Measuring transit service reliability using data from web-based transit user survey: a case study at Brisbane, Australia (original) (raw)

Abstract

Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document.

Figures (8)

Holroyd and Scraggs (1966) suggested that VarH may be expected to approach E’(H) for small value of E(H and tend to a constant as E(H) becomes very large. They proposed a relation of the form: where E(H) and VarH are, respectively, the mean and variance of the time headway H between vehicles. C’ ranges between 0 and 1, where C’ = 0 corresponds to perfectly regular vehicle arrivals and C’ = 1 to the completely random case. As shown in Table 1, the coefficient of variation of headways can be related to the probability P that a given transit vehicle’s absolute headway deviation H; will be off-headway by more than one- half the average scheduled headway E(H), with level of service (LOS) defined according to certain range of C.

Holroyd and Scraggs (1966) suggested that VarH may be expected to approach E’(H) for small value of E(H and tend to a constant as E(H) becomes very large. They proposed a relation of the form: where E(H) and VarH are, respectively, the mean and variance of the time headway H between vehicles. C’ ranges between 0 and 1, where C’ = 0 corresponds to perfectly regular vehicle arrivals and C’ = 1 to the completely random case. As shown in Table 1, the coefficient of variation of headways can be related to the probability P that a given transit vehicle’s absolute headway deviation H; will be off-headway by more than one- half the average scheduled headway E(H), with level of service (LOS) defined according to certain range of C.

Data collected from each respondent in the survey include average waiting time, lateness or earliness of boarding compared to published schedule, in-vehicle time, arrival lateness or earliness compared to published schedule, on-board crowding measured on a five-level scale, and wait buffer time which is the time between passenger’s arrival to wait at a stop and the departure time of his/her targeted service on schedule. Figure 1 illustrates the variations of those variables by a series of box-and-whisker plots. There are six boxes in each picture representing the data from the sub-groups of passengers of (from left to right) off-peak busway 109, off-peak bus 412, off-peak ferry CityCat, peak busway 109 , peak bus 412, and peak ferry CityCat.

Data collected from each respondent in the survey include average waiting time, lateness or earliness of boarding compared to published schedule, in-vehicle time, arrival lateness or earliness compared to published schedule, on-board crowding measured on a five-level scale, and wait buffer time which is the time between passenger’s arrival to wait at a stop and the departure time of his/her targeted service on schedule. Figure 1 illustrates the variations of those variables by a series of box-and-whisker plots. There are six boxes in each picture representing the data from the sub-groups of passengers of (from left to right) off-peak busway 109, off-peak bus 412, off-peak ferry CityCat, peak busway 109 , peak bus 412, and peak ferry CityCat.

According to Kittelson & Associates et al. (2003), at LOS A, passengers are assured that a transit vehicle will arrive soon after they arrive at a stop, thus they don’t need to schedule; at LOS B, service is still relatively frequent, but passengers will consult schedules to minimize their wait time at the transit stop. At LOS C, the wait involved if a service is missed becomes long. At LOS D, service requires passengers to adjust their routines to fit the transit service provided, which is unattractive to choice riders. many UQ attendees must travel from their suburb origins to the CBD, and there interchange with a different transit service to reach UQ. Thus, the service reliability is a big concern to both transit users and operators.

According to Kittelson & Associates et al. (2003), at LOS A, passengers are assured that a transit vehicle will arrive soon after they arrive at a stop, thus they don’t need to schedule; at LOS B, service is still relatively frequent, but passengers will consult schedules to minimize their wait time at the transit stop. At LOS C, the wait involved if a service is missed becomes long. At LOS D, service requires passengers to adjust their routines to fit the transit service provided, which is unattractive to choice riders. many UQ attendees must travel from their suburb origins to the CBD, and there interchange with a different transit service to reach UQ. Thus, the service reliability is a big concern to both transit users and operators.

Figure | Variations of waiting times of three routes during off-peak and peak periods

Figure | Variations of waiting times of three routes during off-peak and peak periods

Figure 2 Confidence intervals (95%) of proportion estimates of three routes at off-peak and peak periods

Figure 2 Confidence intervals (95%) of proportion estimates of three routes at off-peak and peak periods

Table 5 On-time percentage and schedule adherence LOS Coefficient of headway variations

Table 5 On-time percentage and schedule adherence LOS Coefficient of headway variations

Table 6 Coefficient of headway variations and headway adherence LOS DISCUSSIONS

Table 6 Coefficient of headway variations and headway adherence LOS DISCUSSIONS

Figure 3 Comparison of observed and theoretical average waiting times Figure 3 plots the observed average waiting times (Avg(w)) of the three routes surveyed at off-peak and peak periods as well as the theoretical waiting times (E(w)) according to Eq. 3.

Figure 3 Comparison of observed and theoretical average waiting times Figure 3 plots the observed average waiting times (Avg(w)) of the three routes surveyed at off-peak and peak periods as well as the theoretical waiting times (E(w)) according to Eq. 3.

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (11)

  1. Abkowitz, M. and Tozzi, J. (1987). "Research contributions to managing transit service reliability", Journal of Advanced Transportation, 21, 47-65.
  2. Adebisi, O. (1986). "A mathematical model for headway variance of fixed route buses", Transportation Research -B, 20(1), 59-70.
  3. Ceder, A. (2007). Public Transit Planning and Operation: Theory, Modelling and Practice. Elsevier, Oxford, USA. Holroyd, E. M. and Scraggs, D. A. (1966). "Waiting times for buses in central London", Traffic Engineering and Control, 8(3), 158-160.
  4. Islam, M. K. and Vandebona, U. (2010). "Reliability analysis of public transit systems using stochastic simulation". Proceedings of the 33 rd Australasian Transport Research Forum, Canberra, Australia, September
  5. Liu, R. and Sinha, S. (2007). "Modelling urban bus service and passenger reliability". Paper presented at the International Symposium on Transportation Network Reliability, The Hague, July .
  6. Liu, Y. (2009). A Modelling Approach to the Demand Management of Urban Public Transit. Unpublished report of confirmation of doctorate candidature. Queensland University of Technology. Brisbane, Australia.
  7. O'Flachery, C. A. and Mangan, D. O. (1970). "Bus passenger waiting times in central areas", Traffic Engineering and Control, 11(9), 419-421.
  8. Osuna, E. E. and Newell, G. F. (1972). Control strategies for an idealised public transport system", Transportation Science, 6, 52-72
  9. Seddon. P. A. and Day, M. P. (1974). "Bus passenger waiting times in greater Manchester", Traffic Engineering and Control, 15(9), 442-445.
  10. SurveyMonkey (2011). SurveyMonkey User Manual. Online document available at http://s3.amazonaws.com/ SurveyMonkeyFiles/UserManual.pdf. Access on 19 March, 2011.
  11. Kittelson & Associates et al. (2003). Transit Capacity and Quality of Service Manual (2nd Edition). TRCP Report 100. Transportation Research Board, Washington, D.C., USA.