H↗HackFlowTAMU Datathon · November 7–8, 2026 Floor plan mapped · 15 aisles

THE DATATHON IS YOUR DATASET

Get there. Skip the crowds.

An indoor navigator for the TAMU Datathon hall. HackFlow uses one 90-row dataset to forecast congestion for six event periods and compare a direct route against a less-crowded alternative.

Plan a route How it predicts
15Mapped aisle sections
90Modeled aisle records
6Event periods
iEvent-aware estimates. One workbook, 90 records, 15 aisles, six periods.90-ROW MODEL

INTERACTIVE HALL EXPLORER

Interactive floor plan

Tap an aisle to choose your start. Colors reflect the workbook estimates for the current event period.

Shortest Less congested
Original layout redrawn from the team's hall sketch; not to scale.
Shortest walk—

Select a destination to compare routes.

Lower congestion—

Based on the 90-row workbook.

ONE WORKBOOK · SIX EVENT PERIODS

Plan a route from event-based congestion estimates.

HackFlow reads its modeled congestion values from one 90-row dataset: 15 aisle sections for each of six event periods. Every record contains the period, aisle section, assigned team member, estimated pedestrian crossings per minute, and estimated congestion level.

The local College Station clock automatically picks the corresponding event scenario. A weighted route algorithm then compares the shortest schematic path with a path that reduces exposure to high estimated pedestrian traffic.

Limitations: Crossing numbers are modeled estimates, not verified pedestrian counts. This is a rule-based scenario tool, not a live crowd sensor, measured travel-time predictor, or emergency navigation system. The floor plan is not to scale.

View the one-sheet Excel dataset →

01

Dataset
90 estimated aisle records · Rohit, Durgesh & Aditya

02

Event match
Local clock picks one of six scenarios

03

Compare
Direct and lower-congestion routing