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Real-Time Bayesian Micro-Analysis for Metro Traffic Prediction

Published: 07 November 2017 Publication History

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.

References

[1]
American public transport association. http://www.apta.com/mediacenter/ptbenefits/Pages/FactSheet.aspx.
[2]
United states census bureau. population overview. https://www.census.gov/topics/population.html.
[3]
United states department of transportation. bureau of transportation statistics. https://www.bts.gov/topics/passenger-travel.
[4]
Washington metropolitan area transit authority. https://www.wmata.com/.
[5]
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.
[6]
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.
[7]
I. Geva, E. Hauer, and U. Landau. Maximum-likelihood and bayesian methods for the estimation of origin-destination flows.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
W. Mendenhall, R. J. Beaver, and B. M. Beaver. Introduction to probability and statistics. Cengage Learning, 2012.

Cited By

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  • (2024)CNN-BiGRU-Attention: A Time Series-Based Traffic Flow Prediction Model2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML)10.1109/PRML62565.2024.10779611(36-40)Online publication date: 19-Jul-2024
  • (2024)A two-layer graph-convolutional network for spatial interaction imputation from hierarchical functional regionsInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.104163134(104163)Online publication date: Nov-2024
  • (2023)A Bayesian spatial–temporal model for predicting passengers occupancy at Beijing MetroSpatial Statistics10.1016/j.spasta.2023.10075455(100754)Online publication date: Jun-2023
  • Show More Cited By

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cover image ACM Conferences
UrbanGIS'17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics
November 2017
118 pages
ISBN:9781450354950
DOI:10.1145/3152178
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 November 2017

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Cited By

View all
  • (2024)CNN-BiGRU-Attention: A Time Series-Based Traffic Flow Prediction Model2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML)10.1109/PRML62565.2024.10779611(36-40)Online publication date: 19-Jul-2024
  • (2024)A two-layer graph-convolutional network for spatial interaction imputation from hierarchical functional regionsInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.104163134(104163)Online publication date: Nov-2024
  • (2023)A Bayesian spatial–temporal model for predicting passengers occupancy at Beijing MetroSpatial Statistics10.1016/j.spasta.2023.10075455(100754)Online publication date: Jun-2023
  • (2023)Combining knowledge graph into metro passenger flow predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118790213:PAOnline publication date: 1-Mar-2023
  • (2023)Prediction in Smart Environments and Administration: Systematic Literature ReviewAdvanced Information Networking and Applications10.1007/978-3-031-28694-0_4(36-47)Online publication date: 15-Mar-2023
  • (2021)SPATIAL ANALYSIS OF SUBWAY PASSENGER TRAFFIC IN SAINT PETERSBURGGeodesy and cartography10.3846/gac.2021.1198047:1(10-20)Online publication date: 31-Mar-2021
  • (2019)Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural NetworksISPRS International Journal of Geo-Information10.3390/ijgi80602438:6(243)Online publication date: 28-May-2019
  • (2019)Spatiotemporal Bus Route Profiling using Odometer DataProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359350(369-378)Online publication date: 5-Nov-2019

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