Network Screening Methods to Identify Roadway Sites for Safety Investigation: An Examination of Some Critical Issues

Traffic accidents are responsible for about 3,000 deaths and $25 billion in economic losses annually in Canada. One way for transportation authorities to improve safety is to identify potentially hazardous roadway elements through network screening. The process of network screening is a low-cost statistical analysis of highway safety data, which yields a ranked list of sites to be investigated in detail. Critical issues of two network screening methods are investigated in this thesis. The first method is a peak-searching algorithm for screening roadway segments, with attention focused on threshold values of a key user-selected variable, namely the coefficient of variation. The second method examined is a method of screening for high proportions of specific accident types. For this method, parameter estimation techniques are compared, and the effect of the 'critical proportion,' a key user-selected variable in the method, on site rankings is investigated. In addition to the two network screening methods, an investigation is carried out into some aspects of safety performance function calibrated using negative binomial regression. Specific attention is given to how the negative binomial dispension parameter changes over the range of some independent variables.

Calibration of highway safety manual predictive models and validation of safety performance funtions for canadian urban/suburban intersections

In this research, the HSM predictive models for collisions on urban/suburban arterials are calibrated for collision data from the City of Toronto. It has been found that the use of calibration factors for applying HSM models to Toronto intersection data is not appropriate. New collision models are therefore developed by using local data. The HSM and Toronto models are then calibrated to City of Edmonton intersection collision data to determine whether it is better to calibrate HSM models for a Canadian jurisdiction or models from another Canadian jurisdiction. A related aspect of the research is the investigation of models for crash types. There is no safety performance function (SPF) available in the HSM to predict rear end collisions. Instead, rear end collisions are estimated as a proportion of predicted multivehicle collisions. To overcome this deficiency, Toronto data are used in the estimation of models for rear end collisions.

Network Screening Methods to Identify Roadway Sites for Safety Investigation: An Examination of Some Critical Issues

Traffic accidents are responsible for about 3,000 deaths and $25 billion in economic losses annually in Canada. One way for transportation authorities to improve safety is to identify potentially hazardous roadway elements through network screening. The process of network screening is a low-cost statistical analysis of highway safety data, which yields a ranked list of sites to be investigated in detail. Critical issues of two network screening methods are investigated in this thesis. The first method is a peak-searching algorithm for screening roadway segments, with attention focused on threshold values of a key user-selected variable, namely the coefficient of variation. The second method examined is a method of screening for high proportions of specific accident types. For this method, parameter estimation techniques are compared, and the effect of the 'critical proportion,' a key user-selected variable in the method, on site rankings is investigated. In addition to the two network screening methods, an investigation is carried out into some aspects of safety performance function calibrated using negative binomial regression. Specific attention is given to how the negative binomial dispension parameter changes over the range of some independent variables.

Calibration of highway safety manual predictive models and validation of safety performance funtions for canadian urban/suburban intersections

In this research, the HSM predictive models for collisions on urban/suburban arterials are calibrated for collision data from the City of Toronto. It has been found that the use of calibration factors for applying HSM models to Toronto intersection data is not appropriate. New collision models are therefore developed by using local data. The HSM and Toronto models are then calibrated to City of Edmonton intersection collision data to determine whether it is better to calibrate HSM models for a Canadian jurisdiction or models from another Canadian jurisdiction. A related aspect of the research is the investigation of models for crash types. There is no safety performance function (SPF) available in the HSM to predict rear end collisions. Instead, rear end collisions are estimated as a proportion of predicted multivehicle collisions. To overcome this deficiency, Toronto data are used in the estimation of models for rear end collisions.

Crash- and Simulation-Based Safety Performance Evaluation of Freeway Rest Area

The main objective of this study was to evaluate the safety effects caused by altering the lengths of deceleration and acceleration lanes at rest areas on expressways in Korea. Although general conclusions can be found through crash-based safety analysis, to examine more specific optimal conditions considering various traffic conditions, this study proposes a novel framework to explore and evaluate crash-based and simulation-based safety performances. For this purpose, the safety performance function (SPF) and crash modification factor (CMF) were developed to reflect real-world safety impacts. To consider nonlinear trends of the parameters, nonlinearizing link functions were introduced into the analysis. Two types of simulation analyses were conducted to (1) find the combination of surrogate safety measures (SSMs) that best fit with the crash-based safety performance results and (2) determine the optimal lengths of deceleration lane and acceleration lanes for different traffic conditions. The results showed that the best length of deceleration lane of a rest area is between 240 and 260 m, depending on the traffic conditions. The results also indicated that the optimal length of acceleration lane of a rest area is between 385 and 400 m, depending on the traffic parameters. The findings of this study could be used to determine the safety solutions with a micro-traffic simulator.

Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function

Motorcyclists account for more than 380,000 deaths annually worldwide from road traffic accidents. Motorcyclists are the most vulnerable road users worldwide to road safety (28% of global fatalities), together with cyclists and pedestrians. Approximately 80% of deaths are from low- or middle-income countries. Colombia has a rate of 9.7 deaths per 100,000 inhabitants, which places it 10th in the world. Motorcycles in Colombia correspond to 57% of the fleet and generate an average of 51% of fatalities per year. This study aims to identify significant factors of the environment, traffic volume, and infrastructure to predict the number of accidents per year focused only on motorcyclists. The prediction model used a negative binomial regression for the definition of a Safety Performance Function (SPF) for motorcyclists. In the second stage, Bayes' empirical approach is implemented to identify motorcycle crash-prone road sections. The study is applied in Cartagena, one of the capital cities with more traffic crashes and motorcyclists dedicated to informal transportation (motorcycle taxi riders) in Colombia. The data of 2,884 motorcycle crashes between 2016 and 2017 are analyzed. The proposed model identifies that crashes of motorcyclists per kilometer have significant factors such as the average volume of daily motorcyclist traffic, the number of accesses (intersections) per kilometer, commercial areas, and the type of road and it identifies 55 critical accident-prone sections. The research evidences coherent and consistent results with previous studies and requires effective countermeasures for the benefit of road safety for motorcyclists.

Segmentation Effect on the Transferability of International Safety Performance Functions for Rural Roads in Egypt

This paper examines the transferability of the Safety Performance Function (SPF) of the Highway Safety Manual (HSM) and other 10 international SPFs for total crashes on rural multi-lane divided roads in Egypt. Four segmentation approaches are assessed in the transferability of the international SPFs, namely: (1) one-kilometer segments (S1); (2) homogenous sections (S2); (3) variable segments with respect to the presence of curvatures (S3); and (4) variable segments with respect to the presence of both curvatures and U-turns (S4). The Mean Absolute Deviation (MAD), Mean Prediction Bias (MPB), Mean Absolute Percentage Error (MAPE), Pearson χ2 statistic, and Z-score parameters are used to evaluate the performance of the transferred models. The overdispersion parameter (k) for each transferred model and each segmentation approach is recalibrated using the local data by the maximum likelihood method. Before estimating the transferability calibration factor (Cr), three methods were used to adjust the local crash prediction of the transferred models, namely: (1) the HSM default crash modification factors (CMFs); (2) local CMFs; and (3) recalibrating the constant term of the transferred model. The latter method is found to outperform the first two methods. Besides, the results show that the segmentation method would affect the performance of the transferability process. Moreover, the Italian SPFs based on the S1 segmentation method outperforms the HSM and all of the investigated international SPFs for transferring their models to the Egyptian rural roads.

Safety Performance of One-Way Arterials

Safety analysts are generally interested in understanding the differences in the safety performance when a two-way street is converted to a one-way operation or vice-versa. Literature exists to understand and predict the safety of two-way streets. However, safety prediction procedures are currently not available for assessing the safety performance of one-way arterials. This research was undertaken to develop safety prediction models for one-way arterials. To accomplish this objective, data collected in California, Illinois, Michigan, Oregon, and Texas were assembled that included a wide range of geometric design features, traffic control features, traffic characteristics, and crash records. The data were used to calibrate predictive models, each of which included a safety performance function (SPF) and several crash modification factors (CMFs). Separate SPFs were developed for fatal and injury crashes (i.e., fatal, incapacitating injury, non-incapacitating injury, and possible injury crash) and property-damage-only crashes. The SPFs were estimated using the negative binomial modeling structure. Severity distribution functions (SDFs) were also calibrated using the fatal and injury data. These functions can be used with the predictive models to estimate the expected crash frequency for each of four injury severity levels.