Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
SPM: Modeling Spark Task Execution Time from the Sub-stage Perspective Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Fog Computing Based Traffic and Car Parking Intelligent System

Authors : Walaa Alajali, Shang Gao, Abdulrahman D. Alhusaynat

Published in: Algorithms and Architectures for Parallel Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Internet of Things (IoT) has attracted the attention of researchers from both industry and academia. Smart city, as one of the IoT applications, includes several sub-applications, such as intelligent transportation system (ITS), smart car parking and smart grid. Focusing on traffic flow management and car parking systems because of their correlation, this paper aims to provide a framework solution to both systems using online detection and prediction based on fog computing. Online event detection plays a vital role in traffic flow management, as circumstances, such as social events and congestion resulting from accidents and roadworks, affect traffic flow and parking availability. We developed an online prediction model using an incremental decision tree and distributed the prediction process on fog nodes at each intersection traffic light responsible for a connecting road. It effectively reduces the load on the communication network, as the data is processed, and the decision is made locally, with low storage requirements. The spatially correlated fog nodes can communicate if necessary to take action for an emergency. The experiments were conducted using the Melbourne city open data.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A New Bitcoin Address Association Method Using a Two-Level Learner Model
next chapter Semi-supervised Deep Learning for Network Anomaly Detection
Literature
1.
Ahangari, S., Chavis, C., Jeihani, M., Moghaddam, Z.R.: Quantifying the impact of on-street parking information on congestion mitigation using a driving simulator. Technical report (2018)
2.
3.
Alajali, W., Zhou, W., Wen, S.: Traffic flow prediction for road intersection safety. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 812–820. IEEE (2018)
4.
Alajali, W., Zhou, W., Wen, S., Wang, Y.: Intersection traffic prediction using decision tree models. Symmetry 10(9), 386 (2018)CrossRef
5.
Anantharam, P., Thirunarayan, K., Marupudi, S., Sheth, A.P., Banerjee, T.: Understanding city traffic dynamics utilizing sensor and textual observations. In: AAAI, pp. 3793–3799 (2016)
6.
Ángel, A.M., Bartolo, G.J., Ernestina, M.: Predicting recurring concepts on data-streams by means of a meta-model and a fuzzy similarity function. Expert Syst. Appl. 46, 87–105 (2016)CrossRef
7.
Anwar, T.: Spatial partitioning of road traffic networks and their temporal evolution. Ph.D. thesis, Swinburne University of Technology (2017)
8.
Arnott, R., Inci, E.: An integrated model of downtown parking and traffic congestion. J. Urban Econ. 60(3), 418–442 (2006)CrossRef
9.
Asif, M.T., et al.: Spatiotemporal patterns in large-scale traffic speed prediction. IEEE Trans. Intell. Transp. Syst. 15(2), 794–804 (2014)CrossRef
10.
Backfrieder, C., Ostermayer, G., Mecklenbräuker, C.F.: Increased traffic flow through node-based bottleneck prediction and V2X communication. IEEE Trans. Intell. Transp. Syst. 18(2), 349–363 (2017)CrossRef
11.
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11(May), 1601–1604 (2010)
12.
Buyya, R., Dastjerdi, A.V.: Internet of Things: Principles and paradigms. Elsevier, Amsterdam (2016)
13.
Castro-Neto, M., Jeong, Y.S., Jeong, M.K., Han, L.D.: Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst. Appl. 36(3), 6164–6173 (2009)CrossRef
14.
Chen, D.: Research on traffic flow prediction in the big data environment based on the improved RBF neural network. IEEE Trans. Ind. Inform. 13, 2000–2008 (2017)CrossRef
15.
Clark, S.: Traffic prediction using multivariate nonparametric regression. J. Transp. Eng. 129(2), 161–168 (2003)CrossRef
16.
17.
De Francisci Morales, G., Bifet, A., Khan, L., Gama, J., Fan, W.: IoT big data stream mining. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2119–2120. ACM (2016)
18.
Dell’Acqua, P., Bellotti, F., Berta, R., De Gloria, A.: Time-aware multivariate nearest neighbor regression methods for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 16(6), 3393–3402 (2015)CrossRef
19.
Faria, E.R., Gonçalves, I.J., de Carvalho, A.C., Gama, J.: Novelty detection in data streams. Artif. Intell. Rev. 45(2), 235–269 (2016)CrossRef
20.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)CrossRef
21.
Hajibaba, M., Gorgin, S.: A review on modern distributed computing paradigms: cloud computing, jungle computing and fog computing. J. Comput. Inform. Technol. 22(2), 69–84 (2014)CrossRef
22.
Hamed, M.M., Al-Masaeid, H.R., Said, Z.M.B.: Short-term prediction of traffic volume in urban arterials. J. Transp. Eng. 121(3), 249–254 (1995)CrossRef
23.
