Modeling and stimulating labor supply of ride-sourcing drivers with heterogeneous behavioral characteristics
How to stimulate right number of drivers to serve customers at the right time is the core of operations for ride-sourcing platforms.Based on the time expanded network,consider-ing the labor supply heterogeneity of drivers with different behavioral characteristics,this paper assumes that drivers'utility consists of consumption utility and reference utility.A unified frame-work reconciling two behavioral theories is established to depict drivers'labor supply behaviors for working and corresponding equilibrium solution algorithm is proposed to analyze the schedule choices of drivers with different behavioral characteristics in an equilibrium market.Furthermore,two kinds of incentive modes,namely the commission-based one and the threshold-based one,are proposed,and corresponding models are developed to study the responses of drivers with different behavioral characteristics to different incentives.The optimal incentive strategies for the platform are explored.The results show that drivers driven by neoclassical behavior are more likely to work or supply more labor in response to an extra incentive than drivers driven by income-target behavior.Threshold-based incentive is more effective for full-time drivers who prefer to work long hours,while part-time drivers prefer to the commission-based incentive and threshold-based incentive with low threshold.This paper provides a theoretical reference and practical basis for platform to understand drivers'labor supply behaviors and further manage and stimulate labors.
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