Identification of Pedestrian Black Spots using ARCGIS and Improvements in Road Stretches

Varun. R. G.*, L. Durga prashanth**, Vibhav H. V.***
*-*** Department of Civil Engineering, Rashtreeya Vidyalaya College of Engineering, Bangalore, Karnataka, India.
Periodicity:September - November'2020
DOI : https://doi.org/10.26634/jce.10.4.17538

Abstract

It is estimated that 1.35 million individuals expire from road collisions and that over 50 million people are injured globally last year. Road traffic deaths were predicted to be the world's third important reason of death by 2020, and minute work is done to reverse this trend. Road injuries include several dynamic variables that impact the accident to varying degrees and how to identify these variables and differentiate between relationships are the most important problems in trying to avoid and minimize black spots on the route. Based on the category of roads, five road stretches are selected for the study. Data collection are made by collecting the existing road features condition. 223 First Investigation Report (FIR) copies related to study area were obtained from the concerned 12 police stations for three years (2017,2018,2019). The main objectives of the project are analysis of different characteristics of the blackspot such as time of occurrence, type of injuries and the type of vehicle involvement in pedestrian accidents along the stretch. Weighted severity index is used to determine the severity of accidents. Identification of black spot location for pedestrians using kernel density tool in ArcGIS software. Identification of parameters contributing for accidents, and provide countermeasures to reduce the accident rate.

Keywords

Pedestrian Blackspot, Weighted Severity Index (WSI), ArcGIS, Kernel Density, Defects, Counter Measures.

How to Cite this Article?

Varun, R. G., Prashanth, L. D., and Vibhav, H. V. (2020). Identification of Pedestrian Black Spots using ARCGIS and Improvements in Road Stretches. i-manager's Journal on Civil Engineering, 10(4), 1-14. https://doi.org/10.26634/jce.10.4.17538

