---
title: "Empty Miles Reduction Software: A How-To Guide to Route Design, Backhaul Matching & Utilization Analytics"
id: "27131"
type: "post"
slug: "empty-miles-reduction-software"
published_at: "2026-09-15T11:00:00+00:00"
modified_at: "2026-09-27T18:37:20+00:00"
url: "https://locus.sh/blogs/empty-miles-reduction-software/"
markdown_url: "https://locus.sh/blogs/empty-miles-reduction-software.md"
excerpt: "Reduce empty miles with smarter route design, backhaul matching, and utilization analytics. See how AI routing and capacity orchestration cut deadhead costs."
taxonomy_category:
  - "General"
---

#### [General](https://locus.sh/blogs/category/general/)

# Empty Miles Reduction Software: A How-To Guide to Route Design, Backhaul Matching & Utilization Analytics

[Team Locus](/author/team-locus/)

Sep 15, 2026

14 mins read

## Key Takeaways

- Empty miles (deadhead miles) represent the full operational cost of running a vehicle with no freight revenue to offset it; fuel, driver hours, and vehicle wear all accumulate on every unloaded leg
- Telematics dashboards surface deadhead percentages but do not change routing decisions; reducing empty miles requires a mechanism that acts on utilization data, not one that only reports it
- Three levers drive structural empty-mile reduction: smarter route design that plans return legs proactively, backhaul matching that pairs outbound deliveries with return loads, and utilization analytics that expose capacity waste at route and vehicle level
- Locus’s AI routing, DispatchIQ dispatch engine, and Fireworks Routing Engine treat route design, backhaul matching, and utilization analytics as a connected system, not as separate tools, making empty-mile reduction a structural outcome of every planning cycle

[Schedule a Demo With Locus Today](https://locus.sh/schedule-demo/)

Most fleet operations today can tell you their deadhead percentage. Telematics dashboards surface it nightly. Monthly fleet reports include it in the summary slide. And yet in many operations, that number stays flat quarter over quarter.

The problem is that deadhead percentage is a measurement, not a mechanism. Empty miles are a routing and capacity problem. Reducing them requires decisions made at the planning stage, not observations made in a report after vehicles have returned to the depot.

This guide covers the three practical levers for reducing empty miles: smarter route design, backhaul matching, and utilization analytics. It also explains why each lever only produces sustained results when it feeds directly into dispatch and route planning decisions.

## What Are Empty Miles and Why They Quietly Erode Margins

Empty miles, also called deadhead miles, are the distance a commercial vehicle travels without carrying a revenue-generating load. In distribution and [last-mile logistics](https://locus.sh/blogs/last-mile-logistics-software/)
, they accumulate across several common scenarios:

- **Return legs:** Vehicles complete a delivery run and drive back to the depot or origin point without any load assigned for the return trip
- **Repositioning moves:** Vehicles shift between depots, hubs, or staging areas without freight assigned to any segment of the repositioning distance
- **Partial outbound loads:** Vehicles dispatched below optimal fill because planning closes the consolidation window too early, so the vehicle runs under-filled for the entire outbound leg
- **Missed consolidation windows:** Orders that could have been incorporated into an existing route but were not captured in time, generating a separate underloaded trip

Every empty mile carries the full operational cost of a loaded mile: fuel, driver time, vehicle wear, and maintenance. The difference is that no freight revenue offsets it. A report found that empty miles across the US trucking industry [averaged 16.7% in 2024](https://truckingresearch.org/2025/07/new-atri-report-shows-trucking-profitability-severly-squeezed-by-high-costs-low-rates/)
, meaning roughly one in six truck miles generated no freight revenue to offset operating expenses.

For fleets running hundreds of vehicles across multi-depot distribution networks, even a moderate empty-mile rate translates into significant wasted cost per operating day. The compounding effect across a full operating year makes it one of the highest-leverage cost reduction opportunities available to a fleet director.

## Why Telematics Reports Alone Don’t Reduce Empty Miles

The standard response to an empty-miles problem is to add visibility. The fleet gets a telematics platform. Deadhead percentages are tracked. Reports are distributed weekly. And the routes stay largely the same.

