A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana
In the face of severe disasters, some or all of the endangered residents must be evacuated to a safe place. A portion of people, due to various reasons (e.g., no available vehicle, too old to drive), will need to take public transit buses to be evacuated. However, to optimize the operation efficienc...
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ndltd-LSU-oai-etd.lsu.edu-etd-03192009-1542392013-01-07T22:52:01Z A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana Qin, Xiaojun Geography & Anthropology In the face of severe disasters, some or all of the endangered residents must be evacuated to a safe place. A portion of people, due to various reasons (e.g., no available vehicle, too old to drive), will need to take public transit buses to be evacuated. However, to optimize the operation efficiency, the location of these transit pick-up stops and the allocation of the available buses to these stops should be considered seriously by the decision-makers. In the case of a large number of alternative bus stops, it is sometimes impractical to use the exhaustive (brute-force) search to solve this kind of optimization problem because the enumeration and comparison of the effectiveness of a huge number of alternative combinations would take too much model running time. A genetic algorithm (GA) is an efficient and robust method to solve the location/allocation problem. This thesis utilizes GA to discover accurately and efficiently the optimal combination of locations of the transit bus stop for a regional evacuation of the New Orleans metropolitan area, Louisiana. When considering peoples demand for transit buses in the face of disaster evacuation, this research assumes that residents of high social vulnerability should be evacuated with high priority and those with low social vulnerability can be put into low priority. Factor analysis, specifically principal components analysis, was used to identify the social vulnerability from multiple variables input over the study area. The social vulnerability was at the census block group level and the overall social vulnerability index was used to weight the travel time between the centroid of each census block to the nearest transit pick-up location. The simulation results revealed that the pick-up locations obtained from this study can greatly improve the efficiency over the ones currently used by the New Orleans government. The new solution led to a 26,397.6 (total weighted travel time for the entire system measured in hours) fitness value, which is much better than the fitness value 62,736.3 rendered from the currently used evacuation solution. DeWitt Braud Robert Rohli Nina Lam LSU 2009-03-24 text application/pdf http://etd.lsu.edu/docs/available/etd-03192009-154239/ http://etd.lsu.edu/docs/available/etd-03192009-154239/ en unrestricted I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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Geography & Anthropology Qin, Xiaojun A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana |
description |
In the face of severe disasters, some or all of the endangered residents must be evacuated to a safe place. A portion of people, due to various reasons (e.g., no available vehicle, too old to drive), will need to take public transit buses to be evacuated. However, to optimize the operation efficiency, the location of these transit pick-up stops and the allocation of the available buses to these stops should be considered seriously by the decision-makers. In the case of a large number of alternative bus stops, it is sometimes impractical to use the exhaustive (brute-force) search to solve this kind of optimization problem because the enumeration and comparison of the effectiveness of a huge number of alternative combinations would take too much model running time.
A genetic algorithm (GA) is an efficient and robust method to solve the location/allocation problem. This thesis utilizes GA to discover accurately and efficiently the optimal combination of locations of the transit bus stop for a regional evacuation of the New Orleans metropolitan area, Louisiana.
When considering peoples demand for transit buses in the face of disaster evacuation, this research assumes that residents of high social vulnerability should be evacuated with high priority and those with low social vulnerability can be put into low priority. Factor analysis, specifically principal components analysis, was used to identify the social vulnerability from multiple variables input over the study area. The social vulnerability was at the census block group level and the overall social vulnerability index was used to weight the travel time between the centroid of each census block to the nearest transit pick-up location.
The simulation results revealed that the pick-up locations obtained from this study can greatly improve the efficiency over the ones currently used by the New Orleans government. The new solution led to a 26,397.6 (total weighted travel time for the entire system measured in hours) fitness value, which is much better than the fitness value 62,736.3 rendered from the currently used evacuation solution.
|
author2 |
DeWitt Braud |
author_facet |
DeWitt Braud Qin, Xiaojun |
author |
Qin, Xiaojun |
author_sort |
Qin, Xiaojun |
title |
A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana |
title_short |
A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana |
title_full |
A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana |
title_fullStr |
A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana |
title_full_unstemmed |
A Social Vulnerability-Based Genetic Algorithm to Locate-Allocate Transit Bus Stops for Disaster Evacuation in New Orleans, Louisiana |
title_sort |
social vulnerability-based genetic algorithm to locate-allocate transit bus stops for disaster evacuation in new orleans, louisiana |
publisher |
LSU |
publishDate |
2009 |
url |
http://etd.lsu.edu/docs/available/etd-03192009-154239/ |
work_keys_str_mv |
AT qinxiaojun asocialvulnerabilitybasedgeneticalgorithmtolocateallocatetransitbusstopsfordisasterevacuationinneworleanslouisiana AT qinxiaojun socialvulnerabilitybasedgeneticalgorithmtolocateallocatetransitbusstopsfordisasterevacuationinneworleanslouisiana |
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