A Composite Geospatial Index for Road Safety Risk: Integrating Crash Data with Roadway Characteristics

Authors

  • Siqing Chen *

    Melbourne School of Design, Faculty of Architecture Building and Planning, University of Melbourne, Parkville 3010, Australia

DOI:

https://doi.org/10.55121/tdr.v4i1.1024
Received: 15 December 2025 | Revised: 19 February 2026 | Accepted: 26 February 2026 | Published Online: 4 March 2026

Abstract

Road traffic accidents pose significant threats to public safety and urban infrastructure. While effective safety management is a critical component of sustainable transportation planning and public health, traditional approaches often rely heavily on identifying historical crash hotspots. These reactive methods frequently fail to account for the intrinsic environmental risk factors or overlook segments where crash frequency is low, yet the potential for severe outcomes remains high. To address this limitation, this paper presents a proactive, multi-factor geospatial model for calculating a comprehensive Road Safety Risk index at the individual road-segment level. Utilizing the City of Manningham, Victoria, as a case study, the research employs a GIS framework to synthesize official road network and historical crash data. The model incorporates four distinct risk dimensions: (1) accident frequency normalized by segment length; (2) a weighted accident severity index prioritizing serious incidents; (3) a normalized Speed Zone Factor; and (4) a Road Class Factor accounting for the functional hierarchy of the road network. The resulting risk map provides a granular and nuanced visualization of risk distribution, clearly identifying high-risk arterial corridors and intersections. Crucially, the analysis highlights road segments that, despite lower crash locations, pose significant threats due to a confluence of high speeds, road function, and crash severity. This replicable model serves as a powerful evidence-based tool for transport authorities, enabling a paradigm shift from reactive mitigation to proactive safety management.

Keywords:

Road Safety, Risk Assessment, Geospatial Analysis, Traffic Accidents, Transport Planning, Manningham

References

[1] Oestreich, L., Pereira, B.M., Ruiz-Padillo, A., 2022. Identification of Contributing Factors and Hotspots of Pedestrian-Vehicle Collisions in Urban Areas: An Alternative Hybrid Method for Small Cities. Traffic Injury Prevention. 23(7), 416–421. DOI: https://doi.org/10.1080/15389588.2022.2104838

[2] Jayasinghe, B.C., Withanage, N.C., Mishra, P.K., 2025. Evaluating Geographical Variations of Road Traffic Accidents in Matara, Sri Lanka: A Geospatial Perspective to Policy Decisions. Revue Internationale de Géomatique. 34, 707–729. DOI: https://doi.org/10.32604/rig.2025.067395

[3] Ibe, C.C., Nwokedi, T.C., 2025. Geospatial Probability Mapping of Road Incidents for Prioritizing Road Safety Awareness and De-Escalating Associated Death and Injury Burdens in Nigerian. Transportation Research Procedia. 89, 227–242. DOI: https://doi.org/10.1016/j.trpro.2025.05.058

[4] Litman, T., 2025. Evaluating Transportation Equity: Guidance for Incorporating Distributional Impacts in Transport Planning. Victoria Transport Policy Institute: Victoria, BC, Canada.

[5] Petch, R.O., Henson, R.R., 2000. Child Road Safety in the Urban Environment. Journal of Transport Geography. 8(3), 197–211. DOI: https://doi.org/10.1016/S0966-6923(00)00006-5

[6] Hu, L., Wu, X., Huang, J., et al., 2020. Investigation of Clusters and Injuries in Pedestrian Crashes Using GIS in Changsha, China. Safety Science. 127, 104710. DOI: https://doi.org/10.1016/j.ssci.2020.104710

[7] Pucher, J., Dijkstra, L., 2003. Promoting Safe Walking and Cycling to Improve Public Health: Lessons from the Netherlands and Germany. American Journal of Public Health. 93(9), 1509–1516. DOI: https://doi.org/10.2105/ajph.93.9.1509

[8] Tiwari, G., 2023. Systems-Thinking-Based Road Safety Research: The Way Forward. International Journal of Injury Control and Safety Promotion. 30(4), 471–472. DOI: https://doi.org/10.1080/17457300.2023.2282001

[9] Khatun, M.S., Hossain, M.A., Kabir, M.A., et al., 2024. Identification and Analysis of Accident Black Spots Using Geographic Information System (GIS): A Study on Kushtia-Jhenaidah National Highway (N704), Bangladesh. Heliyon. 10(3), e25952. DOI: https://doi.org/10.1016/j.heliyon.2024.e25952

[10] Alkaabi, K., 2023. Identification of Hotspot Areas for Traffic Accidents and Analyzing Drivers’ Behaviors and Road Accidents. Transportation Research Interdisciplinary Perspectives. 22, 100929. DOI: https://doi.org/10.1016/j.trip.2023.100929

[11] Hu, Y., Wang, F., Guin, C., et al., 2018. A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation. Applied Geography. 99, 89–97. DOI: https://doi.org/10.1016/j.apgeog.2018.08.001

[12] Forte, J.P., Brilha, J., Pereira, D.I., et al., 2018. Kernel Density Applied to the Quantitative Assessment of Geodiversity. Geoheritage. 10(2), 205–217. DOI: https://doi.org/10.1007/s12371-018-0282-3

[13] Savolainen, P.T., Mannering, F.L., Lord, D., et al., 2011. The Statistical Analysis of Highway Crash-Injury Severities: A Review and Assessment of Methodological Alternatives. Accident Analysis & Prevention. 43(5), 1666–1676. DOI: https://doi.org/10.1016/j.aap.2011.03.025

