Demo Gallery

Visual walkthrough of the NYC Taxi Zone Recommendation platform.

Scenario: Rainy Friday Evening in Manhattan

The problem

A taxi driver finishes a drop-off in Midtown at 7:30 PM on a rainy Friday. Without guidance, the driver cruises randomly, wasting fuel and time.

Before AI guidance:

Driver circles Midtown for 20 minutes
Finds passenger heading to Brooklyn ($18 fare)
35% utilization across the shift
~$350 daily revenue

AI decision process

Step 1: Demand forecast → JFK airport demand spikes at 8 PM (rain + Friday)
Step 2: Travel time matrix → 35 min from Midtown to JFK via highway
Step 3: Two-step horizon planner → Go to JFK now, pick up airport fare, reposition to Manhattan
Step 4: Recommendation → [JFK Zone 132, Upper East Side Zone 140, Midtown Zone 161]

The result

After AI guidance:

Driver heads to JFK, picks up $62 airport fare within 10 minutes
Then repositions based on next forecast window
52% utilization across the shift
~$570 daily revenue (+$220/day vs cruising)

Benchmark Dashboard

Static diagnostic (3,360 queries)

NDCG comparison

The Two-Step Horizon strategy achieves 0.9565 NDCG@3 on public validation queries — a 21.9% improvement over the naive Hot Zone baseline.

Rollout performance

Pickup comparison

100-seed paired rollout shows consistent improvement: Two-Step delivers +$139/day vs Hot Zone, with the improvement concentrated in the 7-10 AM and 6-9 PM peak windows.

Multi-agent competition

The multi-agent simulator reveals that as fleet size grows, simpler strategies degrade faster than horizon-aware policies. At 50 drivers, Two-Step maintains a +$25/day advantage over Single-Step.

NYC Map Visualization

The project uses NYC’s 263-taxi-zone geography. All-pairs travel times are precomputed via Dijkstra on a directed OD graph built from 1.8M+ training trips.

        Manhattan (Zones 100-199)
        ┌──────────────────────────┐
        │  Upper East   Upper West │
        │  ┌────┬────┐ ┌────┬────┐ │
        │  │140 │141 │ │142 │143 │ │
        │  └────┴────┘ └────┴────┘ │
        │  Midtown                 │
        │  ┌────┬────┐             │
        │  │161 │162 │   → Queens  │
        │  └────┴────┘      (JFK)  │
        │  Downtown          ↓     │
        │  ┌────┬────┐     ┌───┐   │
        │  │113 │114 │     │132│   │
        │  └────┴────┘     └───┘   │
        └──────────────────────────┘

Reproducibility

All results in this gallery are reproducible. Run:

make all          # Full pipeline
make static       # Static diagnostic only
make combined-benchmark  # Combined report

All metrics are checked in as reference snapshots in outputs/ with timestamped validation.

More scenarios

Scenario	Time	Weather	AI Decision	Outcome
Monday morning rush	8:15 AM	Clear	Upper East → Midtown	Commuter demand
Saturday night	11:00 PM	Clear	Greenwich Village → Meatpacking	Nightlife flow
Airport surge	4:00 PM	Thunderstorm	JFK → Manhattan	Airport backlog
Holiday eve	6:00 PM	Snow	Penn Station → residential	Transit hub exit