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Minimum taxi fleet algorithm considering human spatiotemporal behaviors
Zhi-Dan Zhao1,2, Yu Wang1,2, Wei-Peng Nie3,4,5
1Complexity Computation Lab, Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China.
Chaos (Woodbury, N.Y.)
|July 1, 2023
Summary
This study introduces the Spatial-Temporal Hopcroft-Karp (STHK) algorithm for urban travel planning. The algorithm optimizes fleet size and reduces travel time and distance, leading to lower emissions.
Area of Science:
- Urban planning
- Transportation science
- Computer science
Background:
- Growing availability of travel data aids user behavior studies.
- User travel planning is crucial for theoretical and practical applications.
Purpose of the Study:
- To develop an efficient urban travel scheduling solution.
- To minimize fleet size while considering travel time and distance.
Main Methods:
- Proposed the Spatial-Temporal Hopcroft-Karp (STHK) algorithm.
- Developed a travel scheduling solution considering time and space costs.
Main Results:
- STHK algorithm reduced fleet off-load time by 81% and off-load distance by 58%.
- Algorithm retains heterogeneous characteristics of human travel behavior.
- Optimized fleet size meets urban travel demands.
Conclusions:
- The STHK algorithm effectively reduces travel time, distance, energy consumption, and carbon emissions.
- Travel planning results align with human travel characteristics.
- The algorithm holds significant theoretical and practical value for urban mobility.
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