Time Series Traffic Prediction With Vehicle-Type Suggestions
DOI:
https://doi.org/10.63665/5vjrj527Keywords:
Traffic Prediction, LSTM, YOLOv8, Smart Cities, AI, Computer VisionAbstract
Urban traffic congestion is a major challenge in modern cities, leading to increased travel times, fuel consumption, and air pollution. To address this issue, Deep Traffic-VTS presents an intelligent hybrid system that integrates Long Short-Term Memory (LSTM) networks for time series forecasting with YOLO (You Only Look Once) for vehicle detection. The LSTM model analyzes historical traffic data—such as vehicle count, average speed, and congestion levels—to predict traffic conditions over upcoming time intervals. Simultaneously, YOLO processes live video feeds to detect and classify vehicle types on the road, including two-wheelers, cars, buses, and emergency vehicles. Based on both predicted and real-time traffic conditions, the system provides adaptive suggestions on the most suitable vehicle types for efficient navigation—for example, recommending two-wheelers in high-congestion zones due to their maneuverability, while advising larger vehicles to reroute or delay travel. This combined approach enables more effective traffic management, emergency response optimization, and smart urban mobility planning.
Based on both predicted and real-time traffic conditions, the system provides adaptive suggestions on the most suitable vehicle types for efficient navigation. Specifically, when congestion levels are high, the system recommends small vehicles such as two-wheelers due to their maneuverability; for medium congestion, it suggests medium-sized vehicles like cars; and for low congestion, it supports the use of larger vehicles such as buses and trucks for efficient mass transportation. In addition, emergency vehicles like ambulances and fire trucks can be given priority-based navigation routes, reducing response times in critical situations.
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[1] Z. Yang, W. Zhang, and J. Feng, ‘‘Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework,’’ Saf. Sci., vol. 146, Feb. 2022, Art. no. 105522, doi: 10.1016/j.ssci.2021.105522.
[2] F. Zong, X. Chen, J. Tang, P. Yu, and T. Wu, ‘‘Analyzing traffic crash severity with combination of information entropy and Bayesian network,’’ IEEE Access, vol. 7, pp. 63288–63302, 2019, doi: 10.1109/ACCESS.2019.2916691.
[3] Global Status Report on Road Safety 2018, World Health Organization, Geneva, Switzerland, 2018. [Online]. Available: https://apps.who.int/iris/handle/10665/276462
[4] J. Roland, P. D. Way, C. Firat, T.-N. Doan, and M. Sartipi, ‘‘Modeling and predicting vehicle accident occurrence in Chattanooga, Tennessee,’’ Accident Anal. Prevention, vol. 149, Jan. 2021, Art. no. 105860, doi: 10.1016/j.aap.2020.105860.
[5] R. Elvik, F. Sagberg, and P. A. Langeland, ‘‘An analysis of factors influencing accidents on road bridges in Norway,’’ Accident Anal. Prevention, vol. 129, pp. 1–6, Aug. 2019, doi: 10.1016/j.aap.2019.05.002.
[6] L. Eboli, C. Forciniti, and G. Mazzulla, ‘‘Factors influencing accident severity: An analysis by road accident type,’’ Transp. Res. Proc., vol. 47, pp. 449–456, Jan. 2020, doi: 10.1016/j.trpro.2020.03.120.
[7] R. L. Rodriguez, J. Tricia, B. Villamaria, and M. I. Noroña, ‘‘Analysis of factors affecting road traffic accidents in the city of makati Philippines,’’ Tech. Rep., 2021.
[8] I. Pugachev, Y. Kulikov, G. Markelov, and N. Sheshera, ‘‘Factor analysis of traffic organization and safety systems,’’ Transp. Res. Proc., vol. 20, pp. 529–535, Jan. 2017, doi: 10.1016/j.trpro.2017.01.086.
[9] B. Dadashova, B. A. Ramírez, J. M. McWilliams, and F. A. Izquierdo, ‘‘The identification of patterns of interurban road accident frequency and severity using road geometry and traffic indicators,’’ Transp. Res. Proc., vol. 14, pp. 4122–4129, Jan. 2016, doi: 10.1016/j.trpro.2016.05.383.
