Estimating the relationship between accident frequency and homogeneous and inhomogeneous traffic flows

Hiselius, Lena Winslott

doi:10.1016/j.aap.2003.11.002

Cited by 47 publications

(31 citation statements)

References 3 publications

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“…The coefficient signs are negative for all models. This result is in line with Hiselius (2004). The coefficient of Log(%HGV) in the fatalities model is smaller in magnitude than the same coefficient in accident and injury models.…”

Section: Modelling Accidents Injuries and Fatalitiessupporting

confidence: 86%

“…Other studies have been carried out in recent years to establish the relationships between accidents and the effect of different vehicle mix (Tay, 2003;Hiselius, 2004;Wong et al, 2007;Haleem, 2011, Pei et al, 2011), the frequency of intersections (Ivan and O'Mara, 1997), environmental factors (Chang, 2005;Caliendo et al, 2007;Fridstrøm et al, 1995;Shankar et al, 1995), geometric infrastructure characteristics (Chang, 2005;Hauer et al, 2004), number of lanes (Noland and Oh, 2004;Chang, 2005;Milton and Mannering, 1998), and speed limits (Fridstrøm et al, 1995;Ossiander and Cummings, 2002;Wong et al, 2005).…”

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

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The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Rangel

Vassallo

Herraiz

2013

Accident Analysis & Prevention

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The goal of this paper is to evaluate whether the incentives incorporated in toll highway concession contracts in order to encourage private operators to adopt measures to reduce accidents are actually effective at improving safety. To this end, we implemented negative binomial regression models using information about highway characteristics and accident data from toll highway concessions in Spain from 2007 to 2009. Our results show that even though road safety is highly influenced by variables that are not managed by the contractor, such as the annual average daily traffic (AADT), the percentage of heavy vehicles on the highway, number of lanes, number of intersections and average speed; the implementation of these incentives has a positive influence on the reduction of accidents and injuries. Consequently, this measure seems to be an effective way of improving safety performance in road networks.

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Section: Modelling Accidents Injuries and Fatalitiessupporting

confidence: 86%

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Rangel

Vassallo

Herraiz

2013

Accident Analysis & Prevention

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“…This finding is in accordance with the results given by Hiselius [32]. However, one should bear in mind that the model defines only 16% of the variability of the dependent variable, which prevents reliable conclusions.…”

Section: The Influence Of Percentage Of Commercial Vehicles In Total supporting

confidence: 81%

Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

Glavić

Mladenović

Stevanovic

et al. 2016

PROMET

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INTRODUCTIONReducing highway accidents has always been one of the most important tasks for traffic engineers. Estimating the number of accidents resulting from a given highway design is very important in evaluating different design alternatives. Therefore, it is important to understand the relationship between accidents and the characteristics of roadways in order to try to reduce the number of accidents [1]. Accident prediction models have been very useful in estimating the expected number of accidents on intersections and road segments. Essentially, an accident prediction model is a mathematical relationship that expresses the average accident frequency of a site as a function of traffic flow and other site characteristics [2].Road safety modelling has attracted considerable research interest in the past four decades because of its wide variety of applications and important practical implications. Public agencies, such as State Departments of Transportation, are interested in identifying accident-prone areas to promote safety treatments. Similarly, transportation engineers are interested in identifying factors (traffic characteristics, geometric characteristics, etc.) that influence accident frequency and severity to improve roadway design and provide a safer driving environment. High cost of highway accidents paid by societies around the world makes highway safety improvement an important objective of transportation engineering. A significant number of previous studies have indicated that improvements to highway design could produce significant reductions in the number and severity of crashes [3]. Therefore, in the development of feasibility studies it is necessary to properly determine the impact of road and traffic Previous studies utilized different methodologies for accident prediction on highways [4]. Joshua & Garber [5] pointed out that linear regression models do not adequately describe the nature of crash frequency data. Poisson or negative binomial regression models are better suited for defining the random, discrete, and non-negative nature of crash occurrences [6].Accident prediction models have been used elsewhere as a useful tool by road engineers and planners [7]. Fletcher et al. [8] found that due to wide differences in traffic mix, road quality, design and road user behaviour, it would be neither valid nor useful to apply simple multiplicative factors or even devise more complex conversion formulae for models developed elsewhere for another country. The regression models developed for certain conditions, which can vary regionally from country to country, cannot be generalized for all countries, which may have different standards in geometric designs and may create various operational environments for traffic under their jurisdictions.The roadmap of this paper can be summarized in the next few steps. First, the paper provides a comprehensive literature review of the former research of this issue. Special attention is given to several factors which define the quality of similar studies...

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“…Many things however could affect road safety during night-time such as lighting and as such needs further research. Hiselius (2004), on the other hand, showed the importance of the consideration of traffic flows, i.e., the crash rate would be different depending on whether the traffic flows is homogeneous or not.…”

Section: Introductionmentioning

confidence: 99%

Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models

2010

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Abstract:There is an ongoing debate among transport planners and safety policy makers as to whether there is any association between the level of traffic congestion and road safety. One can expect that the increased level of traffic congestion aids road safety and this is because average traffic speed is relatively low in a congested condition relative to an un-congested condition which may result in less severe crashes. The relationship between congestion and safety may not be so straightforward however as there are a number of other factors such as, traffic flow, driver characteristics, road geometry and vehicle design affecting crash severity. Previous studies have employed count data models (either Poisson or negative binomials and their extensions) while developing a relationship between the frequency of traffic crashes and traffic flow or density (as a proxy for traffic congestion). The use of aggregated crash counts at a road segment level or at an area level with the proxy for congestion may obscure the actual relationship. The objective of this study is to explore the relationship between the severity of road crashes and the level of traffic congestion using disaggregated crash records and a measure of traffic congestion while controlling for other contributory factors. Ordered response models such as ordered logit models, heterogeneous choice models and generalised ordered logit (partially constrained) models suitable for both ordinal dependent variables and disaggregate crash data are used. Data on crashes, traffic characteristics (e.g., congestion, flow, speed) and road geometry (e.g., curvature and gradient) were collected from the M25 London orbital motorway between 2003 and 2006. Our results suggest that the level of traffic congestion does not affect the severity of road crashes on the M25 motorway. The impact of traffic flow on the severity of crashes however shows an interesting result. All other factors included in the models also provide results consistent with existing studies.

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Estimating the relationship between accident frequency and homogeneous and inhomogeneous traffic flows

Cited by 47 publications

References 3 publications

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models

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