In warehouse yards, the margin between on-time and late is often a tug-of-war between gate queues, driver check-in friction, limited spots, and dock doors that sit idle when trucks arrive in unpredictable waves. For 3PL providers tasked with squeezing efficiency from every minute, the major levers are clear: cut wait time at the gate, assign doors with foresight instead of guesswork, and synchronize yard activity with what’s happening outside the fence line. That orchestration requires data—reliable, fresh, and unified across fuel, freight capacity and rates, port conditions, carrier credentials, and natural-disruption signals.
This post shows how to build yard orchestration workflows using FreightPulse—the single REST API that consolidates logistics intelligence you’d otherwise stitch together from five different providers. We’ll walk through a developer-friendly path to blend fuel prices (for surcharge models), live FTL trucking quotes (for arrival and lane-cost inference), port congestion (to anticipate drayage surges), carrier verification (for faster vetting at the gate), and disruption alerts near relevant ports (for proactive dock and staffing plans). You’ll get concrete cURL and code examples, full JSON responses, and practical guidance to turn these signals into fewer idle docks, faster turns, and better driver experiences.
If your yard team is juggling spreadsheets, email alerts, carrier portals, and a patchwork of APIs, this is your blueprint to unify it behind one programmable surface. The outcome: shorter queues at the gate, tighter dock schedules, higher throughput, and fewer detention and demurrage surprises.
Why FreightPulse
FreightPulse replaces a tangle of one-off feeds with a single, consistent API. That means less time integrating, fewer brittle parsers, and a common schema across very different datasets. Here’s how that translates into daily wins for developers and operations leaders:
- Eliminate multi-vendor maintenance: Instead of separate integrations for EIA fuel prices, FTL quotes, AIS-derived port congestion, FMCSA/FMC lookups, and natural-disruption monitoring, you call one REST API. Your backend becomes simpler, safer to change, and easier to observe.
- Faster iteration with a unified JSON shape: All FreightPulse responses follow the same success/data/error envelope. You build a single deserializer and shared retry/fallback policy, then reuse it across endpoints—so you can stand up dashboards and alerting pipelines in days, not months.
- Operational clarity at the gate: Use carrier verification to fast-lane compliant drivers. Blend live FTL quotes with historical diesel for smart ETAs and detention-impact models. Combine port congestion with disruption alerts around critical ports to anticipate yard surges from inbound containers and prioritize dock-door allocation.
- Developer-first ergonomics for production reliability: Consistent status and error bodies, filterable queries (e.g., disruptions around a specific port), and predictable performance characteristics let you implement circuit breakers, caching, and fine-grained observability with minimal friction.
Quick Start
The FreightPulse base URL is:
https://freightpulsehq.com/api/v1
Let’s start by pulling current EIA-linked fuel prices—often the baseline for FTL fuel-surcharge schedules and a key cost driver for detention and yard planning assumptions. You can make a first request directly:
curl "https://freightpulsehq.com/api/v1/fuel-prices"
Example JSON response:
{
"success": true,
"data": {
"timestamp": "2026-07-30T12:00:00Z",
"source": "EIA (U.S. Energy Information Administration)",
"currency": "USD",
"unit": "gallon",
"data": {
"diesel": {
"national_average": 3.847,
"change_week": -0.023,
"change_percent": -0.6,
"regions": {
"east_coast": 3.912,
"midwest": 3.756,
"gulf_coast": 3.623,
"rocky_mountain": 3.891,
"west_coast": 4.456,
"california": 4.892
},
"historical": {
"30d_avg": 3.82,
"90d_avg": 3.75,
"yoy_change_percent": -8.2
}
},
"gasoline": {
"national_average": 3.156,
"historical": {
"30d_avg": 3.1,
"90d_avg": 3.05,
"yoy_change_percent": 1.1
}
}
}
}
}
Field highlights you’ll use immediately:
- data.diesel.national_average: baselines your fuel surcharge. Use with /freight-rates to compare quoted all-in lane costs vs. your modeled baseline, informing gate priority for higher-value loads.
