Record Subway Ridership is causing increases in:

  • Delays
  • unpleasant crowding
  • crime / violence
  • fatalities: 50 fatalities per year

Can measurable improvement be achieved through optimizing?

  • Context (measurement driven) train scheduling
  • Limiting quantity of passengers on platforms
  • Measuring traffic patterns

What it does

We've built the data-collection node, which is the cornerstone to a model that collects passenger routes. We can see when a single passenger enters a station and exits another station. Once rolled out we would have a subway model enables forecasting the effect of system changes. Allowing key decisions to be made

  • When to increase / decrease train capacity?
  • How to optimize stops to maximize throughput within safety constraints?
  • How to optimize stops within comfort constraints?
  • Is train capacity expansion cost-effective?
  • Are Delay objectives achieved?

How we built it

The smartphone-beacon transmits a BLE advertisement and the RasPi base-station listens. Changes in bluetooth signal strength (RSSI) is provide proximity estimates

Challenges we ran into

Power variance is non-linear and is a challenge to indicate near-proximity. We created an algorithm that accounted for these fluctuations.

Accomplishments that we're proud of

Proof of concept and objective (detect passer-by) achieved within 8 hours

What we learned

BLE would be a suboptimal solution to run this system to scale. To make this work for a larger population, we would need to use a secure-RFID class radio-technology to process at scale

What's next for Subway Passenger Simulator

  • Integrate data collection into MTA ticketing system-requirements
  • Formulate questions / hypothesis to identify system levers
  • Develop \ Validate Model and measure parameters
  • Simulate experiments and Validate field decisions
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