Roadway Hazard Risk Assessment Framework

• Roadway Hazard Risk Assessment Framework is a structured methodology that defines and quantifies incident probability using integrated weather, traffic, and infrastructure data.
• It employs dual-scale spatiotemporal indexing, using nationwide H3 cells and fine road-segment granularity, to enable real-time nowcasting and forecasting of risks.
• The framework supports modular severity weighting and dynamic data integration, facilitating rapid deployment and informed decision-making in highway safety planning.

A roadway hazard risk assessment framework is a structured methodology for estimating, ranking, and operationalizing roadway risk across spatial, temporal, and functional scales. In the operational forecasting formulation derived from “Road Risk Monitor,” the framework is defined as operational nowcasting/forecasting of roadway incident risk that combines coarse nationwide coverage with fine road-segment granularity, driven by live weather and historical incident archives (Ivchenko, 5 May 2026). A plausible unifying view is that such frameworks map roadway state, traffic actors, infrastructure, and environmental conditions into a quantified hazard estimate—typically a probability of incident occurrence, a severity-aware score, an ISO 26262 risk class, a potential field, or a network-performance loss—then expose that estimate through planning, warning, maintenance, or safety-assurance interfaces.

1. Scope, objectives, and hazard representation

In its deployable forecasting form, the framework targets combined coarse and fine spatial coverage. The nationwide baseline uses an H3 hexagonal grid at resolution 5 with 30,141 candidate cells, while the road-segment pipeline uses TIGER/Line-derived short road segments with 4,074,810 total segments and 676,497 active in the live layer (Ivchenko, 5 May 2026). Temporal resolution is likewise dual: the H3 baseline is built on cell-hour features with weekly refresh, whereas the segment layer uses segment-hour features and rolling 24-hour forecasts driven by live National Weather Service inputs.

Hazards are encoded as feature groups rather than as a single physical variable. The framework represents weather through temperature, dewpoint, humidity, wind speed, and precipitation or wet-hour indicators; road geometry and class through segment length, functional class, intersection proximity, and, where available, curvature, slope or grade, and speed-limit proxies; temporal seasonality through cyclical encodings for hour, day, and month; and historical intensity through counts by cell or segment and hour-of-week (Ivchenko, 5 May 2026). This feature-level encoding makes the framework suitable for both persistent structural risk and short-horizon weather-sensitive risk.

Risk is quantified as a probability of incident occurrence per spatial unit and forecast window, with optional severity weighting. The canonical severity-aware formulation is

$$R = P(\text{incident}\mid X) \times S(\text{severity}),$$

where severity can be ordinal or mapped to expected harm or cost (Ivchenko, 5 May 2026). The basic target is binary incident occurrence per cell-hour or segment-hour, but the framework also accommodates count-based intensity modeling for aggregate layers and severity as an auxiliary target or multiplier.

The broader literature uses related but not identical hazard definitions. In standards-oriented autonomous-driving HARA, risk is framed through Severity, Exposure, and Controllability, and then mapped to Automotive Safety Integrity Levels (Abbaspour et al., 2024). In network-oriented formulations, risk is defined as expected system-performance loss, such as capacity degradation over a transportation graph (Deriba et al., 2024). This suggests that “roadway hazard risk assessment” is best understood as a family of compatible formulations sharing a common objective—formalizing harmful roadway states and their operational consequences—rather than as a single model class.

2. Data integration, spatiotemporal indexing, and system architecture

A deployable framework is an end-to-end stack. In the Road Risk Monitor instantiation, the pipeline begins with offline data staging: ingest and clean FARS fatal crashes, normalize historical weather from NOAA ISD-Lite, build TIGER/Line road segments, and match US-Accidents events to segments. It then proceeds to offline model training, precomputes raster overlays and road tiles, serves predictions through FastAPI and HTML interfaces, and packages runnable artifacts through scripts/build_runtime_bundle.sh with startup logic in scripts/service_startup.py (Ivchenko, 5 May 2026). The abstract characterizes this as a systems problem before it is a modeling problem.

The main data sources play distinct roles. FARS supplies the fatal-crash census and baseline H3 labels. NOAA ISD-Lite provides historical hourly weather, representative-station climatology, and fallback weather when live feeds fail. TIGER/Line provides nationwide road geometry for segment construction and road-level tile serving. US-Accidents supplies segment-level event labels and severities. The NWS API provides live point forecasts for road refresh and point scoring, with optional adapters for Tomorrow.io and OpenWeather. H3 supplies stable spatial indexing for baseline aggregation, candidate-cell expansion, and overlay serving (Ivchenko, 5 May 2026).

