Subway Passenger Simulator

Inspiration

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

Built With

• ble
• express.js
• node.js
• raspberry-pi