|Title||Predictive decision support for real-time crowding prediction and information generation|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Peyman Noursalehi, Haris Koutsopoulos, Jinhua Zhao|
|Conference Name||Transportation Research Board 98th Annual Meeting|
|Conference Location||Washington, D.C.|
This paper proposes a predictive decision support platform for urban rail systems. It provides predictive information of crowding on trains and at stations. Additionally, it generates information on the expected likelihood of being able to board upcoming trains, which can be communicated to passengers. Using this information, passengers can make better-informed decisions as to which train to board.
The proposed decision support platform comprises two components: demand prediction, and an on-line simulation. The first module provides short-term (e.g., 15 minutes) prediction of the number of passengers arriving at each station and their destinations.
Subsequently, the on-line simulation models the interactions of trains and passengers for the duration of prediction horizon. It is assumed that the predictive crowding information are displayed at platforms, influencing passengers’ boarding decisions. The system incorporates the predicted passenger response to the information in its crowding predictions. Outputs from the simulation include the predicted crowding of trains and platforms during that time period, and expected number of passengers who will experience denied boarding.
The decision support platform was tested on a subset of the London Underground transit network. Aggregate automatic fare collection data were used for developing the predictive demand models. The results show the accuracy of denied boarding and platform crowding predictions. The value of providing train crowding information to the passengers waiting on platforms is also discussed. It is shown that as they become more responsive to the information, the number of left-behind passengers decrease.