An Investigation of human Factors Impact on Tehran Urban Crash Severity

Abstract

Abstract-Due to the considerable social and economic burden traffic accidents imposed on societies, strategies to reduce crash severity and potential for crash (frequency) are of interest to transportation agencies. As injury-severity data are generally represented by discrete categories such as injury and property damage only, a variety of discrete choice methodological techniques can be applied to analyze crash-severity data. This paper presents a binary model for predicting severity of Tehran urban car-car collisions which can be used in safety planning and enforcements. Human impact and collision type variables are employed to act as surrogates for point of impact. Results indicate that fastening seat belt decreases the probability of accidents resulting in injury. Furthermore, disregarding regulations, as a human reason of an accident, results in the most severe consequence (injury/fatality) compared to other human reasons. On the other extreme, as a consequence of accidents occurring due to non-human reasons, property damage only is the most probable outcome. Finally, drivers involved in front to front collision types are most prone to injury. Other factors in decreasing order are: front to rear, front to side, other types of collision, rear to side, and side to side.  Index Terms-human factor impacts, crash severity, binary logit, car-car crash

Dynamic Household Vehicle Decision Modeling Considering Plug-In Electric Vehicles

“…These models are mainly econometric-based and typically focus on behaviorally investigating one aspect of vehicle decisions. (Examples of econometric-based models can be found in [ 11 , 12 ].) For instance, discrete choice models are used to estimate the number of vehicles ( 13 ), vehicle type ( 14 ), and vehicle transaction ( 15 ) depending on the lifestyle, travel pattern, socio-economic, and built-environment factors.…”