Accident Analysis
What Transportation Engineers Mean by Accident Analysis
Accident analysis—often called crash analysis in modern practice—is the systematic study of where, when, why, and how roadway crashes occur, and which cost-effective treatments can prevent them. It ties police reports, roadway inventories, operations data, and exposure (traffic volumes) into a defensible workflow that prioritizes projects, designs countermeasures, and verifies results. The objective is simple: fewer fatal and serious-injury crashes, delivered through data-driven engineering.
This guide walks through the full lifecycle: acquiring reliable data; calculating rates and severity; applying proven statistical methods; screening a network for “hot spots” and “hot corridors”; diagnosing contributing factors; selecting countermeasures with Crash Modification Factors (CMFs); and communicating results to decision-makers and the public. Whether you’re building a Vision Zero action plan, preparing a Highway Safety Improvement Program (HSIP) submittal, or evaluating a roundabout conversion, the sections below provide an actionable, standards-aligned playbook.
Did you know?
Targeting just 5% of a network can address more than 50% of severe crashes when sites are selected with robust screening and diagnosis.
Measure risk, diagnose causes, apply proven treatments, and verify the benefit—repeat.
Accident Analysis Data Sources & Quality
Strong analysis starts with strong data. Combine multiple sources to reduce blind spots and increase confidence in decisions. Establish data governance and quality checks so findings are repeatable.
- Crash reports: Police reports (e.g., state crash forms) with location, severity, manner of collision, roadway conditions, and human factors.
- Traffic & exposure: AADT, turning-movement counts, classification, and seasonal factoring to normalize risk.
- Roadway inventory: Lanes, medians, shoulders, curvature, grades, sight distance, control type, speed limits, crosswalks, lighting.
- Operations: Signal timing, phase sequence, detection layouts, speed profiles, queue patterns, incident logs.
- Context layers: Land use, schools, transit stops, freight routes, access density, driveway permits, weather.
- Supplemental evidence: Video, forensic scene data, connected-vehicle events (hard braking, traction control), citizen reports.
Important
Validate geocoding and attach confidence scores. A small percentage of mislocated crashes can distort hot-spot maps and treatment ROI.
Stream Health Check (Illustrative)
Core Safety Metrics Engineers Track
Frequency alone seldom tells the full story. Normalize by exposure and emphasize severity to focus limited budgets where they will save the most life-years.
- Total crashes & severe crashes: Counts by year; KSI (killed + serious injuries) is often the primary performance metric.
- Crash rate (segment): Crashes per vehicle-mile traveled (VMT) for fair comparisons across roads with different volumes and lengths.
- Crash rate (intersection): Crashes per million entering vehicles (MEV) to compare signalized vs. unsignalized sites.
- Severity index: Weighted index emphasizing fatalities/serious injuries to prioritize life-saving treatments.
- Crash types & patterns: Rear-end, angle, left-turn, run-off-road, ped/bike conflicts—by time of day, weather, and lighting.
Common Formulas
Example
An intersection with 12 crashes and 8.5 million entering vehicles yields \(12 \times 10^6 / 8.5 \times 10^6 = 1.41\) crashes/MEV—above peer median, triggering a deeper diagnosis.
Methods & Models Used in Accident Analysis
Sound statistics separate signal from noise. Because crashes are rare events with year-to-year volatility, modern practice blends observed history with expected performance for similar facilities.
- Safety Performance Functions (SPFs): Predict expected crash frequency from exposure and roadway features; often negative binomial.
- Empirical Bayes (EB): Combines observed and predicted values to stabilize estimates and reduce regression-to-the-mean bias.
- Before–after with comparison group: Controls for broader trends (e.g., weather, enforcement) when evaluating projects.
- Surrogate measures: Conflict analysis from video, speed variance, hard-brake events to augment short study windows.
Empirical Bayes (Conceptual)
Did you know?
EB methods can cut false positives in hot-spot lists dramatically, focusing field work and dollars where risk is truly elevated.
Network Screening: Finding High-Value Sites
Network screening identifies locations with potential for safety improvement (PSI). Rank segments and intersections by frequency, rate, or EB Excess (observed minus expected) and group by crash pattern to align with treatments.
- Define peer groups: Functional class, area type, speed, access density, and control type to ensure apples-to-apples comparisons.
- Calculate performance: Frequency, rate, severity-weighted, and EB Excess for each site.
- Cluster spatially: Adjacent segments often share risks; consider corridor treatment opportunities.
- Validate in the field: Verify sight lines, signing, driver workload, pedestrian desire lines, drainage, and skid resistance.
Potential for Safety Improvement (PSI)
Screening Tip
Use severity weights (e.g., K=12, A=5, B=1) when ranking so life-saving treatments outrank cosmetic fixes.
Diagnosing Contributing Factors
Diagnosis links patterns to engineering causes and informs the treatment shortlist. Organize observations into human, roadway, vehicle, and environment buckets, then test hypotheses with data.
