Abstract
The paper presents a simplified simulation model of the operation of a taxi system. The model retains the main features of a real taxi transportation system and despite its simplicity examines the system behavior in different conditions. It was shown that for every request generation rate a critical number of taxis in disposal could be determined. If the real number of taxis is lower than the critical number, the queue of pending requests grows in an unlimited way. On the other hand, if the real number of taxis is significantly higher, the service level is clearly not better and leads to the waste of taxi drivers’ time and fuel. The presented model can be regarded as a queue system; therefore, the well-known queue theory is used to describe its nature. However, this approach has some practical limitations coming from incomplete knowledge on real transportation demands, which additionally undergo significant fluctuations. A method, which optimizes the assignment of vacant taxis to the pending requests was also introduced. It was proven that this method mitigated the influence of the above-mentioned limitations.
Highlights
Nowadays most of population lives in cities of medium or large size
To enhance the efficiency of taxi transportation and diminish the probability of congestion formation at rush hour, the optimization of taxi routes for a shared taxi travel was proposed [13]. Such taxis can take more unrelated passengers instead of just one. These analyses considered the expectations of taxi drivers and passengers
The results showed that the number of taxis in disposal is most important from the point of view of a company, attraction of the potential demand depends mainly on passenger waiting time and prices
Summary
Nowadays most of population lives in cities of medium or large size. mobility needs are growing constantly, but the resources are limited. Salanova et al made a review of different approaches to theoretical modeling of taxi systems and discussed some aspects of taxi system performance, including demand for trips, service supply, vacant distance, costs and access or waiting time [12]. A mathematical model of travel sharing which enabled a capability of dynamic determination of an optimal route was proposed Another area being willingly investigated is the issue of individual decisions made by taxi drivers. The use of a device called Key Performance Indicator connected to a taximeter was proposed [17] It would allow for collection of real data on taxi routes, what could be a base for a theoretical model validation or just for real-time optimization of taxi movement in a city area. In the last section a short summary and conclusions are provided
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