Minimum taxi fleet algorithm considering human spatiotemporal behaviors

With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers' attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft-Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81 % and 58 % and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

Automated summary

With the development of information technology, travel data has become more accessible for researchers to study travel behavior. In this study, we propose a travel scheduling solution that considers time and space costs, namely the Spatial-Temporal Hopcroft-Karp (STHK) algorithm, which significantly reduces off-load time and distance while retaining the characteristics of human travel behavior. Our research shows that the new planning algorithm provides the optimal fleet size to meet urban travel needs, reducing energy consumption and carbon dioxide emissions.