Purpose -The purpose of this paper is to develop an interval method for vehicle allocation and route planning in case of an evacuation. Design/methodology/approach -First, the evacuation route planning system is described and the notations are defined. An inexact programming model is proposed. The goal of the model is to achieve optimal planning of vehicles allocation with a minimized system time under the condition of inexact information. The constraints of the model include four types: number of vehicles constraint, passengers balance constraints, maximum capacity of links constraints and no negative constraints. The model is solved through the decomposition of the inexact model. A hypothetical case is developed to illustrate the proposed model. Findings -The paper finds that the interval solutions are feasible and stable for evacuation model in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving evacuation managers' estimates under different conditions. Originality/value -This method entails incorporation of uncertainties existing as interval values into model formulation and solution procedure, and application of the developed model and the related solution algorithm in a hypothetical case study.

Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

PurposeThe purpose of this paper is to propose a bi‐level programming optimization model to reduce traffic congestion of transportation network while evacuating people to safe shelters during disasters or special events.Design/methodology/approachThe previous optimization model for contra flow configuration only considered the character of the manager. However, the traffic condition is not only controlled by managers, but also depended on the root choice of travelers. A bi‐level programming optimization model, which considered managers and evacuees' character, is proposed to optimize the contra flow of transportation network in evacuation during special events. The upper level model aims to minimize the total evacuation time, while the lower level based on user equilibrium assignment. A solution method based on discrete particle swarm optimization and Frank‐Wolfe algorithm is employed to solve the bi‐level programming problem.FindingsIt is found that the bi‐level programming based contra flow optimization model can improve evacuation efficiency and decrease evacuation time 30 per cent or more. With the increase of traffic demand, the evacuation time will decrease significantly by contra flow configuration.Research limitations/implicationsIn the optimization model, the background traffic is ignored for simplification and the contra flow is configured absolutely as 0 or 1, which ensures vehicles do not go back into the evacuation area.Practical implicationsAn efficient optimization model for traffic managers to reduce congestion and evacuation time of evacuation network.Originality/valueThe new bi‐level programming model not only considers managers' character, but also considers evacuees' reaction. The paper is aimed to optimize contra flow for transportation network.

Driving errors may result from high workload caused by traffic signs with too much information; it could result in traffic crash significantly. Therefore, the information volume of traffic signs must keep to a reasonable level to be recognized well. In order to make drivers recognize traffic signs, the information volume that the driver could endure in different workload levels should be known clearly. In this study, we designed and implemented experiments to analyze drivers' recognition time and workload on different traffic signs information volume. The probe Shannon Theory was adopted to measure the information volume of traffic signs; the participants are required to search the target road name from traffic signs with different information. The Reaction Time (RT) would be compared with the available visual recognition time under real driving scene conditions. If the RT was almost equal to the available visual recognition time, the information volume included in the sign was considered to be the maximum information of the sign under this situation. Subjective difficulty questionnaire was used in the experiment to value driving workload. Results indicate that the information of traffic signs will increase driving workload; the maximum information in different driving speeds is also gained through the experiment. The results contribute to a better understanding of the information volume of traffic signs in different driving speed, it also has positive effects on traffic sign design. 1535 ICTIS 2013 © ASCE 2013 ICTIS 2013 Downloaded from ascelibrary.org by New York University on 08/01/15. Copyright ASCE. For personal use only; all rights reserved. INTRODUCTIONWith the developing economy, the density of road networks in China has been significantly increasing. Drivers are facing various traffic situations and are required to make decisions dependent on the road signs to arrive at the destination safely. High demands are placed on our central-processing resources during the driving. In this context, mental workload is defined as the overall cognitive effort a person invests in his performance while carrying out a task (Hart and Wickens, 1990).The traffic signs and other variety information on road have increased the complexity of the driving environment. When traffic signs contain too much information, the driver's short-term memory capability will get overloaded and result in omissions (Liu, 2005).When driving vehicles, drivers should adjust their driving strategy according to the information that presented in traffic signs. Driving errors occurred when drivers did not follow traffic signs clearly nor had inadequate time to respond. Road guide signs play a very important role in traffic safety system. One of the most important factors that must take into account is the Reaction Time (RT). The RT is defined as the time from the driver find the traffic sign to the time point that the driver takes action according to the sign information.There are several studies employ the static viewing task method to evaluate the ...

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