Abstract
Short-term traffic flow forecasting is an important component of intelligent transportation systems. The forecasting results can be used to support intelligent transportation systems to plan operation and manage revenue. In this paper, we aim to predict the daily floating population by presenting a novel model using taxi trajectory data and weather information. We study the problem of floating traffic flow prediction with weather-affected New York City, and a new methodology called WTFPredict is proposed to solve this problem. In particular, we target the busiest part of the city (i.e., the airports), and identify its boundary to compute the traffic flow around the area. The experimental results based on large scale, real-life taxi and weather data (12 million records) indicate that the proposed method performs well in forecasting the short-term traffic flows. Our study will provide some valuable insights to transport management, urban planning, and location-based services (LBS).
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Notes
- 1.
The trip data was not created by the TLC, and TLC makes no representations as to the accuracy of these data.
References
Antoniou, C., Koutsopoulos, H.N., Yannis, G.: Dynamic data-driven local traffic state estimation and prediction. Transp. Res. Part C: Emerg. Technol. 34, 89–107 (2013)
Barros, J., Araujo, M., Rossetti, R.J.: Short-term real-time traffic prediction methods: a survey. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 132–139. IEEE (2015)
NTL Commission: TLC Trip Record Data. http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
Ding, A., Zhao, X., Jiao, L.: Traffic flow time series prediction based on statistics learning theory. In: Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, pp. 727–730. IEEE (2002)
Ferreira, N., Poco, J., Vo, H.T., Freire, J., Silva, C.T.: Visual exploration of big spatio-temporal urban data: a study of new york city taxi trips. IEEE Trans. Vis. Comput. Graph. 19(12), 2149–2158 (2013)
Ghosh, B., Basu, B., O’Mahony, M.: Bayesian time-series model for short-term traffic flow forecasting. J. Transp. Eng. 133(3), 180–189 (2007)
Haklay, M., Weber, P.: Openstreetmap: user-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)
Holmes, G., Donkin, A., Witten, I.H.: Weka: a machine learning workbench. In: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361. IEEE (1994)
Houbraken, M., Audenaert, P., Colle, D., Pickavet, M., Scheerlinck, K., Yperman, I., Logghe, S.: Real-time traffic monitoring by fusing floating car data with stationary detector data. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 127–131. IEEE (2015)
Lee, K., Hong, B., Lee, J., Jang, Y.: A floating population prediction model in travel spots using weather big data. In: 2015 IEEE Fifth International Conference on Big Data and Cloud Computing (BDCloud), pp. 118–124. IEEE (2015)
Saw, K., Katti, B., Joshi, G.: Literature review of traffic assignment: static and dynamic. Int. J. Transp. Eng. 2(4), 339–347 (2014)
Tan, H., Wu, Y., Shen, B., Jin, P.J., Ran, B.: Short-term traffic prediction based on dynamic tensor completion
Wang, Y., Papageorgiou, M., Messmer, A.: Real-time freeway traffic state estimation based on extended kalman filter: adaptive capabilities and real data testing. Transp. Res. Part A: Policy Prac. 42(10), 1340–1358 (2008)
Wang, Z., Ye, T., Lu, M., Yuan, X., Qu, H., Yuan, J., Wu, Q.: Visual exploration of sparse traffic trajectory data. IEEE Trans. Vis. Comput. Graph. 20(12), 1813–1822 (2014)
Xie, M.Q., Li, X.M., Zhou, W.L., Fu, Y.B.: Forecasting the short-term passenger flow on high-speed railway with neural networks. Comput. Intell. Neurosci. 2014, 23 (2014)
Xu, H., Ying, J., Wu, H., Lin, F.: Public bicycle traffic flow prediction based on a hybrid model. Appl. Math. Inf. Sci 7, 667–674 (2013)
Zheng, W., Lee, D.H., Shi, Q.: Short-term freeway traffic flow prediction: bayesian combined neural network approach. J. Transp. Eng. 132(2), 114–121 (2006)
Acknowledgment
This work was supported in part by the Natural Science Foundation of China under Grant 61502069, 61300087 by the Natural Science Foundation of Liaoning under Grant 2015020003, by the Fundamental Research Funds for the Central Universities under Grant DUT15QY40.
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Xu, X., Su, B., Zhao, X., Xu, Z., Sheng, Q.Z. (2016). Effective Traffic Flow Forecasting Using Taxi and Weather Data. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_35
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