ensure consistency with the characteristics of service perturbations, as represented by a transit operation model, it is important to identify the headway distributions representing service perturbations. A stochastic simulation model is developed for a one-way transit line, which accounts for several service characteristics (dwell time at stops, capacity constraint and arrivals during the dwell time). Samples of headways at the main stops are utilized to build histograms of the headway’s frequencies by their length, which allow to identify the functional forms and parameters of the headway distributions. For these stops, density plots of consecutive headways are also produced. Sensitivity analysis is carried out to identify the effect of key parameters (dispatching headway, maximum load and running time).
Estimation of passenger waiting time using automatically collected transit data
Public Transport, 2020
Among the many ways to improve a transit system is a reduction in travel time as experienced by the passenger. Hence, passenger waiting times remain a topic of interest among transit planners. In this study, the effects of transit vehicle delays on passenger waiting time is investigated, as well as the effects of transfer status, boarding location, time of day, and rider travel frequency. The data used in this study were collected using automatic fare collection (AFC) and automatic vehicle location (AVL) technology. A trip chaining algorithm is used to infer the trajectory of each passenger, and as a result produce measures of passenger waiting time and vehicle delay. An analysis of an arterial Bus Rapid Transit (aBRT) line in Saint Paul, Minnesota reveals a waiting time model consistent with previous literature, a positive relationship between vehicle delay and passenger waiting time, and an insignificant relationship between transfer status and passenger waiting time. Finally, a simple model relating waiting time and vehicle delay is provided for the purpose of transit planning and waiting time estimation.
A relativity theory of traffic along a transit line
2019
Along a transit line, vehicle traffic and passenger traffic are jointly subject to variability in travel time and vehicle load hence crowding. The paper provides a stochastic model of passenger physical time and generalized time, including waiting on platform and in-vehicle run time from access to egress station. Five sources of variability are addressed: (i) vehicle headway which can vary between the stations provided that each service run maintains its rank throughout the local distributions of headways; (ii) vehicle order in the schedule of operations; (iii) vehicle capacity; (iv) passenger arrival time; (v) passenger sensitivity to quality of service. The perspective of the operator, which pertains to vehicle runs, is distinguished from the user’s one at the disaggregate level of the individual trip. After recalling the basic properties from a previous paper [0], this paper provides additional properties and explores some consequences for models of traffic assignment to a transi...
Bus Dwell-Time Model of Main Urban Route Stops
Transportation Research Record: Journal of the Transportation Research Board, 2012
This paper proposes a bus dwell-time model obtained by means of a robust statistical evaluation of boarding passenger data at stops. This model is a change from the generally accepted linear model (i.e., dwell time increases in a fixed rate of time per passenger). The statistical analysis proves the validity of the potential model. This model was derived from a large number of observations on Line 27 of the Transports Municipal Company in Madrid, Spain, and validated with observations from another line (Line 70). The data were gathered by observers who could attest to the influence of occasional incidents in the boarding process. This model can be used to evaluate line capacity more accurately. The analyzed lines were main urban routes with high passenger demand requiring the use of articulated buses. These lines were selected according to two basic criteria: routes with high demand that guarantee good results and routes with similar vehicle types with an onboard payment method. A l...
Trip Timing and Crowding on Rail Transit Lines : Theory and Application
2015
Crowding and travel delays are commonplace on many transit systems in developed and developing countries. 1 A recent roundtable report by the International Transport Forum (OECD, 2014) identifies crowding as a major source of inconvenience that increases the generalized cost of travel. Crowding occurs not only while riding buses and trains, but also when boarding and alighting from them, while waiting on platforms or at bus stops, and while accessing stations by escalator, elevator, or on foot (King et al., 2014). Several recent studies have documented the aggregate cost of crowding on transit networks. For example, Prud'homme et al. (2012) estimate that the 8% increase in densities in the Paris subway between 2002 and 2007 imposed a welfare loss of at least €75 million per year. Veitch et al. (2013) estimate the annual total cost of crowding in Melbourne metropolitan trains in 2011 at AUS $280 million.