Hybrid route generation heuristic algorithm for the design of transit networks

Abstract In this paper we present a Lisp-implemented route generation algorithm (RGA) for the design of transit networks. Along with an analysis procedure and an improvement algorithm, this algorithm constitutes one of the three major components of an AI-based hybrid solution approach to solving the transit network design problem. Such a hybrid approach incorporates the knowledge and expertise of transit network planners and implements efficient search techniques using AI tools, algorithmic procedures developed by others, and modules for tools implemented in conventional languages. RGA is a design algorithm that 1. (a) is heavily guided by the demand matrix, 2. (b) allows the designer's knowledge to be implemented so as to reduce the search space, and 3. (c) generates different sets of routes corresponding to different trade-offs among conflicting objectives (user and operator costs). We explain in detail the major components of RGA, illustrate it on data generated for the transit network of the city of Austin, TX, and report on the numerical experiments conducted to test the performance of RGA.

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