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The emergence of artificial intelligence (AI)-based optimization heuristics like genetic and ant algorithms is useful in solving many complex transportation location optimization problems. The suitability of such algorithms depends on the nature of the problem to be solved. This study examines the suitability of genetic and ant algorithms in two distinct and complex transportation problems: (1) highway alignment optimization and (2) rail transit station location optimization. A comparative study of the two algorithms is presented in terms of the quality of results. In addition, Ant algorithms (AAs) have been modified to search in a global space for both problems, a significant departure from traditional AA application in local search problems. It is observed that for the two optimization problems both algorithms give almost similar solutions. However, the ant algorithm has the inherent limitation of being effective only in discrete search problems. When applied to continuous search spaces ant algorithm requires the space to be sufficiently discretized. On the other hand, genetic algorithms can be applied to both discrete and continues spaces with reasonable confidence. The application of AA in global search seems promising and opens up the possibility of its application in other complex optimization problems.

A public transportation system is a viable alternative to reducing traffic congestion and environmental pollution in urban areas. A metro, subway, or light rail system may be a viable commuting alternative connected with a coordinated service of feeder buses in urban and suburban neighborhoods. The decision to build a rail transit system is largely driven by available land and feasible sites for tracks and stations. Factors like ridership and public perception are considered in identifying suitable rail corridor and station locations. A two-stage analytical model is developed for identifying feasible rail transit station sites based on the real geographical and demographic data. The model uses a genetic algorithm (GA) for optimally locating the stations and works in parallel with a geographical information system (GIS). The model is applied in an example by using real GIS data, road network, and demographic information. The potential station sites are identified in the first stage, and the optimization using the GA is performed in the second stage by minimizing the total cost of locating the stations.