Main Article Content
Abstract
Many have used the prediction of the number of road accidents, but it is still rare to find those who use and test prediction models that are not suitable. Predictive models that have been used to predict road accidents have proven successful, but have not provided model testing with data that is different from the deep learning approach. The LSTM model test is proposed to be tested with 5 different datasets from Kaggle and 3 hidden layer variations. The test results of the LSTM model are that with variations of 4 hidden layers it can achieve higher accuracy results than those without hidden layers and 2 hidden layers. The results are obtained from stability with the lowest average MSLE value and relatively balanced average time. Deep learning-based LSTM model testing was carried out to ensure and prove the stability of the model for predicting the number of road accidents in the future. Stakeholders can predict the number of road accidents using the resulting prediction model.
Keywords
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Copyright (c) 2024 Joko Siswanto, Benny Daniawan, Haryani Haryani, Pipit Rusmandani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
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References
S. Szénási, “Analysis of historical road accident data supporting autonomous vehicle control strategies,” PeerJ Computer Science, vol. 7, p. e399, Feb. 2021, doi: 10.7717/peerj-cs.399.
K. Khan, S. B. Zaidi, and A. Ali, “Evaluating the Nature of Distractive Driving Factors towards Road Traffic Accident,” Civil Engineering Journal, vol. 6, no. 8, pp. 1555–1580, Aug. 2020, doi: 10.28991/cej-2020-03091567.
T. Mphela, “Causes of road accidents in Botswana: An econometric model,” Journal of Transport and Supply Chain Management, vol. 14, Sep. 2020, doi: 10.4102/jtscm.v14i0.509.
J. Mesquitela, L. B. Elvas, J. C. Ferreira, and L. Nunes, “Data Analytics Process over Road Accidents Data—A Case Study of Lisbon City,” ISPRS International Journal of Geo-Information, vol. 11, no. 2, p. 143, Feb. 2022, doi: 10.3390/ijgi11020143.
S. Shahsavari et al., “Analysis of injuries and deaths from road traffic accidents in Iran: bivariate regression approach,” BMC Emergency Medicine, vol. 22, no. 1, p. 130, Dec. 2022, doi: 10.1186/s12873-022-00686-6.
H. Himanshi, “AN ANALYSIS OF ROAD ACCIDENTS IN INDIA,” INDIAN JOURNAL OF APPLIED RESEARCH, 2020, doi: 10.36106/ijar/4219296.
S. Wang, J. Zhao, C. Shao, C. Dong, and C. Yin, “Truck Traffic Flow Prediction Based on LSTM and GRU Methods With Sampled GPS Data,” IEEE Access, vol. 8, pp. 208158–208169, 2020, doi: 10.1109/ACCESS.2020.3038788.
M. Iqbal et al., “COVID-19 Patient Count Prediction Using LSTM,” IEEE Transactions on Computational Social Systems, vol. 8, no. 4, 2021, doi: 10.1109/TCSS.2021.3056769.
J. Deng, L. Lu, and S. Qiu, “Software defect prediction via LSTM,” IET Software, vol. 14, no. 4, pp. 443–450, Aug. 2020, doi: 10.1049/iet-sen.2019.0149.
T. Xayasouk, H. Lee, and G. Lee, “Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models,” Sustainability, vol. 12, no. 6, p. 2570, Mar. 2020, doi: 10.3390/su12062570.
Y. Chen, “Voltages prediction algorithm based on LSTM recurrent neural network,” Optik, vol. 220, p. 164869, Oct. 2020, doi: 10.1016/j.ijleo.2020.164869.
D. Xia et al., “A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction,” Neural Computing and Applications, vol. 33, no. 7, pp. 2393–2410, 2021, doi: 10.1007/s00521-020-05076-2.
M. Marani, M. Zeinali, V. Songmene, and C. K. Mechefske, “Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling,” Measurement, vol. 177, p. 109329, Jun. 2021, doi: 10.1016/j.measurement.2021.109329.
L. Mu, F. Zheng, R. Tao, Q. Zhang, and Z. Kapelan, “Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model,” J Water Resour Plan Manag, vol. 146, no. 9, Sep. 2020, doi: 10.1061/(ASCE)WR.1943-5452.0001276.
A. Behura and A. Behura, “Road Accident Prediction and Feature Analysis By Using Deep Learning,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2020, pp. 1–7. doi: 10.1109/ICCCNT49239.2020.9225336.
T. Vaiyapuri and M. Gupta, “Traffic accident severity prediction and cognitive analysis using deep learning,” Soft Computing, Nov. 2021, doi: 10.1007/s00500-021-06515-5.
Z. Ma, G. Mei, and S. Cuomo, “An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors,” Accident Analysis & Prevention, vol. 160, p. 106322, Sep. 2021, doi: 10.1016/j.aap.2021.106322.
C. Gutierrez-Osorio, F. A. González, and C. A. Pedraza, “Deep Learning Ensemble Model for the Prediction of Traffic Accidents Using Social Media Data,” Computers, vol. 11, no. 9, p. 126, Aug. 2022, doi: 10.3390/computers11090126.
A. Azhar et al., “Detection and prediction of traffic accidents using deep learning techniques,” Cluster Computing, vol. 26, no. 1, pp. 477–493, Feb. 2023, doi: 10.1007/s10586-021-03502-1.
K. R. Ballamudi, “Road Accident Analysis and Prediction using Machine Learning Algorithmic Approaches,” Asian Journal of Humanity, Art and Literature, vol. 6, no. 2, pp. 185–192, Dec. 2019, doi: 10.18034/ajhal.v6i2.529.
