Real-Time Road Crash Severity Prediction for Optimized Resource Allocation

Ranju Mandal; Rupam Deb

doi:10.1007/978-981-96-6966-0_26

Real-Time Road Crash Severity Prediction for Optimized Resource Allocation

Ranju Mandal
, Rupam Deb

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Death, injury, and disability resulting from road traffic accidents remain significant global public health issues. Both developed and developing nations face challenges stemming from growing human and vehicle populations. Consequently, mitigating the severity of traffic accidents is a critical focus for traffic agencies and administrations. This study utilizes a missing value imputation algorithm and Naïve Bayes conditional independence to predict potential injuries following a traffic accident at a specific location in real-time. Using historical traffic accident data from Australia, the research aims to estimate the likely severity of traffic accidents after they occur. The goal is to assist decision-makers in allocating appropriate resources to aid injured individuals at the accident scenes.

Original language	English
Title of host publication	Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings
Editors	Mufti Mahmud, Maryam Doborjeh, Kevin Wong, Andrew Chi Sing Leung, Zohreh Doborjeh, M. Tanveer
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	380-389
Number of pages	10
ISBN (Print)	9789819669653
DOIs	https://doi.org/10.1007/978-981-96-6966-0_26
Publication status	Published - 2024
Event	31st International Conference on Neural Information Processing, ICONIP 2024 - Auckland, New Zealand Duration: 2 Dec 2024 → 6 Dec 2024

Publication series

Name	Communications in Computer and Information Science
Volume	2288 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	31st International Conference on Neural Information Processing, ICONIP 2024
Country/Territory	New Zealand
City	Auckland
Period	2/12/24 → 6/12/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure
SDG 14 Life Below Water

Keywords

Bayesian network
Data mining
Resource allocation
Road asset management
Traffic accident management

Access to Document

10.1007/978-981-96-6966-0_26

Cite this

Mandal, R., & Deb, R. (2024). Real-Time Road Crash Severity Prediction for Optimized Resource Allocation. In M. Mahmud, M. Doborjeh, K. Wong, A. C. S. Leung, Z. Doborjeh, & M. Tanveer (Eds.), Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings (pp. 380-389). (Communications in Computer and Information Science; Vol. 2288 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-6966-0_26

Mandal, Ranju ; Deb, Rupam. / Real-Time Road Crash Severity Prediction for Optimized Resource Allocation. Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. editor / Mufti Mahmud ; Maryam Doborjeh ; Kevin Wong ; Andrew Chi Sing Leung ; Zohreh Doborjeh ; M. Tanveer. Springer Science and Business Media Deutschland GmbH, 2024. pp. 380-389 (Communications in Computer and Information Science).

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abstract = "Death, injury, and disability resulting from road traffic accidents remain significant global public health issues. Both developed and developing nations face challenges stemming from growing human and vehicle populations. Consequently, mitigating the severity of traffic accidents is a critical focus for traffic agencies and administrations. This study utilizes a missing value imputation algorithm and Na{\"i}ve Bayes conditional independence to predict potential injuries following a traffic accident at a specific location in real-time. Using historical traffic accident data from Australia, the research aims to estimate the likely severity of traffic accidents after they occur. The goal is to assist decision-makers in allocating appropriate resources to aid injured individuals at the accident scenes.",

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author = "Ranju Mandal and Rupam Deb",

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Mandal, R & Deb, R 2024, Real-Time Road Crash Severity Prediction for Optimized Resource Allocation. in M Mahmud, M Doborjeh, K Wong, ACS Leung, Z Doborjeh & M Tanveer (eds), Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. Communications in Computer and Information Science, vol. 2288 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 380-389, 31st International Conference on Neural Information Processing, ICONIP 2024, Auckland, New Zealand, 2/12/24. https://doi.org/10.1007/978-981-96-6966-0_26

Real-Time Road Crash Severity Prediction for Optimized Resource Allocation. / Mandal, Ranju; Deb, Rupam.
Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. ed. / Mufti Mahmud; Maryam Doborjeh; Kevin Wong; Andrew Chi Sing Leung; Zohreh Doborjeh; M. Tanveer. Springer Science and Business Media Deutschland GmbH, 2024. p. 380-389 (Communications in Computer and Information Science; Vol. 2288 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Death, injury, and disability resulting from road traffic accidents remain significant global public health issues. Both developed and developing nations face challenges stemming from growing human and vehicle populations. Consequently, mitigating the severity of traffic accidents is a critical focus for traffic agencies and administrations. This study utilizes a missing value imputation algorithm and Naïve Bayes conditional independence to predict potential injuries following a traffic accident at a specific location in real-time. Using historical traffic accident data from Australia, the research aims to estimate the likely severity of traffic accidents after they occur. The goal is to assist decision-makers in allocating appropriate resources to aid injured individuals at the accident scenes.

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Mandal R, Deb R. Real-Time Road Crash Severity Prediction for Optimized Resource Allocation. In Mahmud M, Doborjeh M, Wong K, Leung ACS, Doborjeh Z, Tanveer M, editors, Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 380-389. (Communications in Computer and Information Science). doi: 10.1007/978-981-96-6966-0_26