Traffic Incident Forecasting Eval

traffic-incident-forecasting-eval

Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control — Kinchen et al. (2025) (arXiv:2509.25515, 2025)

What this evaluates

Evaluates spatiotemporal models' ability to localize traffic collision events in time and space, and to forecast network-level congestion and emissions. It probes multi-horizon forecasting accuracy and spatial-temporal coherence under simulated disruption scenarios.

Datasets

• NYC Broadway corridor — total ?; splits: test (-1)

Metrics

• containment_performance (primary) — range: other
  • Counts the number of ground-truth collision events that fall within the model's predicted temporal and spatial intervals. Reported as stacked histograms of actual events captured within predicted ranges.
• network_ce_tti_accuracy — range: other
  • Qualitative and visual assessment of predicted vs. ground-truth Carbon Emissions (CE) and Travel Time Index (TTI) across network links and time steps, evaluating magnitude reproduction and spatial coherence.

Input / output format

Input: Historical and current spatiotemporal traffic state data (vehicle trajectories, speeds, network topology) used to forecast collision events (BiLSTM) or network-level congestion and emissions (DCRNN).

Output: For BiLSTM: predicted intervals for collision time $t$ and spatial coordinates $(x, y)$. For DCRNN: predicted per-link CE emissions and TTI values across time horizons.

Scoring recipe

# Containment Performance
covered = 0
for event in ground_truth:
    if event.t in pred_t_interval and (event.x, event.y) in pred_xy_region:
        covered += 1
containment_score = covered / len(ground_truth)

# Network CE/TTI Accuracy
# Evaluated via visual/magnitude comparison of predicted vs ground-truth link-level time series
# No explicit scalar formula provided; assessed by spatial coherence and peak alignment

Common pitfalls

• Evaluation relies heavily on visual inspection of histograms and plots rather than standardized numerical metrics like RMSE or MAE.
• Containment intervals are reported as stacked histograms of counts, making precise threshold-based accuracy extraction difficult without raw data.
• Network-level metrics are compared qualitatively for spatial distribution and magnitude trends, lacking a unified scalar score.

Evidence (verbatim from paper)

Figs. 3-6 present the containment performance of the BiLSTM in predicting collision time $t$ and spatial coordinates $(x, y) . The stacked histograms report the number of actual events captured within the predicted intervals across different collision types.

Citation

@misc{kinchen2025spatiotemporal,
  title={Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control},
  author={Kinchen et al. (2025)},
  year={2025},
  note={arXiv:2509.25515}
}

• arXiv: 2509.25515