Freight Performance Measures for Arterial Bottlenecks – Linking Traffic Volumes to Congestion — Chapter 4 Analysis

Recommendation: Deploy continuous probe-based monitoring on selected arterials and pair it with targeted signal-timing realignment and truck-priority phases to reduce peak truck delay by roughly 18–25% within one year. Use probe data to trigger adjustments when hourly heavy-vehicle volumes exceed 800 vehicles per hour or when average corridor speeds drop below 25 mph for more than two consecutive peak periods.

Chapter 4 data show a clear quantitative link: corridors in five Midwestern states produced a 0.76 Pearson correlation between heavy-vehicle share and excess delay during AM and PM peaks. The report provides detailed hourly demand curves, queue-length distributions, and sample V/C ratios; agencies should use those thresholds as operational triggers. Examples in the report include a selected arterial where a three-week timing realignment cut median queue length by 35 meters and reduced average truck dwell at intersections by 42 seconds per approach.

Address problems with a short, practical workplan: (1) run a two-month probe campaign to capture seasonal travel variation, (2) host one-on-one meetings with carriers and local associations to capture route-specific needs, and (3) establish a monitoring dashboard that gets updated daily and flags long queues or repeated stops. For corridors with constrained truck park capacity, add targeted parking incentives and off-peak delivery windows so freight movement aligns with corridor capacity.

Apply standards proven across nations and regional agencies: use a common data schema established by freight associations, publish a quarterly report that tracks delay per truck-mile and percent time below free-flow speed, and maintain a prioritized list of selected bottlenecks for mitigation funding. Since many solutions require alignment of planning, operations, and enforcement, schedule one-year pilots, collect detailed performance metrics, and expand programs that show measurable reductions in delay and variability.

Chapter 4: Analytical workflow to link traffic volumes and bottleneck congestion in dataset 481

Apply a rule-based detection first: mark a bottleneck when a 15-minute bin shows volume ≥ 2,200 vehicles and mean speed falls to ≤ 40 km/h for at least three consecutive bins (45 minutes); then compute queued vehicles and delay per bin to quantify severity.

Definition of fields and derived metrics: use raw counts for vehicles and containers, timestamped occupancy, lane-level speed, and detector queue length. Derive five core metrics per segment per 15-minute period: volume, speed ratio (observed/free-flow), queue length, delay (vehicle-hours), and container throughput. Use those metrics to compute a performance index that weights delay 0.5, queue length 0.2, speed ratio 0.2 and container throughput 0.1.

Data preparation procedure: ingest dataset 481, drop records with missing timestamps or negative volumes, synchronize to local timezone, impute gaps under 30 minutes by linear interpolation and treat longer gaps as nulls. Flag weekend records separately: use a weekday versus weekends tag for every row so analysts can stratify commuter trucks and port shift effects.

Segmentation and aggregation: create roadway segments at 500 m granularity; aggregate detectors to segment-level volumes and maximum queue length. Compute peak windows per weekday (06:30–09:30 and 16:00–19:00) and weekends (11:00–15:00). For each segment record portions of freight versus non-freight vehicles using axle-count thresholds and known truck class codes.

Criteria to link volumes to congestion: run logistic regression predicting bottleneck occurrence (binary) with predictors: 15-minute volume percentile, recent 60-minute cumulative volume, percent trucks, nearby port/container gate arrivals, and lane closures. Set an operational decision threshold probability of 0.35 to trigger mitigation alerts; calibrate to achieve ≥ 85% true positive rate on historical events labeled in dataset 481.

Cross-domain enrichments: merge container manifest counts from ports and arrival schedules from airports and intermodal yards to estimate inventory pressure on the roadway. Track consumer-facing indicators (inventory days, delivery delays) to quantify downstream economy effects. When throughput is seized, correlate media reports and incident logs with observed queue growth to identify incident-driven versus demand-driven congestion.

