One step toward lowering traffic accidents is identifying the locations of road accident hotspots and the appropriate assessment technique. This study evaluated the effectiveness of kernel density estimation (KDE) and the weighted severity index (WSI) in locating blackspots using the ArcGIS tool. Finding out accident severity levels is necessary to identify the accident-related blackspots. The formula for the WSI method was applied. This study examines five-year traffic accidents in the Bhopal, Madhya Pradesh, city intersection zone of the roads to envisage the appropriateness for the given technique on the basis of availability, consistency, and type of the data. This study designates five typical road network intersection zones as blackspot sites based on the criterion. This study's general conclusion is that, during the years 2017 to 2019, Govindpura Turning in Bhopal is a highly accident-prone area, and point density estimation is preferable to kernel density estimation for this purpose. Additionally, this study found that between 2015 and 2017, high-traffic accidents in Sukhisewaniya's Balampur Ghati were more likely to occur in intersection zones with a large number of legs. This study suggests that point density estimation be used to investigate high number of traffic accident areas by looking into blackspot locations at the macroscopic level of road networks. Furthermore, it is necessary to evaluate the effectiveness of the designated blackspot site and suggest corrective measures that reduce the number of accidents and their consequences.
Ma J, Ding Y, Cheng JCP, Tan Y, Gan VJL, Zhang J. Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective. IEEE Access. 2019;7:148059–72.
2.
Achu AL, Aju CD, Suresh V, Manoharan TP, Reghunath R. Spatio-Temporal Analysis of Road Accident Incidents and Delineation of Hotspots Using Geospatial Tools in Thrissur District, Kerala, India. KN - Journal of Cartography and Geographic Information. 2019;69(4):255–65.
3.
Zhai C, Li Z, Jiang F, Ma JJ, Xu Z. A Spatial Analysis Methodology Based on Lazy Ensembled Adaptive Associative Classifier and GIS For Examining the Influential Factors on Traffic Fatalities. IEEE Access. 2020;8:117932–45.
4.
Uchida N, Takeuchi S, Ishida T, Shibata Y. Mobile Traffic Accident Prevention System based on Chronological Changes of Wireless Signals and Sensors. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2017;(3):57–66.
5.
Dhamaniya A, Sonu M, Krishnanunni M, Praveen P, Jaijin A. Development of Web Based Road Accident Data Management System in GIS Environment: a Case Study. Journal of the Indian Society of Remote Sensing. 2016;44(5):789–96.
6.
Drosu A, Cofaru C, Popescu M. Relationships between accident severity and weather and roadway adherence factors in crashes occurred in different type of collisions. 2020;251–64.
7.
Mohan A, Landge V. Identification of accident black spots on national highway. International Journal of Civil Engineering and Technology. 2017;(4):588–96.
8.
Xie G, Shangguan A, Fei R, Hei X, Qian J, F. Unmanned system safety decision-making support: Analysis and assessment of road traffic accidents. IEEE/ASME Transactions on Mechatronics. 2020;(2):633–44.
9.
Haghighi F, Karimi E. Evaluation and statistical validation of black-spots identification methods. International Journal of Transportation Engineering. 2018;(1):1–5.
10.
Alam M, Mahmud S, Hoque M. Road accident trends in Bangladesh: A comprehensive study. Annual Paper Meet and 1st Civil Engineering Congress. 2011;172–81.
11.
Mecheri S, Rosey F, Lobjois R. The effects of lane width, shoulder width, and road cross-sectional reallocation on drivers’ behavioral adaptations. Accident Analysis & Prevention. 2017;65–73.
12.
Zhang C, Shu Y, Yan L. A novel identification model for road traffic accident black spots: A case study in Ningbo. China IEEE Access. 2019;140197–205.
13.
Su J, Wang Y, Chang C, Wu P. Application of a geographic information system to analyze traffic accidents using nantou county, Taiwan, as an example. Journal of the Indian Society of Remote Sensing. 2019;101–11.
14.
Isen L, Shibu A, Saran M. Identification and Analysis of Black spots using Geographic information system. International Journal of Innovative Research in Science, Engineering & Technology. 2013;(1):131–9.
15.
Iqbal A, Rehman Z, Ali S, Ullah K, Ghani U. Road traffic accident analysis and identification of black spot locations on highway. Civil Engineering Journal. 2020;(12):2448–56.
16.
Le KG, Liu P, Lin LT. Traffic accident hotspot identification by integrating kernel density estimation and spatial autocorrelation analysis: a case study. International journal of crash worthiness. 2022;27(2):543–53.
