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2020 | OriginalPaper | Buchkapitel

4. In-Memory Deep Learning Computations on GPUs for Prediction of Road Traffic Incidents Using Big Data Fusion

verfasst von : Muhammad Aqib, Rashid Mehmood, Ahmed Alzahrani, Iyad Katib

Erschienen in: Smart Infrastructure and Applications

Verlag: Springer International Publishing

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Abstract

A staggering 1.25 million people die and up to 50 million people suffer injuries annually due to road traffic crashes around the world, causing great socio-economic and environmental damages. Road collisions are a major cause of road congestion. The cost of congestion to the US economy, alone, exceeded 305 billion USD in 2017. Smart infrastructure developments have accelerated the pace of technological advancements and the penetration of these technologies to all spheres of everyday life including transportation. The use of GPS devices to collect data, image processing and artificial intelligence (AI) for traffic analysis, and autonomous driving are but a few examples. This paper brings together transport big data, deep learning, in-memory computing, and GPU computing to predict traffic incidents on the road. Three different kinds of datasets—road traffic, vehicle detector station (VDS), and incident data—are combined together to predict road traffic incidents. The data is acquired from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). We have analyzed over 10 years of road traffic data. This work-in-progress paper reports incident prediction results using 3 months’ data, September to November 2017. The data fusion methodology is explained in detail along with the algorithms. The results for various configurations of deep convolution neural networks are given. Conclusions are drawn from the current status of the results and ideas for future improvements are given.

