Optimization of monetary incentive in ridesharing systems

Abstract

Although ridesharing is a potential transport model for reducing fuel consumption, green-house gas emissions and improving efficiency, it is still not widely adopted due to the lack of providing monetary incentives for ridesharing participants. Most studies regarding ridesharing focus on travel distance reduction, cost savings and successful matching rate in ridesharing systems, which do not directly provide monetary incentives for the ridesharing participants. In this paper, we address this issue by proposing a performance index for ridesharing based on monetary incentives. We formulate a problem to optimize monetary incentives in ridesharing systems as non-linear integer programming problem. To cope with computational complexity, an evolutionary computation approach based on a variant of PSO is adopted to solve the non-linear integer programming problem for ridesharing systems based on cooperative coevolving particle swarms. The results confirm the effectiveness the proposed algorithm in solving the nonlinear constrained ridesharing optimization problem with binary decision variables and rational objective function.