Safety Performance Functions for Traffic Signals:Phasing and Geometry

A significant proportion of urban crashes,especially serious and fatal crashes,occur at traffic signals.Many of the black-spots in both Australia and New Zealand cities occur at high volume and/or high-speed traffic signals.Given this,crash reduction studies often focus on the major signalised intersections.However,there is limited information that links the phasing configuration,degree of saturation and overall cycle time to crashes.While a number of analysis tools are available for assessing the efficiency of intersections,there are very few tools that can assist engineers in assessing the safety effects of intersection upgrades and new intersections.Safety performance functions have been developed to help quantify the safety impact of various traffic signal phasing configurations and level of intersection congestion at low and high-speed traffic signals in New Zealand and Australia.Data from 238 signalised intersection sites in Auckland,Wellington,Christchurch,Hamilton,Dunedin and Melbourne was used to develop crash prediction models for key crash-causing movements at traffic signals.Different variables(road features)effect each crash type.The models indicate that the safety of intersections can be improved by longer cycle times and longer lost inter-green times,especially all-red time,using fully protected right turns and by extending the length of right turn bays.The exception is at intersections with lots of pedestrians where shorter cycle times are preferred as pedestrian crashes increase with longer wait times.A number of factors have a negative impact on safety including,free left turns,more approach lanes,intersection arms operating near or over capacity in peak periods and higher speed limits.

International transferability of macro-level safety performance functions: a case study of the United States and Italy

Safety performance functions(SPFs),or crash-prediction models,have played an important role in identifying the factors contributing to crashes,predicting crash counts and identifying hotspots.Since a great deal of time and effort is needed to estimate an SPF,previous studies have sought to determine the transferability of particular SPFs;that is,the extent to which they can be applied to data from other regions.Although many efforts have been made to examine micro-level SPF transferability,few studies have focused on macro-level SPF transferability.There has been little transferability analysis of macro-level SPFs in the international context,especially between western countries.This study therefore evaluates the transferability of SPFs for several states in the USA(Illinois,Florida and Colorado)and for Italy.The SPFs were developed using data from counties in the United States and provincias in Italy,and the results revealed multiple common significant variables between the two countries.Transferability indexes were then calculated between the SPFs.These showed that the Italy SPFs for total crashes and bicycle crashes were transferable to US data after calibration factors were applied,whereas the US SPFs for total and bicycle crashes,with the exception of the Colorado SPF,could not be transferred to the Italian data.On the other hand,none of the pedestrian SPFs developed was transferable to other countries.This paper provides insights into the applicability of macro-level SPFs between the USA and Italy,and shows a good potential for international SPF transferability.Nevertheless,further investigation is needed of SPF transferability between a wider range of countries.

Investigating the influence of segmentation in estimating safety performance functions for roadway sections

Safety performance functions（SPFs） are crucial to science-based road safety management.Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors： the validity of the statistical inferences for the available data and on how well the data can be organized into distinct homogeneous entities. The latter aspect plays a key role in the identification and treatment of road sections or corridors with problems related to safety. Indeed, the segmentation of a road network could be especially critical in the development of SPFs that could be used in safety management for roadway types, such as motorways（freeways in North America）, which have a large number of variables that could result in very short segments if these are desired to be homogeneous. This consequence, from an analytical point of view, can be a problem when the location of crashes is not precise and when there is an overabundance of segments with zero crashes. Lengthening the segments for developing and applying SPFs can mitigate this problem, but at a sacrifice of homogeneity. This paper seeks to address this dilemma by investigating four approaches for segmentation for motorways, using sample data from Italy. The best results were obtained for the segmentation based on two curves and two tangents within a segment and with fixed length segments. The segmentation characterized by a constant value of all original variables inside each segment was the poorest approach by all measures.

Safety Management of Non-urban Roads in Israel： An Application of Empirical Bayes Evaluation

A safety management system was established to provide for continuously improved safety levels of the non-urban roads in Israel. One of the main functions of the system lies in the identification and treatment of HL （hazardous locations） on existing roads. In line with the state-of-the art in road safety, the HL identification is based on an empirical Bayes evaluation, where an HL is recognized using a high positive difference between the number of accidents expected at the site and that predicted for similar sites. The latter is estimated using safety performance functions that were developed for local conditions, including single- and dual-carriageway road sections, and various types of intersections： signalized/non-signalized, three- and four-legged. The procedure of HL identification is applied annually, serving as a basis for the working programs on road infrastructure improvements. Positive safety effects of such improvements were recently reported in the country. These activities comply with the Road Infrastructure Safety Directive that was recently introduced in the European Union.

Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.