Taxi group

Optimum allocation and incentives for the problems of taxi group travel

Develop the modelling frame to develop the operative policy and to develop TGR incentive schemes. Creates the TGR algorithms that solve the TGR problems in an efficient and effective way by taking full advantages of the IGRG architecture. Conducting computational experimentation with actual taxi driving information from three different towns to understand the efficacy of TGR, the effects of ridership reduction and the importance of drivers' subsidies.

Taxicab Group Route (TGR) is a favourite case of Taxi Route Sharing, in which journeys to and from close starting and finishing points and similar times of departures are combined into a common journey. First we examine the optimum allocation of a series of travelers to help us optimize the overall mileage savings. Secondly, we analyse the different behaviour of travellers and riders in taxi group journeys and examine the best incentive for TGR to maximise efficiencies at optimum occupancy.

Optimum mapping is expressed as an integral linearity coding issue and is further transformed into an equal graphics issue. Whilst the NP issue is tough, effective algorithmic approaches are required for actual online implementation. Developing an accurate TGR solution and a helical TGR solution, we compared the results with a voracious limited fault solution.

Mathematical experimentation indicates that the algorithms are able to solve TGR problems in an efficient way and with good resolution qualities. In order to investigate the best incentive for collective taxi journeys, extensive computational experimentation with taxi dates from New York City (USA), Wuhan (China) and Shenzhen (China) will be used. According to our results, more than 47% of the taxi kilometres can be reduced if the right incentive is created and the passenger experience is optimal.

Taxi group

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