Taxi-Sharing Matching Equilibrium Under Peer-Passenger Incentive Mechanism

doi:10.12141/j.issn.1000-565X.230158

Abstract

Abstract:

In view of the problem that passengers motivate ride-hailing drivers in the form of red packet or dispatching fees to realize self-scheduling, this paper studied the interactive relationship between passenger-ride-hailing matching decision and incentive strategy choice. Based on the matching equilibrium in taxi-sharing, this paper designed a matching equilibrium model for taxi-sharing with peer-passenger incentive mechanism, taking the maximization of total passenger surplus as the goal and considering the constraints such as matching, equilibrium and cost. From the perspective of passengers, it designed the passenger incentive strategy, ride-hailing incentive strategy, passenger and ride-hailing incentive strategy, and the three incentive strategies were embedded into the column generation algorithm to solve the model and to achieve matching equilibrium and pricing equilibrium. By empirical analysis of Dalian taxi data, the results show that, compared with with only motivating drivers from the supply side in the form of random dispatching fees, the implementation of incentive strategies from the demand side and the supply side can promote taxi-share, and the passenger surplus can be increased by 12.6%. When the demand is larger than the supply, about 26% of incentive is transferred among peer passengers for more taxi-share matches. The fare discount rate also affects the flow of incentives. Using incentive strategies and discount strategies simultaneously can avoid malicious competition and ineffective incentives. By increasing the number of taxi-sharing trips, we can simultaneously reduce travel costs for passengers and boost driver incomes.

Key words: transportation planning and management, taxi-sharing, equilibrium matching, incentive-driven mechanism

CLC Number:

U121

PENG Zixuan, CUI Lin, GUO Zhiwei, et al. Taxi-Sharing Matching Equilibrium Under Peer-Passenger Incentive Mechanism[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(2): 95-103.

Figures/Tables 11

Fig.1

Table 1

Table 2

Table of variables and parameters"

类型	符号	描述
集合	$T$	司机集合
	$P$	乘客集合
	$S = s 1, s 2, ⋯, s i ⋯, s n$	出行方案集合
	$S t ⋃ t$	与司机 $t ∈ T$ 相关的出行方案集合
	$S p ⋃ p$	与乘客 $p ∈ P$ 相关的出行方案集合
参数	$μ$	一个乘客-网约车匹配解
	$μ *$	在匹配解 $μ$ 下乘客或司机参与的出行方案
	$s i$	参与出行方案 $s i$ 的乘客和司机总数
	$R t s i$	司机 $t$ 在出行方案 $s i$ 中可获得的收益
	$α$	司机每千米运营成本
	$l t i$	司机 $t$ 在出行方案 $s i$ 中的总行驶里程
	$C p s i$	乘客 $p$ 在出行方案 $s i$ 中的出行成本
	$γ$	乘客每小时时间成本
	$τ p d$	乘客 $p$ 到达时间
	$e p$	乘客 $p$ 最早出发时间
	$M p$	乘客 $p$ 最大可接受出行成本
	$L p$	乘客 $p$ 从起点到终点的最短道路距离
	$ε$	每千米运价
	$ψ$	合乘折扣系数
变量	$ω p t i$	乘客 $p$ 在出行方案 $s i$ 中提供给司机 $t$ 的激励额度
	$ω ¯ p p' i$	乘客 $p$ 在出行方案 $s i$ 中提供给合乘乘客 $p'$ 的激励额度
	$x i$	当出行方案 $s i$ 被选择时，等于1；否则等于0
	$v p s i$	乘客 $p$ 在出行方案 $s i$ 中的剩余

Table 2

Fig.2

Fig.3

Fig.4

Table 3

Fig.5

Fig.6

Table 4

Fig.7

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排序	司机		乘客1		乘客2
排序	出行方案	利润/元	出行方案	成本/元	出行方案	成本/元
1	方案1	14	方案1	21	方案3	9
2	方案3	13	方案3	22	方案2	12
3	方案2	6

激励机制	乘客匹配率/%	乘客总剩余/元	司机平均获得激励/元	司机平均收入/元
自动激励机制	72	3 300	1.7	28
随机调度费机制	65	2 930	2.8	24

算例	计算时间/s		计算时间/s
100-200	16	400-400	40
200-100	11	400-800	43
200-200	18	800-400	39
200-400	35	800-800	68
400-200	28