The purpose of this study is to investigate the representation of driver behavior under extreme conditions, towards development of a micro-simulation modeling framework of traffic flow to support evaluation of management strategies and measures in emergency situations. To accomplish this objective, particular attention is given to understanding and representing so-called “panic behavior” of individuals and how this behavior may be translated into driver actions. Related background from psychology and sociology is examined to provide proper framing and a better understanding of the manifestation and implications of panic for driver behavior.

Following a systematic review and synthesis of previous traffic models, and an assessment of their suitability and limitations vis a vis representation of driver behavior under extreme conditions, a model is selected as a starting point for modification towards the micro-simulation of traffic flow under such conditions.  The model is based on Gipps’ (1981) Car-Following Model, which is combined with a simple representation of the lane changing process. The modification seeks to capture the differences in driving patterns anticipated under certain extreme conditions, and to assess these differences with respect to other traffic models. To evaluate the proposed modification, a prototype implementation is proposed for the micro-simulation of traffic flow on a stretch of highway with simplified geometric features. The vehicle trajectories and aggregate traffic properties, such as volumes and densities, are evaluated with respect to different scenarios and population characteristics, such as the distribution of desired velocities across drivers, through a sensitivity analysis. 




 Recent increases in the frequency of both man-made and natural disasters have required driver and traveler behavior models to better account for the effects of extreme dynamic conditions in addition to normal static conditions. Man-made disasters and emergencies, such as terrorist activities, wild fires, and hazardous spills, occur due to human activity, while natural disasters, such as floods and hurricanes, occur without direct human intervention. The effects of extreme conditions are not exclusive to one domain of study, but have implications in a wide range of disciplines such as the biological and environmental sciences, psychology, urban and regional studies, and engineering. In transportation analysis, modeling and understanding driver behavior under extreme conditions is a relatively new concept and has received only limited attention, and has been insufficiently addressed in past research.

 A similar and related line of research involving the escape behavior of individuals in panic situations has been addressed by several researchers. Particularly notable in this regard is the work of Helbing (2000), which describes a simulation of the escape panic behavior of individuals in a given room with exits. These individuals were conceptualized and modeled as a “self-driven manyparticle system,” with each particle having both physical and socio-psychological attributes. A generalized force model was adopted to describe particle or individual movement. One difference between the crowd evacuation context and vehicular movement is that constraints must be imposed on the direction of movement in the latter. 

In this chapter, extreme conditions are defined and classified into different categories. This study focuses on extreme conditions that may cause panic behavior among drivers. Psychologists have no consensus on the exact definition of “panic”, and in most transportation studies involving panic behavior, an operational definition of panic is typically missing (Helbing 2000). In somewhat general terms, panic is associated with the uncoordinated motion of crowds. The next section discusses panic from a socio-psychological standpoint, in order to set the stage for defining panic behavior in the context of traffic and transportation.

The objective of this thesis is to represent driver behavior under extreme conditions by constructing a micro-simulation model that aims to capture how panic behavior translates into driving actions. For that purpose, the following section presents a classification scheme for extreme conditions. Section 1.3 briefly presents the psychological and social background of “panic” so that it can be related to the traffic characteristics and placed in the context of transportation in Section 1.4. Section 1.5 specifies the research objectives and the approach necessary to accomplish them.

Classification of Extreme Conditions

 As mentioned earlier, “extreme conditions” vary in type and magnitude, and can have different effects on transportation systems and their users.  Extreme conditions result from events that can be classified as either human-caused or naturally occurring. Although these two categories may have some similarities, they also differ in terms of their degree of urgency, their predictability, and the extent to which they may be prevented or otherwise controlled. Naturally occurring extreme conditions include weather conditions and other natural disasters. Accidents, hazardous material releases, terrorist acts, and war are considered human-caused extreme conditions.