Validation and sensitivity: validate model on three months of holdout data and report experienced error metrics: AUC, precision, recall and mean absolute error for predicted queue length. Run scenario tests with ±15% volume shocks to find segments where queues grow nonlinearly. Record which segments have high worker exposure (loading docks, ports) and communicate results to communities and operators soon after validation completes.

Operational outputs and recommendations: produce hourly dashboards showing above-threshold segments, top 20 container chokepoints, and vehicle-versus-container delay comparisons. Export actionable lists for operations: dispatch tow/clear teams when predicted queue > 50 vehicles, deploy temporary lane controls when delay > 25 vehicle-hours per km, and reroute perishable cargo to alternate ports or airports when route performance drops below 0.65 index.

Reporting and lifecycle management: document the procedure and criteria in a reproducible notebook, store model versions and training inventory, and schedule retraining every 12 weeks or after major infrastructure changes. Speaking with stakeholders, present clear definition of bottleneck signals, expected life of alerts, and how interventions will be measured so workers, carriers and planners can find operational value quickly.

Locate arterial bottlenecks in 481: defining segment boundaries and trigger thresholds for investigation

Set segment boundaries at 0.5-mile (800 m) increments centered on intersections with measurable truck activity and trigger an investigation when any one freight-specific threshold is exceeded during a 30-minute monitoring window.

Data inputs and tools

• Use a travel-time analyzer and queue analyzer fed by probe GPS, inductive loop counts, and Bluetooth detections; sample rate: 1-minute resolution, aggregated to 5- and 30-minute intervals.
• Collect truck classification counts, turn movement counts at intersections, and data from transloading or distribution sites that support local truck generation.
• Supplement with work-zone protection logs, enforcement/regulations records, and incident reports to map restrictions and behavioral causes.

Segment boundary rules (simple, repeatable)

1. Primary rule: center each segment on an intersection and extend 0.25 mile upstream and downstream (total 0.5 mile). Use a wider 0.5-mile buffer where spacing between intersections exceeds 1 mile.
2. Split segments where lane configuration, turn bay presence, or signal timing changes occur; add split points where truck lane restrictions are added or dropped.
3. Merge adjacent segments if average peak-hour speed difference is less than 5 mph and truck flow variance is below 10% for three consecutive weekdays.

Trigger thresholds for investigation (quantitative)

• Delay metric: average truck delay per vehicle increases by >= 120 seconds (2 minutes) within a 30-minute window compared with the baseline same-day 15th percentile travel time. This metric uses a mobility-to-freight conversion to calculate cents lost per truck-minute.
• Throughput drop: directional truck count drops by >= 20% and by >= 15 trucks in a 30-minute interval versus the expected flow for that time-of-day.
• Queue growth: queue length increases by >= 200 meters within 30 minutes or reaches > 400 meters absolute, measured by roadside queue sensors or probe stoppage clustering.
• Reliability: travel-time variability (95th–50th percentile) increases by >= 30% over the baseline during peak windows.
• Freight economic trigger: estimated lost profits exceed $50 per truck-hour (equivalently 83 cents per truck-minute) across the segment during the monitoring window; use simple cost-per-minute models to convert minutes to cents.

Investigation procedure and roles

1. Automated alert: analyzer flags segments that meet any trigger and emails a short list to the corridor manager and freight operations contact within 15 minutes of detection.
2. Remote triage (within 30 minutes): verify data integrity, check incident/regulation feeds for restrictions or protection activities, and confirm whether transloading or scheduled deliveries are causing spikes.
3. Field verification (within 2 hours): dispatch traffic operations with simple checklist: queue, signal timing, parked/detoured trucks, enforcement action, lane drops, or new restrictions.
4. Management action: apply temporary timing adjustments, lift or add turn restrictions, or authorize detours; log all changes and mark segments as investigated.
5. Follow-up evaluation (next 3 days): run 30-minute rolling evaluations at the same time-of-day for three weekdays to confirm changes reduced the trigger metrics.

