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AI-assisted routing can help a commercial fleet reduce fuel or energy use by estimating how different vehicles will perform on candidate routes—not simply by choosing the shortest distance. To make that useful, set clear service constraints, provide reliable vehicle and trip data, put recommendations into dispatch and driver workflows, and measure results against comparable trips. Savings vary by fleet; there is no general percentage that can be promised from route optimization alone.
What AI route optimization can do for a fleet
Conventional routing often emphasizes distance or arrival time. Energy-aware routing adds an estimate of how much fuel or energy a particular vehicle is likely to use on each candidate route, then weighs that estimate against travel time and operating constraints.
NLR’s RouteE materials describe estimating vehicle energy use and travel time while accounting for factors such as traffic, speed, road type, lanes, grade, and turns. Those factors explain why fewer miles do not always mean lower fuel use: a longer route with steadier speeds or less climbing may compare favorably with a shorter route involving congestion, steep grades, or frequent turns.
The estimate is only as useful as its fit to the vehicle and work. NLR’s 2025 account of CoVaR describes energy models trained with real-world duty-cycle data, including trip duration, road elevation, cargo weight, and vehicle characteristics. A route that suits one vehicle, load, or schedule may not suit another.
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Set the objective before changing routes
Decide what the routing system should optimize and what it must not compromise. Fuel or energy reduction is one goal alongside delivery windows, service commitments, driver safety, and route feasibility. A route with a favorable energy estimate is not operationally useful if it causes missed appointments or cannot accommodate the vehicle or delivery.
- Define the priority: reduce fuel or energy use, lower operating cost, improve schedule reliability, or balance these goals.
- Set hard constraints such as required arrival times, stop sequence, vehicle suitability, and any fleet-specific operating requirements.
- Choose which trade-offs dispatchers may make—for example, whether a modestly longer trip is acceptable when its predicted energy use is lower.
Prepare the inputs the routing system needs
Before relying on recommendations, check that the system has credible data for the routes and vehicles it will plan. Incomplete or inaccurate inputs can make a precise-looking energy estimate misleading.
- Vehicle details: vehicle characteristics and powertrain information sufficient to distinguish how fleet vehicles use energy.
- Route conditions: road network, road type, elevation or grade, turns, expected traffic, and speed conditions where available.
- Trip and duty-cycle details: stops, trip duration, cargo weight or other relevant load information, and the work the vehicle is expected to perform.
- Schedule requirements: delivery windows, required arrival times, and other dispatch constraints.
- Operational records: trip mileage and, where available, fuel or energy use, idle time, delivery performance, and route adherence for later evaluation.
Telematics can help capture trip mileage and compare actual travel with an efficient or optimal route. NREL’s Telematics Framework for Federal Fleet Applications describes actual-versus-optimal trip-mileage comparisons. Mileage comparison is useful context, but it does not by itself establish fuel savings; pair it with fuel or energy records and comparable operating conditions.
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Compare candidate routes on more than miles
Review the route options in terms dispatch can act on, not just a single score. NLR’s RouteE materials describe analysis of the energy-time trade-off, while its CoVaR account describes routing that considers factors including time, distance, grade, and traffic.
- Estimated fuel or energy: the system’s prediction for the specific vehicle and trip, not a guaranteed outcome.
- Travel time and schedule fit: whether the trip can meet its delivery window under expected conditions.
- Distance and road conditions: miles, grade, road type, turns, speed, and traffic exposure.
- Vehicle and load suitability: whether the proposed path and assumptions fit the assigned vehicle and cargo.
- Operational feasibility: whether the route can be dispatched and completed under the fleet’s requirements.
Use the system’s energy estimate to inform a decision, not to override a missed constraint or an implausible input. Where two routes are close, dispatch may reasonably favor schedule reliability or another fleet priority over a small predicted energy difference.
Put recommendations into dispatch and driver workflows
A route recommendation only has value if the operation can assign it and drivers can use it. NLR’s September 2025 CoVaR account describes a fleet dashboard for assigning vehicles to routes and sending directions to drivers. Treat that as a description of the project’s capabilities, not proof that a commercially available product is ready for your fleet: the report said validation and commercial launch were still needed at that time.
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- Review the proposed assignment: have dispatch check the vehicle, stops, timing, and route constraints before release.
- Send usable directions: deliver the route through the fleet’s driver communication process and make any route changes clear.
- Record exceptions: note diversions, delays, substitutions, or other reasons the actual trip differed from the planned one.
- Use feedback to refine operations: examine whether recommendations are practical for dispatchers and drivers, rather than assuming a model’s preferred route will be followed.
When evaluating software, ask how it estimates energy, which vehicle and route inputs it uses, how it handles time constraints, how recommendations reach dispatch and drivers, and what trip data the fleet can export for evaluation. Verify that a vendor’s claims apply to your vehicle types, routes, and operating conditions.
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Measure fuel-cost changes against a fair baseline
Do not attribute every change in fuel use to routing. Compare trips or operating periods with similar work, and document differences that could affect the result. A practical evaluation should track fuel or energy use alongside the service and route measures needed to interpret it.
- Choose a baseline period or comparable set of trips before changing routing practices.
- Track fuel or energy, miles, delivery performance, idle time, and route adherence.
- Where possible, account for vehicle, cargo load, traffic, weather, and work-mix changes when comparing results.
- Separate predicted energy estimates from pilot observations and from measured operational savings.
- Review whether apparent savings persist without harming delivery performance or other fleet priorities.
This comparison is implementation guidance, not a savings figure established by the cited telematics report. If the fleet changes vehicles, loads, schedules, or routes at the same time, a before-and-after difference may not isolate the effect of routing.
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Combine routing with other fleet-efficiency measures
Routing is one lever, not a complete efficiency program. NLR’s CoVaR account groups routing with vehicle configuration, driver coaching, and dashboard capabilities. A fleet may find that a different intervention matters more for its operations, and changes in fuel use should not automatically be credited to routing alone.
Set up evaluation so that routing changes can be distinguished from coaching or vehicle changes where practical. The goal is to identify which measures work for the fleet’s duty cycles and service requirements, not to assume that a route recommendation will produce the same result everywhere.
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NLR’s September 17, 2025 report describes CoVaR’s modeled potential informed by real-world fleet data; it does not establish a universal fuel-cost savings rate or a quantified saving caused by routing alone. NLR commercial vehicle technologies researcher Setayesh Fakhimi summarized the fleet dependence this way: “The impact of different interventions on energy savings is dependent on the operations of the fleet.”
NLR also reported that Google’s 2023 Environmental Report attributed more than 1.2 million metric tons of carbon emissions prevented to Google Maps fuel-efficient routing across the United States, Canada, and nearly 40 European countries. That is a broad mapping-service emissions figure, not evidence of commercial-fleet fuel savings or of AI routing’s standalone impact.
For a fleet decision, rely on a measured result from comparable fleet work rather than importing a percentage from another service or operation. The available NLR account described CoVaR as still requiring validation and commercial launch in September 2025; its current availability is not established here.
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