Load Passenger Forecasting Towards Future Bus Transportation Network

Authors

  • Clément Vial Alstom Digital Mobility, Villeurbanne, France
  • Vivien Gazeau RATP Group – Régie Autonome des Transports Parisiens, Paris, France

DOI:

https://doi.org/10.13052/jicts2245-800X.831

Keywords:

Smart cities, IoT, urban transportation, load passenger forecast, bus intelligent network

Abstract

Knowing the load of a transport system is one of the critical information of the operators and allows them to take strategic moves to optimize the line, involving more buses or changing the missions of the buses. Having this information in real-time and in the near future opens a new dimension in the management possibilities of the line. The paper describes a prototype that demonstrates this possibility and exposes the methodology used and some examples of the results. The study here presented was possible thanks to a new kind of data: the counting of passengers boarding and dropping out at each stop provide by sensors installed above the doors.

Downloads

Download data is not yet available.

Author Biographies

Clément Vial, Alstom Digital Mobility, Villeurbanne, France

Clément Vial graduated from Ecole Centrale Marseille and currently holds the position of data scientist/data engineer in the innovation team of Alstom Transport, and participate in a project created by the IRT SystemX institute. With a profound background in transports, Clément worked for various transport systems like ticketing, road signalling, platform screen doors, Aesthetic Power Supply. He participated during 4 years to the design, construction and testing of the first tramway project exempt of catenary in Brazil (in Rio de Janeiro) with advanced energy management capabilities (seamless transitions between zones with ground power supply and zones in autonomy), and to the design of the Electrical Road System which will allow electrical trucks to charge while driving in motorways. His passion for programming then led him to create a transportation startup in Salvador de Bahia that connects car owners with drivers that can drive them back home when they can’t. He now succeeded to reconcile his two passions, and work on the data produced by these transport systems that he already knows from experience.

Vivien Gazeau, RATP Group – Régie Autonome des Transports Parisiens, Paris, France

Vivien Gazeau graduated from ESME Sudria school of Engineering (Paris, France) in 2004. He also holds an MBA from IAE de Paris (Sorbonne University) since 2015. After several years of IT consulting, he joined the RATP Group (the main public transport operator in Paris) in 2010 as a system engineer. In 2016, he participated in RATP’s intrapreneur program and developed a traffic and fraud mapping tool based on the exploitation of massive passenger counting and validation data. Since 2017, as an innovation project manager at RATP, he has been working on the development of a fleet manager for autonomous vehicles in public transport and on connected infrastructure projects serving smart cities.

References

Lathia, N., Quercia, D., and Crowcroft, J. (2012). The Hidden Image of the City: Sensing Community Well-Being from Urban Mobility. Pervasive Computing.

Teng, J. and Shen, S. (2015). Modified bus passenger flow forecasting model. 15th COTA International Conference of Transportation Professionals.

Toqué, F., Khouadjia, M., Come, E., Trepanier, M., and Oukhellou, L. (2017). Short & long term forecasting of multimodal transport passenger flows with machine learning methods. IEEE.

Xue, R., Sun, D., and Chen, S. (2015). Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach. Discrete Dynamics in Nature and Society.

Downloads

Published

2020-07-01

How to Cite

Vial, C., & Gazeau, V. (2020). Load Passenger Forecasting Towards Future Bus Transportation Network. Journal of ICT Standardization, 8(3), 185–198. https://doi.org/10.13052/jicts2245-800X.831

Issue

2020: Vol 8 Iss 3

Section

Articles