Behavioral and regulatory considerations

• Map behavioral patterns: high left-turn dwell times, illegal maneuvering near transloading sites, and clustering at curbside deliveries frequently cause repeat triggers.
• Track regulations and managed lane policies that can suddenly increase truck concentration; when an added restriction or new regulation appears, reduce threshold sensitivity for two weeks while patterns stabilize.
• Use protection logs (work zones, police activity) to flag false positives and to prioritize investigations when public safety protections overlap bottleneck locations.

Threshold tuning and documentation

• Tune thresholds using a 12-month baseline; most corridors stabilize after three months of active tuning. Document every threshold change and the rationale in the corridor registry.
• When investigating developing corridors, start with conservative triggers (delay 90 s, throughput drop 15%) and increase sensitivity as more data accumulate.
• Record outcomes in the project ledger for Chapter 4 analysis: list segment ID, time window, metrics triggered, actions taken, and whether delays dropped or behavior changed.

Operational tips

• Prioritize segments that touch transloading hubs, major intersections, or areas with increased enforcement; these generate most freight impact per incident.
• Assign an on-call analyzer operator during peak hours to review wide-area alerts and close the loop between detection and field response.
• Translate delay into direct costs using a cents-per-minute table tied to vehicle type and commodity; present savings and added profits from interventions to funding stakeholders.

Note: apply the above procedure consistently across the corridor to enable comparable evaluations and to support future developing models that predict where the next bottleneck will happen.

Extract freight-specific volumes from mixed traffic records: probe-data classification and axle/weight proxy methods

Use a hybrid, model-based probe-data classifier combined with axle-count and weight-proxy scaling as the primary extraction workflow for freight-specific volumes.

Recommendation: deploy probe classifiers when penetration exceeds 1.5–2.5% and supplement with permanent axle detectors at 0.5–2 km spacing near ramps and interchanges. In trials across three urban corridors (city A, city B, country C), 2.0% probe penetration delivered 82–88% precision for heavy-vehicle labels; adding axle-count-based weighting pushed estimated freight-volume error from ±18% to ±6% on a monthly basis.

Classification methods and sample sizes: train supervised trip-level classifiers on 5,000–10,000 labeled probe trips per corridor using features: axle-proxy (inferred from speed/acceleration pulses), average speed, stop frequency, start/end yard or park coordinates, and time-of-day peaks. Use 10-fold cross-validation and report confusion matrices by class. Expect recall for heavy vehicles of 0.78–0.92 depending on corridor geometry and ramps density.

Calibration and scaling: install at least one WIM per 10–20 km of arterial network or near major interchanges; use axle-counts to compute daily freight share on a 24-hour basis and on weekendoff-peak windows. Recalibrate model-based classifiers quarterly or after any regulatory change (new ordinances or major shipping-hub opening). Expect model drift when a new yards/park complex or distribution house opens; plan an extended calibration window of 4–6 weeks after such events.

Threshold rules and proxies: treat vehicles with inferred axle count ≥3 and sustained average speed <70 km/h in an urban corridor as probable freight; use chaining logic that combines axle proxy, trip endpoint in industrial land-use, and low acceleration variance to reduce false positives from multi-axle buses. Apply an independent override when firm-supplied telematics labels are available; use those labels as ground truth for retraining.

Design decisions tied to money and life: prioritize detectors on segments with highest pavement deterioration risk and highest freight-chain density; allocate budget so that 60% of funding buys WIM/axle sensors and 40% supports probe-data ingestion, storage, and model training. Use freight-volume outputs to justify ordinances or exemption policies that affect ramps and interchanges and to calculate pavement life extension or accelerated maintenance needs.

Operational metrics and reporting: report freight volumes by hour, peaks and weekendoff-peak, by ramp/interchange, and by origin-destination chain on a monthly basis. Include a simple metric: freight share (%) = (axle-scaled freight trips / total trips) * 100. Target an operational uncertainty band of ±7% for monthly freight share and ±12% for hourly peaks when probe penetration is low.

Practical implementation steps: 1) map candidate sensor locations near shipping yards, ramps and interchanges; 2) collect 6–8 weeks of synchronized probe and axle data; 3) build a model-based classifier and validate against WIM; 4) deploy scaling factors and publish freight volumes on a stable basis; 5) budget for extended validation after policy changes or new logistics facilities.

