暴雨内涝影响下的城市道路交通拥挤特征识别

doi:10.18306/dlkxjz.2018.06.004

Abstract

Abstract:

In recent years, pluvial flash floods (PFF) have caused serious traffic congestion and disruption in many big cities. This study investigated the traffic congestion pattern due to PFFs in an intra-urban area through a macro traffic simulation method. A macro traffic flow model was built based on trip distribution and the characteristics of roads and routing. We incorporated rainfall data and simulated the hourly traffic volume on each road segment under two PFF scenarios of 50-year and 100-year return periods by the macro traffic flow model. Next, the variation of volume/capacity (V/C) on each road was calculated to derive the spatial pattern of traffic condition under different PFF scenarios. The results were contrasted to demonstrate the change of congestion pattern on the main roads and entrances/exits of the expressways in the city center of Shanghai to analyze the influence of different PFF scenarios on traffic congestion. The results indicate that ①PFFs of 50-year return period may have a marginal effect on the traffic system, but PFFs of 100-year return period can pose great threat to the traffic system in the central urban area of Shanghai. Seven exits and entrances of the expressways are closed due to serious inundation and a large portion of the road network becomes more congested; ②the effect of PFFs on V/C also exhibits spatial disparity over the entire network. Overall, 13.35% of the roads become more congested under 100-year return period. Additionally, the most obvious change of congestion pattern is found on the first-class highways (23.31%), such as Dalian Road, Wuning Road, Zhoujiazui Road, and Changshou Road.

Key words: macro traffic simulation, pluvial flash flood, road network, volume/capacity, Shanghai

Wenyan HU, Mengya LI, Jun WANG, Qingyu HUANG. Urban traffic congestion caused by rainstorms and innudation[J].PROGRESS IN GEOGRAPHY, 2018, 37(6): 772-780.

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	快速路/ (m/s)	主干道路/(m/s)	一级道路/(m/s)	二级道路/(m/s)	支路/ (m/s)
道路限速	22.22	16.67	13.89	11.11	8.33
校正车速	10.27	7.71	6.42	5.14	3.85

	A	B	C	D	E	F	OUT
A	1.12	2.23	1.40	0.56	1.96	2.79	2.23
B	2.23	1.12	1.12	1.40	1.96	2.23	2.23
C	1.40	1.12	1.12	1.68	1.68	1.68	5.03
D	0.56	1.40	1.68	1.12	1.68	1.68	0.56
E	1.96	1.96	1.68	1.68	1.12	2.79	5.03
F	2.79	2.23	1.68	1.68	2.79	1.12	4.47
OUT	2.23	2.23	5.03	0.56	4.47	4.47	1.12

	行驶路程/m	行驶时间/s	行驶速度/(m/s)
高德地图_平均值	8236.17	1255.77	6.56
模拟_平均值	9021.22	1187.65	7.60

时间	验证路口模拟小时交通总量/pcu	验证路口实际小时交通总量/pcu
6:00	16369	11104
8:00	54570	56445
10:00	43660	43490
12:00	38197	37938
16:00	43660	43490
18:00	54570	56445
22:00	27282	27760

出入口代号	变拥堵的高架出入口		出入口代号	变顺畅的高架出入口
出入口代号	晴天饱和度	100年一遇暴雨饱和度	出入口代号	晴天饱和度	100年一遇暴雨饱和度
入口4	0.13	1.11	入口16	0.38	0.13
入口30	0.69	0.91	入口43	1.83	1.58
入口33	1.77	2.31	入口44	0.56	0.36
入口39	1.44	1.92	入口47	1.73	1.19
入口46	2.19	2.94	入口48	1.16	0.93
入口50	1.11	1.44	出口16	0.40	0.12
出口4	0.05	0.46	出口23	2.68	0.00
出口33	0.59	0.95	出口28	0.55	0.34
出口39	2.12	2.41	出口44	0.77	0.32
出口40	2.13	2.51	出口47	1.31	0.60
出口43	0.40	0.65	出口48	1.47	1.19

Urban traffic congestion caused by rainstorms and innudation

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