Development of BRT Operation System with Frequency Stability
Date Issued
2015
Date
2015
Author(s)
Lou, Shiuan-Yu
Abstract
BRT possesses the benefits of bus and MRT. It costs less, requires less time to construct, has more capacity to alleviate traffic congestion in a short period, and can be used as a foundation for MRT system in the future. So far BRT has become an important solution for solving traffic problems in cities around the world. Therefore, Taichung City Government developed BRT to improve traffic conditions in Taichung. The goal of this study is to boost citizen’s willingness to take BRT, assist in its operation, and to reduce the negative effects BRT has on the transportation network in Taichung by developing strategies to maintain the stability of frequency of BRT to increase its reliability. The study targets at two performance indexes, punctuality and headway, to plan strategies for maintenance and improvement to deal with the delay and bus bunching that may occur, and to lower the possibility of stopping at the traffic lights and the frequency of using priority signals. The study makes use of two strategies, which are Schedule-Based Control and Headway-Based Control. Schedule-Based Control comprises “Normal Schedule-Based Model” and “Driver Characteristics Prediction Schedule-Based Model”. Headway-Based Control includes “Driver Characteristics Prediction Model” and “Green Light Extension Model”. The two models in this strategy can be used in combination so there are four scenarios, which are no driver characteristics prediction nor green light extension (NN), green light extension only (NG), driver characteristics prediction only (AN), and driver characteristics prediction with green light extension (AG). The sequence of implementing of strategy is: Implement Schedule-Based Control when a delay occurs. If the BRT’s arrival time deviates from the timetable (i.e. an “error” occurs) after implementing Schedule-Based Control, we define this situation as failure of schedule maintain. We will then implement Headway-Based Control to maintain a certain headway between two buses. The predictive algorithm employed in these two strategies is “Artificial Neural Network”, whose data input consists of delay and expected travel speed. BRT conducts manipulation according to the speed calculated by Artificial Neural Network. Since BRT has not yet been officially operated, the data required for building Artificial Neural Network need to be gathered using VISSIM, and the examination of the model’s performance is conducted in a mock environment. We chose A09 and A10 on BRT blue line to do the performance valuation. Schedule-Based Control can be compared according to MAPE figure. Driver Characteristics Prediction Model’s performance (0.0473) is superior than Normal Schedule-Based Model (0.0961). For Headway-Based Control Strategy, MAPE figure, presented from the smallest to the largest, are AG(0.0518), AN(0.0704), NG(0.1541), NN(0.1970). The possibility of stopping at the traffic light, ranked from the lowest to the highest, are AG(2/72), AN(5/72), NG(7/72), NN(15/72).
Subjects
Taichung City BRT
Bus travel time control
Artificial Neural Network
Headway
Schedule
Type
thesis
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