17.
Mirza Aghasi H, N. Introducing GIS as legitimate instrument to deal with road accident data: a case study of Iran, Tehran. Spatial information research. 2017;151–9.
18.
Das D, Ojha A, Kramsapi H, Baruah P, Dutta M. Road network analysis of Guwahati city using GIS. SN Applied Sciences. 2019;1–1.
19.
Özcan M, Küçükönder M. Investigation of spatiotemporal changes in the incidence of traffic accidents in Kahramanmaraş, Turkey, using GIS-based density analysis. Journal of the Indian Society of Remote Sensing. 2020;(7):1045–56.
20.
Chaudhari R, Khode B. Identification & Analysis of Black Spots on National Highways-A Current Scenario. InIOP Conference Series: Earth and Environmental Science. 2024;1326(1):12108.
21.
Harirforoush H, Bellalite L. A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke. Accident Analysis & Prevention. 2019;62–74.
22.
Zambom A, Dias R. A review of kernel density estimation with applications to econometrics. International Econometric Review. 2013;(1):20–42.
23.
Hu Y, Wang F, Guin C, Zhu H. A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied geography. 2018;99:89–97.
24.
Le K, Liu P, Lin L. Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam. Geo-spatial Information Science. 2020;(2):153–64.
25.
Dereli M, Erdogan S. A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transportation Research Part A: Policy and Practice. 2017;106–17.
26.
Iyanda A. Geographic analysis of road accident severity index in Nigeria. International journal of injury control and safety promotion. 2019;(1):72–81.
27.
Fancello G, Carta M, Serra P. Data Envelopment Analysis for the assessment of road safety in urban road networks: A comparative study using CCR and BCC models. Case studies on transport policy. 2020;(3):736–44.
28.
Shafabakhsh G, Famili A, Bahadori M. GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. Journal of traffic and transportation engineering. 2017;(3):290–9.
29.
Sandhu H, Singh G, Sisodia M, Chauhan R. Identification of black spots on highway with kernel density estimation method. Journal of the Indian Society of Remote Sensing. 2016;457–64.
30.
Colak H, Memisoglu T, Erbas Y, Bediroglu S. Hot spot analysis based on network spatial weights to determine spatial statistics of traffic accidents in Rize. Turkey Arabian Journal of Geosciences. 2018;1–1.
31.
Park S, Kim D, Kho S, Rhee S. Identifying hazardous locations based on severity scores of highway crashes. 2010;11–5.
32.
Elvik R, Sagberg F, Langeland P. An analysis of factors influencing accidents on road bridges in Norway. Accident Analysis & Prevention. 2019;1–6.
33.
Amiri A, Nadimi N, Khalifeh V, Shams M. GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques. International journal of injury control and safety promotion. 2021;(3):325–38.
34.
Silverman B. Density estimation for statistics and data analysis. Routledge. 2018;
35.
Pulugurtha S, Krishnakumar V, Nambisan S. New methods to identify and rank high pedestrian crash zones: An illustration. Accident Analysis & Prevention. 2007;(4):800–11.
36.
Zheng Z, Wang Z, Zhu L, Jiang H. Determinants of the congestion caused by a traffic accident in urban road networks. Accident Analysis & Prevention. 2020;105327.
37.
Reddy D, Kavuri N. Black spot analysis using weighted severity index method at Bhongir Zone-Rachakonda Police Commissionerate-Hyderabad. Civil engineering and architecture. 2021;(6):1687–97.
38.
Olusina J, Ajanaku W. Spatial analysis of accident spots using weighted severity index (WSI) and density-based clustering algorithm. Journal of applied sciences and environmental management. 2017;(2):397–403.
39.
Jima D, Sipos T. Examining traffic crash scene using density estimation and its relevance to determine intersection zone road network blackspot location. The Egyptian Journal of Remote Sensing and Space Science. 2023;26(3):595–606.
40.
Gupta S. Solution for Reduction of Traffic Congestion at Polytechnic Roundabout. IJSRD.
41.
Achu A, Aju C, Suresh V, Manoharan T, Reghunath R. Spatio-temporal analysis of road accident incidents and delineation of hotspots using geospatial tools in Thrissur District. 2019;69(4):255–65.
42.
Islam M, Dinar Y. Evaluation and spatial analysis of road accidents in Bangladesh: an emerging and alarming issue. Transportation in developing economies. 2021;7:1–4.
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