Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways

Ying Lee; Chien-Hung Wei; Kai-Chon Chao

doi:10.5604/01.3001.0010.4228

Authors

Ying Lee National Kaohsiung Marine University, Kaohsiung, Taiwan Author
Chien-Hung Wei National Cheng Kung University, Tainan, Taiwan Author
Kai-Chon Chao THI Consultants Incorporation, Taipei, Taiwan Author

DOI:

https://doi.org/10.5604/01.3001.0010.4228

Keywords:

accident on freeway, accident duration, effect evaluating, correlation, artificial neural networks, k-nearest neighbour method

Abstract

Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.

References

BUSTILLOS, B. I., CHIU, Y. C., 2011. Real-time freeway-experienced travel time prediction using N-curve and K nearest neighbor methods, Transportation Research Record, 2243, pp. 127–137.

CHAN, K. S., LAM, W. H. K., TAM, M. L., 2009. Real-time estimation of arterial travel times with spatial travel time covariance relationships, Transportation Research Record, 2121, pp. 102–109.

CHEN, H., RAKHA, H. A., 2014. Real-time travel time prediction using particle filtering with a non-explicit state-transition model, Transportation Research Part C, 43 (1), pp. 112–126.

CHIEN, I. J., DING, Y., WEI, C. H., 2002. Dynamic Bus Arrival Time Prediction with Artificial Neural Networks, Journal of Transportation Engineering, 128(5), pp. 429–438.CHOI, H. K., 1996. Predicting Freeway Traffic Incident Duration an Expert System Context Using Fuzzy Logic, PhD Dissertation, University of Southern California, Los Angeles, USA.

CHUNG, Y. S., CHIOU, Y. C., LIN, C. H., 2015. Simultaneous equation modeling of freeway accident duration and lanes blocked, Analytic Methods in Accident Research, 7, pp. 16–28.

CHUNG, Y., 2010. Development of an accident duration prediction model on the Korean Freeway Systems’, Accident Analysis Preview, 42(1), pp. 282–289.

DIMITRIOU, L., VLAHOGIANNI, E. I., 2015. Fuzzy modeling of freeway accident duration with rainfall and traffic flow interactions, Analytic Methods in Accident Research, 5-6, pp. 59–71.

GARIB, A., RADWAN, A.E., AL-DEEK, H., 1997. Estimating magnitude and duration of incident delays, Journal of Transportation Engineering, 123(6), pp. 459–466.

GUO, B., NIXON, M. S., 2009. Gait feature subset selection by mutual information, IEEE Transactions on Systems, Man, and Cybernetics- Part A: Systems and Humans, 39 (1), pp. 36–46.

HOJATI, A. T., FERREIRA, L., WASHINGTON, S., CHARLES, P., 2013. Hazard based models for freeway traffic incident duration, Accident Analysis and Prevention, 52, pp. 171–181.

KHATTAK, A., WANG, X., ZHANG H., 2012. Incident management integration tool: dynamically predicting incident durations, secondary incident occurrence and incident delays, IET Intelligent Transport Systems, 6(2), pp. 204–314.

KIM, W., CHANG, G. L., 2011. Development of a Hybrid Prediction Model for Freeway Incident Duration: A Case Study in Maryland. International Journal of Intelligent Transportation Systems Research, 10(1), pp. 22–33.

LEE, Y., WEI, C. H., 2009. Freeway travel time forecast using Artificial Neural Networks with Cluster Method, 12th International Conference on Information Fusion, pp. 1331–1338.

LEWIS, C. D., 1982. Industrial and Business Forecasting Method. London: Butterworth Scientific.

LI, R., 2015. Traffic incident duration analysis and prediction models based on survival analysis approach, IET Intelligent Transport Systems, 9(4), pp. 351–358.

LI, R., PEREIRA, F. C., BEN-AKIVA, M. E., 2015. Competing risks mixture model for traffic incident duration prediction, Accident analysis and prevention, 75, pp, 192–201.

LI, T., YANG, Y., WANG, Y., CHEN, C., YAO, J., 2016. Traffic fatalities prediction based on support vector machine, Archives of Transport, 39(3), pp. 21–30.

NAM, D., MANNERING F., 2000. An exploratory hazard-based analysis of highway incident duration, Transportation Research Part A, 34(2), pp. 85–102.

Pamula, T., 2012. Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks, Archives of Transport, 24(4), pp. 519–529.

QI, Y., TENG, H., 2008. An Information-Based Time Sequential Approach to Online Incident Duration Prediction, Journal of Intelligent Transportation Systems, 12(1), pp. 1–12.

SMITH, K., SMITH, B. 2001. Forecasting the Clearance Time of Freeway Accidents, Research Report STL-2001-01. Center for Transportation Studies, University of Virginia, Charlottesville, VA.

Spławińska, M., 2015. Development of models for determining the traffic volume for the analysis of roads efficiency, Archives of Transport, 33(1), pp. 81–91.

VALENTI, G., LELLI, M., CUCINA, D., 2010. A comparative study of models for the incident duration prediction, European Transport Research Review, 2(2), pp. 103–111.

VLAHOGIANNI, E. I., KARLAFTIS, M. G., 2013. Fuzzy-entropy neural network freeway incident duration modeling with single and competing uncertainties, Computer-Aided Civil and Infrastructure Engineering, 28(6), pp. 420–433.

WANG, W., CHEN, H., BELL, M. C., 2005. Vehicle breakdown duration modeling’, Journal of Transportation and Statistics, 8(1), pp. 75–84.

WINSTON, C., LANGER, A., 2006. The effect of government highway spending on road users’ congestion costs. Journal of Urban Economics, 60(3), pp. 463–483.

YU, B., LAM, W. H. K., TAM, M. L., 2011. Bus arrival time prediction at bus stop with multiple routes’, Transportation Research Part C, 19(6), pp. 1157–1170.

ZHAN, C., GAN, A., HADI, M., 2011. Prediction of lane clearance time of freeway incidents using the M5P tree algorithm. IEEE Transactions on Intelligent Transportation Systems, 12(4), pp. 1549–1557.

ZHANG, H. M., 2000. Recursive prediction of traffic conditions with neural network models’, Journal of Transportation Engineering, 126(6), pp. 472–481.