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