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In Orlando, Transit Signal Priority and Bus Rapid Transit systems were estimated to reduce travel times up to 26 percent for all vehicles and reduce delays up to 64 percent for buses.

Part of the planned future regional transit system in Central Florida, which will include commuter rail, Light Rail Transit (LRT), and Bus Rapid Transit (BRT).

Date Posted
07/31/2017
Identifier
2017-B01153
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Methodology

Microsimulation and statistical analysis were used to develop BRT models. Analysis was performed on the I-Drive test corridor to compare performance before and after the implementation of BRT with Conditional and Unconditional TSP. Eight scenarios were developed and modeled using the VISSIM model. These scenarios consisted of three main levels: No-TSP and No-BRT, TSP only, and BRT with TSP. For the scenarios with TSP, two different Conditional TSP scenarios were modeled: three minutes and five minutes behind (indicating that the TSP will only activate for buses that are either three or five minutes behind schedule).

The following data was collected for the model:

 

  • Google maps, engineering drawings, and field visits were utilized to obtain accurate geometric data for the corridor.
  • Pneumatic tube counters were used to collect traffic volumes throughout the corridor for two working days.
  • Signal control data and split histories for each of the traffic signals in the test corridor were provided by the City of Orlando Traffic Management Center (TMC).
  • Passenger data was provided by LYNX (the local government agency responsible for area transit service) and several field visits were made to confirm the bus travel time and delay. These data were reviewed to determine peak passenger volumes.


Based on these field data, a base VISSIM model was developed, calibrated, and validated. VISSIM was then used to evaluate the entire corridor before and after applying the TSP and BRT systems with respect to various performance measures, including total travel time (seconds), speed (feet per second or fps), total delay (seconds), and number of stops (per one-way trip).

Results

 

 

  • Simulation results showed that TSP and BRT scenarios were effective in reducing travel times (up to 26 percent for all vehicles) and delays (up to 64 percent for buses), as well as increasing the speed (up to 47 percent for all vehicles), compared to the base scenario.
  • The most effective scenarios were achieved by combining BRT and TSP. BRT with Conditional TSP three minutes behind was the most effective scenario in reducing travel times and delays, while having the least traffic impact on crossing streets in the I-Drive corridor.
  • Unconditional TSP scenarios produced significant crossing street delays, especially at high traffic intersections, indicating that these scenarios are impractical for implementation.

 

Goal Areas
Results Type
Deployment Locations