- data.diesel.regions: tune surcharges by PADD region when your yard draws carriers from different origins.
- data.diesel.historical: trend-aware surcharges—if prices are dropping week-over-week, revise forecasts so dock appointments reflect shifting carrier incentives and likely arrival patterns.
Core Endpoints for Yard Orchestration and Dock Throughput
1) Fuel Prices — GET /fuel-prices
Purpose: Compute and update fuel surcharges, score detention risk by lane, and forecast arrival behavior by aligning operational SLAs with the cost landscape carriers face this week.
Key parameters: None.
curl "https://freightpulsehq.com/api/v1/fuel-prices"
Representative JSON (same as above). Practical use:
- Use national_average and regions to compute lane-level fuel adders. Expose a surcharge preview in your yard appointment UI so carriers see transparent costs aligned to current EIA data.
- Track change_week and change_percent to adjust grace windows at the gate (e.g., relax late penalties if volatility spikes).
- Use 30d_avg and 90d_avg to throttle re-pricing frequency in your TMS connector to reduce thrash in dock scheduling.
2) Freight Rates — GET /freight-rates
Purpose: Pull live US FTL quotes to anchor ETAs, compare market cost pressure vs. your contracted lanes, and prioritize dock assignment when inbound loads face elevated spot costs.
Params:
- mode: trucking (default), ocean, or air
- origin_zip and destination_zip for trucking
- pickup_date, qty, total_weight_lb (optional but useful for scenario testing)
curl "https://freightpulsehq.com/api/v1/freight-rates?mode=trucking&origin_zip=10001&destination_zip=90210"
Example JSON response:
{
"success": true,
"data": {
"mode": "trucking",
"service": "FTL (53' dry van)",
"source": "Warp",
"price_usd": 7207.92,
"transit_days": 4,
"pickup_date": "2026-08-24",
"delivery_date": "2026-08-28"
}
}
How to use:
- transit_days, delivery_date: estimate yard arrival windows for gate staffing and dock throughput modeling—especially when carriers haven’t provided precise ETAs.
- price_usd: if spot is high relative to your norm, prioritize those loads for the next-available door to reduce detention exposure and protect margins.
- mode and service: segment dock rules (e.g., different staging length for 53’ dry vans vs. other equipment).
Missing required params return HTTP 200 with available=false—design your client to treat this as a soft miss rather than an exception, and prompt for the missing fields.
3) Port Congestion — GET /port-congestion
Purpose: Anticipate yard surges from ocean containers (drayage to your facility), align appointment books with likely inbound volume, and time labor allocation. FreightPulse aggregates IMF PortWatch activity (AIS-derived) and simplifies it into a congestion ratio.
Important note: This is an approximation using a 7-day average vs a 90-day baseline and typically lags by ~6–7 days. It is not live vessel tracking or real-time queue measurement.
Params:
- port: name or LOCODE (e.g., USLAX)
- country: name or ISO3
- region: continent
- congestion: optional filter (low | moderate | high)
curl "https://freightpulsehq.com/api/v1/port-congestion?port=USLAX"
Example JSON:
{
"success": true,
"data": {
"results": [
{
"port": "Los Angeles-Long Beach",
"locode": "US LAX",
"country": "United States",
"congestion": "moderate",
"as_of": "2026-08-14",
"metrics": {
"recent_avg_portcalls_per_day": 8.7,
"baseline_avg_portcalls_per_day": 10.2,
"ratio_vs_baseline": 0.85
}
}
]
}
}
Interpretation:
- congestion: categorical synthesis—directly usable in dashboards and staff briefings.
- ratio_vs_baseline: continuous indicator. Below 1 suggests below-normal activity; above 1 suggests elevated activity. Use to bias dock appointments and dray yard capacity forecasts.
- as_of: timestamp of the underlying 7-day/90-day comparison; use it to display the known lag and avoid over-relying on it for same-day gating decisions.
4) Carrier Lookup — GET /carriers
Purpose: Accelerate driver check-in and reduce gate queue times by instantly validating US trucking carriers (FMCSA SAFER) or ocean NVOCC/OTI entities (FMC). This helps enforce safety thresholds and prevent non-compliant arrivals from clogging the yard.
