Yi-Chang Chiu

Modeling no-notice mass evacuation using a dynamic traffic flow optimization model

This paper presents a network transformation and demand specification approach for no-notice evacuation modeling. The research is aimed at formulating the Joint Evacuation Destination–Route-Flow-Departure (JEDRFD) problem of a no-notice mass evacuation into a system optimal dynamic traffic assignment model. The proposed network transformation technique permits the conversion of a typical transportation planning network to an evacuation network configuration in which a hot zone, evacuation destinations, virtual super-safe node and connectors are established. Combined with a demand specification method, the JEDRFD problem is formulated as a single-destination cell-transmission-model-based linear programming model. The advantage of the proposed model compared with prior studies in the literature is that the multi-dimensional evacuation operation decisions are jointly obtained at the optimum of the JEDRFD model. The linear single-destination structure of the proposed model implies another advantage in computational efficiency. A numerical example is given to illustrate the modeling procedure and solution properties. Real-time operational issues and data requirements are also discussed.

Determinants of guest loyalty to international tourist hotels - a neural network approach.

This study aims to understand how service attributes influence guest loyalty of business travelers toward international tourist hotels by employing Artificial Neural Networks (ANNs). ANNs were trained to establish the mapping between eight extracted service aspects and attitudinal loyalty measures. The results of this study show that ANN models demonstrate satisfactory capability in modeling and predicting loyalty behavior of business travelers. ANN models also provide comparable and similar results to previous studies on guest loyalty behaviors.

Online Behavior-Robust Feedback Information Routing Strategy for Mass Evacuation

Disaster response to manmade and natural events involves the quick evacuation of the affected population to safer areas. Given the potential for large-scale loss of life and property, there is a need for effective emergency strategies to mitigate the adverse effects of these disasters. Most existing evacuation traffic management strategies focus on increasing network capacity along the evacuation direction such as contraflow lanes, but other information or routing strategies have not been fully explored. Optimal routing strategies can be presented to evacuees as recommended routes. Advising evacuees that take system-optimal routes help balance the distribution of evacuation flows among multiple evacuation routes. However, a critical aspect in evaluating the effectiveness of such strategies is to properly account for the possible evacuation route-choice behavior. This study analyzed the situation in which evacuees are given a set of system-optimal paths; evacuees choose their evacuation routes, following a certain route-choice behavior (rational, panic, etc.). Discussions focus on the extent to which the routing effectiveness can be properly estimated, subject to the route-choice behavior. This paper further proposes a behavior-robust feedback information routing (FIR) strategy to further improve system performance. The FIR is based on the concept of closed-loop control that reacts to the system state and updates the advised routes. The FIR that targets the system-optimal routing strategy has been shown to be effective and robust for real-time evacuation traffic management.

Evaluating Regional Contra-Flow and Phased Evacuation Strategies for Texas Using a Large-Scale Dynamic Traffic Simulation and Assignment Approach

After Hurricane Rita, the need for systematic and quantitative assessments of evacuation planning and operations strategies was recognized. Both contra-flow operation and phased evacuation were put into rigorous analysis through a large-scale regional traffic simulation modeling approach. After extensive data preparation and model developments, the Central Texas Evacuation network (CTE) in Dynamic Urban Systems in Transportation (DynusT) was created. The analysis results are presented and elaborated through relevant measures of effectiveness as well as innovative visualization techniques in this paper. The results show that the contra-flow strategy yields considerable improvements in all evacuation corridors in spite of several hot spots requiring further mitigation. The phased evacuation strategy in conjunction with the contra-flow strategy brings forth further improvements, particularly for the coastal high risk zones.

Integrated Land Use-Transport Model System with Dynamic Time-Dependent Activity-Travel Microsimulation

The development of integrated land use–transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity–travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand–supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity–travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity–travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs.

A Network Flow Algorithm for the Cell-Based Single-Destination System Optimal Dynamic Traffic Assignment Problem

The cell-transmission model-based single-destination system optimal dynamic traffic assignment problem proposed by Ziliaskopoulos was mostly solved by standard linear programming (LP) methods, e.g., simplex and interior point methods, which produce link-based flows involving vehicle-holding phenomenon. In this paper we present a network flow algorithm for this problem. We show that the problem is equivalent to the earliest arrival flow and then solve the earliest arrival flow on a time-expanded network. In particular, a scaled flow scheme is proposed to deal with the situation in which the ratio of backward wave speed to forward wave speed is less than one. The proposed algorithm produces path-based flows exhibiting realistic nonvehicle-holding properties. Complexity and numerical analyses show that the algorithm runs more efficiently than LP.

Modeling of evacuation and background traffic for optimal zone-based vehicle evacuation strategy

This paper discusses details of developing an optimal zone-based vehicle evacuation strategy based on an optimization–simulation approach. The optimal egress strategy is obtained by solving a universal quickest flow problem, and the solution is implemented and evaluated in a mesoscopic simulation model. Evacuees would follow optimal routes to safe locations outside the hot zone and then select behaviorally realistic routes to their final destinations. Background traffic is included in the model to simulate more realistic traffic conditions. The route choice of background traffic in response to the evacuation strategy and driver information strategies is carefully addressed. Operational issues such as temporal loading intensity and queuing at parking lots are also modeled and discussed. The modeling framework has been applied to a bomb threat scenario at a football stadium. The case study shows that the proposed methods generate reasonable and meaningful results for the intended no-notice scenario.

Approach to Modeling Demand and Supply for a Short-Notice Evacuation

As part of disaster mitigation and evacuation planning, planners must be able to develop effective tactical and operational strategies to manage traffic and transportation needs during an evacuation. One aspect of evacuation planning is the estimation of how many people must be evacuated to provide strategies that are responsive to the number and location of these people. When such estimates are available, it may be possible to implement tactical and operational strategies that closely match the likely demand on the road network during the evacuation. With short notice for an evacuation, people may need to be evacuated directly from current locations. In addition, for some disasters, the spatial extent of the evacuated area may change over time. This problem may be exacerbated by congestion around the evacuated area. An estimation process is proposed for a short-notice evacuation. The method uses on-hand data typically generated through existing travel demand models at many metropolitan planning organizations. It estimates demand using convenient models for trip generation, trip distribution, and travel time generation for these trips, considering a staged evacuation. These demand estimates feed a dynamic simulation model, DynusT, that is used to model the supply characteristics of the roadway network during the evacuation. Such models can be applied using a case study based on a short-notice flooding scenario for Phoenix, Arizona.

Traffic scheduling simulation and assignment for area-wide evacuation

Large-scale evacuation is called for when a natural or man-made extreme event strikes a populated area so that the population is exposed to immediate or foreseeable life-threatening danger. Evacuating a large population is an extremely complicated and difficult task, which primarily relies on efficient utilization of transportation systems, and effective evacuation schemes. This paper presents research that is aimed at developing an emergency evacuation modeling capability, which entails a careful integration of an optimal evacuation time and route choice model, as well as a traffic simulation model. Experiment results demonstrate the proposed methodologys operational planning capability for large-scale emergency evacuation management.

Hybrid real-time dynamic traffic assignment approach for robust network performance

A hybrid dynamic traffic assignment (HDTA) approach is presented that results in robust performance of the traffic network under various scenarios. The HDTA approach envisions a hierarchical routing decision process achieved through the careful interplay between a centralized DTA (CDTA) model and a decentralized DTA (DDTA) capability. The CDTA model supplies anticipatory a priori routing decisions, whereas the DDTA model generates locally optimized solutions online. Analytical and experimental analyses are conducted to illustrate the satisfactory performance of such an HDTA approach.