[10] A. Montella, L. L. Imbriani, V. Marzano, and F. Mauriello, ‘‘Effects on speed and safety of point-to-point speed enforcement systems: Evaluation on the urban motorway A56 Tangenziale di Napoli,’’ Accident Anal. Prevention, vol. 75, pp. 164–178, Feb. 2015, doi: 10.1016/j.aap.2014.11.022.
[11] F. Gross and E. T. Donnell, ‘‘Case–control and cross-sectional methods for estimating crash modification factors: Comparisons from roadway lighting and lane and shoulder width safety effect studies,’’ J. Saf. Res., vol. 42, no. 2, pp. 117–129, Apr. 2011, doi: 10.1016/j.jsr.2011.03.003.
[12] H. Martensen, K. Diependaele, S. Daniels, W. Van den Berghe, E. Papadimitriou, G. Yannis, I. Van Schagen, W. Weijermars, W. Wijnen, A. Filtness, R. Talbot, P. Thomas, K. Machata, E. Aigner Breuss, S. Kaiser, T. Hermitte, R. Thomson, and R. Elvik, ‘‘The European road safety decision support system on risks and measures,’’ Accident Anal. Prevention, vol. 125, pp. 344–351, Apr. 2019, doi: 10.1016/j.aap.2018.08.005.
[13] R. Sakhapov and R. Nikolaeva, ‘‘Traffic safety system management,’’ Transp. Res. Proc., vol. 36, pp. 676–681, Jan. 2018, doi: 10.1016/j.trpro.2018.12.126.
[14] G. Fancello, M. Carta, and P. Fadda, ‘‘A decision support system for road safety analysis,’’ Transp. Res. Proc., vol. 5, pp. 201–210, Jan. 2015, doi: 10.1016/j.trpro.2015.01.009.
[15] F. Gargiulo, S. Silvestri, M. Ciampi, and G. De Pietro, ‘‘Deep neural network for hierarchical extreme multi-label text classification,’’ Appl. Soft Comput., vol. 79, pp. 125–138, Jun. 2019, doi: 10.1016/j.asoc.2019.03.041.
[16] T. M. Allen, ‘‘A factor analysis of accident records,’’ Highway Res. Rec., vol. 79, pp. 17–25, Jan. 1965.
[17] T. Champahom, P. Wisutwattanasak, K. Chanpariyavatevong, N. Laddawan, S. Jomnonkwao, and V. Ratanavaraha, ‘‘Factors affecting severity of motorcycle accidents on Thailand’s arterial roads: Multiple correspondence analysis and ordered logistics regression approaches,’’ IATSS Res., vol. 46, no. 1, pp. 101–111, Apr. 2022, doi: 10.1016/j.iatssr.2021.10.006.
[18] T. Tsubota, C. Fernando, T. Yoshii, and H. Shirayanagi, ‘‘Effect of road pavement types and ages on traffic accident risks,’’ Transp. Res. Proc., vol. 34, pp. 211–218, Jan. 2018, doi: 10.1016/j.trpro.2018.11.034.
[19] L.-Y. Chang, H.-C. Chu, D.-J. Lin, and P. Lui, ‘‘Analysis of freeway accident frequency using multivariate adaptive regression splines,’’ Proc. Eng., vol. 45, pp. 824–829, Jan. 2012, doi: 10.1016/j.proeng.2012.08.245.
[20] G. Guido, S. S. Haghshenas, S. S. Haghshenas, A. Vitale, V. Astarita, Y. Park, and Z. W. Geem, ‘‘Evaluation of contributing factors affecting number of vehicles involved in crashes using machine learning techniques in rural roads of Cosenza, Italy,’’ Safety, vol. 8, no. 2, p. 28, Apr. 2022, doi: 10.3390/safety8020028.
[21] S. Kumar and D. Toshniwal, ‘‘A data mining framework to analyze road accident data,’’ J. Big Data, vol. 2, no. 1, Dec. 2015, doi: 10.1186/s40537- 015-0035-y.
[22] T. Chen, C. Zhang, and L. Xu, ‘‘Factor analysis of fatal road traffic crashes with massive casualties in China,’’ Adv. Mech. Eng., vol. 8, no. 4, pp. 1–11, Apr. 2016, doi: 10.1177/1687814016642712.
