The data behind a corridor-day
The interface is built around a single freeway corridor observed over a day, in GMNS-compatible form. Everything below is a visual analytics use case for GUI4GMNS: the same folder that feeds the desktop NeXTA tools feeds these panels.
- GMNS node / link network — freeway mainline, ramps, detectors as nodes/links.
- 5-minute detector state —
{speed, flow, density}per link per interval. - Recurring historical profiles — the typical day-type curve, per link and time.
- Abnormal-cell labels — link×interval cells flagged as departing from the profile.
- Incident / event records — time, location, and type of reported events.
- Base OD & path-flow inputs — origin–destination demand and path flows.
- Path–link incidence — which paths traverse which links (loads the bottleneck).
- Optional solver / AI state — a submission's reconstructed state, rendered the same way.
{speed, flow, density} indexed by link and
5-minute interval — the atomic cell of every panel on this page.ANetwork Explorer
Pick a corridor, direction, date, and time window; toggle layers to see the freeway links, detectors, reported incidents, bottleneck candidates, and the current queue-tail extent on the GMNS network.
Layers redraw as you change corridor/date. The bottleneck ring marks a candidate from the state field; the queue tail is the current-frame extent from the time slider in panel B.
BTraffic State Field
The space-time speed field — milepost × 5-minute interval — measured on top, the recurring profile below. Where the measured panel is redder than the profile is exactly the non-recurring event. Toggle the feed to flow or density, or overlay a solver's reconstruction.
CQueue Object Inspector
Turn the heatmap into an operational incident picture: each red region in the state field becomes a queue object with a lifecycle and geometry you can read off directly.
DOD and Path Attribution
Which OD/path flows actually load this bottleneck? Select the bottleneck link and see the contributing paths, their demand, and their share of the loaded link flow — a diagnostic view of "who loads this queue?", not a scoring metric.
- P-07 O1→D2 · 1,030 veh/h · 54% of loaded flow
- P-12 O2→D2 · 560 veh/h · 29%
- P-19 O3→D4 · 320 veh/h · 17%
EIncident and Evidence Panel
Tie the incident record and abnormal-cell labels back to the observed state field. The panel grounds an interpretation in evidence and states its confidence — it does not assert causality.
Abnormal cells cluster downstream-to-upstream from 07:35; measured speed dips to 12 km/h where the recurring profile stays above 56 km/h.
Evidence tier: high. Phrased as evidence-grounded explanation — recurring congestion was already present; the 07:35 event coincides with and deepens it. Alternatives (pure recurring / uncertain) are down-weighted, not excluded.
The GUI4GMNS pipeline
The panels above are the last stage of a practical pipeline — from raw detector states to a self-contained dashboard and NeXTA-compatible GMNS exports.
# generate the corridor-day visuals and NeXTA-ready GMNS files cd 04_nexta_ai_gen python make_html_viz.py 210E 2026-06-19 # space-time + network + calendar HTML python nexta_export.py 210E 2026-06-19 # node/link/link_performance for desktop NeXTA
Swap the corridor id and date; point nexta_export.py --state my_submission.csv at a
solver's output to render an AI-generated state exactly like ground truth.
Export
Take the current corridor-day out of the browser — as a self-contained dashboard, GMNS performance tables, NeXTA files, or an observed-vs-solver comparison.