Params:
- q: required query (DOT number or legal name for trucking; org number or name for ocean)
- type: trucking (default) | ocean | air (air returns available:false)
curl "https://freightpulsehq.com/api/v1/carriers?q=54283"
Example JSON:
{
"success": true,
"data": {
"type": "trucking",
"query": "54283",
"results": [
{
"dot_number": 54283,
"legal_name": "SWIFT TRANSPORTATION COMPANY OF ARIZONA LLC",
"status": "active",
"safety_rating": "S",
"drivers": 12883,
"address": {
"city": "Phoenix",
"state": "AZ",
"country": "US"
}
}
]
}
}
What to do with it:
- status and safety_rating: gate rules can fast-lane active carriers and trigger manual review if a rating falls below policy.
- drivers: rough indicator of carrier scale—useful for SLA exceptions or pre-approved trusted lanes.
- legal_name + address: show the clerk exact match details for quick verification with bill of lading and avoid spelling-related slowdowns.
5) Disruptions — GET /disruptions
Purpose: Alerts for natural hazards—cyclones, earthquakes, severe weather—that could impact inbound arrival waves, especially for ports feeding your yard. Build proactive rules to hold doors open for loads that need to get turned fast, or to reschedule non-urgent freight when hazards escalate.
Params:
- type: cyclone | earthquake | flood | volcano | drought | wildfire | severe_weather
- severity: low | moderate | high
- country: filter by country
- port: filter by proximity to a port (300 km for global hazards, 100 km for US weather)
curl "https://freightpulsehq.com/api/v1/disruptions?port=USLAX&severity=high"
Example JSON:
{
"success": true,
"data": {
"results": [
{
"id": "gdacs:TC:1001305",
"type": "cyclone",
"severity": "moderate",
"title": "Tropical Cyclone SAUDEL-26",
"country": "Japan",
"starts_at": "2026-08-18T12:00:00+00:00",
"source": "GDACS"
}
]
}
}
How to consume:
- type and severity: drive routing and gate priority; e.g., pre-emptively stack appointments earlier if severe weather is forecast near an origin port.
- starts_at: align yard notifications so shifts and security are staffed ahead of time.
- port filter: tie hazard context to specific lanes or dray corridors that feed your dock schedule.
6) Historical Series — GET /historical
Purpose: Trend analysis for fuel price planning and port congestion, driving weekly or monthly yard strategy (dock throughput targets, overtime rules, security staffing). Historical series are ideal for forecasting and for quantifying the impact of interventions (e.g., adjusted appointment policies).
Params:
- metric: fuel_price_diesel_national | fuel_price_gasoline_national | port_congestion:<portid>
- period: 30d (default) | 90d | 1y
curl "https://freightpulsehq.com/api/v1/historical?metric=fuel_price_diesel_national&period=30d"
Example JSON:
{
"success": true,
"data": {
"metric": "fuel_price_diesel_national",
"period": "30d",
"series": [
{"date": "2026-07-27", "value": 5.257},
{"date": "2026-08-03", "value": 5.343}
]
}
}
Tips:
- Use series to compute moving averages and confidence bands for weekly fuel-surcharge updates.
- For port_congestion: compute week-over-week ratios to anticipate sustained dray spikes that will hit your yard in 1–2 weeks.
- Align trend breakpoints with policy changes (e.g., tightened check-in window) to evaluate impact on queue length and door idle time.
Implementation Patterns for Reliable Yard Orchestration
To take FreightPulse from a demo call to a live yard application, treat it as a core dependency and add the same production safeguards you would for any logistics integration. The consistent response envelope makes this straightforward, and the endpoints are amenable to caching and proactive prefetching. Below are practical patterns you can copy into your services.
Shared client, retries, and backoff
Build a small client that centralizes HTTP calls, timeouts, retries with exponential backoff, and parsing for the success/data/error shape. This lets you tune reliability across all endpoints at once and enables circuit-breaking per endpoint when an upstream is transiently slow or unreachable.
