Prospect and evolution of the demand-responsive transport: price, subsidies, and market dynamics

Chuan Zhao, Yutong Yin, Xin Shen and Kun Wang

Transportation Research Part A: Policy and Practice, 2026, vol. 209, issue C

Abstract: Demand-responsive transport (DRT) is increasingly being implemented as an alternative to private transport and a complement to traditional public transport (TPT), aiming to bridge service gaps and alleviate urban congestion. However, policy uncertainty undermines the prospects of DRT, leading to a preference among riders for TPT or private transportation and causing transport providers to hesitate in supporting this initiative. Building on the complex interactions among the riders, transport providers, and the local government, this study employs a prospect-evolutionary game with stochastic disturbances to explore the price, the government subsidies, and the market dynamics of the DRT promotion, aiming to find the optimal strategy combinations and evolution paths. The research finds that: (1) DRT tends to be more effective in low-density areas, while both excessive and insufficient demand density may hinder the government’s infrastructure development for DRT; (2) fare subsidy policy can exhibit a better effect in promoting DRT when the market share is relatively high, whereas infrastructure subsidy policy can suppress excessive fares and penalties for DRT; (3) the increase in service-conscious riders prompts local government to accelerate fare subsidy, encouraging transport providers to adopt DRT as a complement to TPT; (4) the government should implement fare subsidies for DRT in low-density areas and infrastructure subsidies for TPT in high-density areas to mitigate the impacts of market stochastic disturbances.

Keywords: Demand-responsive transport; Public transport policy; Prospect-evolutionary game; Stochastic disturbance risk (search for similar items in EconPapers)
JEL-codes: C73 E61 E71 L91 R41 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.tra.2026.105016

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