Safety Performance Functions for Low-Volume Roads

Gianluca Dell’Acqua, Francesca Russo


This paper analyzes roadway safety conditions using the network approach for a number of Italian roadways within the Province of Salerno. These roadways are characterized by low-volume conditions with a traffic flow of under 1000 vpd and they are situated partly on flat/rolling terrain covering 231.98 km and partly on mountainous terrain for 751.60 km. Since 2003, the Department of Transportation Engineering at the University of Naples has been conducting a large-scale research program based on crash data collected in Southern Italy. The research study presented here has been used to calibrate crash prediction models (CPMs) per kilometer per year. The coefficients of the CPMs are estimated using a non-linear multi-variable regression analysis utilizing the least – square method. In conclusion, two injurious crash prediction models were performed for two-lane rural roads located on flat/rolling area with a vertical grade of less than 6% and on mountainous terrain with a vertical grade of more than 6%. A residuals analysis was subsequently developed to assess the adjusted coefficient of determination and p-value for each assessable coefficient of the prediction model. CPMs are a useful tool for estimating the expected number of crashes occurring within the roads’ geometric components (intersections and road sections) as a function of infrastructural, environmental, and roadway features. Several procedures exist in the scientific literature to predict the number of crashes per kilometer per year. CPMs can also be used as a tool for safety improvement project prioritization.


crashes; prediction models; road safety analysis

Full Text:



Achwan, N.; Rudjito, D. 1999. Accident Characteristics on Low-Volume Roads in Indonesia, Transportation Research Record 1652: 103–110. doi:10.3141/1652-14

Banihashemi, M.; Dimaiuta, M. 2005. Maximizing Safety Improvement Benefits in Crash Prediction Models with Accident Modification Factors, Transportation Research Record 1908: 9–18. doi:10.3141/1908-02

Chobya, L. A. K.; Eck, R. W.; Wyant, W. D. 1999. Teamwork and Technology Transfer in Low-Volume Road Safety, Transportation Research Record 1652: 59–67. doi:10.3141/1652-08

Dell’Acqua, G.; Russo, F. 2010. Speed Factors on Low-Volume Roads for Horizontal Curves and Tangents, The Baltic Journal of Road and Bridge Engineering 5(2): 89–97. doi:10.3846/bjrbe.2010.10

Dell’Acqua, G.; Lamberti, R.; Russo, F. 2010. Road Safety Management Using Crash Prediction Models, International Road Federation Bulletin Special Edition, Rural Transport 1(1): 22–23.

Fridstrøm, L.; Ifver, J.; Ingebrigtsen, S.; Kulmala, R.; Thomsen, L. K. 1995. Measuring the Contribution of Randomness, Exposure, Weather, and Daylight to the Variation in Road Accident Counts, Accident Analysis and Prevention 27(1): 1–20.

Giummarra, G. J. 2003. Establishment of a Road Classification System and Geometric Design and Maintenance Standards for Low-Volume Roads, Transportation Research Record 1819: 132–140. doi:10.3141/1819a-20

Gross, F.; Eccles, K.; Nabors, D. 2011. Low-Volume Roads and Road Safety Audits: What We’ve Learned, in Proc. of the 90th Annual Meeting of the Transportation Research Board. January 23–27, 2011, Washington, DC.

Hauer, E.; Council, F. M.; Mohammedshah, Y. 2004. Safety Models for Urban Four-Lane Undivided Road Segments, Transportation Research Record 1897: 96–105. doi:10.3141/1897-13

Lank, C.; Steinauer, B. 2011. Increasing Road Safety by Influencing Drivers’ Speed Choice with Sound and Vibration, in Proc. of the 90th Annual Meeting of the Transportation Research Board. January 23–27, 2011, Washington, DC.

Lazda, Z.; Smirnovs, J. 2009. Evaluation of Road Traffic Safety Level in the State Main Road Network of Latvia, The Baltic Journal of Road and Bridge Engineering 4(4): 156–160. doi:10.3846/1822-427X.2009.4.156-160

Lord, D.; Persaud, B. N. 2000. Accident Prediction Models with and without Trend – Application of the Generalized Estimating Equations Procedure, Transportation Research Record 1717: 102–108. doi:10.3141/1717-13

Mahgoub, H.; Selim, A.; Pramod, K. C. 2011. Quantitative Assessment of Local Rural Road Safety – Case Study, in Proc. of the 90th Annual Meeting of the Transportation Research Board. January 23–27, 2011, Washington, DC.

Mattar-Habib, C.; Polus, A.; Farah, H. 2008. Further Evaluation of the Relationship between Enhanced Consistency Model and Crash-Rates of Two-Lane Rural Roads in Israel and Germany, European Journal of Transport and Infrastructure Research – EJTIR 8(4): 320–332.

Mayora, J. M. P.; Manzo, R. B.; Orive, A. C. 2006. Refinement of Accident Prediction Models for Spanish National Network, Transportation Research Record 1950: 65–72. doi:10.3141/1950-08

Ratkevičiūtė, K.; Čygas, D.; Laurinavičius, A.; Mačiulis, A. 2007. Analysis and Evaluation of the Efficiency of Road Safety Measures Applied to Lithuanian Roads, The Baltic Journal of Road and Bridge Engineering 2(2): 81–87.

Stamatiadis, N.; Jones, S.; Aultman-Hall, L. 1999. Causal Factors for Accidents on Southeastern Low-Volume Rural Roads, Transportation Research Record 1652: 111–117. doi:10.3141/1652-15

Tarko, A. 2006. Calibration of Safety Prediction Models for Planning Transportation Networks, Transportation Research Record 2103: 83–91. doi:10.3141/1950-10

Vogt, A.; Bared, J. 1998. Accident Models for Two-Lane Rural Segments and Intersections, Transportation Research Record 1635: 18–29. doi:10.3141/1635-03

DOI: 10.3846/bjrbe.2011.29


  • There are currently no refbacks.

Copyright (c) 2011 Vilnius Gediminas Technical University (VGTU) Press Technika