Integration of Big Data to Predict Transportation for Smart Cities

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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.

Keywords: Big data, bus headway prediction, machine learning, public transportation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315633

References:

[1] Korea Planners Association, Urban Planning, Bosunggak, 2009, pp. 36-37.
[2] J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future generation computer systems, vol. 29, no. 7, 2013, pp. 1645-1660.
[3] W.-K. Lee, M.-K. Kim, Y.-S. Kim and J.-H. Lee, “Study on implementation plan of flexible headway service of city bus,” Busan Development Institute, 2009. (in Korea)
[4] K.-W. Kim, “Study on the city bus use demand and flexible service during precipitation,” Ph. D. Dissertation, Pusan National University, 2012. (in Korea)
[5] S.-J. Lee, “Big Data for Transportation Policies and Their Applications,” The Korea Transport Institute, 2013. (in Korea)
[6] J.-W. Yi and I.-K. Kim, “A Study on The Integrate Evaluation of Urban Bus Service in Seoul,” Journal of Transport Research, vol. 20, no. 4, 2013, pp. 131-145. (in Korea)
[7] L. Eboli and G. Mazzulla, “A Methodology for Evaluating Transit Service Quality Based on Subjective and Objective Measures from the Passenger’s Point of View,” Transport Policy, vol. 18, issue 1, 2011, pp. 172-181.
[8] M. Friman, “Implementing Quality Improvements in Public Transport,” Journal of Public Transportation, vol. 7, no. 4, 2004, pp. 49-65.
[9] T. Liebig, N. Piatkowski, C. Bockermann and K. Morik, “Dynamic route planning with real-time traffic predictions,” Information Systems, vol. 64, 2017, pp. 258-265.
[10] H. S. Lee, J. H. Park, S. H. Jo and B. J. Yun, “Development of Optimal Bus Scheduling Algorithm with Multi-constraints,” Journal of Korean Society of Transportation, vol. 24, no. 7, 2006, pp. 129-138. (in Korea)
[11] M. Ruan and J. Lin, “An investigation of bus headway regularity and service performance in Chicago bus transit system,” in Transport Chicago, Annu. Conf., Vol. 14, June 2009.
[12] S.-Y. Ko, J.-S. Ko and J.-S. Jeon, “Development of Real Time Vehicle Scheduling Model for Public Transportation,” Journal of the Research Institute of Industrial Technology, vol. 18, 1999, pp. 181-186 (in Korean)
[13] T. Maze, M. Agarwai and G. Burchett, “Whether weather matters to traffic demand, traffic safety, and traffic operations and flow,” Transportation research record: Journal of the transportation research board, no. 1948, 2006, pp. 170-176.
[14] C. Dobre and F. Xhafa, “Intelligent services for big data science”, Future Generation Computer Systems, vol. 37, 2014, pp. 267-281.
[15] Open Data Portal - https://www.data.go.kr/ (2017. 10. 9.)
[16] Gyeonggi Bus Information System (GBIS) - http://www.gbis.go.kr/ (2017. 10. 9.)
[17] National Weather Center - https://data.kma.go.kr/cmmn/main.do (2017. 10. 9.)
[18] Intelligent Transport Society of Korea - http://www.itskorea.kr/02_sta/sta1.jsp (2017. 10. 9.)