[14] Pinna, M., Serra, P., Porcu, C., et al., 2025. An Integrated Approach for Urban Road Safety Analysis. Transportation Research Procedia. 90, 702–709. DOI: https://doi.org/10.1016/j.trpro.2025.06.089

[15] Hashimoto, S., Yoshiki, S., Saeki, R., et al., 2016. Development and Application of Traffic Accident Density Estimation Models Using Kernel Density Estimation. Journal of Traffic and Transportation Engineering (English Edition). 3(3), 262–270. DOI: https://doi.org/10.1016/j.jtte.2016.01.005

[16] Aghajani, M.A., Dezfoulian, R.S., Arjroody, A.R., et al., 2017. Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (Case Study: Ilam Province, Iran). Transportation Research Procedia. 25, 2126–2138. DOI: https://doi.org/10.1016/j.trpro.2017.05.409

[17] Macedo, M.R.O.B.C., Maia, M.L.A., Robbani, E.R.K., et al., 2022. Traffic Accident Prediction Model for Rural Highways in Pernambuco. Case Studies on Transport Policy. 10(1), 278–286. DOI: https://doi.org/10.1016/j.cstp.2021.12.009

[18] Zhang, H., Zhang, M., Zhang, C., et al., 2021. Formulating a GIS-Based Geometric Design Quality Assessment Model for Mountain Highways. Accident Analysis & Prevention. 157, 106172. DOI: https://doi.org/10.1016/j.aap.2021.106172

[19] Gu, Y., Cho, E., Oh, C., et al., 2025. Riding Safety Evaluation of Food Delivery Motor Scooters Based on Associating Sensor-Based Riding Behavior and Road Traffic Characteristics. Accident Analysis & Prevention. 211, 107871. DOI: https://doi.org/10.1016/j.aap.2024.107871

[20] Pokorny, P., Jensen, J.K., Gross, F., et al., 2020. Safety Effects of Traffic Lane and Shoulder Widths on Two-Lane Undivided Rural Roads: A Matched Case-Control Study from Norway. Accident Analysis & Prevention. 144, 105614. DOI: https://doi.org/10.1016/j.aap.2020.105614

[21] Babić, D., Brijs, T., 2021. Low-Cost Road Marking Measures for Increasing Safety in Horizontal Curves: A Driving Simulator Study. Accident Analysis & Prevention. 153, 106013. DOI: https://doi.org/10.1016/j.aap.2021.106013

[22] Wang, C., Quddus, M., Ison, S., 2009. The Effects of Area-Wide Road Speed and Curvature on Traffic Casualties in England. Journal of Transport Geography. 17(5), 385–395. DOI: https://doi.org/10.1016/j.jtrangeo.2008.06.003

[23] Aultman-Hall, L., Kaltenecker, M.G., 1999. Toronto Bicycle Commuter Safety Rates. Accident Analysis & Prevention. 31(6), 675–686. DOI: https://doi.org/10.1016/S0001-4575(99)00028-7

[24] Chen, C., Anderson, J.C., Wang, H., et al., 2017. How Bicycle Level of Traffic Stress Correlates with Reported Cyclist Accident Injury Severities: A Geospatial and Mixed Logit Analysis. Accident Analysis & Prevention. 108, 234–244. DOI: https://doi.org/10.1016/j.aap.2017.09.001

[25] Erdogan, S., 2009. Explorative Spatial Analysis of Traffic Accident Statistics and Road Mortality Among the Provinces of Turkey. Journal of Safety Research. 40(5), 341–351. DOI: https://doi.org/10.1016/j.jsr.2009.07.006

[26] Ahmed, A., Sadullah, A.F.M., Yahya, A.S., 2019. Errors in Accident Data, Its Types, Causes and Methods of Rectification: Analysis of the Literature. Accident Analysis & Prevention. 130, 3–21. DOI: https://doi.org/10.1016/j.aap.2017.07.018

[27] Jiang, F., Yuen, K.K.R., Lee, E.W.M., 2020. Analysis of Motorcycle Accidents Using Association Rule Mining-Based Framework with Parameter Optimization and GIS Technology. Journal of Safety Research. 75, 292–309. DOI: https://doi.org/10.1016/j.jsr.2020.09.004

[28] Wen, X., Xie, Y., Wu, L., et al., 2021. Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident Analysis & Prevention. 159, 106261. DOI: https://doi.org/10.1016/j.aap.2021.106261

[29] Al-Omari, M.M.A., Abdel-Aty, M., Cai, Q., 2021. Crash analysis and development of safety performance functions for Florida roads in the framework of the context classification system. Journal of Safety Research. 79, 1–13. DOI: https://doi.org/10.1016/j.jsr.2021.08.004

[30] Papadimitriou, E., Filtness, A., Theofilatos, A., et al., 2019. Review and ranking of crash risk factors related to the road infrastructure. Accident Analysis & Prevention. 125, 85–97. DOI: https://doi.org/10.1016/j.aap.2019.01.002

[31] Lord, D., Mannering, F., 2010. The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives. Transportation Research Part A: Policy and Practice. 44(5), 291–305. DOI: https://doi.org/10.1016/j.tra.2010.02.001

[32] Rodrigues, D.S., Ribeiro, P.J.G., da Silva Nogueira, I.C., 2015. Safety Classification Using GIS in Decision-Making Process to Define Priority Road Interventions. Journal of Transport Geography. 43, 101–110. DOI: https://doi.org/10.1016/j.jtrangeo.2015.01.007

[33] Matović, B., Damjanović, M., Ilić, V., et al., 2025. Identification and Ranking of High-Risk Road Segments and Intersections in the Municipality of Bačka Palanka. Transportation Research Procedia. 91, 806–813. DOI: https://doi.org/10.1016/j.trpro.2025.10.103

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