-امیر گلرو، مرتضی باقری، زهرا فتح الهی (1395). تشخیص خرابی تیغههای سوزن راه آهن با استفاده از پردازش تصویر.
-حامد پایدار، سعید محمدزاده (1399). تحلیل مکانیک شکست ترک ستارهای درز ریل و معرفی پارامترهای مؤثر بر نرخ رشد ترک. 237–249.
-Attoh-okine, N. (2014). Big Data Challenges in Railway Engineering. 7–9.
-Babu, A. R. P. P. S. (2022). Solar Powered Fault Detection System for Railway Tracks.
-Babuska, R., & Schutter, B. De. (2016). Deep Convolutional Neural Networks for Detection of Rail Surface Defects. 2584–2589.
-Bergmeir, C., Gregorio, S., Mart, C., & Ben, M. (2013). A Study on the Use of Machine Learning Methods for Incidence Prediction in High-Speed Train Tracks. 674–675.
-Chen, Z., & Fan, W. (David). (2019). A multinomial logit model of pedestrian-vehicle crash severity in North Carolina. International Journal of Transportation Science and Technology, 8(1), 43–52. https://doi.org/10.1016/j.ijtst.2018.10.001
-DOT, U. S. (2007). Role of Human Factors in Rail Accidents. Accessed on 5 May 2021.
-Fay. (2000). A fuzzy knowledge-based system for railway traffic control. Eng Appl Artif Intell 13(6):719–729.
doi.org/10.%0A1016/S0952-1976(00)00027-0FRA. (2012). SAFETY.
-Ghofrani, F., He, Q., Goverde, R. M. P., & Liu, X. (2018). Recent applications of big data analytics in railway transportation systems : A survey ☆. Transportation Research Part C, 90(January), 226–246. doi.org/10.1016/j.trc.2018.03.010
-Hadj-mabrouk, H. (2019). Contribution of Artificial Intelligence to Risk Assessment of Railway Accidents. 5, 104–122.
-Hao, W., Kamga, C., & Wan, D. (2016). The Effect of Time of Day on Driver ’ s Injury Severity at Highway-rail Grade Crossings in the United States ScienceDirect The effect of time of day on driver ’ s injury severity at highway-rail grade crossings in the United States. January. doi.org/10.1016/j.jtte.2015.10.006
-Harsha, P. K. S. B. P. (2020). Analysis of Causes of Rail Derailment in India and Corrective Measures.
-Hayward. (2018). Big data & the digital railway.
-Hughes. (2018). Making the railway safer with big data.
http://www.railtechnologymagazine.com/Comment/%0Amaking-the-railway-safer-with-big-data
-Jindal, A., & Mukherji, S. (2005). World report on road traffic injury prevention. Medical Journal Armed Forces India,
61(1), 91. doi.org/10.1016/s0377-1237(05)80135-2
-Kawprasert, A., & Barkan, C. P. L. (2010). Effect of train speed on risk analysis of transporting hazardous materials by rail. Transportation Research Record, 2159, 59–68. doi.org/10.3141/2159-08
-Keramati, A., Lu, P., Tolliver, D., & Wang, X. (2020). Geometric effect analysis of highway-rail grade crossing safety performance. Accident; Analysis and Prevention, 138, 105470. doi.org/10.1016/j.aap.2020.105470
-Keramati, A., Lu, P., Zhou, X., & Tolliver, D. (2020). A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings : The Competing Risks Method. 2020(1).
-Lai, J., Xu, J., Wang, P., Yan, Z., Wang, S., Chen, R., & Sun, J. (2020). Numerical Investigation of Dynamic Derailment Behavior of Railway. Engineering Failure Analysis, 105132. doi.org/10.1016/j.engfailanal.2020.105132
-Lee, J. S. (2019). Estimation of crack width based on shape-sensitive kernels and semantic segmentation. November, 1–21. doi.org/10.1002/stc.2504
-Lin, P.-S.; Fabregas, A.; Kourtellis, A.; Bryant, E.; Lall, S. (2014). Planning and design for prevention of incorrect turns onto highway-rail grade crossings. ascelibrary.org/doi/10.1061/9780784413364.044
-Marr. (2017). How Siemens is using big data and IoT to build the internet of trains. https://www.forbes.com/%0Asites/bernardmarr/2017/05/30/how-siemens-is-using-big-dataand-%0Aiot-to-build-the-internet-of-trains/#2b7a4b6e72b8
-Naweed, A. (2013). Psychological factors for driver distraction and inattention in the Australian and New Zealand rail industry. 60, 193–204.
-Rampriya, R. S., Suganya, R., Nathan, S., Perumal, P. S., Suganya, R., Nathan, S., & Perumal, P. S. (2022). A Comparative Assessment of Deep Neural Network Models for Detecting Obstacles in the Real Time Aerial Railway Track Images A Comparative Assessment of Deep Neural Network Models. In Applied Artificial Intelligence, Vol. 36, Issue 1. Taylor & Francis. doi.org/10.1080/08839514.2021.2018184
-Reis, V., Fabian Meier, J., Pace, G., & Palacin, R. (2013). Rail and multi-modal transport. Research in Transportation Economics, 41(1), 17–30.doi.org/10.1016/j.retrec.2012.10.005
-Rowan, H. M., Faculty, E., & Lavrenz, S. M. (2021). Rowan Digital Works Fatal crashes at highway rail grade crossings : A U . S . based study Recommended Citation.
-Santur, Y., Karaköse, M., & Ak, E. (2017). A New Rail Inspection Method Based on Deep Learning Using Laser Cameras.
-Shah, A. A., Chowdhry, B. S., Memon, T. D., & Kalwar, I. H. (2020). Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform. 4(2), 53–60. doi.org/10.33166/AETiC.2020.02.005
-Shin, W. J. (2020). Grinding effect analysis according to control variables of compact rail surface grinding machine.
-Silla, A.; Luoma, J. (2012). Main characteristics of train–pedestrian fatalities on finnish railroads.
https://www.google.com/search?q=Main+characteristics+of+train–pedestrian+fatalities+on+finnish+railroads&sxsrf=APq-WBuB0ELTF1N-LiMV4yVJzcdhknhWQg%3A1650536589318&ei=jTBhYpX8EoX-7_UP8fCZ0Ak&ved=0ahUKEwjV9uD496T3AhUF_7sIHXF4BpoQ4dUDCA4&uact=5&oq=Main+charact
-Tey, L., Ferreira, L., & Wallace, A. (2011). Measuring driver responses at railway level crossings. 43, 2134–2141.
doi.org/10.1016/j.aap.2011.06.003
-Thaduri, A., Galar, D., & Kumar, U. (2015). Railway assets : A potential domain for big data analytics. 53, 457–467.
doi.org/10.1016/j.procs.2015.07.323
-Xie, Y., Zhao, K., & Huynh, N. (2012). Analysis of driver injury severity in rural single-vehicle crashes. Accident Analysis and Prevention, 47, 36–44.doi.org/10.1016/j.aap.2011.12.012
-Zhao, S., & Khattak, A. J. (2017). Factors associated with self-reported inattentive driving at highway-rail grade crossings. Accident Analysis and Prevention, 109(February), 113–122.doi.org/10.1016/j.aap.2017.10.013
