Analysis of flood evacuation mode and route choice behavior of households in a developing country

Name: Lim, Jr., Hector Ruiz

keyword: Evacuation

; Modeling

; Determinants

; Route choice

; Mode choice

; Flood

Abstract: Increasing frequency and severity of hazards that lead to devastating disaster impacts demand building substantial response capability. Evacuation is seen as one of the effective measures to avert disaster impacts. Planning and modeling of effective evacuation incorporate evacuation travel behavior. Evacuation modeling usually done in a sequential manner follows the classic four-step travel demand modeling. Essential decisions based from the classic four-step travel demand model include evacuation decision, departure time choice, destination choice, mode choice and route choice. Evacuation decision is the choice of households at risk to either leave their place and move to a safe place or stay at home. Departure time choice describes when the household actually leaves the area at risk. Destination choice describes where the households go when leaving the area at risk. Mode choice describes which mode of transport is preferred when leaving the area at risk. And route choice describes what route evacuees take when moving from area at risk to their chosen destination. All these decisions involve complex behavioral factors influencing each household of various characteristics and situations at the period of choosing (e.g. Simonovic and Ahmad, 2005). This study seeks to investigate the mode and route choice of evacuees as a continuation of the study made by M.B Lim (2016). Understanding the mode and the route choices of evacuees use when evacuating has implications on the traffic flow in road network available during evacuation. This however, are affected by certain factors such as household socio-demographic, travel related decisions and hazard-related information, among others. Identifying and understanding how these factors affect mode and route choices of evacuees can help government officials develop strategies whenever a disaster happens such who to prioritize, investments for disaster management that makes evacuation effective and efficient, resulting to reduction of casualties. There are limited studies on understanding the mode choice behavior. Sadri et al. (2014a) emphasized the need for more studies to investigate and reveal other factors that are important to evacuees’ decision making. Also, earlier evacuation planning studies were car-based and transit-based (e.g. Murray-Tuite, 2007 ; Balakrishna et al., 2008 ; Chen and Xiao, 2008 ; Klunder et al., 2009 ; Mastrogiannidou et al., 2009 ; Noh et al., 2009 ; Chan, 2010 ; Huibregste et al., 2010 ; Wang et al., 2010 ; Pel et al., 2011 ; An et al., 2013). Case studies in most developing countries are still lacking socio-demographic characteristics of the population, hazard-related information, and evacuees’ travel related decisions, in addition to available transportation infrastructures that are unique. Majority of people in developing countries do not have personal vehicles. They depend greatly on available modes of transport for evacuation. It was also recommended by Abdelgawad and Abdulhai (2010a) in their large scale evacuation multimodal study that modes such as walking and cycling that are readily available in urban cities could be integrated to evacuation planning. Allowing people to evacuate on foot is believed to be faster than vehicular evacuation within two-kilometer region (Shiwakoti et al., 2013). This is an important mode of evacuation at the onset of disasters especially when road networks are congested. For route choice, previous studies indicate that one of the core aspects of evacuation is routing. One of the impetus for this is that the travel demand during emergency evacuation exceed the capacity of the transportation networks (Pel et al., 2010), and congestion is likely to happen. Thus, a number of researches focused on optimal evacuation considering different strategies on routes for evacuees to reach safety, were evaluated through simulation models (e.g. Ng and Waller, 2010 ; Sayyady and Eksioglu, 2010 ; Campos et al., 2012 ; Na et al., 2012 ; Bish and Sherali, 2013). Evaluated strategies with substantial reduction of clearance time against the lead time of the hazard are translated to evacuation plan. However, simulation models do not necessarily capture behaviors of the decision makers. This indicates that empirical studies on observed route choices of evacuees are then valuable. Travel behavior could be incorporated in evacuation simulations in order to identify optimal routing strategies (Fang and Edara, 2013). This recommendation is also consistent with previous studies that recognized route decisions of evacuees as an important part of evacuation simulation/traffic modeling (e.g. Dow and Cutter, 2002 ; Pel et al., 2010 ; Pel et al., 2011). Research attempts to fill this gap is done by shifting efforts to understanding how evacuees choose the route they take during evacuation (e.g. Akbarzadeh and Wilmot, 2015 ; Sadri et al., 2014). Researchers are becoming more adept to solve evacuation problems that will generalize how evacuees (e.g. individuals/households) decide on certain aspects (e.g. evacuation decision, departure time, destination, mode, and route) whenever a disaster strikes. However, in places where household characteristics are heterogeneous, households’ decisions might considerably vary. Household decisions can be affected by culture, demography, and environment, among others. Hence, it is an imperative to understand household’s preferences in different communities in making decisions during certain events such as evacuation. In line with the above gaps, the main goal of this study to understand determinants of household evacuation mode and route choice behavior. Understanding the variables that affect evacuees’ choice can assist emergency planners in preparing for future evacuation plans, for instance in determining what mode to use for evacuation, which route can be congested, and which routes to recommend for evacuees to take in order to reach destination in a timely manner. This study then seeks to identify and understand the determinants of households' mode and route choices in a developing country, particularly the Philippines. Discrete choice models were estimated and validated from original data