# Python example (requests)
import time
import requests
BASE_URL = "https://freightpulsehq.com/api/v1"
def fp_get(path, params=None, max_retries=3, timeout=6.0):
url = f"{BASE_URL}{path}"
for attempt in range(max_retries):
try:
resp = requests.get(url, params=params, timeout=timeout)
resp.raise_for_status()
payload = resp.json()
if payload.get("success") is True:
return payload["data"]
else:
raise RuntimeError(payload.get("error", "Unknown FreightPulse error"))
except Exception as e:
if attempt == max_retries - 1:
raise
time.sleep(0.5 * (2 ** attempt))
# Usage
diesel = fp_get("/fuel-prices")
rates = fp_get("/freight-rates", params={"mode": "trucking", "origin_zip": "30301", "destination_zip": "07030"})
Browser dashboard with fetch, caching, and graceful errors
// JavaScript example
const BASE_URL = "https://freightpulsehq.com/api/v1";
async function fpGet(path, params = {}) {
const qs = new URLSearchParams(params);
const url = `${BASE_URL}${path}${qs.toString() ? "?" + qs.toString() : ""}`;
const res = await fetch(url, { method: "GET" });
if (!res.ok) throw new Error(`HTTP ${res.status}`);
const json = await res.json();
if (!json.success) throw new Error(json.error || "FreightPulse error");
return json.data;
}
// Example: prefetch port congestion + disruptions for a port dashboard
Promise.all([
fpGet("/port-congestion", { port: "USLAX" }),
fpGet("/disruptions", { port: "USLAX", severity: "high" })
]).then(([congestion, hazards]) => {
// render cards, show as_of lag on UI
}).catch(err => {
// show fallback cards and toasts; do not block critical dashboard
console.error("FreightPulse fetch error:", err);
});
Best practices to adopt on day one:
- Circuit breakers per endpoint: e.g., keep /fuel-prices cached for 1–4 hours; if a fetch fails, serve cached data with a visual “stale” badge.
- Regional routing: deploy your backend close to your yard operations to minimize latency when your clerks trigger on-demand lookups at the gate.
- Observability: log endpoint, latency, success flag, and the as_of timestamp for lagged datasets like /port-congestion to prevent misinterpretation by operations.
- Governance: in multi-app environments, assign per-service credentials and audit logs so you can trace who consumed what data when—especially important for SLA root-cause investigations.
Interpreting Responses and Handling Errors
Every FreightPulse response follows a predictable envelope: { "success": true|false, "data": {...}, "error": "message" (when success=false) }. Your client should always check success first, then consume data. Most validation issues return structured messages; for example, /freight-rates returns an “available” indicator when lane parameters are insufficient rather than throwing an exception response body.
When building orchestration logic:
- Treat port-congestion lag intentionally: display the as_of date; if it’s older than your threshold, mute any aggressive adjustments to dock staffing derived from it.
- For disruptions, debounce notifications so your team doesn’t receive multiple alerts as a storm track updates. Track each disruption id and severity, and notify only on severity escalation or a new id.
- Cache infrequently changing data (historical series, national/regional prices) and refresh on a schedule to minimize UI flicker and backend load.
- In UI workflows at the gate, degrade gracefully: if /carriers lookup fails temporarily, let clerks accept a fallback document capture rather than halting the lane.
From Data to Throughput: End-to-End Flows
Here are concrete ways to translate FreightPulse data into fewer idle doors, faster gate processing, and more predictable driver experiences.
Flow A: Arrival Prediction and Door Assignment
- Use /freight-rates to fetch transit_days for unscheduled loads where precise ETA is unknown. Combine with pickup_date to estimate arrival windows.
- Overlay /disruptions filtered by port or country on those windows. If severe weather is forecast en route, shift door assignments earlier and notify clerks to keep those doors free 30–60 minutes ahead.
- Reference /fuel-prices to score each load’s detention risk (higher spot prices and higher diesel often correlate with lower driver flexibility). Prioritize these high-risk loads at the gate and in the dock queue.
