Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment
Flooding can severely disrupt transportation systems. When safety measures are limited to road closures, vehicles affected by the flooding have an origin, destination, or path segment that is closed or soon-to-be flooded during the trip's duration. This thesis introduces a framework to provide...
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ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-799112020-09-29T05:39:43Z Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment Hannoun, Gaby Joe Civil and Environmental Engineering Heaslip, Kevin Patrick Murray-Tuite, Pamela Marie Hancock, Kathleen L. flooding routing assistance connected vehicles in-vehicle navigation systems Flooding can severely disrupt transportation systems. When safety measures are limited to road closures, vehicles affected by the flooding have an origin, destination, or path segment that is closed or soon-to-be flooded during the trip's duration. This thesis introduces a framework to provide routing assistance and trip cancellation recommendations to affected vehicles. The framework relies on the connected vehicle environment for real-time link performance measures and flood data and evaluates the trip of the vehicle to determine whether it is affected by the flood or not. If the vehicle is affected and can still leave its origin, the framework generates the corresponding routing assistance in the form of hyperpath(s) or set of alternative paths. On the other hand, a vehicle with a closed origin receives a warning to wait at origin, while a vehicle with an affected destination is assigned to a new safe one. This framework is tested on two transportation networks. The evaluation of the framework's scalability to different network sizes and the sensitivity of the results to various flood characteristics, policy-related variables and other dependencies are performed using simulated vehicle data and hypothetical flood scenarios. The computation times depends on the network size and flood depth but have generally an average of 1.47 seconds for the largest tested network and deepest tested flood. The framework has the potential to alleviate the impacts and inconveniences associated with flooding. Master of Science 2017-11-02T08:00:24Z 2017-11-02T08:00:24Z 2017-11-01 Thesis vt_gsexam:13253 http://hdl.handle.net/10919/79911 In Copyright http://rightsstatements.org/vocab/InC/1.0/ ETD application/pdf Virginia Tech |
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flooding routing assistance connected vehicles in-vehicle navigation systems |
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flooding routing assistance connected vehicles in-vehicle navigation systems Hannoun, Gaby Joe Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment |
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Flooding can severely disrupt transportation systems. When safety measures are limited to road closures, vehicles affected by the flooding have an origin, destination, or path segment that is closed or soon-to-be flooded during the trip's duration. This thesis introduces a framework to provide routing assistance and trip cancellation recommendations to affected vehicles. The framework relies on the connected vehicle environment for real-time link performance measures and flood data and evaluates the trip of the vehicle to determine whether it is affected by the flood or not. If the vehicle is affected and can still leave its origin, the framework generates the corresponding routing assistance in the form of hyperpath(s) or set of alternative paths. On the other hand, a vehicle with a closed origin receives a warning to wait at origin, while a vehicle with an affected destination is assigned to a new safe one. This framework is tested on two transportation networks. The evaluation of the framework's scalability to different network sizes and the sensitivity of the results to various flood characteristics, policy-related variables and other dependencies are performed using simulated vehicle data and hypothetical flood scenarios. The computation times depends on the network size and flood depth but have generally an average of 1.47 seconds for the largest tested network and deepest tested flood. The framework has the potential to alleviate the impacts and inconveniences associated with flooding. === Master of Science |
author2 |
Civil and Environmental Engineering |
author_facet |
Civil and Environmental Engineering Hannoun, Gaby Joe |
author |
Hannoun, Gaby Joe |
author_sort |
Hannoun, Gaby Joe |
title |
Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment |
title_short |
Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment |
title_full |
Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment |
title_fullStr |
Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment |
title_full_unstemmed |
Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment |
title_sort |
framework for better routing assistance for road users exposed to flooding in a connected vehicle environment |
publisher |
Virginia Tech |
publishDate |
2017 |
url |
http://hdl.handle.net/10919/79911 |
work_keys_str_mv |
AT hannoungabyjoe frameworkforbetterroutingassistanceforroadusersexposedtofloodinginaconnectedvehicleenvironment |
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1719345057009500160 |