Modeling the Effectiveness of Infrastructure and Travel Demand Management Measures to Improve Traffic Congestion during Typhoons

台风 准备 运输工程 洪水(心理学) TRIPS体系结构 交通拥挤 自然灾害 计算机科学 应急管理 业务 运筹学 地理 工程类 气象学 经济 心理学 经济增长 管理 心理治疗师
作者
Wisinee Wisetjindawat,Sybil Derrible,Amirhassan Kermanshah
出处
期刊:Transportation Research Record [SAGE Publishing]
卷期号:2672 (1): 43-53 被引量:18
标识
DOI:10.1177/0361198118791909
摘要

Many commuters find themselves stranded during natural disasters like typhoons. In the Tokai region in Japan, many road sections become heavily congested during typhoons, with some commuters reporting homebound trips taking more than four times longer than usual because of road flooding at several locations. Although large typhoons are considered extreme events (in terms of magnitude), they occur frequently (i.e., several times per year), substantiating the need for better preparedness. Nonetheless, it is impossible to predict exactly which roads are going to be flooded during a typhoon. As a result, in this study, a stochastic modeling approach was used that assigns a failure probability to each road segment based on climate model outputs for the region. Using this stochastic model, the travel time reliability between any given origin–destination pair can be determined. By applying this model to the road network of the Tokai region, two major measures were identified that could be implemented to reduce severe congestion during a typhoon. First, targeted infrastructure management measures can be implemented to strengthen heavily used roads, thus reducing the failure probability of major roads. Second, travel demand management measures can be implemented, such as asking commuters to leave their workplace or school one or two hours after their normal departure time. Overall, it was found that strengthening heavily used roads has a bigger impact in relieving congestion than delaying departure time, but that combining both strategies achieves the best results.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
vvvv发布了新的文献求助10
1秒前
辞忧发布了新的文献求助200
1秒前
1秒前
小马甲的应助被刘加鑫采纳,获得10
1秒前
吨吨完成签到,获得积分10
1秒前
qiqi发布了新的文献求助10
2秒前
2秒前
搜集达人的应助被董帅铭采纳,获得10
3秒前
sjdove发布了新的文献求助10
3秒前
一二发布了新的文献求助10
3秒前
PeterBlue完成签到,获得积分10
3秒前
隋玉发布了新的文献求助10
3秒前
SuJudy发布了新的文献求助10
3秒前
3秒前
4秒前
5秒前
自然代亦发布了新的文献求助10
5秒前
珈998完成签到,获得积分10
5秒前
hu的应助被惊蛰采纳,获得10
6秒前
cdercder的应助被Redinn采纳,获得10
6秒前
俭朴南风发布了新的文献求助30
6秒前
black的hole完成签到,获得积分10
6秒前
Peachchips完成签到,获得积分10
7秒前
摩西完成签到 ,获得积分10
8秒前
8秒前
小二郎的应助被金色热浪采纳,获得10
8秒前
再睡五分钟完成签到,获得积分10
8秒前
无敌发布了新的文献求助10
8秒前
黄先生完成签到,获得积分20
8秒前
8秒前
8秒前
充电宝的应助被惊蛰采纳,获得10
9秒前
SuJudy完成签到,获得积分10
9秒前
9秒前
10秒前
小蘑菇的应助被终梦采纳,获得10
10秒前
10秒前
爱妍完成签到,获得积分20
11秒前
紫毛鼹鼠完成签到,获得积分10
11秒前
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Modern Artificial Intelligence 1000
Особенности технологий и методов информационно-психологических войн СССР с Великобританией и США в период 1939-1953 гг 1000
Das Matthäusevangelium: Kapitel 1:1–17 1000
IRTSD: Open-Source Data and Toolset for Electromagnetic Transient Analysis of Disturbances and IBR Control Malfunctions in Transmission Systems 1000
Challenging Attachment Theory 600
Analyses de reconnaissances de dettes passées devant les échevins d'Ypres (1249-1291) 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8138774
求助须知:如何正确求助?哪些是违规求助? 9539310
关于积分的说明 21634867
捐赠科研通 7722959
什么是DOI,文献DOI怎么找? 3420633
关于科研通互助平台的介绍 2545884
邀请新用户注册赠送积分活动 2462572