S. Mićić, R. Vujadinović, G. Amidžić, M. Damjanović, and B. Matović, “Accident Frequency Prediction Model for Flat Rural Roads in Serbia,” Sustainability, vol. 14, no. 13, p. 7704, Jun. 2022, doi: 10.3390/su14137704.
A. Borucka, E. Kozłowski, P. Oleszczuk, and A. Świderski, “Predictive analysis of the impact of the time of day on road accidents in Poland,” Open Engineering, vol. 11, no. 1, pp. 142–150, Dec. 2020, doi: 10.1515/eng-2021-0017.
Y. Boo and Y. Choi, “Comparison of mortality prediction models for road traffic accidents: an ensemble technique for imbalanced data,” BMC Public Health, vol. 22, no. 1, p. 1476, Dec. 2022, doi: 10.1186/s12889-022-13719-3.
X. Jin, J. Zheng, and X. Geng, “Prediction of Road Traffic Accidents Based on Grey System Theory and Grey Markov Model,” International Journal of Safety and Security Engineering, vol. 10, no. 2, pp. 263–268, Apr. 2020, doi: 10.18280/ijsse.100214.
R. PEČELIŪNAS, V. ŽURAULIS, P. DROŹDZIEL, and S. PUKALSKAS, “Prediction of Road Accident Risk for Vehicle Fleet Based on Statistically Processed Tire Wear Model,” Promet, vol. 34, no. 4, Jul. 2022, doi: 10.7307/ptt.v34i4.3997.
T. Lei, “Great Britain Road Accidents 2005_2016.” Accessed: Jun. 20, 2023. [Online]. Available: https://www.kaggle.com/datasets/nichaoku/gbaccident0516
D. Fisher-Hickey, “1.6 million UK traffic accidents.” Accessed: Jun. 20, 2023. [Online]. Available: https://www.kaggle.com/datasets/daveianhickey/2000-16-traffic-flow-england-scotland-wales
S. Jalalian, “Road Accident in UK.” Accessed: Jun. 20, 2023. [Online]. Available: https://www.kaggle.com/datasets/sadeghjalalian/road-accident-in-uk
silicon99, “UK Car Accidents 2005-2015.” Accessed: Jun. 20, 2023. [Online]. Available: https://www.kaggle.com/datasets/silicon99/dft-accident-data
T. R, “Thailand Fatal Road Accident [2011-2022].” Accessed: May 10, 2023. [Online]. Available: https://www.kaggle.com/datasets/thaweewatboy/thailand-fatal-road-accident
M. M. Patel, S. Tanwar, R. Gupta, and N. Kumar, “A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions,” Journal of Information Security and Applications, vol. 55, no. August, p. 102583, 2020, doi: 10.1016/j.jisa.2020.102583.
H. K. Dam, T. Tran, T. Pham, S. W. Ng, J. Grundy, and A. Ghose, “Automatic Feature Learning for Predicting Vulnerable Software Components,” IEEE Transactions on Software Engineering, vol. 47, no. 1, pp. 67–85, 2021, doi: 10.1109/TSE.2018.2881961.
A. Mokhtar et al., “Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms,” IEEE Access, vol. 9, pp. 65503–65523, 2021, doi: 10.1109/ACCESS.2021.3074305.
M. Nabipour, P. Nayyeri, H. Jabani, S. S., and A. Mosavi, “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis,” IEEE Access, vol. 8, pp. 150199–150212, 2020, doi: 10.1109/ACCESS.2020.3015966.
Y. Jung, J. Jung, B. Kim, and S. Han, “Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea,” J Clean Prod, vol. 250, p. 119476, Mar. 2020, doi: 10.1016/j.jclepro.2019.119476.
S. Ghimire, Z. M. Yaseen, A. A. Farooque, R. C. Deo, J. Zhang, and X. Tao, “Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks,” Sci Rep, vol. 11, no. 1, p. 17497, Sep. 2021, doi: 10.1038/s41598-021-96751-4.
Md. A. Istiake Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), IEEE, Oct. 2020, pp. 87–92. doi: 10.1109/NILES50944.2020.9257950.
D. Xu, Q. Zhang, Y. Ding, and D. Zhang, “Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting,” Environmental Science and Pollution Research, vol. 29, no. 3, pp. 4128–4144, Jan. 2022, doi: 10.1007/s11356-021-15325-z.
S. Basheer, R. M. Mathew, and M. S. Devi, “Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 127–133, Oct. 2019, doi: 10.35940/ijitee.L3473.1081219.
M. S. Devi, R. M. Mathew, and R. Suguna, “Regressor Fitting Of Feature Importance For Customer Segment Prediction With Ensembling Schemes Using Machine Learning,” Int J Eng Adv Technol, vol. 8, no. 6, pp. 952–956, Aug. 2019, doi: 10.35940/ijeat.F8255.088619.
M. S. Devi, S. Basheer, and R. M. Mathew, “Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 134–139, Oct. 2019, doi: 10.35940/ijitee.L3474.1081219.
N. Singh, P. Sharma, N. Kumar, and M. Sreejeth, “Short-Term Load Forecasting Using Artificial Neural Network and Time Series Model to Predict the Load Demand for Delhi and Greater Noida Cities,” in Lecture Notes in Networks and Systems, vol. 177 LNNS, 2021, pp. 443–455. doi: 10.1007/978-981-33-4501-0_41.