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Minimum taxi fleet algorithm considering human spatiotemporal behaviors.

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Summary
This summary is machine-generated.

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.

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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.