Abstract
Effective means for transportation form a critical city infrastructure, particularly for resource-constrained smart cities. Rapid advancements in information and communication technologies have paved the path for intelligent transportation system (ITS), specifically designed for optimal effectiveness and safety with existing transportation infrastructure. A key function of ITS is its ability to aggregate large volumes of data across various sources for event detection. However, prediction accuracy remains a challenge since ITS event detection is characterized by very stringent latency requirements necessitating the use of lightweight detection schemes, thus seriously compromising the efficiency of ITS. This paper attempts to tackle this problem by introducing an IoT-integrated distributed context-aware fog-cloud ensemble that intelligently manages context instances at fog nodes ensuring availability of context instances for ITS. This system enhances prediction accuracy by utilizing a hybrid convolutional neural network (CNN) where each vehicle within the system retains only local information, while adjacent fog nodes gain access to global events via continual federated learning, updating regularly between fog and cloud models. Experiments presented herein illustrate the superiority of the CNN model, yielding an accuracy of more than 95%, which is an improvement of around 3% compared to the LeNet with same RGB input images.
Similar content being viewed by others
References
Zhu L, Yu FR, Wang Y, Ning B, Tang T (2018) Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 20(1):383–398
Ali ZH, Ali HA (2021) Towards sustainable smart IoT applications architectural elements and design: opportunities, challenges, and open directions. J Supercomput 77:5668–5725
Njoku JN, Nwakanma CI, Amaizu GC, Kim D-S (2023) Prospects and challenges of metaverse application in data-driven intelligent transportation systems. IET Intel Transport Syst 17(1):1–21
Maleknasab Ardakani M, Tabarzad MA, Shayegan MA (2022) Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm. J Supercomput 78(14):16303–35
Schilit B, Adams N, Want R (1994) Context-aware computing applications, In: 1994 First Workshop on Mobile Computing Systems and Applications, pp. 85–90, IEEE
Manaligod HJT, Diño MJS, Ghose S, Han J (2020) Context computing for internet of things
Minh QT, Kamioka E, Yamada S (2018) Cfc-its: context-aware fog computing for intelligent transportation systems. IT Professional 20(6):35–45
Demetriadis SN, Papadopoulos PM, Stamelos IG, Fischer F (2008) The effect of scaffolding students’ context-generating cognitive activity in technology-enhanced case-based learning. Comput Educ 51(2):939–954
Wang J, Chen Q (2021) A traffic prediction model based on multiple factors. J Supercomput 77:2928–2960
Balico LN, Loureiro AA, Nakamura EF, Barreto RS, Pazzi RW, Oliveira HA (2018) Localization prediction in vehicular ad hoc networks. IEEE Commun Surv Tutor 20(4):2784–2803
Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873
Tong M, Duan H, Luo X (2021) Research on short-term traffic flow prediction based on the tensor decomposition algorithm. J Intell Fuzzy Syst 40(3):5731–5741
Xu H, Jiang C (2020) Deep belief network-based support vector regression method for traffic flow forecasting. Neural Comput Appl 32:2027–2036
Wang Y, Ren Q, Li J (2023) Spatial-temporal multi-feature fusion network for long short-term traffic prediction. Expert Syst Appl 224:119959
Zhao L, Hu Q, Wang W (2015) Heterogeneous feature selection with multi-modal deep neural networks and sparse group lasso. IEEE Trans Multimed 17(11):1936–1948
Khairdoost N, Shirpour M, Bauer MA, Beauchemin SS (2020) Real-time driver maneuver prediction using lstm. IEEE Trans Intell Veh 5(4):714–724
Maqueda AI, Loquercio A, Gallego G, García N, Scaramuzza D (2018) Event-based vision meets deep learning on steering prediction for self-driving cars, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5419–5427
Tian Y, Pei K, Jana S, Ray B (2018) Deeptest: Automated testing of deep-neural-network-driven autonomous cars, In: Proceedings of the 40th International Conference on Software Engineering, pp 303–314
Falk A, Granqvist D (2017) Combining deep learning with traditional algorithms in autonomous cars
Mohanta BK, Jena D, Mohapatra N, Ramasubbareddy S, Rawal BS (2022) Machine learning based accident prediction in secure IoT enable transportation system. J Intell Fuzzy Syst 42(2):713–725
Huval B, Wang T, Tandon S, Kiske J, Song W, Pazhayampallil J, Andriluka M, Rajpurkar P, Migimatsu T, Cheng-Yue R, et al (2015) An empirical evaluation of deep learning on highway driving, arXiv preprint arXiv:1504.01716
Zeng T, Ferdowsi A, Semiari O, Saad W, Hong CS (2023) Convergence of communications, control, and machine learning for secure and autonomous vehicle navigation, arXiv preprint arXiv:2307.02663
Olabiyi O, Martinson E, Chintalapudi V, Guo R (2017) Driver action prediction using deep (bidirectional) recurrent neural network, arXiv preprint arXiv:1706.02257
Yan L, Gong Y, Chen Z, Li Z, Guo J (2021) Automatic identification method for driving risk status based on multi-sensor data, Personal and ubiquitous computing, pp. 1–17
Malik M, Nandal R, Dalal S, Jalglan V, Le D-N (2022) Deriving driver behavioral pattern analysis and performance using neural network approaches. Intell Autom Soft Comput. https://doi.org/10.32604/iasc.2022.020249
Roy DS, Behera RK, Reddy KHK, Buyya R (2018) A context-aware fog enabled scheme for real-time cross-vertical IoT applications. IEEE Internet Things J 6(2):2400–2412
Reddy KHK, Behera RK, Chakrabarty A, Roy DS (2020) A service delay minimization scheme for Qos-constrained, context-aware unified IoT applications. IEEE Internet Things J 7(10):10527–10534
Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite, In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 3354–3361, IEEE
KingaD A (2015) A method for stochastic optimization, In: Anon. International Conference on Learning Representations. SanDego: ICLR
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788
Author information
Authors and Affiliations
Contributions
KHK Reddy performed the modeling and implementing. RSG contributed to writing and designing. DSR assisted in designing, implementing and proofreading. All authors revised the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Reddy, K.H.K., Goswami, R.S. & Roy, D.S. A deep learning-based smart service model for context-aware intelligent transportation system. J Supercomput 80, 4477–4499 (2024). https://doi.org/10.1007/s11227-023-05597-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-023-05597-2