The operational scale achieved in the published rebuild is explicit: 322,268 cleaned FARS incidents, 296,464 weather rows, 30,141 H3 candidate cells, 4,074,810 segments, and 5,976,955 matched events across 49 states, with live NWS integration for 148 of 149 stations (Ivchenko, 5 May 2026). Those numbers are central because they define the engineering envelope within which the framework was demonstrated.

The framework relies on consistent spatiotemporal keys. H3 cell-hour and segment-hour indices support lagged history, hour-of-week features, and rolling windows. For H3, neighbor expansion is used to define candidate cells and optional spatial smoothing. For segments, event-to-road association is a core preprocessing stage: US-Accidents events are matched to segments using BallTree nearest candidates followed by exact point-to-polyline shortest distance, with reported median 2.7 m, mean 68.6 m, and 95th percentile 542.3 m (Ivchenko, 5 May 2026).

Layer	Unit and cadence	Role
Nationwide baseline	H3 cell-hour, weekly refresh	Consistent national coverage
Road-segment layer	Segment-hour, rolling 24-hour forecast	Fine-grained actionable prediction
Serving layer	Raster tiles and JSON road tiles	Web, API, and map delivery

The same systems pattern recurs in adjacent work. RLDM-based occlusion frameworks build layered local dynamic maps with static, quasi-static, transient, and dynamic layers for visibility reasoning at intersections (Damerow et al., 2023). Performance-based network methods couple asset states to graph-level capacity solvers (Deriba et al., 2024). This indicates that data integration, indexing, and runtime orchestration are not secondary implementation details; they are part of the framework definition itself.

3. Quantification, feature construction, and model design

The dual-scale design of the forecasting framework separates robustness from local actionability. The nationwide H3 baseline uses FARS fatal crashes mapped to H3 cells and hours, with hour-of-week counts and neighbor expansion to define candidate cells. The stored model contains 16 features: latitude and longitude, cyclical hour, day, and month terms, historical cell and same-hour counts, and weather covariates including temperature, dewpoint, humidity, wind speed, and wet-hour. Its role is to provide robust national coverage and stable weekly overlays even when segment-level live signals are sparse (Ivchenko, 5 May 2026).

The road-segment pipeline uses US-Accidents events matched to segments, with negatives sampled from segment-hours without events. The stored bundle contains 26 features spanning static geometry and class, temporal variables, historical counts, and weather covariates, with particularly strong historical-count features (Ivchenko, 5 May 2026). The target is binary incident occurrence per segment-hour, with severity available for later prioritization.

Temporal feature engineering is explicit. Best-practice cyclical encodings are

$$f_{\sin}(t;T)=\sin(2\pi t/T), \qquad f_{\cos}(t;T)=\cos(2\pi t/T),$$

and best-practice lags include the previous 1, 6, 12, and 24 hours, the 7-day same-hour lag, and the 28-day aggregate (Ivchenko, 5 May 2026). Historical intensity can also be represented by cell or segment counts by hour-of-week and rolling windows such as 7-day and 30-day windows.

The paper does not specify the exact algorithm used for the tabular predictors, but the framework explicitly lists common choices for geospatial tabular risk: logistic regression, gradient-boosted trees such as XGBoost or LightGBM, and regularized linear models (Ivchenko, 5 May 2026). The standard binary formulations included in the framework are logistic probability, cross-entropy loss, and Brier score. The corresponding expressions are

$$L = -\frac{1}{N}\sum_{i=1}^N\left[y_i \log p_i + (1-y_i) \log(1-p_i)\right],$$

and

$BS = \frac{1}{N}\sum_{i=1}^N (p_i - y_i)^2.$

Class imbalance is handled in the segment pipeline through sampled negatives, with class weights, focal loss, and stratified sampling listed as best-practice extensions (Ivchenko, 5 May 2026). Feature selection is likewise treated pragmatically through permutation importance, SHAP, regularization, and domain constraints to avoid leakage.

Severity handling remains deliberately modular. The paper includes severity in US-Accidents but does not specify a final severity model; the framework therefore treats severity either as an auxiliary target or as a post hoc multiplier. Listed best-practice options include a two-head model predicting both $P(\text{incident})$ and $E[\text{severity}\mid \text{incident}]$, and ordinal regression for severity levels (Ivchenko, 5 May 2026). This modularity is important because it allows the same infrastructure to support both raw incident probability and consequence-weighted operational ranking.