Flow B: Drayage Surge Anticipation from Ports
- Poll /port-congestion weekly for your top feeder ports (e.g., USLAX, CNSHA). Calculate ratio_vs_baseline trends over 2–4 weeks.
- When ratios rise steadily, align yard appointment capacity and staffing for the week likely to see elevated inbound. Use /historical port_congestion to quantify how long to maintain the surge plan.
- Use /disruptions near those ports to fine-tune: if a cyclone is forecast, expect batch arrivals before the storm and a lull after.
Flow C: Gate Fast-Lane with Carrier Verification
- At check-in, trigger /carriers with a DOT number or legal name from documents. If status=active and safety_rating meets policy, direct the driver to a fast-lane for immediate yard entry.
- When the lookup fails or flags an issue, route to manual verification without blocking the entire lane; capture images and continue asynchronously.
- Aggregate lookup results to build a “trusted carriers” list which bypasses repeated manual checks for repeat arrivals.
Complete Endpoint Examples and Field Breakdown
Fuel Prices: Additional annotated example
{
"success": true,
"data": {
"timestamp": "2026-07-30T12:00:00Z", // point-in-time reference
"source": "EIA (U.S. Energy Information Administration)",
"currency": "USD",
"unit": "gallon",
"data": {
"diesel": {
"national_average": 3.847, // primary surcharge anchor
"change_week": -0.023, // $/gal change vs last week
"change_percent": -0.6, // percent change vs last week
"regions": {
"east_coast": 3.912,
"midwest": 3.756,
"gulf_coast": 3.623,
"rocky_mountain": 3.891,
"west_coast": 4.456,
"california": 4.892
},
"historical": {
"30d_avg": 3.82,
"90d_avg": 3.75,
"yoy_change_percent": -8.2
}
},
"gasoline": {
"national_average": 3.156,
"historical": {
"30d_avg": 3.1,
"90d_avg": 3.05,
"yoy_change_percent": 1.1
}
}
}
}
}
Practical use: Display a yard “cost pressure” indicator combining change_percent with live /freight-rates deltas to help dispatchers pick which trucks to shepherd through the gate first during peak hours.
Freight Rates: Handling missing fields
curl "https://freightpulsehq.com/api/v1/freight-rates?mode=trucking&origin_zip=10001"
Possible soft-miss pattern (illustrative):
{
"success": true,
"data": {
"available": false,
"message": "Missing required parameter: destination_zip"
}
}
Client tip: Prompt the user for the missing field without failing the entire transaction. Log the occurrence so your ops team sees common data gaps.
Port Congestion: Multi-port query
curl "https://freightpulsehq.com/api/v1/port-congestion?country=United%20States&congestion=high"
Example JSON:
{
"success": true,
"data": {
"results": [
{
"port": "New York-New Jersey",
"locode": "US NYC",
"country": "United States",
"congestion": "high",
"as_of": "2026-08-12",
"metrics": {
"recent_avg_portcalls_per_day": 12.1,
"baseline_avg_portcalls_per_day": 9.8,
"ratio_vs_baseline": 1.23
}
},
{
"port": "Savannah",
"locode": "US SAV",
"country": "United States",
"congestion": "high",
"as_of": "2026-08-12",
"metrics": {
"recent_avg_portcalls_per_day": 10.4,
"baseline_avg_portcalls_per_day": 8.3,
"ratio_vs_baseline": 1.25
}
}
]
}
}
Use this to build a weekly “dray surge watchlist” so appointment books are not blindsided on Monday mornings.
Carriers: Ocean entities
curl "https://freightpulsehq.com/api/v1/carriers?type=ocean&q=Example%20Logistics"
Illustrative JSON (structure focus):
{
"success": true,
"data": {
"type": "ocean",
"query": "Example Logistics",
"results": [
{
"org_number": "OTI-123456",
"legal_name": "EXAMPLE LOGISTICS INC",
"status": "active",
"address": { "city": "Jersey City", "state": "NJ", "country": "US" }
}
]
}
}
Even if your yard is inland, verifying ocean entities for inbound containers helps upstream vetting before a truck ever gets dispatched to your facility.
