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A large percentage of automobile accidents in city traffic occur at speeds below 15 mph. Unfortunately there is a scarcity of experimental crash data at these low speeds to help investigators to reconstruct accidents. Accident reconstruction experts have consequently attached a low level of confidence to speed predictions from vehicle crush at the low end of the speed spectrum. The need for more experimental crash data, especially in a low speed range, has repeatedly been mentioned by accident investigators. The University of British Columbia Accident Research Croup has constructed a crash test facility in conjunction with the Insurance Corporation of British Columbia to address this need. The lCBC-UBC barrier is a low speed crash test facility. A description of the ICBC-UBC crash barrier, its systems and crash testing techniques at the ICBC-UBC facility are presented in this thesis. Also multiple impacts on the same vehicle are investigated to see if this technique provided accumulated crush data that reproduced known high speed crashes. In addition, the preliminary findings are presented on the impact speed to initiate permanent crush and subsequent implications toward vehicle crush characteristics in a low speed range. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Conventional road safety models rely on historical crash data. Locations with high crash injury statistics are given primary interventions. However, crash data are subject to errors, under-reportings, inaccuracy, and requires years to get updated, as crash events are infrequent and partly random(Gettman, Pu, Sayed and Shelby, 2008), as well as road conditions might change. With the advances in connected vehicle technologies, vehicles can be used as mobile sensors that collects driver behavior information. And if found correlated with the crash data, the driver behavior indices can act as safety surrogate measures. This dissertation focuses first on presenting an algorithm for mapping a vehicle sensing big dataset to a digital road network, in a metropolitan city, using the accompanied GPS trajectories. This is a challenging task since the data collected from the on-board-diagnostic port of the vehicle is sampled at a low ping rate, adding to that the excess of GPS noise in urban canyons, which makes the map matching problem even harder. Next, a spatial correlation study is presented. It investigates the spatial relationship between the driver behavior indices (i.e. speed parameters, hard braking and hard acceleration) and crashes (crash frequencies and crash rates, normalized with traffic volume). Highways and non-highway roads are bucketed separately. The other focus of this dissertation is developing an injury-prediction network screening model, that provide safety ranking of road intersections. Novel explanatory variables are derived from the telematics data, such as intersection traffic maneuvers and traffic conflicts. The non-linearity between the explanatory variables as well as the spatial dependency between road intersection is also tested.

Crashes resulting from automobiles running a red light are typically severe in nature. One way to try to reduce the number and severity of these types of crashes is by increasing the red clearance interval of a traffic signal. In Portland, Oregon, eight intersections received a variety of treatments including red extensions. Determining which treatment had what effect can be difficult to weed out. Using a combination of crash analysis and a model simulating an intersection with red extensions, this paper describes the estimated impact of red light running intersection upgrades and red extensions on crashes. By performing a variety of before and after crash analysis, a reduction of angle crashes after treatments was detected, with a crash modification factor of 0.64 +/- 0.28 using the Empirical-Bayes method. Output from the simple simulation also suggest that red light running crashes can be reduced with red extension technology and confirms crash modification values determined from the Empirical-Bayes method.

Indiana University-Purdue University Indianapolis (IUPUI) / Pedestrian and vehicle interaction analysis in a naturalistic driving environment can provide useful information for designing vehicle-pedestrian crash warning/mitigation systems. Many researchers have used crash data to understand and study pedestrian behaviors and interactions between vehicles and pedestrian during crash. However, crash data may not provide detailed pedestrian-vehicle interaction information for us. In this thesis, we designed an automatic pedestrian tracking and status analysis method to process and study pedestrian and vehicle interactions. The proposed pedestrian tracking and status analysis method includes pedestrian detection, pedestrian tracking and pedestrian status analysis modules. The main contributions of this thesis are: we designed a new pedestrian tracking method by learning the pedestrian appearance and also their motion pattern. We designed a pedestrian status estimation method by using our tracking results and thus helped estimate the possibility of collision. Our preliminary experiment results using naturalistic driving data showed promising results.