4. Alternative formulations in the research literature

The forecasting architecture above is only one branch of the literature. A second branch treats roadway hazard risk assessment as a standards-based hazard analysis process. In the AI-augmented HARA workflow for autonomous driving, the stages are Item Definition, Function Extraction, Malfunction Derivation, Hazard identification, Risk Assessment using Severity, Exposure, and Controllability, and Safety Goals, with ASIL assignment following the ISO 26262 matrix and Safety Goals inheriting the highest ASIL among the hazards they mitigate (Abbaspour et al., 2024). The empirical AEB case study reported up to 80% reduction in time, from 100 hours to 20 hours, and 20% improvement in coverage, from 80% to 100%, when compared with conventional manual HARA (Abbaspour et al., 2024).

A third branch treats risk as explicit residual-risk management rather than only classification. The Risk Management Core formalizes initial risk, residual risk, accepted risk, and explicit risk reduction plus integrity allocations, and evaluates residual risk against criteria such as Positive Risk Balance, ALARP, and MEM (Salem et al., 2023). In related ISO 26262 work for unmanned protective vehicles, HARA is coupled to operating modes such as Manual Mode, Safe Halt, Follow Mode, and Coupled Mode, and safety goals include preventing steering actuation beyond specification, preventing overrunning hard shoulder markings, and ensuring detection of relevant obstacles (Stolte et al., 2017). The companion hazardous-event-identification method combines a skill graph, an adapted HAZOP analysis, and discretized scenes to enumerate hazardous events at concept phase (Bagschik et al., 2018).

A fourth branch represents hazard as a spatiotemporal field. The dynamic hazard field used in QP-MPC planning defines total hazard as

$H(l,d,t) = H_s(l,d) + H_d(l,d,t),$

and evaluates candidate trajectories through the exposure integral

$J_h = \int H(l(t),d(t),t)\,dt$

under the hard safety condition $H(l,d,t) \le H_{\max}$ (Tian et al., 31 Aug 2025). The Composite Safety Potential Field instead combines a subjective field for drivers’ perceived proximity risk and an objective field for imminent collision probability, thereby covering both longitudinal and lateral interactions (Zuo et al., 29 Apr 2025). The Streetscope Collision Hazard Measure takes a near-miss perspective and defines pairwise hazard through relative speed squared over separation distance (Antonsson et al., 2022).

A fifth branch emphasizes uncertainty. Beta Distribution Learning models fatal crash risk from satellite imagery as a full Beta distribution rather than a point estimate, reporting recall improvements of 17–23% over baselines and better calibration, including lower ECE and lower Brier score (Elallaf et al., 7 Nov 2025). Conformal Risk Tube Prediction models spatiotemporal uncertainty for risk object identification, reporting higher coverage and better Risk-IoU than prior approaches while reducing nuisance braking alerts (Fu et al., 25 Mar 2026). Hierarchical Bayesian extreme-value models estimate crash risk from 2D TTC block minima and produce site-specific crash-risk estimates and credible intervals in near real time (Anis et al., 2024).

A sixth branch moves to corridor or network scale. Performance-based risk assessment for large-scale transportation networks defines system risk as expected network-capacity loss and computes it as the evidence of a TMCMC posterior, while the final posterior samples yield asset importance measures (Deriba et al., 2024). ROADFIRST, by contrast, uses Random Forest and SHAP to identify and rank crash contributing factors such as alcohol-impaired driving, distracted driving, and speeding at road-segment scale for systemic safety planning (Reyya et al., 2024). These variants differ in mathematics, but they share the same underlying objective: convert roadway states and failure mechanisms into interpretable, decision-relevant risk quantities.

5. Validation, calibration, and operational serving

The reported evaluation for the forecasting stack is intentionally asymmetric. On the H3 task, the baseline achieved AUROC 0.894 and Average Precision 0.715 on a held-out year. On the road-segment task, the internal same-pipeline year holdout with sampled negatives achieved AUROC 0.9999 and Average Precision 0.9999, but the paper explicitly cautions that this task is likely easier due to persistence and split design, so these segment metrics are treated as diagnostic rather than definitive (Ivchenko, 5 May 2026). That caution is central to the framework because it distinguishes operational usefulness from overinterpreted benchmark performance.

Recommended validation extensions are extensive: roll-forward backtesting by month or quarter across years; prospective testing as live data arrive; geographic holdouts such as state-level leave-out; cold-segment holdouts; source holdouts excluding subsets of US-Accidents; and temporal generalization tests for seasonal shift (Ivchenko, 5 May 2026). Baseline comparisons should include climatology-only, weather-only, history-only, and constant-prior models, while additional metrics should include log loss, Brier score, PR-AUC, and hit rate or false alarm rate at operational thresholds.

