Performance evaluation in BRT systems: An analysis to predict the BRT systems planning. Issue 3 (September 2021)

• Record Type: Journal Article
• Authors: Dantas, Renata; Dantas, Jamilson; Melo, Carlos; Maciel, Paulo
• Is Part Of: Case studies on transport policy. Volume 9:Issue 3(2021)
• Page Start: 1141
• Page End: 1150
• Publication Date: 2021-09
• Subjects: BRT system -- Performance evaluation -- Stochastic model -- Petri nets; Transportation and state -- Case studies -- Periodicals; Transportation -- Planning -- Case studies -- Periodicals; Transportation -- Research -- Case studies -- Periodicals; 388.05
• DOI: 10.1016/j.cstp.2021.06.002
• Languages: English
• ISSNs: 2213-624X

Abstract

Highlights: Our work uses a methodology capable of evaluating a BRT system. We evaluated the probability that the user will miss the bus (Discard probability). The model contemplates metrics such as the Mean Queue Size and the Mean System Size. We show a parameterizable stochastic model to evaluate performance metrics based on BRT scenarios. Finally, we depicted a Case study in BRT system planning. Abstract: Mobility issues may create various inconveniences in urban areas due to traffic light delays, traffic jams and acidents. Alternatives have been created to improve public transportation. One of them is the Bus rapid transit system (BRT), delivering convincing results from a cost-effective perspective; i.e., lower deployment costs and a greater number of passengers. This paper proposes a Stochastic Petri Net (SPN) model for performance evaluation of the BRT system, focusing on the mean system size, mean queue size, mean queue time and the probability that the user will miss the bus (discard probability). Scenarios based on the BRT system were created, improving a set of solutions being with a variation in headways and in the number of vehicles on the route. The results show that, from a management perspective, it is up to the decision maker to define which is the most important metric at a given moment and, thus, to define the start intervals, as well as the number of vehicles to have on the route. Under the perspective in study, the best scenario is presented with headways of 300s and 5 vehicles in the route, operating as the one that leads to lower waiting times for passengers.