Speed and behavior signals: use speed variance and stop frequency to separate long-haul trucks from local delivery units; chain arrival/departure patterns at yards indicate distribution activity and help attribute counts to specific firms. Include speed thresholds specific to corridor type to avoid misclassification where motorists and freight mix closely.

Governance and use: provide a framework for data sharing with shipping firms under confidentiality agreements to improve label quality; allow city or country agencies to request temporary exemptions for sensors on private property when yards are obstructed. Produce recommendations for ordinances and investment that align with measured freight impacts so authorities can make data-driven decisions rather than rely on anecdote.

Attribute congestion to freight vs background demand: stepwise delay attribution with temporal and directional controls

Start by implementing a three-step attribution that separates background demand, freight insertion, and directional-temporal adjustment; apply this on each corridor segment and verify with a validation pass.

Step 1 – establish a baseline background delay model: train a regression on low-freight windows (midnight–04:00 local, weekend mid-days) and holiday-removed days using flow, occupancy, and speed as predictors. Use PCU-adjusted volumes (HGV PCU = 2.5) and include fixed effects for day-of-week and month. Output B(t,d) = modeled background delay by time interval t and direction d. Note model assumptions and log residuals so analysts can flag where assumptions wasnt satisfied.

Step 2 – freight injection test: run a counterfactual “no-freight” simulation and a “full-observed” simulation for the same t,d using the trained model. Compute freight-attributable delay ΔF(t,d) = Delay_full(t,d) − B(t,d). Use recorded HGV counts from weigh-in-motion, roadside classifiers or company telematics to populate the injection. For corridors like highways30 between interchanges, use 5-minute bins; require at least 10 bins in the peak hour to pass statistical significance.

Step 3 – directional and temporal controls: subtract corresponding directional flows from adjacent links to account for spillback and diversion. Apply a directional influence matrix that captures transfer of delay between inflow and outflow directions at each interchange. If delay shifts away from the bottleneck more than 250 m, attribute proportional delay to upstream links using momentary travel time gradients. Analysts must treat bidirectional peaks independently when HGV share differs by >8 percentage points.

Table schema for the analyzer: columns = {segment_id, county, timestamp, direction, total_flow, HGV_count, HGV_share, B_delay, Observed_delay, ΔF, non-recurrent_flag, notes}. Populate non-recurrent_flag from incident feeds and probe speed drops (>20% vs rolling median) lasting >30 minutes. Use this table to summarize per-market and per-company contributions to trade movements when company trip logs are available for validation.

Behavioral adjustment and supply-side controls: include lane-change and platooning factors in the meth by adding a behavioral multiplier β(d,t) estimated from platoon length distributions and lane-use shares; for single-lane bottlenecks set β = 1.15 when HGV_share > 12%. Control for geometry: number of interchanges, ramp spacing and shoulder width materially change capacity; code those supply attributes into the model so estimated freight impact isolates demand effects from roadway supply changes.

Non-recurrent handling and criteria: exclude or separately label intervals with non-recurrent events; if an interval’s observed delay exceeds modeled B_delay by >50% and incident_flag = true, mark as non-recurrent and do not roll it into season-long freight attribution. For steady-state market attribution, declare a segment freight-dominant when annual-average ΔF accounts for ≥15% of total delay and HGV_share ≥12%; otherwise attribute to background demand.

Validation, pass/fail and sensitivity: perform a backcast on three months of held-out data and require that the analyzer reproduces observed delays within ±10% for ≥70% of peak intervals to pass. Run sensitivity sweeps on PCU (±0.5), β (±10%) and temporal window (±30 minutes) and report corresponding ΔF ranges so decision-makers see the uncertainty envelope.

Practical recommendations for implementation: deploy the stepwise routine on corridors identified by trade volume (example: highways30 corridor and adjacent county links), integrate incident feeds and company telematics where available, and produce weekly tables of segment-level ΔF and suggested improvements. Prioritize interventions where freight-attributable delay constitutes an excellent indication of avoidable delay (ΔF ≥25% of total) and where supply changes at interchanges can pass measurable improvements to reliability and safe operations.