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Vorheriges Kapitel Automatic Detection and Validation of Smart City Events Using HPC and Apache Spark Platforms
Nächstes Kapitel Hybrid Statistical and Machine Learning Methods for Road Traffic Prediction: A Review and Tutorial
Literatur
1.
2.
3.
4.
Schrank, D., Eisele, B., Lomax, T.: TTI’s 2012 urban mobility report. Texas A&M Transportation Institute. The Texas A&M University System 4 (2012)
5.
El Hatri, C., Boumhidi, J.: Traffic management model for vehicle re-routing and traffic light control based on multi-objective particle swarm optimization. Intell. Decis. Technol. 11(2), 199–208 (2017)CrossRef
6.
Kim, H.J., Hoi-Kyun, C.: A comparative analysis of incident service time on urban freeways. IATSS Res. 25(1), 62–72 (2001)CrossRef
7.
Skabardonis, A., Varaiya, P., Petty, K.: Measuring recurrent and nonrecurrent traffic congestion. Transp. Res. Rec. J. Transp. Res. Board 1856(1), 118–124 (2003)CrossRef
8.
Ghosh, I., Savolainen, P.T., Gates, T.J.: Examination of factors affecting freeway incident clearance times: a comparison of the generalized f model and several alternative nested models. J. Adv. Transp. 48(6), 471–485 (2014)CrossRef
9.
Asakura, Y., Kusakabe, T., Nguyen, L.X., Ushiki, T.: Incident detection methods using probe vehicles with on-board gps equipment. Transp. Res. C Emerg. Technol. 81, 330–341 (2017)CrossRef
10.
D’Andrea, E., Marcelloni, F.: Detection of traffic congestion and incidents from gps trace analysis. Expert Syst. Appl. 73, 43–56 (2017)CrossRef
11.
Oskarbski, J., Zawisza, M., Żarski, K.: Automatic incident detection at intersections with use of telematics. Transp. Res. Procedia 14, 3466–3475 (2016)CrossRef
12.
Ayres, G., Mehmood, R.: On discovering road traffic information using virtual reality simulations. In: 11th International Conference on Computer Modelling and Simulation, 2009. UKSIM’09, pp. 411–416. IEEE, Piscataway (2009)
13.
Mehmood, R.: Towards understanding intercity traffic interdependencies. In: Proceedings of the 14th World Congress on Intelligent Transport Systems (ITS), held Beijing, October 2007 (2007)
14.
Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017)CrossRef
15.
Fouladgar, M., Parchami, M., Elmasri, R., Ghaderi, A.: Scalable deep traffic flow neural networks for urban traffic congestion prediction. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2251–2258. IEEE, Piscataway (2017)
16.
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y., et al.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
17.
Jia, Y., Wu, J., Du, Y.: Traffic speed prediction using deep learning method. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 1217–1222. IEEE, Piscataway (2016)
18.
Yu, R., Li, Y., Shahabi, C., Demiryurek, U., Liu, Y.: Deep learning: a generic approach for extreme condition traffic forecasting. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 777–785. SIAM, Philadelphia (2017)CrossRef
19.
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. C Emerg. Technol. 54, 187–197 (2015)CrossRef
20.
El Hatri, C., Boumhidi, J.: Fuzzy deep learning based urban traffic incident detection. Cogn. Syst. Res. 50, 206–213 (2018)CrossRef
21.
Sun, J., Sun, J.: A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data. Transp. Res. C Emerg. Technol. 54, 176–186 (2015)CrossRef
22.
Ki, Y.K., Heo, N.W., Choi, J.W., Ahn, G.H., Park, K.S.: An incident detection algorithm using artificial neural networks and traffic information. In: Cybernetics & Informatics (K&I), 2018, pp. 1–5. IEEE, Piscataway (2018)
23.
Agarwal, S., Kachroo, P., Regentova, E.: A hybrid model using logistic regression and wavelet transformation to detect traffic incidents. IATSS Res. 40(1), 56–63 (2016)CrossRef
24.
Li, R., Pereira, F.C., Ben-Akiva, M.E.: Overview of traffic incident duration analysis and prediction. Eur. Transp. Res. Rev. 10(2), 22 (2018)CrossRef
25.
Boyles, S., Fajardo, D., Waller, S.T.: A naive bayesian classifier for incident duration prediction. In: 86th Annual Meeting of the Transportation Research Board, Washington, DC, Citeseer (2007)
26.
Nam, D., Mannering, F.: An exploratory hazard-based analysis of highway incident duration. Transp. Res. A Policy Pract. 34(2), 85–102 (2000)CrossRef
27.
Lee, J.Y., Chung, J.H., Son, B.: Incident clearance time analysis for Korean freeways using structural equation model. In: Proceedings of the Eastern Asia Society for Transportation Studies (The 8th International Conference of Eastern Asia Society for Transportation Studies, 2009), vol. 7, pp. 360–360. Eastern Asia Society for Transportation Studies, Tokyo (2009)
28.
Zhan, C., Gan, A., Hadi, M.: Prediction of lane clearance time of freeway incidents using the m5p tree algorithm. IEEE Trans. Intell. Transp. Syst. 12(4), 1549–1557 (2011)CrossRef
29.
Vlahogianni, E.I., Karlaftis, M.G.: Fuzzy-entropy neural network freeway incident duration modeling with single and competing uncertainties. Comput. Aided Civ. Infrastruct. Eng. 28(6), 420–433 (2013)CrossRef
30.