[23] L. Liu, X. Ye, T. Wang, X. Yan, J. Chen, and B. Ran, ‘‘Key factors analysis of severity of automobile to two-wheeler traffic accidents based on Bayesian network,’’ Int. J. Environ. Res. Public Health, vol. 19, no. 10, p. 6013, May 2022, doi: 10.3390/ijerph19106013.
[24] T. Beshah and S. Hill, ‘‘Mining road traffic accident data to improve safety: Role of road-related factors on accident severity in Ethiopia,’’ in Proc. AAAI Spring Symp. Ser., Mar. 2010. [25] H. Chen, Y. Zhao, and X. Ma, ‘‘Critical factors analysis of severe traffic accidents based on Bayesian network in China,’’ J. Adv. Transp., vol. 2020, pp. 1–14, Nov. 2020, doi: 10.1155/2020/8878265.
[26] E. Sacchi, T. Sayed, and A. Osama, ‘‘Developing crash modification functions for pedestrian signal improvement,’’ Accident Anal. Prevention, vol. 83, pp. 47–56, Oct. 2015, doi: 10.1016/j.aap.2015.07.009.
[27] E. Sacchi and T. Sayed, ‘‘Accounting for heterogeneity among treatment sites and time trends in developing crash modification functions,’’ Accident Anal. Prevention, vol. 72, pp. 116–126, Nov. 2014, doi: 10.1016/j.aap.2014.06.016.
[28] R. B. Patel, F. M. Council, and M. S. Griffith, ‘‘Estimating safety benefits of shoulder rumble strips on two-lane rural highways in Minnesota: Empirical Bayes observational before-and-after study,’’ Transp. Res. Rec., J. Transp. Res. Board, vol. 2019, no. 1, pp. 205–211, Jan. 2007, doi: 10.3141/2019-24.
[29] F. Gross, C. Lyon, B. Persaud, and R. Srinivasan, ‘‘Safety effectiveness of converting signalized intersections to roundabouts,’’ Accident Anal. Prevention, vol. 50, pp. 234–241, Jan. 2013, doi: 10.1016/j.aap. 2012.04.012.
[30] M. A. Raihan, P. Alluri, W. Wu, and A. Gan, ‘‘Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models,’’ Accident Anal. Prevention, vol. 123, pp. 303–313, Feb. 2019, doi: 10.1016/j.aap.2018.12.009.
[31] A. Osama, T. Sayed, and E. Sacchi, ‘‘Crash modification functions for installation of left-turn lanes at signalized intersection approaches,’’ Transp. Res. Rec., J. Transp. Res. Board, vol. 2583, no. 1, pp. 42–49, Jan. 2016, doi: 10.3141/2583-06.
[32] Q. Li, Z. Wang, M. Li, R. Yang, P.-S. Lin, and X. Li, ‘‘Development of crash modification factors for roadway illuminance: A matched case-control study,’’ Accident Anal. Prevention, vol. 159, Sep. 2021, Art. no. 106279, doi: 10.1016/j.aap.2021.106279.
[33] F. Gross and P. P. Jovanis, ‘‘Estimation of safety effectiveness of changes in shoulder width with case control and cohort methods,’’ Transp. Res. Rec., J. Transp. Res. Board, vol. 2019, no. 1, pp. 237–245, Jan. 2007, doi: 10.3141/2019-28. [34] A. Calvi and C. Petrella, ‘‘An evaluation of the effectiveness of countermeasures for improving the safety of dilemma zones: A driving simulator study,’’ Transp. Res. F, Traffic Psychol. Behav., vol. 87, pp. 295–312, May 2022, doi: 10.1016/j.trf.2022.04.013.
[35] Q. Hussain, W. K. M. Alhajyaseen, K. Brijs, A. Pirdavani, and T. Brijs, ‘‘Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study,’’ Accident Anal. Prevention, vol. 134, Jan. 2020, Art. no. 105349, doi: 10.1016/j.aap.2019.105349.
[36] M. Paul, I. Ghosh, and M. M. Haque, ‘‘The effects of green signal countdown timer and retiming of signal intervals on dilemma zone related crash risk at signalized intersections under heterogeneous traffic conditions,’’ Saf. Sci., vol. 154, Oct. 2022, Art. no. 105862, doi: 10.1016/j.ssci.2022.105862.