collected in selected sub-districts in Quezon City, Philippines. Face to face interviews were conducted with randomly selected households in sub-districts. Random selection of households was done using the cluster sampling technique. During the interview process, the respondents were given a brief introduction on the study being conducted. This was to ensure they understand the context which is the basis of their answers to the questions. The interviewers also made sure that the household experienced flood during the Mid-August 2013 before proceeding with the interviews. To do this, interviewees were first inquired if they experienced flood during the 2013 event. The flood details including the level of flood in their house, the number of days they were flooded, the level of damage the flood caused their house, and whether they received evacuation warning and its source. Information on the distance of house location from the flood hazard was also solicited. Then, information on evacuation-related decisions was also elicited including the type of evacuation decision, the timing of evacuation, their evacuation destination, the mode they used and the route they took when evacuating. Routing strategies available to households were identified and included in the survey instrument. The route recommended by the government, usually available in other studies was not made available to households in this current study during issuance of evacuation warning. During the interviews, a map of possible routes to identified evacuation centers were shown from which interviewees were asked which route they took during evacuation to their specific destinations. They were then asked of the reason of taking such route rather than taking other available ones. From these, three routing options were identified including the route that they usually take during normal days (most familiar route to them that was also confirmed as not the nearest to their destination), the nearest route or where they thought they can take to evacuate faster, and the route without flood waters. These were the list of options made available to households during the rest of interviews in addition to “others” in case of the existence of route option not identified during the pilot survey. After data collection, however, the third option was removed from analysis as it was found that such route was not available to them that time or it is the only route available. In addition, there were no “other” route options made available in the data collected. The 2 remaining routes are then used for analysis in this study. These route options are defined as nearest and familiar (the main road most familiar that they usually take during normal days but not the nearest). The second part of the interview solicited suggestions and comments for better situations in future evacuation from floods. The third part of the interview aided solicitation of socio-demographic information of the head of the household and other household information that includes age, gender, marital status, educational attainment and type of work of the head of the household, presence of health problem and insurance, household monthly income, number of household members, age of members, the presence of small children and senior citizens, number of years the household has been living in that residence, type of house ownership and materials, the number of house floor levels, vehicle ownership, and pet ownership. Six hundred thirty two interviews were completed out of 640 total number of households approached. Out of the 632 interviews, 340, 150, and 142 were from Bagong Silangan, Bahay Toro, and Sto. Domingo, respectively. All the data collected was tabulated in excel sheet. The data was verified and cross checked by ensuring that information provided by the households were according to the questions asked. Data obtained with a lot of missing information and inconsistency based from the needed information was excluded from the analysis. Also, data from households that decided not to evacuate was not included in the analysis. These were then processed to and the logit models used for analysing the mode and route choices of evacuees. Findings revealed important determinants that can help evacuation planners and managers develop strategies for future flood evacuation operations. For the mode that household choose, those who traveled on-foot take into account departure timing, destination type, age, gender and educational attainment of the head of the household, presence of small children, presence of health problem, house ownership, number of years living in the residence, vehicle ownership, source of warning, distance traveled to safety and cost of evacuation. These results provide insights that can be useful for the government to plan for future evacuations. For instance, the government can encourage the households with personal vehicle to use them in future evacuations, while providing for those without personal vehicle and needs to travel longer distances. The government can also encourage households living in high flood risk areas to prepare household evacuation plan. In terms of the route that households choose, the level of flood, vehicle ownership, mode chosen, departure timing, and house ownership show influence to decision making. Based on results, planners can analyze and evaluate appropriate actions such as which timing, evacuation routes, and destinations to advice households at risk in case of future floods. Given findings in this study, pedestrian and gender-based evacuation behavior can be modeled and included in the evacuation simulation framework understanding key determinants of gender-based evacuation travel behavior in the area of evacuation, departure time, destination, mode and route choice behavior can be investigated in the future. Increasing sample size and collecting data in other sub-districts may be helpful for further investigation. While transferability of estimated models to other hazards such as earthquake, is an area worthy to study.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Mongkut Piantanakulchai

Role: advisor

Created: 2017

Modified: 2021-05-28

Issued: 2021-05-28

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2017.208

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

©copyrights Thammasat University

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