Hojati, A.T., Ferreira, L., Washington, S., Charles, P., Shobeirinejad, A.: Modelling total duration of traffic incidents including incident detection and recovery time. Accid. Anal. Prev. 71, 296–305 (2014)CrossRef
31.
Pan, B., Demiryurek, U., Shahabi, C., Gupta, C.: Forecasting spatiotemporal impact of traffic incidents on road networks. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 587–596. IEEE, Piscataway (2013)
32.
Miller, M., Gupta, C.: Mining traffic incidents to forecast impact. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, pp. 33–40. ACM, New York (2012)
33.
Chung, Y., Recker, W.W.: A methodological approach for estimating temporal and spatial extent of delays caused by freeway accidents. IEEE Trans. Intell. Transp. Syst. 13(3), 1454–1461 (2012)CrossRef
34.
Javid, R.J., Javid, R.J.: A framework for travel time variability analysis using urban traffic incident data. IATSS Res. 42(1), 30–38 (2018)CrossRef
35.
Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds.): Smart Societies, Infrastructure, Technologies and Applications. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), vol. 224. Springer International Publishing, Cham (2018)
36.
Tawalbeh, L., Basalamah, A., Mehmood, R., Tawalbeh, H.: Greener and smarter phones for future cities: characterizing the impact of gps signal strength on power consumption. IEEE Access 4, 858–868 (2016)CrossRef
37.
Mehmood, R., Alam, F., Albogami, N.N., Katib, I., Albeshri, A., Altowaijri, S.M.: UTiLearn: a personalised ubiquitous teaching and learning system for smart societies. IEEE Access 5, 2615–2635 (2017)CrossRef
38.
Muhammed, T., Mehmood, R., Albeshri, A., Katib, I.: UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6, 32258–32285 (2018)CrossRef
39.
Büscher, M., Coulton, P., Efstratiou, C., Gellersen, H., Hemment, D., Mehmood, R., Sangiorgi, D.: Intelligent mobility systems: some socio-technical challenges and opportunities. In: International Conference on Communications Infrastructure. Systems and Applications in Europe, pp. 140–152. Springer, Berlin (2009)
40.
Mehmood, R., Graham, G.: Big data logistics: a health-care transport capacity sharing model. Procedia Comput. Sci. 64, 1107–1114 (2015). ElsevierCrossRef
41.
Arfat, Y., Mehmood, R., Albeshri, A.: Parallel shortest path graph computations of united states road network data on apache spark. In: International Conference on Smart Cities, Infrastructure, Technologies and Applications, pp. 323–336. Springer, Cham (2017)
42.
Mehmood, R., Meriton, R., Graham, G., Hennelly, P., Kumar, M.: Exploring the influence of big data on city transport operations: a Markovian approach. Int. J. Oper. Prod. Manag. 37(1), 75–104 (2017)CrossRef
43.
Mehmood, R., Lu, J.A.: Computational Markovian analysis of large systems. J. Manuf. Technol. Manag. 22(6), 804–817 (2011)CrossRef
44.
Aqib, M., Mehmood, R., Albeshri, A., Alzahrani, A.: Disaster management in smart cities by forecasting traffic plan using deep learning and gpus. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds.) International Conference on Smart Cities, Infrastructure, Technologies and Applications (SCITA 2017): Smart Societies, Infrastructure, Technologies and Applications, pp. 139–154. Springer International Publishing, Cham (2018)CrossRef
45.
Alazawi, Z., Altowaijri, S., Mehmood, R., Abdljabar, M.B.: Intelligent disaster management system based on cloud-enabled vehicular networks. In: ITS Telecommunications (ITST), 2011 11th International Conference on, pp. 361–368. IEEE, Piscataway (2011)
46.
Alazawi, Z., Abdljabar, M.B., Altowaijri, S., Vegni, A.M., Mehmood, R.: Icdms: an intelligent cloud based disaster management system for vehicular networks. In: International Workshop on Communication Technologies for Vehicles, pp. 40–56. Springer, Berlin (2012)
47.
Alazawi, Z., Alani, O., Abdljabar, M.B., Altowaijri, S., Mehmood, R.: A smart disaster management system for future cities. In: Proceedings of the 2014 ACM International Workshop on Wireless and Mobile Technologies for Smart Cities, pp. 1–10. ACM, New York (2014)
48.
Alazawi, Z., Alani, O., Abdljabar, M.B., Mehmood, R.: An intelligent disaster management system based evacuation strategies. In: Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014 9th International Symposium on, pp. 673–678. IEEE, Piscataway (2014)
49.
Arfat, Y., Aqib, M., Mehmood, R., Albeshri, A., Katib, I., Albogami, N., Alzahrani, A.: Enabling smarter societies through mobile big data fogs and clouds. Procedia Comput. Sci. 109, 1128–1133 (2017)CrossRef
50.
Graham, G., Mehmood, R., Coles, E.: Exploring future cityscapes through urban logistics prototyping: a technical viewpoint. Supply Chain Manag. 20(3), 341–352 (2015)CrossRef
51.
Mehmood, R., Nekovee, M.: Vehicular ad hoc and grid networks: discussion, design and evaluation. In: Proceedings of the 14th World Congress on Intelligent Transport Systems (ITS), held Beijing, October 2007 (2007)
52.
Gillani, S., Shahzad, F., Qayyum, A., Mehmood, R.: A survey on security in vehicular ad hoc networks. In: International Workshop on Communication Technologies for Vehicles, pp. 59–74. Springer, Berlin (2013)
53.