Compute performance measures for operations and planning: selecting and calculating volume-to-delay metrics suitable for arterials

Use a class-weighted volume-to-delay (V/D) metric defined as total delay (vehicle-minutes) divided by total adjusted vehicles per peak hour; set an operational trigger at V/D > 10 vehicle-minutes per vehicle and a planning threshold at V/D > 15 vehicle-minutes per vehicle for corridors with >15% truck share.

Define input data and collection cadence: 15-minute volumes (V15), peak hourly volume (Vpeak), vehicle classification counts, signal timing, free-flow travel time (t0), and observed travel time (tobs). Compute peak hour factor PHF = Vpeak / (4 * max(V15)); if PHF < 0.85 flag under-saturation or data issues. Convert trucks to passenger-car equivalents (PCE) using locally developed values; if local values are unavailable apply PCEs = 1.0 (cars), 1.5 (medium trucks), 2.5 (heavy trucks), and adjust after sensitivity testing.

Calculate delay per vehicle (d): d = (tobs – t0). Compute corridor V/D = (sum over all vehicles of d * adjusted_volume) / (sum of adjusted_volume). For signalized intersections use the HCM control delay meth for approach-level delay and aggregate by lane groups to reflect turning movement restrictions and pedestrian phases.

Incorporate non-recurring impacts by tagging days with incidents, weather, or recreational events and computing separate V/D statistics: V/D_base (no incident, clear weather) and V/D_total (all days). Use video and probe-data timestamps to isolate incident duration and estimate non-recurring share; if non-recurring accounts for >20% of excess delay, prioritize operations countermeasures (more responsive incident management) over capital projects.

Adjust for demand elasticity: monitor prices and purchasing patterns at freight terminals and destination clusters; high retail prices or seasonal purchasing spikes drive peak freight volumes and raise V/D. Conducting short origin-destination surveys at key terminals and using Bluetooth/GPS traces helps attribute delays to freight flows and supports targeted truck-priority signal timing.

Estimate reliability metrics alongside V/D: compute 95th-percentile travel time minus free-flow (buffer index) and percent time congested (percent of peak minutes where speed < 0.8*free-flow). Report absolute delay (vehicle-minutes) and normalized metrics (delay per 1,000 adjusted vehicles, delay per truck-tonne) for prioritizing corridors that serve national freight routes or cross-border movements between nations.

Apply sensitivity testing: run the V/D calculation under three scenarios – baseline (average weekday), peak-season (highest monthly peak), and stressed (severe-weather + incident). Use weather and restriction overlays (snow, flood, temporary lane closure) to model V/D increases; for arterials expect delay multipliers of 1.2–1.6 for wet conditions and 1.5–2.5 when lanes are closed.

Integrate pedestrian impacts and regulations: incorporate pedestrian crossing time and protection phases into intersection delay; where pedestrian volumes exceed 100 per hour, expect approach delays to rise by 10–30%. Review local truck restriction ordinances and routing regulations and reflect them in destination assignment – restricted links require reassigning volumes and recalculating V/D for alternate corridors.

Use practical tools and workflows: deploy video analytics for turning-movement counts, loop detectors for continuous volumes, and cloud-based processing to compute V/D hourly. Conducting batch runs weekly and producing a monthly dashboard that highlights corridors with V/D > thresholds lets operators act within 24 hours. Tamara, the corridor analyst, reduced peak V/D by 18% after implementing signal retiming informed by this workflow.

Prioritizing interventions: rank corridors by incremental delay per heavy truck (vehicle-minutes per heavy truck) and by delay per ton delivered to reflect economic impact. For corridors with similar V/D, prioritize those with higher truck shares, proximity to terminals, or critical access to freight destinations. Therefore allocate operations budgets to the top 20% of corridors that account for 60% of total freight delay.

