考虑出行全过程的城市群多方式交通流分配模型与算法

doi:10.3976/j.issn.1002-4026.2022.01.012

摘要/Abstract

摘要：

为优化城市群旅客运输网络结构,提出了组合出行模式下城市群多方式交通流分配模型,研究网络均衡时城市群多方式旅客运输网络的流量分布状态。基于对城市群出行全过程的充分分析,利用超网络理论构建了城市群多方式交通超网络模型,将超网络的路段划分为上网路段、行驶路段、换乘路段和下网路段等4类,其中行驶路段包括城市交通网络上的行驶路段以及城际交通网络上的行驶路段。考虑影响旅客出行方式选择的多种因素,分别对各类路段的广义费用函数及超路径的广义费用函数进行定义。在此基础上,提出城市群多方式交通系统的随机平衡分配模型,并基于相继平均算法和有效超路径搜索算法设计了相应的求解算法。最后,通过一个数值算例对模型和算法进行说明和验证。

关键词: 城市群, 出行全过程, 多方式, 超网络, 广义费用, 有效超路径, 随机用户均衡

Abstract:

To optimize the structure of passenger transportation networks in an urban agglomeration, a multi-modal traffic flow assignment model under combined mode is proposed to study the flow distribution of multi-modal passenger transport networks in the urban agglomeration when the network achieves equilibrium. Based on the analysis of the entire travel process in an urban agglomeration, the super network theory is used to construct a super network model of multi-modal transportation in the urban agglomeration, and the links of the super network are divided into four types—boarding links, travel links, transfer links, and alighting links—among which travel links include travel links on urban transportation networks and travel links on intercity transportation networks. Considering the various factors that affect the choice of passenger travel mode, the generalized cost function of different links and hyperpaths are defined. Based on this, the stochastic equilibrium assignment model of a multi-modal transportation system in an urban agglomeration is proposed and the corresponding solution algorithm is designed based on the method of successive average algorithm as well as the search algorithm of the effective hyperpath. Finally, a numerical example is given to illustrate and verify the effectiveness of the model and algorithm.

Key words: urban agglomeration, entire travel process, multi-modal, super network, generalized cost, effective hyperpath, stochastic user equilibrium

中图分类号:

徐璞. 考虑出行全过程的城市群多方式交通流分配模型与算法[J]. 山东科学, 2022, 35(1): 88-98.

XU Pu. Multi-modal traffic assignment model and algorithm for an urban agglomeration considering entire travel process[J]. Shandong Science, 2022, 35(1): 88-98.

图/表 11

图1

图2

图3

表1

城际交通方式出行信息"

交通方式	路段	$t a 2 m$ ( $t a 2 0$ )/h	$C a 2 m$ /(人·h^-1)	$l a 2$ /km	$x a 2 m 0$ /veh
小汽车	24	1.0	1500	100	1000
城际铁路	25	0.5	1000	90	—
城际客车	26	1.5	1000	100	—

表1

表2

地铁出行信息"

路段	$t a 2 m$ /h	$C a 2 m$ /(人·h^-1)	$x a 2 m 0$ /(人·h^-1)	路段	$t a 2 m$ /h	$C a 2 m$ /(人·h^-1)	$x a 2 m 0$ /(人·h^-1)
16	0.067	1000	500	39	0.067	1000	500
17	0.050	1000	600	40	0.100	1000	500
18	0.050	1000	500	41	0.125	1000	600
19	0.067	1000	700	42	0.125	1000	600

表2

表3

公交车出行信息"

路段	$t a 2 m$ /h	$C a 2 m$ /(人·h^-1)	$x a 2 m 0$ /(人·h^-1)	路段	$t a 2 m$ /h	$C a 2 m$ /(人·h^-1)	$x a 2 m 0$ /(人·h^-1)
20	0.083	1000	300	43	0.125	1000	300
21	0.067	1000	400	44	0.125	1000	300
22	0.167	1000	300	45	0.125	1000	400
23	0.200	1000	300	46	0.167	1000	300

表3

表4

小汽车出行信息"

路段	$t a 2 0$ /h	$C a 2 m$ /(人·h^-1)	$l a 2$ /km	$x a 2 m 0$ /(人·h^-1)	路段	$t a 2 0$ /h	$C a 2 m$ /(人·h^-1)	$l a 2$ /km	$x a 2 m 0$ /(人·h^-1)
4	0.125	1000	5	500	27	0.100	800	3	800
5	0.125	800	5	800	28	0.125	1000	4	1000
6	0.100	700	4	700	29	0.100	800	3	400
7	0.100	1000	4	1000	30	0.067	1000	3	1000
8	0.200	800	4	800	31	0.167	700	5	700
9	0.083	1000	4	1000	32	0.125	800	5	800
10	0.125	800	4	800	33	0.167	1000	5	1000
11	0.083	700	4	500	34	0.125	1000	4	1000
12	0.067	1000	3	500	35	0.067	800	3	800
13	0.167	1000	4	600	36	0.167	800	5	800
14	0.083	700	5	400	37	0.167	800	5	800
15	0.167	800	5	500	38	0.125	1000	4	1000

表4

表5

换乘路段出行信息"

路段	$T a 3$ /h	$T a 3 wait$ /h	路段	$T a 3$ /h	$T a 3 wait$ /h	路段	$T a 3$ /h	$T a 3 wait$ /h
47	0	0	52	0.167	0.33	57	0.250	0.167
48	0.083	0.5	53	0.25	0.33	58	0.083	0
49	0.167	0.5	54	0	0	59	0.167	0.050
50	0.250	0.5	55	0.167	0	60	0.250	0.167
51	0.083	0.33	56	0.167	0.05

表5

表6

上/下网路段出行信息"

路段	$T a 1$ ( $T a 3 wait$ )/h	$T a 3 wait$ /h	路段	$T a 1$ ( $T a 3 wait$ )/h	$T a 3 wait$ /h
1	0.083	—	61	0.083	—
2	0.250	0.05	62	0.250	—
3	0.167	0.167	63	0.167	—

表6

图4

图5

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