HiMCM 2015 · Problem set
Two human-systems problems that reward careful behavioral modeling and disciplined data analysis. Problem A asks how drivers actually behave when a highway lane closes — and what signage and education would push them toward both fairness and throughput. Problem B hands you a fictional city's two weeks of police-report data and asks for a defensible safety rating, plus a clean memo to the mayor.
| Contest dates | November 12 – November 16, 2015 (5-day window) [illustrative] |
| Participation | ~700 teams, primarily United States and China [illustrative] |
| Problem A | Preventing Road Rage — lane-merge driver behavior, fairness vs. throughput |
| Problem B | City Crime and Safety — crime statistics, safety rating, mayoral memo |
| Official results | 2015 HiMCM problems & commentary |
The two problems
Preventing Road Rage
Compare "fair" zipper merging against "efficient" early/late merging when a highway lane closes. Extend to three lanes and to secondary roads, then produce driver-education guidelines and DOT signage recommendations.
City Crime and Safety
Mine two weeks of police-report data for a fictional 2.8-million-person city. Build a safety rating that combines incidence, severity, and clearance, then translate it into a short non-technical memo to the mayor.
Why this year is good practice
- Both are people problems. A is a traffic-flow + behavioral model; B is a crime-analytics problem. Neither rewards heavy machinery — both reward clean assumptions and a clear measure of merit.
- Two different data postures. A is a from-scratch simulation (cellular automata or follow-the-leader); B is a structured-data analysis on a small but rich dataset. Good practice for the two modes HiMCM keeps testing.
- Strong stakeholder framing. A asks for driver-education guidance and DOT signage; B asks for a mayoral memo. Both demand a clean one-page non-technical writeup — the exact letter format judges look for.