Why so many people? Explaining Nonhabitual Transport Overcrowding With Internet Data

@article{Pereira2015WhySM,
  title={Why so many people? Explaining Nonhabitual Transport Overcrowding With Internet Data},
  author={Francisco C. Pereira and Filipe Rodrigues and Evgheni Polisciuc and Moshe E. Ben-Akiva},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2015},
  volume={16},
  pages={1370-1379}
}
Public transport smartcard data can be used for detection of large crowds. By comparing statistics on habitual behavior (e.g., average by time of day), one can specifically identify nonhabitual crowds, which are often very problematic for transport systems. While habitual overcrowding (e.g., peak hour) is well understood both by traffic managers and travelers, nonhabitual overcrowding hotspots can become even more disruptive and unpleasant because they are generally unexpected. By quickly… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • We verified that such information could reduce the root mean squared error (RMSE) by more than 50% in each OD.

Citations

Publications citing this paper.
SHOWING 1-10 OF 17 CITATIONS

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

  • IEEE Transactions on Intelligent Transportation Systems
  • 2018
VIEW 6 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Detecting Pickpocketing Gangs on Buses with Smart Card Data

Xia Zhao, Yong Zhang, +4 authors Baocai Yin
  • IEEE Intelligent Transportation Systems Magazine
  • 2019
VIEW 1 EXCERPT
CITES BACKGROUND

Real-Time Taxi Demand Prediction using data from the web

  • 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
  • 2018
VIEW 1 EXCERPT
CITES BACKGROUND

Exploring crowdsourcing information to predict traffic-related impacts

  • 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
  • 2017
VIEW 1 EXCERPT

References

Publications referenced by this paper.
SHOWING 1-10 OF 24 REFERENCES

Latent Dirichlet Allocation

VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Infer.NET 2.5

T. Minka, J. Winn, J. Guiver, D. Knowles
  • 2012, microsoft Research Cambridge. http://research.microsoft.com/infernet.
  • 2012
VIEW 2 EXCERPTS