Behavioural foundations of two-fluid model for urban traffic

• The two-fluid model was derived in an expected utility framework.
• The theory predictions were tested using historical two-fluid model data from various cities.
• Contrary to traditional assumptions the two parameters of the two-fluid model were shown to be correlated.
• Impact of network features were estimated on perception of: likelihood and severity of the crash.

Traditionally traffic flow models have been based on analogous physical phenomena. Though these models have been successful in representing traffic flow, there is a need to provide a systematic behavioural explanation for their existence. One such model is the two-fluid model which is analogous to the principles of Bose–Einstein condensation of particles at low temperatures. The model has been extensively used to characterize the quality of traffic on urban networks and arterial streets. The two parameters of the model essentially represent ‘free flow’ travel time and level of interaction among vehicles. Though the studies have found the parameters of the two-fluid model to be significantly correlated with driver behaviour (aggressive/conservative) and crash rates, no systematic behavioural explanation has been found. This paper proposes a behavioural framework based on individual trade-off behaviour to explain the two-fluid model phenomenon. The two-fluid model is derived based on a driver’s attempt to maximize his quality of travel, by travelling fast while maintaining safety. Contrary to earlier assumptions the proposed framework shows the two parameters to be correlated. The theoretical framework was tested using two-fluid model data from various cities. The data was also used to estimate the effects of geometric factors on the perception of likelihood of a crash and the severity of the crash that affect the two-fluid model. Increase in the fraction of one-way streets was found to reduce the driver’s perception of likelihood to crash. While reduction in the fraction of one way streets and increase in average number of lanes per street, signal density and fraction of actuated signals increased the perceived level of severity of a crash.