- Human: Speeding, distraction, impairment, gap acceptance, pedestrian compliance, cyclist positioning.
- Roadway: Geometry (radius, superelevation), inconsistent lane drops, sight distance, access density, skewed intersections.
- Operations: Split failures, short turn bays, offset drift, yellow/red timings, permissive vs. protected phasing.
- Environment: Nighttime lighting, glare, wet/icy surfaces, ponding, foliage, construction staging.
- Vehicle & freight: Large vehicle turning paths, off-tracking, brake grades, runaway risks, WIM evidence.
Consideration
Pair crash patterns with operational surrogates from video or probe data (speed variance, hard-brake hotspots) to strengthen the diagnosis at low-volume or newly built sites.
Selecting Countermeasures & Using CMFs
Countermeasures range from quick-build markings to geometric rebuilds. Use Crash Modification Factors (CMFs) to estimate effect sizes, then run benefit–cost (B/C) to prioritize.
- Speed management: Gateway treatments, lane narrowing, speed feedback signs, variable limits, median islands.
- Conflict reduction: Roundabouts, turn-lane additions, signal phasing (PPLT to protected), access management, raised crosswalks.
- Visibility & guidance: LED backplates, high-visibility crosswalks, lighting upgrades, chevrons, post-mounted delineators.
- Ped/Bike safety: Curb extensions, refuge islands, leading pedestrian intervals, protected intersections, separated bikeways.
- Run-off-road mitigation: Shoulder widening, rumble strips, clear zones, barriers where warranted.
Applying CMFs & B/C
Example
At a high-speed curve with night crashes, combine chevrons (CMF≈0.75) + lighting (CMF≈0.85) for combined effect \(0.75 \times 0.85 \approx 0.64\): a 36% expected reduction.
Special Focus: Work Zones & Vulnerable Road Users
Temporary traffic control and multimodal contexts require tailored analysis to prevent surges in severe crashes. Monitoring and near-real-time adjustments are essential when patterns shift quickly.
- Work zones: Track speeds, queue lengths, and taper crashes; verify taper length and device spacing; adjust based on measured compliance.
- Pedestrians: Heat-map crossings, desire lines, and delay; design for slow walkers; prioritize LPI, refuge islands, and continuous lighting.
- Cyclists & micromobility: Separate conflict movements, provide protected space through intersections, calm turning speeds.
- Near schools & transit: Shorten crossings, manage curbside chaos, and coordinate signals with dwell times.
Did you know?
Lowering turning speeds with tighter corner radii or hardened centerlines can dramatically cut pedestrian injury risk without hurting progression.
Reporting, Dashboards, and Communication
Decision-makers and communities need clear, credible storytelling. Present both the why and the what—diagnosis, treatment options, expected benefits, and timelines—using consistent visuals.
- Hot-spot map: Severity-weighted clusters with EB Excess labels and treatment status.
- Before–after panels: KSI trendlines, rate changes, reliability bands, and counterfactuals where applicable.
- Project sheets: Problem summary, photos/diagrams, CMFs, B/C, and implementation steps.
- Equity lens: Compare KSI rates and investment by neighborhood, land use, and mode share.
Important
Document assumptions and data vintages on every graphic. Transparency builds trust and speeds approvals.
Accident Analysis: Frequently Asked Questions
Isn’t “accident” a misnomer?
Many agencies prefer “crash” because most events stem from preventable factors. We use the keyword “Accident Analysis” for search clarity while applying modern crash-analysis methods.
How much data do I need?
Three to five years is common to balance sample size with currency. For new facilities or low volumes, supplement with conflicts, speed profiles, and similar-site SPFs.
What if my network changed recently?
Use segmented before–after windows and control corridors. EB methods help isolate treatment effects from broader trends and regression to the mean.
Which tool should I use?
Choose any platform that supports SPFs, EB, robust geocoding, and reproducible dashboards. The key is data quality, transparent assumptions, and peer-reviewed methods.
How do I reflect severity in rankings?
Apply severity weights or prioritize KSI first, then use EB Excess of severe crashes. Pair with B/C to ensure funding targets life-saving projects.
Conclusion
Accident analysis equips transportation engineers to save lives with data-driven investments. The formula is consistent: gather high-quality crash and exposure data; normalize and weight by severity; apply SPFs and Empirical Bayes to find true risk; diagnose contributing factors in the field; select countermeasures with credible CMFs; and verify outcomes via before–after studies and clear dashboards.
When agencies institutionalize this workflow—backed by quality control, transparent methods, and iterative refinement—funding flows to the highest-impact sites and public trust grows. Start small, publish your assumptions, learn from each project, and scale. The payoff is measured in fewer funerals, fewer life-altering injuries, and safer trips for everyone.
Focus on severe harm, use evidence-based treatments, and prove the benefit—this is engineering at its best.