# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 1 Introduction  
The urban transport system is an important feature of today's urban areas. The design of high-quality urban transport systems comprises a major issue for modern cities due to their development, it and affects both pollution and environmental matters. In the corresponding literature, two main types of urban transport systems are defined [1]: (i) public transportation networks and (ii) private transportation networks. Nowadays, people throughout the world recognize the importance of having efficient urban public transport systems [2]. As a matter of fact, many different transportation means are included in the public transportation system, for example, busses, metro, trams, etc. Efficient public transportation systems can reduce negative effects of private transportation networks [3]. The quality of bus services is mainly determined both by the minimization of waiting and in-vehicle travel times as well as the reduction of en route vehicle changes. Indeed, it is extremely difficult to have an ideal bus service, that is, a bus service which satisfies all passengers' needs while simultaneously maintaining all operator costs in an acceptable level [4]. Usually, throughout the world, it is the responsibility of bus companies to design efficient bus routes and schedules for an urban area. On the other hand, there are also some cases where the local authorities have the responsibility todetermine all bus routes and schedules [5]. Of course, local governments' main concern is to satisfy all needs of the travelling public, while bus companies focus on the maximization of their profit [6]. Above all is the satisfaction of the passengers; otherwise the use of the offered transportation services will be low. Another issue is that it is not easy for local authorities to sustain underutilized bus routes since they have to report to the local communities who usually provide the funding [7]. Furthermore, there are other constraints imposed by the bus operators which have to keep the operational cost as low as possible. These constraints include restrictions in the number of available buses and the number and lengths of operating bus routes [8]. From the opposing point of view, the local authorities are obliged to maintain an acceptable transportation service level, limit the negative effects of vehicles' operation (e.g., emissions) and make sure that all policies and regulations concerning local transport are met [9]. In the last decades, some interesting commercial software packages have been introduced in order to help local authorities to construct efficient public transportation networks. These interactive tools, which are mainly used for decision support and visualization, include, among others, Emme [10], SATURN [11] and VISUM [12]. Moreover, the scientific community has attempted to create a well-established scientific framework for solving the urban transit routing problem (UTRP) ([2, 13]). Furthermore, many efficient methods for solving variations of the UTRP have been designed by researchers who also made their input datasets available for experimentation and comparison ([14, 15, 16, 17]). Finally, in the last decades, scheduling and urban routing problems have attracted much attention of the respective scientific society, and many researchers have attempted to solve scheduling and urban routing problems by applying many different soft computing techniques. Some of these attempts are presented in short in the related work section (see Section 1.1). In this research paper, a cat swarm optimization (CSO)-based algorithm to cope with the UTRP is presented.