Railway and metro transport systems (RS) are becoming one of the popular choices of transportation among people, especially those who live in urban cities. Urbanization and increasing population due to rapid development of economy in many cities are leading to a bigger demand for urban rail transit. Despite being a popular variant of Traveling Salesman Problem (TSP), it appears that the universal formula or techniques to solve the problem are yet to be found. This paper aims to develop an optimization algorithm for optimum route selection to multiple destinations in RS before returning to the starting point. Bee foraging behaviour is examined to generate a reliable algorithm in railway TSP. The algorithm is then verified by comparing the results with the exact solutions in 10 test cases, and a numerical case study is designed to demonstrate the application with large size sample. It is tested to be efficient and effective in railway route planning as the tour can be completed within a certain period of time by using minimal resources. The findings further support the reliability of the algorithm and capability to solve the problems with different complexity. This algorithm can be used as a method to assist business practitioners making better decision in route planning.

The travelling salesman problem in transit planning is an extremely complex and non-deterministic polynomial problem. Many optimisation algorithms have been tested but failed to facilitate passengers getting to multiple stations by way of a complex rail transit network. Although various algorithms based on social insect species have successfully solved complex problems, bee behaviour in nature has inspired more significant solutions. The study reported in this paper aimed to identify the most efficient algorithm and develop a mathematical model based on artificial bee colony optimisation to solve this problem in light rail transit systems. For validation, the developed model was contrasted with empirical routes generated by human experience. The model was able to generate the optimal route in all cases without any error or miscalculation and could thus be used in any rail transit network in the world to determine the optimal route for multiple stations.

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