Commuter traffic - Datacity

Commuter traffic

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Experiment

BACKGROUNDER


How can we provide high-quality, reliable information on commuter traffic and congestion in order to improve the public transport experience?

The problem we are trying to solve is the sense of discomfort often found when commuters use congested modes of public transportation. Commuters are often faced with overcrowding when it is already too late, i.e. once they have already committed to using public transport and cannot choose an alternative route or schedule. We could avoid this if we could provide high-quality, reliable information on congestion at the moment when the commuter begins his or her journey. Even if this means choosing an alternative method of transportation, such information would guide commuters to more appropriate forms of transportation, depending on the current state of congestion.

The idea is to combine several information sources (for example ticketing counters, weight sensors, Web Listening, heat sensors in train stations and on the trains, geolocation, crowdsourcing, etc.) to determine a scaled “Crowd Index” related to the geographic area (bus station, metro stop) or the type of transport (bus, train). Information on commuter traffic congestion could then be provided in real-time, in the form of an archive or as a commute time forecast based on archival data.

Cities

Corporates

Startups

  • Use case & expected benefits

  • Available ressources

  • Business Opportunities for Partners

  • Startup Laureate

Use case

Who hasn’t found themselves in the following situation: once already inside a train station during rush hour, the subway is overcrowded at the back but carriages are empty at the front; or an overcrowded bus is followed a few minutes later by an empty bus, etc.

 

Understanding and sharing congestion information upstream would allow commuters to adapt their choices to find the comfort they seek traveling to and from work (depart later, wait for the following train, etc.). This would further guarantee that commuters could find a seat on a subsequent bus or train, for example.

Expected benefits

The idea is to create an index of information on public transport congestion (real time data, archival information, travel forecast) in a specified area (either fixed or mobile) that can then be accessed through WebServices contacted by third parties.

 

There are three types of actors associated with this project:

 

  1. Route calculation applications (RATP or CityMapper). Traffic congestion information allows users to alter the proposed route and improve the public transportation experience. Commuters can choose to change the departure time, type of transport, and even find a more spacious subway carriage, resulting in less commuter congestion.
  2. Transportation services that can optimize their plans by referencing archival data and forecasting commuter traffic.
  3. Local authorities who can adapt their infrastructure and services in relation to traffic conditions

Datasets

  • CISCO can provide video counting software after assessing a camera located in a proposed location
  • Business partners could provide commuter counting technology
  • City of Paris: provision of beacon sensors at tramway stations
  • Open data:

RATP for real-time transit times : https://data.ratp.fr/explore/?sort=modified

Expertise

  • Suez, Sopra Steria et Setec: proven expertise in data sciences and data usability
  • Sopra Steria: secure cloud data hosting

Experimentation field

The testing area should be a station somewhere on Paris’ tramway line. The City of Paris will make a few tramway stations available in order to track the amount of commuter traffic on a particular line. This will also allow it to maximize the use of collected data.

 

If needed, Sopra-Steria could provide a cloud environment for the testing period.

  • Setec has already positioned itself on AMO passenger information projects. The testing period results would help expand this position and support clients to integrate new information resources for a new type of use of sustainable mobility

 

  • SUEZ hopes to explore new data sources, better understand public transport usage, and consequently improve its consulting offers for “local authority” clients

 

  • Sopra Steria is interested in the results because they would allow it to complete and develop a Traveler Information business offer

Affluences

Affluences is a startup specialized on measurement system, forecast and real-time communication of the level of attendance (waiting time and/or occupancy rate) in high traffic locations, such as libraries, museums, administrations, public swimming pools, student restaurants…

GeoUniq

GeoUniq is a location business intelligence company that leverages a proprietary SDK for mobile apps as a data source. We collect 100+ positions/device per day with very high accuracy and inconsequential impact on battery; then turn raw data into valuable insights on users, places and journeys.

 

Partners' Experts


Jean-Noël Barneron


Chief Product Officer
Connecthings

Sébastien Chicou


Innovation Manager
Sopra Steria

Jean-Philippe Clément


Chief Data Officer
Paris Council

Thierry Gohon


Director of Utilities Development
Sopra Steria