Calibration is not detailed in the Road Risk Monitor paper, so the framework lists post hoc methods rather than claiming an implemented solution. Platt scaling or isotonic regression are proposed, with reliability diagrams and Expected Calibration Error

$$ECE = \sum_{m=1}^M \frac{|B_m|}{N}\left|\text{acc}(B_m) - \text{conf}(B_m)\right|$$

for assessment (Ivchenko, 5 May 2026). Uncertainty is likewise not specified in the paper, but bootstrap confidence intervals, model ensembles, quantile regression for counts, and temporal cross-validation are listed as best-practice options.

Operational serving is explicit. The system exposes FastAPI with 14 public paths: 10 machine endpoints and 4 HTML product pages, including an interactive map, about page, and contact page with SMTP or logging fallback (Ivchenko, 5 May 2026). Prediction artifacts are served as weekly raster tiles for the H3 baseline and JSON road tiles refreshed on a rolling 24-hour cadence. The serving layer supports health checks, live point scoring, segment queries, detail inspection, timeline summaries, and tile endpoints.

Operational usage is illustrated through weather-driven scenarios. In a winter storm nowcasting scenario across the Midwest, falling temperature, rising winds, and precipitation transitioning to snow are combined with snow-related incident history, and segment-hours for the next 24 hours are ranked by $$f_{\sin}(t;T)=\sin(2\pi t/T), \qquad f_{\cos}(t;T)=\cos(2\pi t/T),$$0; the example dispatches anti-icing to the top decile and refreshes tiles hourly as NWS updates (Ivchenko, 5 May 2026). In a tropical-storm rainfall scenario on Southeast arterials, precipitation, wind, and intersection density elevate risk, thresholds are lowered for arterials, alerts are pushed to maintenance, and calibration drift is monitored as the storm progresses (Ivchenko, 5 May 2026).

6. Governance, limitations, adaptation, and future directions

The generalized framework includes explicit governance and monitoring layers. Data lineage documentation, a source-catalog-style registry, geometry-integrity checks, event-to-road match-distance validation, and missingness reports are listed as recommended controls (Ivchenko, 5 May 2026). Model monitoring includes data freshness, feature drift, calibration drift, drift detection such as Population Stability Index, recalibration triggers, and periodic retraining. Feedback loops should ingest recent incidents, update counts, evaluate alert hit rates, and close the loop with operations teams.

Adaptation to other regions is defined primarily as substitution rather than redesign. FARS and US-Accidents can be replaced by national police crash databases or insurance claims; TIGER/Line can be replaced by OpenStreetMap, national cadastral datasets, or DOT-maintained networks; NWS can be replaced by national meteorological services or global products such as ERA5, ICON, or GFS; and H3 remains globally applicable provided the resolution is chosen for local road density (Ivchenko, 5 May 2026). The same pattern appears in rural-weather infrastructure frameworks, which combine visibility bands and friction regimes into a ProbabilitySeverity score and graduated advisory actions, and in V2I microsimulation frameworks, which combine TTC, DRAC, TIT, Road Hazard Warning, and Emergency Electronic Brake Light logic for rear-end crash mitigation (Saba et al., 26 Aug 2025, Porfyri et al., 2020).

The main limitations of the forecasting-oriented formulation are explicitly acknowledged. The baseline is anchored on fatal crashes; the segment pipeline relies on US-Accidents with sampled negatives and persistent segment identities; the live layer uses weather only and lacks exposure, traffic speeds, work zones, and driver-behavior signals; and the segment evaluation is internal and likely optimistic, requiring robust geographic and temporal holdouts plus prospective tests (Ivchenko, 5 May 2026). These limitations explain why the framework is presented as deployable but not exhaustive.

The forward roadmap is correspondingly concrete: integrate traffic speeds from probe data, AADT or exposure, work-zone feeds, and weather-impact markers such as snow or ice accumulation; develop severity modeling and cost-weighted alert optimization; perform harder evaluations including cold-start segments, state holdouts, multi-source validation, and live prospective backtests; and incorporate higher-resolution weather, seasonal recalibration, and causal feature exploration (Ivchenko, 5 May 2026). A plausible implication is that future roadway hazard risk assessment frameworks will continue to converge across currently separate traditions—forecasting systems, HARA processes, field-based planning, uncertainty quantification, and network-performance analysis—while preserving a shared core of explicit hazard representation, traceable risk computation, and operational decision support.