Extracting the Inconsistencies in the Urban Highways Considering the Crash Occurrences

Document Type : Research Paper


1 Assistant professor, Department of Civil Engineering, Payame Noor University(PNU), P. O. Box, 19395-3697 Tehran, Iran

2 PhD candidate, Department of Civil Engineering, Payame Noor University(PNU), P. O. Box, 19395-3697 Tehran, Iran


The present study aims to identify the inconsistencies resulting from the interferences between the geometric features of the highway, access points and traffic control devices by extracting the frequent association rules and discovering the patterns that contribute to the recurrent crashes. A case study was conducted on a 117-kilometer urban highway around Mashhad. The data for five years of crashes were procured from the corresponding authorities and placed on the network under a geo spatial software environment. Then, the all features of the highway were collected. The trip times between the nodes and existing features were calculated via coding based on the data obtained from one of the route planning software. The operating speed was subsequently estimated. According to the obtained databank and considering the geometric features of the road (horizontal and vertical), the access points, road width, operating speed and position of the traffic control devices (signs and speed control camera), efforts were made to divide the road to the sections. Allocating the crash data to the sections and using the FP-Growth Algorithm, the frequent rules affecting the recurrent crash occurrences were extracted. The results showed that there are different combinations of the geometric road-related factors such as flat-straight alignment and horizontal curve along with cases like right-hand exit, consecutive entrance and exit, and reduced road width in addition to the presence of speed control camera and advanced direction signs on the highway sections together cause inconsistencies like the recurrent crash occurrences.


