Probabilistic Modeling of Bus Rapid Transit Station Loading Area Selection for Bus Capacity Estimation
place - australasia, place - urban, mode - bus rapid transit, infrastructure - station, operations - capacity, ridership - elasticity, planning - methods, planning - service quality
Boarding and alighting, Bus rapid transit, Probability, Quality of service, Station operations, Vehicle capacity
The critical station of a bus rapid transit facility governs facility capacity. This study developed an improved model of a typical station with three linear offline loading areas and a passing lane, using a probabilistic approach to the modeling of loading area selection and blockage of buses. The principal operational parameter of the model was station effective utilization: the expected number of buses that are clearing or dwelling. Bus arrival rate, theoretical capacity, and degree of saturation were determined for each loading area. The critical loading area controls station operation. The model quantified station excess time to evaluate passenger quality of service. It quantified interior, upstream, and total utilizations to evaluate queuing. It quantified production elasticity as a function of dwell utilization (output) and excess utilization (input), which was important to understanding station capacity because small values reflect where little improvement is possible. The model was calibrated using data collected during twenty-four 6-min episodes across a range of effective utilization at a busy Brisbane station. The association between observed and model estimates of loading area selection probabilities was significant. Existing deterministic methodologies assume that all loading areas contribute to station capacity homogeneously, however, the results of this study highlighted the need to model each loading area discretely. Elasticities calculated for each episode were used to qualify operation from very efficient to congested. Congested operation must be considered case by case. The new model provided a far richer appreciation of station operation through model output parameters, including those that mark congestion.
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Bunker, J.M. (2022). Probabilistic Modeling of Bus Rapid Transit Station Loading Area Selection for Bus Capacity Estimation. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2676(7), pp. 711-725.