Eric Lin
ABSTRACT
Metro transport plays a large role in major cities around the world as an easily accessible and convenient means of transit. We propose a novel approach to forecast the metro network flow of passengers, which is exceptionally useful for city planning. For instance, accurate estimations of passenger outflow provide valuable insight in deciding where and when to add new trains and stations. We present a micro-prediction approach to predict individual passenger's destination station and arrival time. As a global apriori model we empirically learn a probability distribution of origin-destination (OD) station-pairs using analysis on historical data and estimate travel times between stations. Then, we condition the OD probability distribution by the current travel time of an individual passenger using Bayesian learning. For each station, the summation of the probability distribution of each passenger in the network produces the expected outflow. Our experimental evaluation shows that our model outperforms baseline approaches, thus showing that our model can be successfully implemented for a wide array of passenger traffic flow data for smart city planning.
- American public transport association. http://www.apta.com/mediacenter/ptbenefits/Pages/FactSheet.aspx.Google Scholar
- United states census bureau. population overview. https://www.census.gov/topics/population.html.Google Scholar
- United states department of transportation. bureau of transportation statistics. https://www.bts.gov/topics/passenger-travel.Google Scholar
- Washington metropolitan area transit authority. https://www.wmata.com/.Google Scholar
- M. Dou, T. He, H. Yin, X. Zhou, Z. Chen, and B. Luo. Predicting passengers in public transportation using smart card data. In Australasian Database Conference, pages 28--40. Springer, 2015.Google ScholarCross Ref
- J. Froehlich, J. Neumann, N. Oliver, et al. Sensing and predicting the pulse of the city through shared bicycling. In IJCAI, volume 9, pages 1420--1426, 2009. Google ScholarDigital Library
- I. Geva, E. Hauer, and U. Landau. Maximum-likelihood and bayesian methods for the estimation of origin-destination flows.Google Scholar
- A. M. Hendawi, J. Bao, M. F. Mokbel, and M. Ali. Predictive tree: An efficient index for predictive queries on road networks. In Data Engineering (ICDE), 2015 IEEE 31st International Conference on, pages 1215--1226. IEEE, 2015.Google ScholarCross Ref
- A. Kaltenbrunner, R. Meza, J. Grivolla, J. Codina, and R. Banchs. Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6(4):455--466, 2010. Google ScholarDigital Library
- H.-P. Kriegel, M. Renz, M. Schubert, and A. Züfle. Statistical density prediction in traffic networks. In SDM, volume 8, pages 200--211. SIAM, 2008.Google Scholar
- B. Leng, J. Zeng, Z. Xiong, W. Lv, and Y. Wan. Probability tree based passenger flow prediction and its application to the beijing subway system. Frontiers of Computer Science, 7(2):195--203, 2013. Google ScholarDigital Library
- Y. Li, Y. Zheng, H. Zhang, and L. Chen. Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, page 33. ACM, 2015. Google ScholarDigital Library
- W. Mendenhall, R. J. Beaver, and B. M. Beaver. Introduction to probability and statistics. Cengage Learning, 2012.Google Scholar
Recommendations
Optimal Real-Time Traffic Control in Metro Stations
Train movements across railway stations are still operated by human dispatchers. Motivated by an application provided by Azienda Trasporti Milanesi (ATM), the major Italian municipal transport company, we developed a real-time automated traffic control ...
Timetable Optimization for Metro Lines Connecting to Intercity Railway Stations to Minimize Passenger Waiting Time
In major metropolitan cities, metro and intercity rail services usually share stations or have stations in close proximity to facilitate smooth connections for passengers. A large number of passengers may arrive on metro platforms within a short period of ...
Attention Based Short-Term Metro Passenger Flow Prediction
Knowledge Science, Engineering and ManagementAbstractAccurate short-term passenger flow prediction can help metro managers optimize train scheduling and formulate useful operation plan, so as to reduce metro transportation pressure and improve passenger comfort. However, existing research can’t ...
Comments