- Abellán, J., López, G., & OñA, J. de (2013). Analysis of traffic accident severity using decision rules via decision trees. Expert Systems with Applications, 40(15), 6047–6054.
- Abugessaisa, I. (2008). Knowledge discovery in road accidents database-integration of visual and automatic data mining methods. International Journal of Public Information Systems, 4(1).
- Chand, A., Jayesh, S., & Bhasi, AB (2021). Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Materials Today: Proceedings, 47, 5135–5141.
- Chang, L.-Y., & Wang, H.-W. (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019–1027.
- Chen, W.-H., & Jovanis, P. P. (2000). Method for identifying factors contributing to driver-injury severity in traffic crashes. Transportation research record, 1717(1), 1–9.
- Chong, M. M., Abraham, A., & Paprzycki, M. (2004). Traffic accident analysis using decision trees and neural networks. arXiv preprint cs/0405050.
- Comi, A., Polimeni, A., & Balsamo, C. (2022). Road accident analysis with data mining approach: evidence from Rome. Transportation research procedia, 62, 798–805.
- Das, S., Dutta, A., Avelar, R., Dixon, K., Sun, X., & Jalayer, M. (2019). Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures. International Journal of Urban Sciences, 23(1), 30–48.
- Demirel, N., Emil, M. K., & Duzgun, H. S. (2011). Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery. International journal of Coal geology, 86(1), 3–11.
- Deng, X., Zeng, D., & Shen, H. (2018). Causation analysis model: Based on AHP and hybrid Apriori-Genetic algorithm. Journal of Intelligent & Fuzzy Systems, 35(1), 767–778.
- Depaire, B., Wets, G., & Vanhoof, K. (2008). Traffic accident segmentation by means of latent class clustering. Accident Analysis & Prevention, 40(4), 1257–1266.
- Farhangi, F., Sadeghi-Niaraki, A., Nahvi, A., & Razavi-Termeh, S. V. (2022). Spatial modelling of accidents risk caused by driver drowsiness with data mining algorithms. Geocarto International, 37(9), 2698–2716.
- Feng, M., Zheng, J., & Ren, J. (2019). Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access, 7, 106111–106123.
- Gariazzo, C., Stafoggia, M., Bruzzone, S., Pelliccioni, A., & Forastiere, F. (2018). Association between mobile phone traffic volume and road crash fatalities: a population-based case-crossover study. Accident Analysis & Prevention, 115, 25–33.
- Geurts, K., Wets, G., Brijs, T., & Vanhoof, K. (2003). Profiling of high-frequency accident locations by use of association rules. Transportation research record, 1840(1), 123–130.
- Kuhnert, P. M., Do, K.-A., & McClure, R. (2000). Combining non-parametric models with logistic regression: an application to motor vehicle injury data. Computational Statistics & Data Analysis, 34(3), 371–386.
- Kumar, S., & Toshniwal, D. (2015). A data mining framework to analyze road accident data. Journal of Big Data, 2(1), 1–18.
- Kumar, S., & Toshniwal, D. (2016). A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24(1), 62–72.
- Kwon, O. H., Rhee, W., & Yoon, Y. (2015). Application of classification algorithms for analysis of road safety risk factor dependencies. Accident Analysis & Prevention, 75, 1–15.
- M Feng, J Zheng, J Ren, Y Xi (Ed.). 2019. Association Rule Mining for Road Traffic Accident Analysis: A Case Study from UK: Springer.
- Meißner, K., & Rieck, J. (2022). Strategic planning support for road safety measures based on accident data mining. IATSS research.
- Momeni Kho, S., Pahlavani, P., & Bigdeli, B. (2022). Analyzing and Predicting Fatal Road Traffic Crash Severity Using Tree-Based Classification Algorithms. International Journal of Transportation Engineering, 9(3), 635–652.
- Montella, A. (2011). Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types. Accident Analysis & Prevention, 43(4), 1451–1463.
- Nafis, S. R., Alluri, P., Wu, W., & Kibria, B. G. (2021). Wrong-way driving crash injury analysis on arterial road networks using non-parametric data mining techniques. Journal of Transportation Safety & Security, 1–29.
- Pakgohar, A., Tabrizi, R. S., Khalili, M., & Esmaeili, A. (2011). The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach. Procedia Computer Science, 3, 764–769.
- Pang-Ning, T., Steinbach, M., & Kumar, V. (2006). Introduction to data mining: Pearson Addison Wesley Boston.
- S Das, X. S. (Ed.). 2014. Investigating the pattern of traffic crashes under rainy weather by association rules in data mining. : 14-1540: Transportation Research Board Washington DC.
- S Priya, R. A. (Ed.). 2018. Association rule mining approach to analyze road accident data: IEEE.
- Samerei, S. A., Aghabayk, K., Mohammadi, A., & Shiwakoti, N. (2021). Data mining approach to model bus crash severity in Australia. Journal of safety research, 76, 73–82.
- Sanmiquel, L., Rossell, J. M., & Vintró, C. (2015). Study of Spanish mining accidents using data mining techniques. Safety science, 75, 49–55.
- Shirmohammadi, H., Hadadi, F., & Saeedian, M. (2019). Clustering analysis of drivers based on behavioral characteristics regarding road safety. International Journal of Civil Engineering, 17(8), 1327–1340.
- Sohn, S. Y., & Shin, H. (2001). Pattern recognition for road traffic accident severity in Korea. Ergonomics, 44(1), 107–117.
- Tavakoli Kashani, A., Shariat-Mohaymany, A., & Ranjbari, A. (2011). A data mining approach to identify key factors of traffic injury severity. PROMET-Traffic&Transportation, 23(1), 11–17.
- Tesema, T. B., Abraham, A., & Grosan, C. (2005). Rule mining and classification of road traffic accidents using adaptive regression trees. International Journal of Simulation, 6(10), 80–94.
- Wedyan, S. (2014). Review and comparison of associative classification data mining approaches. International Journal of Computer, Information, Systems and Control Engineering, 8(1), 34–45.
- Weng, J., Zhu, J.-Z., Yan, X., & Liu, Z. (2016). Investigation of work zone crash casualty patterns using association rules. Accident Analysis & Prevention, 92, 43–52.
- WHO (2018). https://www.who.int/.
- Wong, J.-T., & Chung, Y.-S. (2008). Comparison of methodology approach to identify causal factors of accident severity. Transportation research record, 2083(1), 190–198.
- Xiong, H. (2006). Association Analysis: Basic Concepts and Algorithms. URL http://www. columbia. edu/~ jwp2128/Teaching W, 4721, 185–203.
- Xu, C., Bao, J., Wang, C., & Liu, P. (2018). Association rule analysis of factors contributing to extraordinarily severe traffic crashes in China. Journal of safety research, 67, 65–75.
- Z Gao, R Pan, R Yu, X Wang (Ed.). 2018. Research on automated modeling algorithm using association rules for traffic accidents: IEEE.
- Zelalem, R. (2009). Determining the degree of driver’s responsibility for car accident: the case of Addis Ababa traffic office. Addis Ababa, Addis Ababa University.