Document assumptions and update regularly: record PCE values, PHF calculations, incident tagging rules, and the meth for computing control delay. Revisit choices after major regulatory changes, shifts in purchasing behavior, or new infrastructure. Considering modest sample sizes, use bootstrapping to estimate confidence intervals for V/D and report uncertainty with every recommendation, hoping to reduce misallocation of funds and to make measures useful for planning and operations across roads of different condition and demand.

Implement data validation and fusion: sensor QC rules, AVL integration, missing-data imputation, and manifest cross-checks

Enforce automated sensor QC rules that flag and quarantine readings immediately: reject speed outside 0–80 mph, volume per 5-min interval >3,000 vehicles, occupancy <0% or >100%, and timestamp drift >2 seconds; mark sensors as “failed” after three consecutive 5‑minute violations and drop them from live aggregation until technician verification.

Apply spike and stuck-value detection using rolling windows: if a value changes by >40 percent relative to a 30-minute median or remains identical for four consecutive 5‑minute bins, tag the interval and calculate a confidence score. Use the 95th percentile travel-time baseline to detect severe surges: when observed travel time exceeds baseline95 by >25 percent at peaks, generate an alert for immediate review.

Integrate AVL feeds to fuse vehicle traces with fixed sensors: map GPS points to link IDs within a 25‑meter tolerance and a ±30‑second time window, then reconcile counts by matching vehicle passage events to loop activations. For transit and freight fleets, require vehicle ID, axle count, and timestamp from AVL; when AVL-derived counts differ from loop counts by >10 percent for the same interval, create a discrepancy ticket and retain both sources for imputation.

Impute missing data using tiered rules: short gaps (<5 minutes) use linear interpolation; medium gaps (5–60 minutes) use time-of-week median from the last 28 days weighted at 0.6 historic + 0.4 recent trend; long gaps (>60 minutes) use a state-space Kalman filter trained on 90 days of data and supply a variance estimate. Always add an “imputed” flag and a quality score (0–100); treat imputed values with quality <70 as provisional for planning metrics.

Cross-check manifests against measured events: compare declared load count, container numbers and gross vehicle weight to weigh-in-motion and axle sensors; flag any weight discrepancy >5 percent or container count mismatch >1 for customs and operations. Log waiting times and correlate with manifest mismatches–each flagged manifest should include recorded waiting minutes, estimated fuel burn (use fleet average of ~1 gallon per truck-hour unless fleet reports say otherwise) and an estimate of profit loss per truck-hour provided by operations.

Quantify operational impact: run weekly reports that show number and percent of intervals with imputation, number of sensor failures, and lost-capacity minutes at peaks. For example, if 10 percent of peak intervals require imputation and those intervals account for 15 percent of delay minutes, classify the corridor as “under-investigation” and allocate technician support within 48 hours.

Define escalation steps and roles: automate alerts to on‑call field techs and to a named analyst (e.g., Mike) for manifest disputes. Create SLAs: respond to severe QC alerts within 2 hours, resolve AVL-sensor mismatches within 24 hours, and provide a root-cause report for any loss of capacity that exceeds a predefined threshold (for example, >20 percent capacity loss sustained for >30 minutes).

Document data designations and public reporting rules: tag sensors as “residential,” “arterial,” or “commercial” and include those designations in fusion logic so that residential peaks do not pollute freight performance metrics. Provide a change log showing times and reasons for imputations or manifest overrides; attach this log to Chapter 4 analyses used for policy or customs audits.

Create dashboards that display percentile-based KPIs (50th, 85th, 95th), outstanding concerns, and number of unresolved manifest flags. Use these dashboards to support decision-making when volumes surge or when timing changes for lanes create capacity relief opportunities. Possibly schedule biweekly review meetings for stakeholders to inspect flagged intervals and approve backfill procedures.

When investigating anomalies, combine quantitative thresholds with manual checks: run parallel automated checks across sensor arrays, AVL, and manifests; if flagged items exceed a set number or if waiting times surged >30 minutes at multiple adjacent links, escalate to operations immediately and record estimated fuel and profit impacts for after-action reports.