Henry, K.: To build or not to build: infrastructure challenges in the years ahead and the role of the government: address to the conference on the economics of infrastructure in a globalised world: issues, lessons and future challenges (2010)
24.
Hu, P., Dhelim, S., Ning, H., Qiu, T.: Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98, 27–42 (2017)CrossRef
25.
Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Disc. 23(1), 128–168 (2011)MathSciNetCrossRef
26.
Jeong, Y.S., Byon, Y.J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 14(4), 1700–1707 (2013)CrossRef
27.
Jia, R., Jiang, P., Liu, L., Cui, L., Shi, Y.: Data driven congestion trends prediction of urban transportation. IEEE Internet Things J. 5, 581–591 (2017)CrossRef
28.
Kim, Y.J., Hong, J.S., et al.: Urban traffic flow prediction system using a multifactor pattern recognition model. IEEE Trans. Intell. Transp. Syst. 16(5), 2744–2755 (2015)CrossRef
29.
Lana, I., Del Ser, J., Velez, M., Vlahogianni, E.I.: Road traffic forecasting: recent advances and new challenges. IEEE Intell. Transp. Syst. Mag. 10(2), 93–109 (2018)CrossRef
30.
Lee, S., Fambro, D.: Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp. Res. Rec.: J. Transp. Res. Board 1678, 179–188 (1999)CrossRef
31.
Levy, J.I., Buonocore, J.J., Von Stackelberg, K.: Evaluation of the public health impacts of traffic congestion: a health risk assessment. Environ. Health 9(1), 65 (2010)CrossRef
32.
Lindley, J.A.: Urban freeway congestion: quantification of the problem and effectiveness of potential solutions. ITE J. 57(1), 27–32 (1987)
33.
Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A.: A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans. Intell. Transp. Syst. 17(2), 557–569 (2016)CrossRef
34.
Min, X., Hu, J., Zhang, Z.: Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1535–1540. IEEE (2010)
35.
Mitrovic, N., Asif, M.T., Dauwels, J., Jaillet, P.: Low-dimensional models for compressed sensing and prediction of large-scale traffic data. IEEE Trans. Intell. Transp. Syst. 16(5), 2949–2954 (2015)CrossRef
36.
Mouss, H., Mouss, D., Mouss, N., Sefouhi, L.: Test of page-hinckley, an approach for fault detection in an agro-alimentary production system. In: 2004 5th Asian Control Conference (IEEE Cat. No. 04EX904), vol. 2, pp. 815–818. IEEE (2004)
37.
Networking, T.D.: Connected vehicular transportation. IEEE Veh. Technol. Mag. 12, 42–54 (2017)CrossRef
38.
Schrank, D., Eisele, B., Lomax, T., Bak, J.: Urban mobility scorecard. Texas A&M Transportation Institute and the Texas A&M University System (2015)
39.
Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1–8. IEEE (2014)
40.
Bureau of Transport and Regional Economics: Estimating urban traffic and congestion cost trends for Australian cities (2007)
41.
Wibisono, A., Jatmiko, W., Wisesa, H.A., Hardjono, B., Mursanto, P.: Traffic big data prediction and visualization using fast incremental model trees-drift detection (FIMT-DD). Knowl.-Based Syst. 93, 33–46 (2016)CrossRef
42.
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)CrossRef
43.
Wongcharoen, S., Senivongse, T.: Twitter analysis of road traffic congestion severity estimation. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6. IEEE (2016)
44.
Xiao, J., Xiao, Z., Wang, D., Bai, J., Havyarimana, V., Zeng, F.: Short-term traffic volume prediction by ensemble learning in concept drifting environments. Knowl.-Based Syst. 164, 213–225 (2019) CrossRef
45.
Yang, Z., Al-Dahidi, S., Baraldi, P., Zio, E., Montelatici, L.: A novel concept drift detection method for incremental learning in nonstationary environments. IEEE Trans. Neural Netw. Learn. Syst. (2019)
46.
Yannuzzi, M., Milito, R., Serral-Gracià, R., Montero, D., Nemirovsky, M.: Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 325–329. IEEE (2014)
47.
Yi, S., Hao, Z., Qin, Z., Li, Q.: Fog computing: platform and applications. In: 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp. 73–78. IEEE (2015)
48.
Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp. 37–42. ACM (2015)
49.
Zhan, H., Gomes, G., Li, X.S., Madduri, K., Sim, A., Wu, K.: Consensus ensemble system for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 19, 3903–3914 (2018)CrossRef
50.
Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)CrossRef
51.
Zhang, W., Qi, G., Pan, G., Lu, H., Li, S., Wu, Z.: City-scale social event detection and evaluation with taxi traces. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 40 (2015)
52.
Zhang, Y., Niyato, D., Wang, P., Kim, D.I.: Optimal energy management policy of mobile energy gateway. IEEE Trans. Veh. Technol. 65(5), 3685–3699 (2016)CrossRef
53.
Zhao, J., Sun, S.: High-order gaussian process dynamical models for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 17(7), 2014–2019 (2016)CrossRef
Metadata
Title
Fog Computing Based Traffic and Car Parking Intelligent System
Authors
Walaa Alajali
Shang Gao
Abdulrahman D. Alhusaynat
Copyright Year
2020
Book
Algorithms and Architectures for Parallel Processing

Print ISBN: 978-3-030-38960-4

Electronic ISBN: 978-3-030-38961-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-38961-1_32

Premium Partner

    Image Credits