Mongkut Piantanakulchai

A household-level flood evacuation decision model in Quezon City, Philippines

AbstractEvacuation is one of the important preparedness measures in disaster management. It requires careful modeling and planning to minimize chaos and confusion during evacuation operations. The choice of decision-makers, whether to evacuate or stay in the area threatened by hazard, is an important aspect of evacuation travel behavior research. This is considered an essential input for evacuation modeling and planning. This study investigates the effects of various factors determining evacuation decision. A discrete choice model is proposed using the data collected through a face-to-face post-event survey from flood-affected households in Quezon City, Philippines. The model allows a choice among three alternatives of full, partial, and no evacuation. Results show that evacuation decision is determined by a combination of household characteristics and capacity-related factors (gender, educational level, presence of children, and number of years living in the residence, house ownership, number of house floor levels, type of house material), as well as hazard-related factors (distance from source of flood, level of flood damage, and source of warning). Findings in the study provide insights that can be considered by policy-makers in preparing for future evacuations. Appropriate programs can be designed to encourage full evacuation compliance of households that live nearest to the flood source and those living in houses with two or more floor levels who are more likely not to evacuate. Households with children can also be educated for full evacuation compliance since these households have higher probability to partially evacuate.

Determinants of household flood evacuation mode choice in a developing country

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. This study seeks to identify and understand the effects of determinants on households’ mode choice behavior in a developing country. Discrete choice models were estimated and validated from original data collected in selected sub-districts in Quezon City, Philippines. Findings revealed important determinants that can help evacuation planners and managers develop strategies for future flood evacuation operations. Determinants to evacuees who traveled on-foot include 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. Results of this study provide insights that can be useful for the government to plan for future evacuations. For instance, the government can encourage the households with a personal vehicle to use them in future evacuations, while providing for those without a personal vehicle and needing to travel longer distances. The government can also encourage households living in high flood risk areas to prepare household evacuation plans.

The integration of classification tree and Sequence Alignment Method for exploring groups of population based on daily time use data

This study proposes a method of classifying homogenous groups of population in the time use database.The model utilizes activity sequence patterns and socio-economic data in the time use data.An Integration of Classification tree And Sequence alignment method is proposed.The advantage is the ability to handle sequential, continuous, and discrete variables.The model was applied with real world data, using the 2004 Bangkok time use data from Thailand. Searching homogenous groups of individuals is one of the important steps in activity based travel demand modeling development. This study proposes an Integration of Classification tree And Sequence alignment method (ICAS) as a new classification method. The main advantage is the ability to explore all sources of lifestyle variations that have various data types including: sequential data, continuous variables, and discrete variables. These data are, for example, activity sequential patterns, socio-economic characteristics, and socio-demographic characteristics. Results from ICAS can also be used as both an activity classifier and an activity generator in an activity based travel demand modeling system. The proposed ICAS concept was evaluated with real world data, using the 2004 Bangkok time use data from Thailands National Statistical Office (NSO).

Modified Bayesian data fusion model for travel time estimation considering spurious data and traffic conditions

Abstract This paper presents a framework for the development of the travel time estimation model using multiple sources of data with consideration of spurious data and traffic conditions. A modified Bayesian data fusion approach, combined with the Gaussian mixture model, is used to fuse the travel time data, which are estimated from different types of sensors to improve accuracy, precision, as well as completeness of data, in terms of spatial and temporal distribution. Two additional features are added into existing models including the difference of traffic conditions classified by the Gaussian mixture model and the bias estimation from individual sensor by introducing a non-zero mean Gaussian distribution which learned from the training dataset. The methodology and computational procedure are presented. The Gaussian mixture model is used to classify states of traffic into predefined number of traffic regimes. Once a traffic condition is classified, the modified Bayesian data fusion approach is used to estimate travel time. The proposed model provides explicit advantages over the basic Bayesian approach, such as being robust to noisy data, reducing biases of an individual estimation, and producing a more precise estimation of travel time. Two different real-world datasets and one simulated dataset are used to evaluate the performance of the proposed model under three different traffic regimes: free flow, transitional flow and congested flow regimes. The results when compared with the results from benchmark models show significant improvement in the accuracy of travel time estimation in terms of mean absolute percentage errors (MAPE) in the range of 3.46% to 16.3%.