{"title":"Integration of Big Data to Predict Transportation for Smart Cities","authors":"Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin","volume":130,"journal":"International Journal of Architectural and Environmental Engineering","pagesStart":1473,"pagesEnd":1481,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10008486","abstract":"
The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system. The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.<\/p>\r\n","references":"[1]\tKorea Planners Association, Urban Planning, Bosunggak, 2009, pp. 36-37.\r\n[2]\tJ. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, \u201cInternet of Things (IoT): A vision, architectural elements, and future directions,\u201d Future generation computer systems, vol. 29, no. 7, 2013, pp. 1645-1660.\r\n[3]\tW.-K. Lee, M.-K. Kim, Y.-S. Kim and J.-H. Lee, \u201cStudy on implementation plan of flexible headway service of city bus,\u201d Busan Development Institute, 2009. (in Korea)\r\n[4]\tK.-W. Kim, \u201cStudy on the city bus use demand and flexible service during precipitation,\u201d Ph. D. Dissertation, Pusan National University, 2012. (in Korea)\r\n[5]\tS.-J. Lee, \u201cBig Data for Transportation Policies and Their Applications,\u201d The Korea Transport Institute, 2013. (in Korea)\r\n[6]\tJ.-W. Yi and I.-K. Kim, \u201cA Study on The Integrate Evaluation of Urban Bus Service in Seoul,\u201d Journal of Transport Research, vol. 20, no. 4, 2013, pp. 131-145. (in Korea)\r\n[7]\tL. Eboli and G. Mazzulla, \u201cA Methodology for Evaluating Transit Service Quality Based on Subjective and Objective Measures from the Passenger\u2019s Point of View,\u201d Transport Policy, vol. 18, issue 1, 2011, pp. 172-181.\r\n[8]\tM. Friman, \u201cImplementing Quality Improvements in Public Transport,\u201d Journal of Public Transportation, vol. 7, no. 4, 2004, pp. 49-65.\r\n[9]\tT. Liebig, N. Piatkowski, C. Bockermann and K. Morik, \u201cDynamic route planning with real-time traffic predictions,\u201d Information Systems, vol. 64, 2017, pp. 258-265.\r\n[10]\tH. S. Lee, J. H. Park, S. H. Jo and B. J. Yun, \u201cDevelopment of Optimal Bus Scheduling Algorithm with Multi-constraints,\u201d Journal of Korean Society of Transportation, vol. 24, no. 7, 2006, pp. 129-138. (in Korea)\r\n[11]\tM. Ruan and J. Lin, \u201cAn investigation of bus headway regularity and service performance in Chicago bus transit system,\u201d in Transport Chicago, Annu. Conf., Vol. 14, June 2009.\r\n[12]\tS.-Y. Ko, J.-S. Ko and J.-S. Jeon, \u201cDevelopment of Real Time Vehicle Scheduling Model for Public Transportation,\u201d Journal of the Research Institute of Industrial Technology, vol. 18, 1999, pp. 181-186 (in Korean)\r\n[13]\tT. Maze, M. Agarwai and G. Burchett, \u201cWhether weather matters to traffic demand, traffic safety, and traffic operations and flow,\u201d Transportation research record: Journal of the transportation research board, no. 1948, 2006, pp. 170-176.\r\n[14]\tC. Dobre and F. Xhafa, \u201cIntelligent services for big data science\u201d, Future Generation Computer Systems, vol. 37, 2014, pp. 267-281.\r\n[15]\tOpen Data Portal - https:\/\/www.data.go.kr\/ (2017. 10. 9.)\r\n[16]\tGyeonggi Bus Information System (GBIS) - http:\/\/www.gbis.go.kr\/ (2017. 10. 9.)\r\n[17]\tNational Weather Center - https:\/\/data.kma.go.kr\/cmmn\/main.do (2017. 10. 9.)\r\n[18]\tIntelligent Transport Society of Korea - http:\/\/www.itskorea.kr\/02_sta\/sta1.jsp (2017. 10. 9.)","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 130, 2017"}