Disruptions: Country filter
curl "https://freightpulsehq.com/api/v1/disruptions?country=United%20States&type=severe_weather&severity=high"
Example JSON:
{
"success": true,
"data": {
"results": [
{
"id": "nws:SW:987654",
"type": "severe_weather",
"severity": "high",
"title": "Excessive Heat Warning - Southern Plains",
"country": "United States",
"starts_at": "2026-08-19T10:00:00+00:00",
"source": "NOAA/NWS"
}
]
}
}
Tie this to regional labor rules (e.g., additional breaks or heat-related delays) when calculating realistic appointment windows.
Historical: Port congestion trend
curl "https://freightpulsehq.com/api/v1/historical?metric=port_congestion:USLAX&period=90d"
Illustrative JSON:
{
"success": true,
"data": {
"metric": "port_congestion:USLAX",
"period": "90d",
"series": [
{"date": "2026-06-01", "value": 0.93},
{"date": "2026-06-08", "value": 0.98},
{"date": "2026-06-15", "value": 1.05}
]
}
}
Compute a simple momentum indicator to shift yard capacity plans forward by two weeks if the ratio persistently exceeds 1.0.
Real-World Use Cases You Can Ship This Quarter
1) Gate Fast-Lane and Carrier Vetting Panel
At check-in, your clerk enters a DOT number and sees an instant profile with status and safety rating. If compliant, the driver goes straight through a dedicated lane to get staged at a door; otherwise, a secondary process kicks in without blocking the queue. This reduces manual calls and document shuffling. Endpoints used: /carriers.
2) Dock Prioritization Backed by Market Cost and Fuel Pressure
When doors are scarce, prioritize loads that face the highest spot cost pressure. Pull live FTL quotes for inbound lanes and overlay current diesel averages and weekly deltas. Trucks with above-median combined pressure get first docking rights. Endpoints used: /freight-rates, /fuel-prices.
3) Inbound Dray Surge Monitor with Shift Planning
For facilities fed by ocean ports, a weekly surge monitor ingests port congestion ratios and nearby disruptions. When ratios trend high and severe weather looms, your system automatically adds appointment slots on the likely arrival days and pings staffing with recommended shifts. Endpoints used: /port-congestion, /historical, /disruptions.
4) Fuel-Surcharge Calculator with Trend-Aware Guardrails
Publish a transparent surcharge calculator to carriers so they know what to expect on arrival. Base the calculation on national_average and regional PADD values, capped by 30d_avg. This stabilizes expectations and reduces disputes at the gate. Endpoints used: /fuel-prices.
5) Yard-Risk Heat Map for Dispatchers
Create a yard “risk meter” combining disruptions within 300 km of your feeder ports, current congestion category, and the cost weight from live FTL quotes. Dispatchers see a single color-coded signal to decide whether to compress appointments or hold some docks open. Endpoints used: /disruptions, /port-congestion, /freight-rates.
FAQ
How often do FreightPulse fuel prices update?
Fuel prices track EIA releases and include national and regional breakdowns plus historical averages. Treat the data as authoritative for surcharge calculations and refresh on your chosen schedule (for example, daily or weekly) to keep dock policies aligned with current market conditions.
Can I look up ocean carriers as well as trucking carriers?
Yes. The /carriers endpoint supports both trucking (FMCSA SAFER) and ocean NVOCC/OTI entities (FMC directory). Use type=ocean to validate ocean carrier entities upstream of dray dispatch to your yard.
Does the port congestion data reflect real-time vessel wait times?
No. FreightPulse presents a 7-day average versus a 90-day baseline with a typical lag of about 6–7 days. It is designed for planning and trend insight rather than minute-by-minute queue tracking; combine it with disruptions to fine-tune staffing and appointment books.
What lanes and modes are supported for live freight rates?
US FTL trucking quotes are supported across domestic ZIP-to-ZIP lanes. Ocean and air modes are limited to Asian origins into US destination ZIPs, making them useful for dray and transload planning into your facilities.
How should I handle partial or missing parameters in my client?