References

[1]. Abdel-Aty, M. A., & Radwan, A. E. (2000). Modeling traffic accident occurrence and involvement. Accident Analysis & Prevention, 32(5), 633-642. https://doi.org/10.1 016/S0001-4575(99)00094-
[2]. Abdulhafedh, A. (2017). A novel hybrid method for measuring the spatial auto correlation of vehicular crashes: Combining Moran's index and Getis-Ord G* i statistic. Open Journal of Civil Engineering, 7(02), 208–221.
[3]. Bíl, M., Andrášik, R., Svoboda, T., & Sedoník, J. (2016). The KDE+software: A tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology, 31(2), 231-237. https://doi.org/10.10 07/s10980-015-0265
[4]. Chen, X., Huang, L., Dai, D., Zhu, M., & Jin, K. (2018). Hotspots of road traffic crashes in a redeveloping area of Shanghai. International Journal of Injury Control and Safety Promotion, 25(3), 293-302. https://doi.org/10.1080/174 57300.2018.1431938
[5]. Chen, Y., & He, Z. K. (2012). Analysis and improvement of road black spots in Ningbo City. In CICTP 2012: Multimodal Transportation Systems - Convenient, Safe, Cost-Effective, Efficient (pp. 2639-2649).
[6]. Dereli, M. A., & Erdogan, S. (2017). A new model for determining the traffic accident black spots using GISaided spatial statistical methods. Transportation Research Part A: Policy and Practice, 103, 106-117. https://doi.org/1 0.1016/j.tra.2017.05.031
[7]. Erdogan, S., Yilmaz, I., Baybura, T., & Gullu, M. (2008). Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar. Accident Analysis and Prevention, 40(1), 174-181. https://doi.org/ 10.1016/j.aap.2007.05.004.
[8]. Guifang, S., Yuan, H., Cheng, J., & Huang, X. (2009). Pedestrian safety consideration and improvement. Proceedings of the 2nd International Conference on Transportation Engineering (pp. 899-904).
[9]. Harirforoush, H., & Bellalite, L. (2016). A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke. Accident Analysis & Prevention, 130, 62- 74. https://doi.org/10.1016/j.aap.2016.08.015
[10]. Hauer, E. (1980). Bias-by-selection: Overestimation of the effectiveness of safety countermeasures caused by the process of selection for treatment. Accident Analysis & Prevention, 12(2), 113-117.
[11]. Indian Road Congress. (2012). Guidelines for pedestrian facilities (IRC 103-2012). Indian Road Congress. Retrieved https://go.itdp.org/download/attachments/ 60296563/IRC%202012%20%28Guidelines%20For%20Pe destrian%20Facilities%29.pdf
[12]. Indian Road Congress. (2014). Manual of specifications and standards for four laning of highways through public private partnership (IRC:SP: 84-2014). Indian Road Congress Special Publication. https://law.resource. org/pub/in/bis/irc/irc.gov.in.sp.084.2014.pdf
[13]. Indian Road Congress. (2015a). Code of practice for road making (IRC 35-2015). Indian Road Congress.
[14]. Indian Road Congress. (2015b). Code of practice for road signs (IRC 67-2015). Indian Road Congress.
[15]. Indian Road Congress. (2016). Road accident forms A1 and A4 (IRC 53-2016). Indian Road Congress.
[16]. Latour, B. (2019). Analysis of the causes of road blackspots based on the improved rough sets theory. Journal of Chemical Information and Modeling, 53(9), 1689-99.
[17]. Liu, Y. (2013). Highway traffic accident black spot analysis of influencing factors. In ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation (pp. 2295-2300). https://doi.org/10.1061/9780784413159.333
[18]. Lloyd, C. D. (2010). Spatial data analysis: An introduction for GIS users. Oxford: Oxford University Press.
[19]. Mohaymany, A. S., Shahri, M., & Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-spatial Information Science, 16(2), 113-119. https:// doi.org/10.1080/10095020.2013.766396
[20]. MORTH. (2019). Road accidents in India - 2018. Ministry of Road Transport and Highways, Government of India. Retrieved https://morth.nic.in/sites/default/files/ Road_Accidednts.pdf
[21]. Okabe, A., & Sugihara, K. (2012). Spatial analysis along networks. Hoboken, NJ: John Wiley & Sons.
[22]. Persaud, B., Lyon C., & Nguyen T. (1999). Empirical bayes procedure for ranking sites for safety investigation by potential for safety improvement. Transportation Research Record: Journal of the Transportation Research Board, 1665(1), 7-12.
[23]. Thakali, L., Kwon, T. J., & Fu, L. (2015). Identification of crash hotspots using kernel density estimation and kriging methods: A comparison. Journal of Modern Transportation, 23(2), 93–106. https://doi.org/10.1007/s40534-015-0068-0
[24]. Vadlamani, S., Chen, E., Ahn, S., & Washington, S. (2011). Identifying large truck hot spots using crash counts and PDOEs. Journal of Transportation Engineering, 137(1), 11-21.
[25]. Vemulapalli, S. S., Ulak, M. B., Ozguven, E. E., Sando, T., Horner, M. W., Abdelrazig, Y., & Moses, R. (2017). GISbased spatial and temporal analysis of aging-involved accidents: A case study of three counties in florida. Applied Spatial Analysis and Policy, 10(4), 537-563. https://doi.org/ 10.1007/s12061-016-9192-4
[26]. Ver Hoef, J. M., Peterson, E. E., Hooten, M. B., Hanks, E. M., & Fortin, M. J. (2018). Spatial auto regressive models for statistical inference from ecological data. Ecological Monographs, 88(1), 36-59. https://doi.org/10.1002/ec m.1283
[27]. Xie, Z., & Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), 396-406.
[28]. Zahran, E. S. M. M., Tan, S. J., Tan, E. H. A., Mohamad Asri Putra, N. A. A. B., Yap, Y. H., & Abdul Rahman, E. K. (2019). Spatial analysis of road traffic accident hotspots: evaluation and validation of recent approaches using road safety audit. Journal of Transportation Safety and Security, 1–30. https:// doi.org/10.1080/19439962.2019.165867
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