For [fleet tracking and dispatching](https://locus.sh/blogs/fleet-tracking-and-dispatching/)
 operations, the gap between a dashboard showing 28% deadhead and a route plan that reduces it to 18% is closed by a planning system that incorporates utilization data into dispatch decisions.

|  | Telematics Reporting | AI Routing + Capacity Orchestration |
| --- | --- | --- |
| What it does | Measures empty miles after the fact | Optimizes routes and capacity to prevent empty miles before dispatch |
| When it acts | Post-trip reports, lag of hours or days | Pre-dispatch, using live order and capacity data in real time |
| Decision impact | Informs periodic review meetings | Directly changes the next route plan generated |
| Feedback loop | Manual: planner reviews data, decides what to change | Automated: utilization data feeds back into next planning cycle |
| Scale value | Consistent at any fleet size | Compounds at higher fleet complexity and order volume |

### The difference between measuring waste and acting on it

A telematics platform that shows 28% deadhead tells you how much empty mileage your fleet accumulated last week. It does not identify:

- Which specific routes or route segments produced the deadhead
- What consolidation or sequencing change would eliminate it in tomorrow’s plan
- Whether the same structural problem exists in routes that have not departed yet
- What backhaul opportunity exists on the return legs generating the most empty distance

Reducing deadhead requires a decision engine. The mechanism that moves the needle is a routing and capacity orchestration system that incorporates return-leg planning, order consolidation logic, and backhaul matching into every route it generates, before vehicles depart.

| Source: ChatGPTAlt text: Split comparison diagram showing a passive telematics reporting cycle on the left (measure, report, review) and an active AI routing and capacity orchestration cycle on the right (measure, plan, optimize, dispatch) with a closed feedback loopCaption: Telematics reporting and AI routing orchestration both use fleet data, but only one changes what happens at next dispatch. The difference is whether utilization data feeds back into the planning layer or stops at the reporting layer. |
| --- |

## The Three Levers of Empty Miles Reduction Software

Empty-mile reduction operates across three distinct levers, and a platform that handles only one of them leaves the others unaddressed. True [distribution routing software](https://locus.sh/blogs/distribution-routing-software/)
 treats all three as interconnected inputs to the same planning decision:

- **Route design:** Eliminating avoidable empty legs at the planning stage, before vehicles depart
- **Backhaul matching:** Pairing outbound deliveries with return loads to fill vehicle capacity on return trips
- **Utilization analytics:** Measuring volume, weight, and distance efficiency to expose structural waste and feed better planning decisions

### Lever 1: Smarter route design

Route design is the first and most consequential lever for empty-mile reduction.

Most empty legs are designed into routes before a vehicle departs, through territory structures, planning assumptions, or consolidation cutoffs that do not account for the return trip. Fixing them at the planning stage prevents the empty miles from occurring; identifying them after the fact reduces them only if planning changes in the next cycle.

Practical steps for return-aware route design:

- **Plan return legs alongside outbound legs:** Routes that incorporate both in one optimization cycle produce materially fewer empty return legs. Treating outbound and inbound as separate planning problems generates deadhead by default
- **Consolidate stops before locking routes:** Order consolidation logic that groups compatible stops by proximity, time window, and vehicle type improves outbound fill and reduces the number of underloaded route legs generated per planning cycle
- **Sequence for delivery density:** Routee optimized for stop density over raw mileage minimization cluster stops tightly, reducing the unladen distance between productive stops and minimizing backtrack distance at zone edges
- **Build zone structure around load balance:** Territory design that accounts for delivery volume per zone prevents chronic over-routing in sparse zones that generate disproportionate empty distance per vehicle

[Automated route planning](https://locus.sh/blogs/automated-route-planning/)
 systems that evaluate all four of these factors simultaneously produce structurally fewer empty miles than those optimizing one factor at a time, because the interactions between stop density, zone balance, and return-leg feasibility only become visible when evaluated together.

### Lever 2: Backhaul matching

Backhaul matching is the practice of coordinating outbound deliveries with inbound pickups so that return legs carry a load. It is one of the most direct ways to eliminate deadhead on vehicle return trips and one of the most underused in enterprise distribution operations. That is largely because it requires outbound and inbound planning to happen in the same decision window.

Steps to build backhaul matching into your operation:

- **Map recurring outbound and inbound lanes:** Identify which delivery lanes pass through supplier, manufacturer, or return-pickup locations with consistent return volume. These are the structural backhaul opportunities that scheduling changes can address immediately
- **Quantify available capacity on return legs:** Use utilization data to calculate payload capacity unused on each return segment. This becomes the matching target: the volume or weight available for backhaul assignment before it defaults to empty miles
- **Coordinate pickups in the same planning window:** Backhaul matching only produces results when pickup and delivery scheduling happen simultaneously. Integrating pickup coordination into the outbound route planning stage closes the window that sequential scheduling leaves open
- **Extend coordination to your carrier network:** For lanes without in-house backhaul options, carrier network tools that surface available capacity across 3PL partners can fill return legs that owned fleet vehicles cannot. [Locus ShipFlex](https://locus.sh/ship-flex/) extends this coordination to 160+ active carriers from a broader network of 1,000+ pre-integrated partners

| Source: ChatGPTAlt text: Route diagram showing two scenarios side by side: a standard one-way delivery route on the left with an empty return leg, and a backhaul-optimized route on the right where the return leg carries an inbound pickup load, reducing deadhead distance to near zeroCaption: Backhaul matching pairs outbound delivery legs with inbound pickup loads on the return trip. Routes planned with both legs in the same optimization window produce far fewer empty return miles than those planned in separate scheduling cycles. |
| --- |