Always check the success flag and then interpret the data body. For endpoints like /freight-rates, insufficient lane parameters return a non-exception soft miss (e.g., available=false), so prompt users for the missing fields while keeping your UI responsive.
Performance and Operability Tips for Yard Systems
- Prefetch and cache: Schedule background refresh for /fuel-prices and /port-congestion (with explicit as_of labeling) so the yard app is instant. Keep a 4–6 hour TTL for fuel, and weekly cadence for port congestion unless conditions warrant more frequent pulls.
- Fallback chains: If /freight-rates for a lane is temporarily unavailable, fall back to a historical transit_days average and widen the dock arrival window accordingly.
- Health checks: Add a canary call to a lightweight endpoint (e.g., /fuel-prices) at service startup and on a periodic heartbeat; alert if the success flag is false or latency spikes.
- Granular logging: Log endpoint, parameters (sanitized), success flag, and timing. For port congestion, also log ratio_vs_baseline and as_of so ops can see when decisions were based on lagged data.
- Data locality: Co-locate your yard orchestration service close to your primary facility for minimal egress latency to the gate kiosks and dock monitors.
Putting It All Together: Sample Orchestration Script
Below is a simplified Python routine that ingests FreightPulse signals and outputs a door priority list for the next four hours. In production, you’d integrate with your TMS/WMS and real-time check-in events, but the data flow remains the same.
# Python pseudo-implementation for door prioritization
from datetime import datetime, timedelta
def prioritize_doors(inbound_loads):
# inbound_loads: list of dicts with origin_zip, destination_zip, pickup_date, port_locode (optional)
priorities = []
diesel = fp_get("/fuel-prices")["data"]["diesel"]["national_average"]
for load in inbound_loads:
# Estimate arrival window using live quote
fr = fp_get("/freight-rates", params={
"mode": "trucking",
"origin_zip": load["origin_zip"],
"destination_zip": load["destination_zip"],
"pickup_date": load.get("pickup_date", "")
})
# Default transit_days if unavailable
transit_days = fr.get("transit_days", 3)
eta = datetime.utcnow() + timedelta(days=transit_days)
# Cost pressure weight
price_usd = fr.get("price_usd", 0)
cost_weight = (price_usd / 1000.0) + (diesel - 3.0) # simple composite
# Port surge signal if applicable
surge = 0.0
if load.get("port_locode"):
pc = fp_get("/port-congestion", params={"port": load["port_locode"]})
if pc["results"]:
surge = pc["results"][0]["metrics"]["ratio_vs_baseline"] - 1.0
# Disruption bump
bump = 0.0
if load.get("port_locode"):
hazards = fp_get("/disruptions", params={"port": load["port_locode"], "severity": "high"})
bump = 0.2 if hazards.get("results") else 0.0
score = cost_weight + max(0, surge) + bump
priorities.append({ "load_id": load["id"], "eta": eta.isoformat(), "score": round(score, 2) })
# Highest score => earlier door
priorities.sort(key=lambda x: x["score"], reverse=True)
return priorities
This sketch blends current fuel pressure, spot price signals, port-driven surges, and hazard proximity into one quantitative score—exactly the kind of unified logic that moves your dock schedule from reactive to proactive.
Conclusion + Call to Action
Gate queues and idle docks aren’t inevitable—they’re symptoms of fragmented data and lagging signals. With FreightPulse, you replace scattered feeds and brittle glue code with one consistent API that supplies the core intelligence your yard decisions depend on: fuel prices to set fair surcharges, live FTL quotes to anticipate arrivals and costs, port congestion to prepare for dray surges, carrier lookups to fast-lane compliant drivers, and disruption alerts to adjust before the storm hits.
As a developer or operations leader, the shortest path from idea to production is a cohesive surface you can query, cache, observe, and trust. That’s exactly what FreightPulse provides. Build dashboards your clerks will love, automate door assignment that cuts wait time, and staff intelligently based on what’s coming next week—not just what arrived today.
Start turning data into throughput. Explore the endpoints, plug them into your orchestration service, and roll out your first fast-lane gate in days, not months. Learn more at FreightPulse and Try FreightPulse for free.