ACCIDENT PREDICTION MODELS FOR URBAN UNSIGNALISED INTERSECTIONS

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy. This has been increasing over years. Regulating traffic on roads is an important task. There by reducing accidents in accident prone zones. The accident was drastically increased over a decade from 4% to 31%. This is a alarming issue. The analysis and identification of such road accident prone zones is essential to reduce the accidents. A model was developed based on intersection parameters and no. of accidents by regression analysis.

2000

The main objective of this thesis is to develop microscopic accident prediction models for estimating the safety potential of 4-leg signalised intersections in the City of Vancouver, B.C. and describes the applications of these models in traffic safety analysis. The aim, therefore, is to examine the traffic variables that appear to underlie the occurrence of accidents of these intersections and explain, in a statistical sense, the generation of accidents as a function of these variables. Generalised linear regression was employed to develop the models because of its superiority over conventional linear regression in modelling accident occurrence. The statistical software package GLIM4 was used to accomplish this task. The study made use of a sample of 8466 accidents that occurred at 170 4-leg signalised intersections during the years of 1994-1996. The data on accident frequencies and traffic volumes were obtained from the City of Vancouver. Several models that have different applica...

MATEC web of conferences, 2018

Open Journal of Civil Engineering

The quantity and severity of traffic accidents have increased with the development of machinery life and traffic growth in cities and roads in the past 50 years. Among the road users, pedestrians are the most vulnerable groups to be exposed to high risks. Vehicle crashes with pedestrian are almost inevitable and cause injury or death to pedestrian. Crash investigation and statistical studies indicate that percentage of pedestrian deaths caused by vehicle accidents are much more than all deaths. A considerable amount of accidents occur at signalized and urban intersections which are the intensive crash places. Therefore in this paper appropriate models that could specify safety indicators have been indicated with existing information by characterized parametric and nonparametric variables for twenty signalized intersections. Categories and correlations of variables also have been investigated. Three models including Regression, Poisson, and Negative binomial with defined variables ha...

In the past few years traffic accidents have become an epidemic. They cause the death of over one million people per year worldwide including those walking and riding bikes. Recent studies have shown that Jordan suffers massive human and economical losses as well as social and emotional effects from traffic accidents every year. Despite the efforts of the public and private sectors, traffic accidents are still increasing and exhaust Jordan's resources at the price of other areas of development and construction.The main objectives of this study are: to analyze traffic accidents in Jordan and their main causes; to reduce the number of traffic accidents and their severity. Also, to study the effect of driver behavior mistakes on traffic accidents and their severity. In addition, to determine and build prediction statistical regression models, which relates the number of accidents (dependent variable), with drivers behavior mistakes (independent variable) by using the Statistical Package for Social Sciences (SPSS) computer software.The study was conducted based on accident data provided by the Jordan Traffic Institute from the year 2000 to year 2010. The study investigates 394188 total accidents during the period of the study with five independent variables (close following, lane violation, speeding or violation of speed limit, wrong passing and red light violation).Regression techniques were used to analyze the collected data and to create four models .The models were developed by SPSS statistical package computer program. The first developed predicted model was for the total accident, the result indicated that the close following and lane violation are the most causes of accidents .The second developed model was for the fatal accidents, and the results indicated that the violation of speed limit and the lane violation are the most causes of the fatalities. The third and fourth models were developed for the slight and sever injuries; the result showed that the same independent variables causes of fatalities are applicable for injuries.The accident prediction model can be used to develop warrants and standards for law enforcement, geometric design, and traffic operation and to improve the required countermeasures in order to reduce the traffic accidents especially fatal and injury accidents.

Passer journal of basic and applied sciences, 2024

Road crash reduction depends on the precise identification of High Crash Locations (HCLs) and suggesting appropriate solutions and preventative measures. Though not all crashes are owing to defective characteristics of the roadway, a concentration of crashes at one location suggests that there may be a failure in the highway system. Identification of these HCLs can be achieved by detailed investigation of crash records, and further evaluations can then result in improvements that will decrease the number and severity of future crashes. The primary goal of this study is to identify HCLs in Duhok City and rank the signalized intersections using mathematical methods such as crash frequency method, crash rate method and critical crash rate method and identify possible treatments that reduce crashes at signalized intersections using the Highway Safety Manual. Distribution of crashes by type indicates that the rear end, angle, and sideswipe are common types of crashes that occur at these intersections. The results indicated that of intersections, Tax, Benavi 1, Benavi 2, Commerce, and Etite intersections are hazardous locations. The Highway Safety Manual (HSM) predictive method allows the design engineer in a road agency to estimate the measurable safety impacts of several design proposals and offer explanations for their design decisions. The results show that one approach/countermeasure to crash prevention may work effectively; however, a combination of approaches and/or countermeasures will have a greater impact. Furthermore, the results showed that there is a significant effect on the probable average crash frequency after all treatments applied at intersections.

Accident Analysis & Prevention, 2007

Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.

Journal of Advanced College of Engineering and Management, 2019

Every year globally 1.3 million people lose their lives from road traffic crashes (RTAs). Similarly, increasing rate of RTAs has been observed in Nepal including Kathmandu valley. This study is focused on the analysis of crash trends and respective site specific geometric features of urban road intersections in Kathmandu valley. Seventeen major intersections based on the data availability and traffic volume, are considered for the analysis of crash type. Previous crash data and traffic volume records of one year have been analysed. Common types of three and four legged intersections were taken for the study. Classified traffic volume at those intersections were converted into the Annual Average Daily Traffic. Evaluation factors for the crash analysis were determined by using predictive method. Crash frequency, crash rate, critical crash rate and crash prediction methods were used for ranking of the intersection. Priority for the safety improvement is prepared based on the results of...

This paper summarizes the results of the bibliographic study on accident prediction models applied to urban areas. Several types of models where analyzed, namely for accidents involving pedestrians or cyclists, collisions between motorized vehicles, total accidents, non injury accidents, accidents with fatalities, injury accidents, night time accidents and accidents involving only vehicles. Models with different levels of disagregation were also studied: aggregated models describe general safety trends (national or regional); disaggregated models represent specific changes in the transportation system. This study is part of the "IRUMS -Safer Roads in Urban Areas" project, carried out at the National Laboratory of Civil Engineering and at the Department of Engineering of the University of Coimbra, financed by the Foundation for Science and Technology. This project intends to develop methods for safety management of urban road networks. Procedures for the estimation of expected accident frequencies, identification of sites with a promise and selection of efficient corrective measures are being developed as well. The case study is being applied in Lisbon.