### Lever 3: Utilization analytics

Utilization analytics is the measurement infrastructure that makes route design and backhaul matching improvements visible, trackable, and sustainable. Without it, improvements are estimated and isolated; with it, they are measurable, attributable, and fed back into the next planning cycle.

Three dimensions of utilization to track:

- **Volume utilization:** The percentage of cubic vehicle capacity filled per trip. Low volume utilization on routes that are geographically efficient signals that consolidation logic is not grouping orders optimally before dispatch
- **Weight utilization:** The percentage of maximum payload capacity used per departure. Weight-constrained operations need this metric alongside volume to identify which constraint is binding the fill rate on specific vehicle types
- **Distance utilization:** The ratio of loaded miles to total miles driven, the most direct measure of deadhead exposure and the metric that empty-mile reduction efforts ultimately move

What utilization analytics should do for your operation:

- Surface underloaded routes and vehicles by name
- Identify which specific route segments consistently generate deadhead, so route design changes target the right geographic patterns
- Flag capacity available for backhaul matching on specific lanes, quantifying the backhaul opportunity before it defaults to empty miles
- Feed utilization signals back into the route planning layer automatically

A unified real-time visibility layer within Locus’s agentic TMS makes volume, weight, and distance utilization trackable at route and vehicle level throughout the delivery day, not only in post-trip summaries. This means the planning layer receives live utilization signals before the next dispatch window closes, enabling same-day adjustments when structural waste is detected mid-cycle.

**Also read:** [TMS Analytics: The Metrics That Drive Logistics ROI](https://locus.sh/blogs/tms-analytics/)

## How AI Routing and Capacity Orchestration Turn These Levers Into Results

Applying the three levers manually produces incremental improvement at best. Route design, backhaul matching, and utilization analytics each require data from the other two to make the optimal decision. Manual planning cannot hold all three in a consistent decision context across a large fleet operating across multiple depots and carrier partners.

AI routing and capacity orchestration resolves this by treating all three levers as inputs to a single optimization problem, evaluated simultaneously for every route in every planning cycle.

Locus is the world’s first Decision-Intelligent, Agentic TMS. Its approach to empty-mile reduction is built into the [dispatch management](https://locus.sh/dispatch-management-software/)
 and route planning architecture from the ground up:

- **DispatchIQ:** Manages carrier-order matching and allocation across multiple fulfillment nodes, incorporating capacity fill, return-leg feasibility, and SLA constraints simultaneously to prevent underloaded dispatches before vehicles depart
- **Fireworks Routing Engine:** Processes 250+ real-world constraints per route, optimizing stop consolidation, load sequencing, and zone balance in a single pass to structurally reduce avoidable empty legs across the fleet
- **Mycroft AI Co-Pilot:** Surfaces empty-leg risk signals in plain language before routes lock, enabling dispatchers to act on utilization gaps before departure
- **Sense-Decide-Execute-Learn loop:** Ensures each completed delivery cycle’s utilization outcomes feed back into the next planning cycle, compounding empty-mile reduction over time without requiring manual recalibration

Understanding [how AI route optimization works](https://locus.sh/blogs/how-ai-route-optimization-works/)
 at the constraint level explains why orchestration produces structurally fewer empty miles than rule-based routing. When the optimization engine evaluates return-leg planning, zone balance, and order consolidation in one pass, it finds solutions that sequential or siloed planning consistently misses.

| Source: https://locus.sh/route-optimization/route-optimization-software/Alt text: Locus Fireworks Routing Engine interface showing multi-constraint route planning across vehicle load capacity, backhaul coordination, delivery windows, and real-time traffic for an enterprise distribution fleetCaption: The Fireworks Routing Engine evaluates 250+ constraints simultaneously per route, incorporating return-leg planning and order consolidation in the same pass as outbound sequencing. Empty-mile reduction emerges from the optimization output, not from a separate reporting step. |
| --- |