[37] S. Kazemzadehazad, S. Monajjem, G. S. Larue, and M. J. King, ‘‘Evaluating new treatments for improving driver performance on combined horizontal and crest vertical curves on two-lane rural roads: A driving simulator study,’’ Transp. Res. F, Traffic Psychol. Behav., vol. 62, pp. 727–739, Apr. 2019, doi: 10.1016/j.trf.2019.03.002.
[38] D. R. Ragland, A. A. Zabyshny, and E. Org. (2003). UC Berkeley Research Reports Title Intersection Decision Support Project: Taxonomy of Crossing-Path Crashes At Intersections Using GES 2000 Data Permalink Https://escholarship.org/uc/item/0201j0v2 Publication Date. [Online]. Available: https://escholarship.org/uc/item/0201j0v2
[39] A. P. Chassiakos, C. Panagolia, and D. D. Theodorakopoulos, ‘‘Development of decision-support system for managing highway safety,’’ J. Transp. Eng., vol. 131, no. 5, pp. 364–373, Apr. 2005, doi: 10.1061/(ASCE)0733- 947X(2005)131:5(364).
[40] D. Shinar and E. Hauer, ‘‘Crash causation, countermeasures, and policy– editorial,’’ Accident Anal. Prevention, vol. 201, Jun. 2024, Art. no. 107543, doi: 10.1016/j.aap.2024.107543.
[41] R. Elvik, ‘‘Risk factors as causes of accidents: Criterion of causality, logical structure of relationship to accidents and completeness of explanations,’’ Accident Anal. Prevention, vol. 197, Mar. 2024, Art. no. 107469, doi: 10.1016/j.aap.2024.107469.
[42] M. Bello, G. Nápoles, R. Sánchez, R. Bello, and K. Vanhoof, ‘‘Deep neural network to extract high-level features and labels in multi-label classification problems,’’ Neurocomputing, vol. 413, pp. 259–270, Nov. 2020, doi: 10.1016/j.neucom.2020.06.117.
[43] Y. Xu, Q. Huang, W. Wang, P. Foster, S. Sigtia, P. J. B. Jackson, and M. D. Plumbley, ‘‘Unsupervised feature learning based on deep models for environmental audio tagging,’’ IEEE/ACM Trans. Audio, Speech, Language Process., vol. 25, no. 6, pp. 1230–1241, Jun. 2017, doi: 10.1109/TASLP.2017.2690563.
[44] L. Chen, Y. Wang, and H. Li, ‘‘Enhancement of DNN-based multilabel classification by grouping labels based on data imbalance and label correlation,’’ Pattern Recognition., vol. 132, Dec. 2022, Art. no. 108964, doi: 10.1016/j.patcog.2022.108964.
[45] A. Maxwell, R. Li, B. Yang, H. Weng, A. Ou, H. Hong, Z. Zhou, P. Gong, and C. Zhang, ‘‘Deep learning architectures for multi-label classification of intelligent health risk prediction,’’ BMC Bioinf., vol. 18, no. S14, pp. 121–131, Dec. 2017, doi: 10.1186/s12859-017-1898-z.
[46] H. Liu, X. Li, and S. Zhang, ‘‘Learning instance correlation functions for multilabel classification,’’ IEEE Trans. Cybern., vol. 47, no. 2, pp. 499–510, Feb. 2017, doi: 10.1109/TCYB.2016.2519683.
[47] H. H. Mohammed, E. Dogdu, A. K. Gorur, and R. Choupani, ‘‘Multi-label classification of text documents using deep learning,’’ in Proc. IEEE Int. Conf. Big Data (Big Data), Dec. 2020, pp. 4681–4689, doi: 10.1109/BigData50022.2020.9378266.
[48] F. Subhan, S. Zhao, E. B. Diop, Y. Ali, and H. Zhou, ‘‘Public intention to pay for road safety improvement: A case study of Pakistan,’’ Accident Anal. Prevention, vol. 160, Sep. 2021, Art. no. 106315, doi: 10.1016/j.aap.2021.106315.
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