Alvi, A., Greaves, D., Mehmood, R.: Intra-vehicular verification and control: a two-pronged approach. In: 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010), pp. 401–405. IEEE, Piscataway (2010)
54.
Nabi, Z., Alvi, A., Mehmood, R.: Towards standardization of in-car sensors. In: International Workshop on Communication Technologies for Vehicles, pp. 216–223. Springer, Berlin (2011)
55.
Alam, F., Mehmood, R., Katib, I.: D2TFRS: an object recognition method for autonomous vehicles based on RGB and spatial values of pixels. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 224, pp. 155–168. Springer, Cham (Nov 2018)
56.
Schlingensiepen, J., Mehmood, R., Nemtanu, F.C., Niculescu, M.: Increasing sustainability of road transport in European cities and metropolitan areas by facilitating autonomic road transport systems (ARTS). In: Wellnitz, J., Subic, A., Trufin, R. (eds.) Sustainable Automotive Technologies 2013, pp. 201–210. Springer International Publishing, Ingolstadt (2014)CrossRef
57.
Schlingensiepen, J., Nemtanu, F., Mehmood, R., McCluskey, L.: Autonomic transport management systems-enabler for smart cities, personalized medicine, participation and industry grid/industry 4.0. In: Intelligent Transportation Systems–Problems and Perspectives, pp. 3–35. Springer, Cham (2016)
58.
Schlingensiepen, J., Mehmood, R., Nemtanu, F.C.: Framework for an autonomic transport system in smart cities. Cybern. Inf. Technol. 15(5), 50–62 (2015)
59.
Suma, S., Mehmood, R., Albugami, N., Katib, I., Albeshri, A.: Enabling next generation logistics and planning for smarter societies. Procedia Comput. Sci. 109, 1122–1127 (2017)CrossRef
60.
Suma, S., Mehmood, R., Albeshri, A.: Automatic event detection in smart cities using big data analytics. In: International Conference on Smart Cities, Infrastructure, Technologies and Applications, pp. 111–122. Springer, Cham (2017)
61.
Alomari, E., Mehmood, R.: Analysis of tweets in Arabic language for detection of road traffic conditions. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 224, pp. 98–110. Springer, Cham (Nov 2018)
62.
Mehmood, R., Faisal, M.A., Altowaijri, S.: Future networked healthcare systems: a review and case study. In: Boucadair, M., Jacquenet, C. (eds.) Handbook of Research on Redesigning the Future of Internet Architectures, pp. 531–558. IGI Global, Hershey, PA (2015)CrossRef
63.
Usman, S., Mehmood, R., Katib, I.: Big data and HPC convergence: the cutting edge and outlook. In: International Conference on Smart Cities, Infrastructure, Technologies and Applications (SCITA 2017). Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 224, pp. 11–26. Springer, Cham (Nov 2018)
64.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S.: Deep learning for visual understanding: a review. Neurocomputing 187, 27–48 (2016)CrossRef
65.
Berkeley, U.: Caltrans (2005) freeway performance measurement system (PEMS) 5.4. pems. eecs. berkeley. edu/Public (2005). Accessed 30 June 2005
66.
Aqib, M., Mehmood, R., Alzahrani, A., Albeshri, A.P: A smart disaster management system for future cities using deep learning, GPUs, and in-memory computing. In: Mehmood, R., See, S., Katib, I., Chlamtac, I. (eds.) Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies. Springer (2019). https://​doi.​org/​10.​1007/​978-3-030-13705-2_​7
67.
Hojati, A.T., Ferreira, L., Washington, S., Charles, P., Shobeirinejad, A.: Modelling the impact of traffic incidents on travel time reliability. Transp. Res. C Emerg. Technol. 65, 49–60 (2016)CrossRef
68.
Park, H., Haghani, A.: Real-time prediction of secondary incident occurrences using vehicle probe data. Transp. Res. C Emerg. Technol. 70, 69–85 (2016)CrossRef
69.
Paule, J.D.G., Sun, Y., Moshfeghi, Y.: On fine-grained geolocalisation of tweets and real-time traffic incident detection. Inf. Process. Manag. 56, 1119–1132 (2018)CrossRef
70.
Zhang, Z., He, Q., Gao, J., Ni, M.: A deep learning approach for detecting traffic accidents from social media data. Transp. Res. C Emerg. Technol. 86, 580–596 (2018)CrossRef
71.
Gu, Y., Qian, Z.S., Chen, F.: From twitter to detector: real-time traffic incident detection using social media data. Transp. Res. C Emerg. Technol. 67, 321–342 (2016)CrossRef
72.
Gutiérrez, C., Figueiras, P., Oliveira, P., Costa, R., Jardim-Goncalves, R.: An approach for detecting traffic events using social media. In: Emerging Trends and Advanced Technologies for Computational Intelligence, pp. 61–81. Springer, Cham (2016)CrossRef
73.
Nguyen, H., Liu, W., Rivera, P., Chen, F.: Trafficwatch: real-time traffic incident detection and monitoring using social media. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 540–551. Springer, Cham (2016)CrossRef
74.
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)CrossRef
Metadaten
Titel
In-Memory Deep Learning Computations on GPUs for Prediction of Road Traffic Incidents Using Big Data Fusion
verfasst von
Muhammad Aqib
Rashid Mehmood
Ahmed Alzahrani
Iyad Katib
Copyright-Jahr
2020
Buch
Smart Infrastructure and Applications

Print ISBN: 978-3-030-13704-5

Electronic ISBN: 978-3-030-13705-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-13705-2_4