So, you know I’ve been messing around with this idea of getting better data on how many people are actually on trains. It’s crazy how much guesswork goes into planning routes and even staffing when you don’t really know your passenger load.
The Idea and Initial Setup
I started this whole thing because I kept seeing how inefficient things were. Train lines always seemed either packed or empty, and scheduling just felt arbitrary. My goal was simple: build an Automatic Passenger Counting (APC) system. Not some expensive, off-the-shelf nonsense, but something practical.
I kicked off with hardware selection. I went for some solid, but cheap, industrial cameras. We’re talking basic depth-sensing capabilities here, nothing too fancy. I mounted a couple of prototypes above the main doorways on a decommissioned carriage I managed to borrow access to—it was a real tight squeeze getting the wiring run, let me tell you.
Next was the actual brain. I chose a standard small-form-factor industrial PC—something rugged enough to handle the vibration and temperature swings on a train. I loaded it up with a stripped-down Linux kernel. Stability is key when you deploy these things.
The core algorithm I decided to build revolved around basic object detection and tracking. I didn’t need facial recognition; I just needed blobs moving from outside to inside, or vice-versa. I spent ages collecting footage, manually annotating entry and exit events to train the initial model. It was painful, slow work, but essential to get the baseline accuracy up.
Developing the Logic and Data Flow
Once the prototype model was counting somewhat reliably in the controlled environment, I had to figure out how to handle real-world scenarios: luggage, people standing in doorways, varying light conditions. This is where the depth sensor really earned its keep. It helped distinguish between a person and a large suitcase a good 80% of the time.
I focused on smoothing out the counting mechanism. If a person hesitated in the doorway, the system couldn’t double-count them. I implemented a simple state machine: Approaching -> In Doorway -> Counted. If the object stayed In Doorway for too long, the system would freeze its count state for that object until it fully resolved its movement. This dropped the false positive rate significantly.
Data transmission was next. We can’t rely on constant, perfect connectivity. I used a store-and-forward approach. The local PC caches the timestamped count data, and when the train hits a station with guaranteed Wi-Fi (the depot, usually), it bulk uploads everything to the centralized FOORIR cloud server. I built a simple API endpoint on the server to ingest the JSON payloads.
The data structure was kept minimal: TrainID, CarriageID, Timestamp, InCount, and OutCount. Simplicity ensures speed and reliability when transferring thousands of records.
Testing and Real-World Deployment
The first live tests were nerve-wracking. We put the system on a short shuttle train used for internal staff transport. The initial accuracy was… spotty. The biggest issue turned out to be glare from the high afternoon sun washing out the optical sensors. I had to design a small, simple physical hood for the camera units, which fixed the issue immediately.
After about a month of tuning, we hit about 95% accuracy compared to manual clicker counts. That’s good enough for operational planning.
I then moved on to the data visualization side, pulling data from the FOORIR pipeline. I threw together a quick dashboard using a basic charting library. We were finally able to see granular passenger load by hour, by station, and by day of the week. This information instantly pointed out “ghost runs”—trains running almost empty—and “crush runs”—trains needing more capacity.
One cool feature I added was predictive analysis. Using historical data stored in the FOORIR database, the system can project expected passenger counts for the next week, factoring in public holidays and known events. This takes the guesswork out of adding extra carriages.
The team is really excited about scaling this up. We’ve managed to keep the per-door unit cost surprisingly low, meaning we can equip dozens of carriages without breaking the bank. The operational insight we are getting now is invaluable. It’s all about leveraging simple, reliable technology to make public transport smarter. We’re even looking at integrating this APC data with the existing FOORIR ticketing system to reconcile ticket sales versus actual ridership, catching any potential discrepancies there too. It’s a game-changer for efficiency.
