Optimization of Taxi Allocation for Minimizing CO2 Emissions Based on Heuristics Algorithms

Recently, the rapid climate change caused by increasing CO2 emissions has become a global concern. Efficient transportation systems are necessary to reduce in cities. Taxi services an essential part of system, both urban areas with high demand and rural inadequate public transportation. Inefficient taxi cause problems such as increased idle times, resulting emissions. This study proposes allocation model that minimizes time costs for efficient service operation. We also propose three heuristic algorithms solve proposed model. At last, we conduct case using real data Nagaoka, Japan. By comparing algorithms, dynamic greedy algorithm produced best result terms cost CPU time. The findings indicate minimizing reducing number taxis, it is possible achieve significant 81.84% reduction within sector. Further, order estimate sensitivity considered.