An AI‐based approach for transit route system planning and design

TLDR

Past approaches to transit network design have been categorized as optimization formulations, heuristic methods, or ad hoc guidelines based on planners’ experience. The study proposes an AI‑based hybrid solution to the transit network design problem, motivated by the complexity of the task and the limitations of existing methods, to better capture planners’ expertise. The approach combines AI search techniques with planners’ knowledge through a Lisp‑implemented route generation algorithm, the TRUST analysis procedure, and a route improvement algorithm, integrating modules written in conventional languages. An illustrative example demonstrates the application of the proposed method.

Abstract

Abstract We present an AI‐based solution approach to the transit network design problem (TNDP). Past approaches fall into three categories: optimization formulations of idealized situations, heuristic approaches, or practical guidelines and ad hoc procedures reflecting the professional judgement and practical experience of transit planners. We discuss the sources of complexity of the TNDP as well as the shortcomings of the previous approaches. This discussion motivates the need for AI search techniques that implement the existing designer's knowledge and expertise to achieve better solutions efficiently. Then we propose a hybrid solution approach that 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. The three major components of the solution approach are presented, namely, the lisp‐implemented route generation design algorithm (RGA), the analysis procedure TRUST (Transit Route Analyst), and the route improvement algorithm (RIA). An example illustration is included.