Study on the Multi-Period Allocation Method of Vehicle Resource in Hybrid Service Mode

doi:10.12141/j.issn.1000-565X.230182

Abstract

Abstract:

Ride-sharing is one of the typical models in the context of the sharing economy. With the advantages of reducing travel costs and carbon emissions, it has occupied an important share in the urban travel market. Vehicle resource allocation is the core link in optimizing carpooling services. To realize the overall management of travel modes, this paper comprehensively considered the influence of multiple travel modes and passengers’ selection behavior on the allocation scheme. Focusing on the supply and demand matching of the mixed travel mode based on ride-sharing travel, the internal correlation between the passenger travel mode selection behavior and the allocation of vehicle resources between regions was analyzed. A passenger-to-driver matching function was introduced to describe the relationship between vehicle supply and passenger demands. Then the passengers’ travel selection behavior was simulated by the Logit model, and an inter-regional resource allocation optimization model was constructed. In solving the optimization model, the allocation problem was divided into two stages: passenger travel mode allocation and vehicle supply allocation. The Lagrangian relaxation and the gradient descent algorithms were integrated to solve the decision-making problems involved in each stage. The solutions of the two stages were updated based on the feedbacks from passenger selection preference and vehicle allocation, so as to establish the regional multi-period traffic resource allocation algorithm. Finally, the resource allocation algorithm was tested by random generation of examples on a road network with 100 sub-regions. Results show that the method proposed in this paper can effectively balance the mobility allocation among regions with mixed travel modes. As the number of ride-sharing vehicles decreased, the decreasing proportion of total revenue increased from 7.32 % to 25.37 %. Compared with the vehicle allocation method based on mileage, the vehicle allocation algorithm proposed in this paper helps to improve the total revenue.

Key words: urban traffic, ride-sharing, resource allocation algorithm, multi-period

CLC Number:

SHEN Yutong, LI Ming, CUI Zhiyong, et al. Study on the Multi-Period Allocation Method of Vehicle Resource in Hybrid Service Mode[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 89-98.

Figures/Tables 15

Table 1

Parameters and variables used in the model"

符号	含义
m	参数上标， $m ∈ t x, r s$ ，表示出行模式，tx代表独乘，rs代表合乘模式
i，j	参数下标， $i, j ∈ Ω$ ，区域编号
R	区域集合
$Ω$	子交通网络集合
$γ 1, γ 2, γ 3$	匹配率相对于未匹配车辆数、等待乘客数及路网平均速度的弹性系数
$α m$	选择出行模式m的乘客的时间价值系数
$β m$	出行模式m下单位行驶里程的出行费用
$d i j m$	决策变量，平台预估区域i至区域j出行模式m的等待服务乘客数量
$D i j m a x$	从区域i至区域j内的等待响应乘客数量的最大值
$D i j$	从区域i至区域j内的等待服务乘客数量
$D i$	区域i内等待服务乘客数量
$c i j m$	决策变量，出行模式m下从区域i至区域j调派的车辆数
$C i m$	出行模式m下区域i内的平台在线车辆数量
$z i j m$	系统能够服务的从区域i至区域j选择出行模式m的乘客数量
$l i j$	区域i至区域j独乘乘客的平均行驶里程
$θ i j m$	出行模式m下从区域i至区域j的单位行驶里程运行成本
$u i j m$	区域i至区域j选择出行模式m的效用值
$v i j$	区域i和区域j路网的车辆平均运行速度
$t i j m$	出行模式m下从区域i至区域j乘客的平均出行时间
$w i j m$	出行模式m下从区域i至区域j乘客的平均等待时间
$r i j r s$	合乘出行模式下从区域i至区域j乘客的平均绕行时间
$B i j m$	出行模式m下从区域i至区域j乘客的匹配率（%）

Table 1

Fig.1

Fig.2

Table 2

Values of the parameters in the passenger-to-driver matching function"

参数	取值
匹配函数中匹配率A	0.000 05
匹配函数中车辆数量权值 $γ 1$	0.9
匹配函数中乘客数量权值 $γ 2$	0.75
匹配函数中车辆行驶速度权值 $γ 3$	0.9
独乘时间价值系数 $α 1$	-6.867 1
合乘车辆时间价值系数 $α 2$	-6.867 1
独乘旅程费用系数 $β 1$	-0.279 3
合乘车辆旅程费用系数 $β 2$	-0.195 5

