This dissertation presents a system-wide approach, based on genetic algorithms, for the optimization of transfer times for an entire bus transit system. Optimization of transfer times in a transit system is a complicated problem because of the large set of binary and discrete values involved. The combinatorial nature of the problem imposes a computational burden and makes it difficult to solve by classical mathematical programming methods.The genetic algorithm proposed in this research attempts to find an optimal solution for the transfer time optimization problem by searching for a combination of adjustments to the timetable for all the routes in the system. It makes use of existing scheduled timetables, ridership demand at all transfer locations, and takes into consideration the randomness of bus arrivals.Data from Broward County Transit are used to compute total transfer times. The proposed genetic algorithm-based approach proves to be capable of producing substantial time savings compared to the existing transfer times in a reasonable amount of time.The dissertation also addresses the issues related to spatial and temporal modeling, variability in bus arrival and departure times, walking time, as well as the integration of scheduling and ridership data.
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机译:本文提出了一种基于遗传算法的全系统方法,用于优化整个公交系统的换乘时间。由于涉及大量的二进制和离散值,因此在运输系统中优化传输时间是一个复杂的问题。该问题的组合性质增加了计算负担,并且难以用经典的数学编程方法解决。本研究中提出的遗传算法试图通过寻找对调整的组合来找到传输时间优化问题的最优解。系统中所有路线的时间表。它利用现有的时间表,所有换乘地点的载客量需求,并考虑公交车到站的随机性。来自Broward County Transit的数据用于计算总换乘时间。与现有的转乘时间相比,基于遗传算法的方法在合理的时间内能够节省大量时间。本文还解决了与时空建模,公交车到站和出发时间的可变性有关的问题。步行时间,以及日程安排和乘客数据的集成。
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