## How to Evaluate Empty Miles Reduction Software

When evaluating [freight optimization software](https://locus.sh/blogs/freight-optimization-software/)
 for empty-mile reduction, the questions that distinguish an orchestration platform from a reporting tool are operational, not feature-based. Use this checklist to pressure-test vendor claims:

| Evaluation Question | What to Look For |
| --- | --- |
| Does it act or only report? | The platform must change the next route plan, not just surface deadhead metrics. Ask to see how a utilization signal triggers a routing change |
| Does it plan return-aware routes? | Outbound and return-leg optimization should happen in the same planning pass. Sequential planning produces systematic empty return miles by design |
| Does it support backhaul matching automatically? | Backhaul assignment should be part of standard route generation. Manual post-planning coordination breaks down at scale and misses time-sensitive opportunities |
| Does it surface utilization in real time? | Volume, weight, and distance utilization must be available before routes lock each day. Post-trip data cannot influence the current morning’s dispatch |
| Does it integrate with OMS and WMS? | Live order and inventory data are required for same-day optimization. Platforms requiring batch file transfers introduce lag that defeats real-time consolidation decisions |
| Does it scale across depots and fleet types? | Test multi-depot, multi-fleet-type scenarios explicitly. Single-depot tools frequently do not disclose scale limitations during a standard demo |
| Does it handle multi-carrier coordination? | Backhaul opportunities extend beyond owned fleet capacity. The platform should make carrier network capacity visible and assignable in the same planning context |

## Turning Empty-Mile Reduction Into a Competitive Advantage With Locus

The three levers of empty-mile reduction, route design, backhaul matching, and utilization analytics, only produce sustained competitive advantage when they are connected. A routing engine that improves outbound consolidation without incorporating return-leg planning captures a fraction of the available improvement.

Utilization analytics that surface structural waste without feeding back into route planning describe a problem and stop there.

Locus connects all three into a single architecture. DispatchIQ and the Fireworks Routing Engine handle route and capacity optimization with 250+ constraints evaluated simultaneously.

ShipFlex extends that optimization to the carrier network. Mycroft AI Co-Pilot makes utilization risk actionable at the dispatch level before routes lock. And a unified real-time visibility layer within Locus’s agentic TMS ensures that utilization data is available to the planning layer before tomorrow’s routes are finalized.

Across 360+ enterprise customers in 30+ countries, Locus has driven $320M+ in cumulative logistics cost savings through better [AI route optimization](https://locus.sh/blogs/ai-route-optimization/)
 and dispatch orchestration, with a 20% reduction in logistics costs and a 45% improvement in fleet utilization documented across enterprise deployments.

Gartner has recognized Locus for seven consecutive years, including the 2026 Hype Cycle for Supply Chain Execution and Logistics Technologies and the 2025 Market Guide for Last-Mile Delivery Technology Solutions.

Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus in October 2025 following a global evaluation of logistics software providers. Locus continues to operate independently. Built for the real world, backed for the long run.

[Schedule a demo with Locus](https://locus.sh/schedule-demo/)
 to see how AI routing and capacity orchestration make empty-mile reduction a structural output of every planning cycle, not a reporting exercise that follows it.

## Frequently Asked Questions

### How does Locus approach backhaul matching across a fleet?

Locus handles backhaul matching as part of the standard route generation process, not as a separate post-planning step. The Fireworks Routing Engine incorporates return-leg feasibility into the same optimization pass as outbound sequencing. ShipFlex extends this to multi-carrier coordination, identifying backhaul opportunities across 160+ active carriers from a broader network of 1,000+ pre-integrated partners where in-house fleet coverage is not available for a given return lane.

### Can Locus reduce empty miles in a multi-carrier, multi-depot operation?

Yes, Locus is built for enterprise logistics complexity. DispatchIQ manages allocation across multiple fulfillment nodes simultaneously, and the Fireworks Routing Engine maintains performance across high-volume multi-depot networks. ShipFlex coordinates [delivery fleet management](https://locus.sh/blogs/delivery-fleet-management-software/)
 across owned and 3PL carriers in the same planning context, so backhaul and capacity-fill opportunities extend across the full network.

### How does Locus’s approach differ from a telematics reporting platform?

Telematics platforms measure what happened to your fleet. Locus changes what happens next. A unified real-time visibility layer within Locus’s agentic TMS tracks vehicle utilization throughout the day and feeds that data back into the planning layer before routes lock. DispatchIQ and the Fireworks Routing Engine then use that utilization signal to generate better-consolidated, backhaul-aware routes for the next dispatch window. The [transportation route optimization](https://locus.sh/blogs/transportation-route-optimization-software/)
 architecture is built to reduce empty miles as an output of every planning cycle.

MEET THE AUTHOR

Team Locus

Written by the Locus Solutions Team—logistics technology experts helping enterprise fleets scale with confidence and precision.

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## Empty Miles Reduction Software: A How-To Guide to Route Design, Backhaul Matching & Utilization Analytics

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