Table 2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 3

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

References 16

1	KRUEGER R， RASHIDI T H， ROSE J M ．Preferences for shared autonomous vehicles［J］．Transportation Research Part C：Emerging Technologies，2016，69：343-355.
2	MA J， LI X， ZHOU F，et al ．Designing optimal autonomous vehicle sharing and reservation systems：a linear programming approach［J］．Transportation Research Part C：Emerging Technologies，2017，84：124-141.
3	LEVIN M W ．Congestion-aware system optimal route choice for shared autonomous vehicles［J］．Transportation Research Part C：Emerging Technologies，2017，82：229-247
4	BONGIOVANNI C， KASPI M， GEROLIMINIS N ．The electric autonomous dial-a-ride problem［J］．Transportation Research Part B：Methodological，2019，122：436-456
5	MAHMOUDI M， ZHOU X ．Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows：a dynamic programming approach based on state-space-time network representations ［J］．Transportation Research Part B：Methodological，2016，89：19-42
6	MASOUD N， JAYAKRISHNAN R ．A decomposition algorithm to solve the multi-hop Peer-to-Peer ride-matching problem［J］．Transportation Research Part B：Methodological，2017，99：1-29.
7	丁冉．出租车动态合乘匹配问题研究［D］．南京：东南大学，2015.
8	肖强，何瑞春，张薇，等．基于模糊聚类和识别的出租车合乘算法研究［J］．交通运输系统工程与信息，2014，14（5）：119-125.
	XIAO Qiang， HE Ruichun， ZHANG Wei，et al ．Algorithm research of taxi carpooling based on fuzzy clustering and fuzzy recognition［J］．Journal of Transportation Systems Engineering and Information Technology，2014，14（5）：119-125.
9	HAME L， HAKULA H ．A maximum cluster algorithm for checking the feasibility of dial-a-ride instances［J］．Transportation Science，2015，49（2）：295-310.
10	郑建国，李园园．基于改进差分进化算法的出租车合乘问题研究［J］．交通运输系统工程与信息，2018，18（1）：121-126.
	ZHENG Jianguo， LI Yuanyuan ．Shared taxi problem based on the improved differential evolution algorithm［J］．Journal of Transportation Systems Engineering and Information Technology，2018，18（1）：121-126.
11	郭羽含，伊鹏．车辆合乘问题的分布式复合变邻域搜索算法［J］．计算机科学与探索，2019，13（2）：330-341.
	GUO Yuhan， YI Peng ．Distributed hybrid variable neighborhood search algorithm for carpooling problem［J］．Journal of Frontiers of Computer Science & Technology，2019，13（2）：330-341.
12	YANG H， LEUNG C W Y， WONG S C，et al ．Equilibria of bilateral taxi-customer searching and meeting on networks［J］．Transportation Research Part B：Methodological，2010，44（8/9）：1067-1083．
13	RAMEZANI M， NOURINEJAD M ．Dynamic modeling and control of taxi services in large-scale urban networks：A macroscopic approach［J］．Transportation Research Part C：Emerging Technologies，2018，94：203-219.
14	CAO Z， CEDER A ．Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic［J］．Transportation Research Part C：Emerging Technologies，2019，102：370-395.
15	ABE R ．Introducing autonomous buses and taxis：quantifying the potential benefits in Japanese transportation systems［J］．Transportation Research Part A：Policy and Practice，2019，126：94-113.
16	FISHER M L ．An applications oriented guide to Lagrangian relaxation［J］．Interfaces，1985，15（2）：10-21.

车辆数量变化量	独乘车辆		合乘车辆
车辆数量变化量	收益值/元	变化百分比/%	收益值/元	变化百分比/%
0	18 188.72		18 188.72
-50	17 652.51	-2.90	16 857.65	-7.32
-100	17 317.51	-4.78	15 675.89	-13.82
-150	17 132.98	-5.80	14 711.37	-19.12
-200	17 058.79	-6.21	14 013.83	-22.95
-250	17 036.16	-6.34	13 574.83	-25.37

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