Abstract
The rapid growth in the number of road users and poor road management have been deemed responsible for the upsurge in road congestions and fatalities in recent times. Many of the lives lost was due to inadequate or inefficient public-accessible alerts system and rerouting mechanisms during emergencies. The Intelligent Transportation System (ITS) was anticipated as a solution to the numerous road networks usage problems. Recently, some developed countries have implemented some forms of ITS initiatives. But the transition of the road networks to a fully integrated ITS has been slow and daunting due to the huge cost of implementation. The use of mobile devices as backbone infrastructure for ITS networks during public emergencies has been proposed. Despite the advantage of being a cheap alternative, low computing power of mobile devices limit their potentials to support the expected Big Data ITS traffic. In this paper, we propose a cloud-based context-sensitive ITS infrastructure that uses the cloud as a primary aggregator of traffic messages plus a hybrid Data Analytics algorithm. The algorithm combines the enhanced features of Apache-Spark and Kafka frameworks blended with collaborative filtering using the ensemble machine learning classifier. The novelty of our approach stems from its ability to provide load balancing routing services based on the users’ profiles, and avoid congestion-using the Dynamic Round Robin scheduling algorithm to reroute users with similar profiles.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Toroyan, T.: Global status report on road safety. Inj. Prev. 15(4), 286–289 (2009)
Zheng, K., Zheng, Q., Chatzimisios, P., Xiang, W., Zhou, Y.: Heterogeneous vehicular networking: a survey on architecture, challenges, and solutions. IEEE Commun. Surv. Tutor. 17(4), 2377–2396 (2015)
U.S. Department of Transportation (2020) About ITS - Frequently Asked Questions. https://www.its.dot.gov/about/faqs.htm. Accessed 28 Apr 2020
U.S. Department of Transportation Effects on Intelligent Transportation Systems Planning and Deployment in a Connected Vehicle Environment (2018). https://ops.fhwa.dot.gov/publications/fhwahop18014/fhwahop18014.pdf. Accessed 24 Apr 2020
Ezell, S.: Explaining international IT application leadership: intelligent transportation systems. ITIF-The Information Technology & Innovation Foundation, Washington, DC (2010). https://itif.org/files/2010-1-27-ITS-Leadership.pdf. Accessed 3 May 2020
Vanajakshi, L., Ramadurai, G., Anand, A.: Intelligent Transportation Systems Synthesis Report on ITS Including Issues and Challenges in India, Centre of Excellence in Urban Transport (2010)
Young, K.: Overcoming Barriers to ITS Deployment in Korea, Presentation to the ITS World Congress (2008)
Li, R., Jiang, C., Zhu, F., Chen, X.: Traffic flow data forecasting based on interval type-2 fuzzy sets theory. IEEE/CAA J. Autom. Sinica 3(2), 141–148 (2016)
Vorhies, B.: The Big Deal About Big Data: What’s Inside-Structured, Unstructured, and Semi-Structured Data (2013). http://data-magnum.com/the-big-deal-about-big-data-whats-inside-structured-unstructured-and-semi-structureddata/. Accessed 24 Apr 2020
Brooks, R.R., Sander, S., Deng, J., Taiber, J.: Automobile security concerns. IEEE Veh. Technol. 4(2), 52–64 (2009)
Zhu, L., Yu, F.R., Wang, Y., Ning, B., Tang, T.: Big data analytics in intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 20(1), 383–398 (2019). https://doi.org/10.1109/TITS.2018.2815678
Hilbert, M., López, P.: The world’s technological capacity to store, communicate, and compute information. Science 332(6025), 60–65 (2011)
Wang, W., Krishnan, R., Diehl, A.: Advances and Challenges in Intelligent Transportation: The Evolution of ICT to Address Transport Challenges in Developing Countries (2015). https://openknowledge.worldbank.org/handle/10986/25006. Accessed 24 Apr 2020
Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A.S., Buyya, R.: Big Data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79(80) 3–15 (2013)
Waze Apps (2020). https://www.waze.com/. Accessed 3 May 2020
Chowdhury, M., Apon, A., Dey, K.: Data Analytics for Intelligent Transportation Systems. Amsterdam, The Netherlands (2017)
Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., Shook, E.: Mapping the global Twitter heartbeat: the geography of Twitter. 18(5)(2013). https://firstmonday.org/article/view/4366/3654. Accessed 3 May 2020
Inrix Traffic Apps (2016). http://inrix.com/mobile-apps/. Accessed 3 May 2020
Moovit Apps (2018). http://moovitapp.com/. Accessed 3 May 2020
Gaode Apps2 (2019). http://gaode.com/. Accessed 3 May 2020
Balogun, V.F., Obe, O.O., Balogun, T.M.: Location-based mobile alert system for intelligent transportation system. Int. J. Adv. Res. Eng. Appl. Sci. (IJAREAS) 3(1), 11–26 (2017)
Ringhand, M.: Factors influencing drivers’ urban route choice. Ph.D. Dissertation (2019). https://www.researchgate.net/publication/335422150-Factors-influencing-drivers%27-urban-route-choice. Accessed 24 Apr 2020
Bovy, P.H.L., Stern, E.: Route Choice: Wayfinding in Transport Networks, vol. 9. Springer, Dordrecht (1990)
Segadilha, A.B.P., da Penha Sanches, S.: Identification of factors that influence cyclistś route choice. Procedia-Soc. Behav. Sci. 160, 372–380 (2014)
Tawfik, A.M., Rakha, H.A.: Network route-choice evolution in a real-time experiment: a necessary shift from network to driver oriented modeling. In: 91st Annual Meeting of Transportation Research Board Paper Compendium DVD 12-1640 (2012)
Papinski, D., Scott, D.M., Doherty, S.T.: Exploring the route choice decision-making process: a comparison of planned and observed routes obtained using person-based GPS. Transp. Res. Part F: Traffic Psychol. Behav. 12(4), 347–358 (2009). https://doi.org/10.1016/j.trf.2009.04.001
Chamali, H., Baman Bandara, W.S.: Analysis of factors affecting pedestrian route choice. J. Chem. Inf. Model. 53(9), 1689–1699 (2013)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1) 269–271(1959)
Farooq, M.U., Shakoor, A., Siddique, A.: An efficient dynamic round robin algorithm for CPU scheduling. In: IEEE International Conference on Communication, Computing and Digital Systems, pp. 244–248 (2017)
Seshasayee, A., Lakshmi, J.V.N.: An insight into tree based machine learning techniques for big data analytics using Apache Spark. In: International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 1740–1743 (2018)
Sarumi, O.A., Leung, C.K., Adetunmbi, O.A.: Spark-based data analytics of sequence motifs in large omics data. Procedia Comput. Sci. 126, 596–605 (2018)
Le Noac’h, P., Costan, A., Bougé, L.: A performance evaluation of Apache Kafka in support of big data streaming applications. In: IEEE International Conference on Big Data (Big Data), pp. 4803–4806 (2017)
Jiang, F., Leung, C.K., Sarumi, O.A., Zhang, C.Y.: Mining sequential patterns from uncertain big DNA in the Spark framework. In: IEEE BIBM, pp. 874–88 (2016)
Matei, Z., et al.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)
Sarumi, O.A., Leung, C.K.: Scalable data science and machine learning algorithm for gene prediction. In: The 7th International Conference on Big Data Applications, pp. 118–126 (2019)
Cardoso, P.V., Barcelos, P.P.: Definition of an architecture for dynamic and automatic checkpoints on Apache Spark. In: IEEE 37th Symposium on Reliable Distributed Systems (SRDS), pp. 271–272 (2018)
Venil, P., Vinodhini, G., Suban, R.: Performance evaluation of ensemble based collaborative filtering recommender system. In: IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5 (2019)
Thepade, S.D., Kalbhor, M.M.: Ensemble of machine learning classifiers for improved image category prediction using fractional coefficients of Hartley and sine transforms. In: Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Balogun, V., Sarumi, O.A., Obe, O.O. (2021). A Context-Sensitive Cloud-Based Data Analytic Mobile Alert and Optimal Route Discovery System for Rural and Urban ITS Penetration. In: Martins, A.L., Ferreira, J.C., Kocian, A., Costa, V. (eds) Intelligent Transport Systems, From Research and Development to the Market Uptake. INTSYS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-030-71454-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-71454-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71453-6
Online ISBN: 978-3-030-71454-3
eBook Packages: Computer ScienceComputer Science (R0)