PUBLIC POLICY · AGENCY CASE: RURAL MOBILITY PLAN
“Anticipating the adoption of a shared mobility plan in low-density areas over 5 years.”
Completed case for a regional mobility agency facing the rollout of a shared mobility plan across rural territories.
THE CONTEXT
A structuring plan on a territory with a low propensity to share.
A regional mobility agency was preparing the rollout of a shared mobility plan across the rural territories of a French region: organized carpooling, peer-to-peer car-sharing, on-demand shuttles, electric mobility subsidies. The planned budget was €82 million over 5 years, with the goal of reaching a 34% adoption rate of shared services among rural workers at a 60-month horizon.
The challenge was structural: the rural territories concerned had a strong historical anchoring in solo driving (one personal vehicle per household or per worker), with carpooling rates below 3% at the start. Comparable previous programs deployed in other regions had run into difficulties: services oversized relative to effective demand, low utilization rates, a high cost per user.
The agency's leadership mobilized our system to test different sizing and sequencing configurations, with a 5-year projection of real adoption trajectories by rural worker typology.
THE INQUIRY
Three insights that recomposed the rollout plan.
Adoption is not linear: it comes in waves triggered by signal events.
Our system identified a structural dynamic of shared mobility adoption in rural territory: it does not follow a linear progression but successive waves, triggered by signal events external to the program: a lasting rise in fuel prices, a prolonged breakdown of a personal vehicle, the closure of a local service, the arrival of a new operator. Between waves, adoption stagnates, whatever the intensity of institutional communication. The strategy must be calibrated to absorb the waves, not to promote usage uniformly.
A service sized for 8%, expandable to 22% in 6 months, is more profitable than a service sized for 15% from the start.
Our system modeled several sizing configurations. A service sized from the outset for 15% adoption runs at an average of 34% of its capacity over the first 3 years, with a high cost per user. A service sized for 8% with rapid extension capacity to 22% within 6 months runs at an average of 78% of its capacity, with a cost per user 2.4 times lower. Scalable sizing is structurally more profitable than immediate sizing.
Communication must prepare for the waves, not promote usage.
Our system tested several communication strategies. Continuous communication promoting shared mobility usage (the classic register) generates little adoption between waves. Communication centered on preparing for the waves (a pedagogical register: “when you need it, here is how it works”) generates adoption 3.4 times higher when the waves actually come. The change of register transforms the effectiveness of the communication budget.
THE METHOD
How we built the inquiry.
Our system rebuilt a synthetic population of 2.4 million rural workers of the region concerned, calibrated on INSEE public data, regional mobility surveys and the agency's proprietary data on observed mobility behaviors. The population was structured into 11 typologies of relationship to rural solo driving, crossing age, professional constraints, family structure, distance to the town center and the relationship to collective services.
Our system interviewed 3,200 synthetic workers on the 5 tested service configurations, with dynamic follow-ups on the tipping points. Adoption trajectories were projected over 60 months, with modeling of the probable signal events (fuel price rises, breakdowns, service closures, operator arrivals) and their influence on adoption waves.
THE DEPLOYMENT
What was decided, what happened.
The regional agency retained the strategy combining initial sizing for 8% adoption with rapid extension capacity to 22% within 6 months, communication centered on preparing for the waves, a signal-event monitoring capability to anticipate service extension, and partnerships with private mobility operators for rapid scalability.
At 36 months into the rollout, two major adoption waves were observed: a lasting rise in fuel prices in year 2 and a local service closure in year 3. The program absorbed both waves with utilization above 82% of extended capacity. Cumulative adoption at 36 months is 24% (close to the projected 22%). The cost per user is 42% below the projection for a service sized from the outset for 22%. The methodology has been referenced as a model by other regions preparing similar plans.
- PROJECTED ADOPTION AT 5 YEARS
- 12% → 34%with the recommended approach
- COST OF A MISTIMED ROLLOUT
- +€18Mof anticipated over-investment avoided
- CAPACITY UTILIZATION
- 82%vs 34% with immediate sizing
- COST PER USER
- −42%vs a service sized for 22%
THE LESSONS
Two principles transposable to usage transformation policies.
Usage transformation policies advance in waves triggered by signal events, not by institutional communication.
This case confirmed a recurring dynamic of long-horizon usage transformations: shared mobility, residential energy transition, remote work adoption, dietary change. Real adoption does not follow the curve of institutional communication but that of external signal events. This principle implies calibrating programs to absorb the waves, with rapid extension capacity, rather than to promote the target usage uniformly.
Scalable sizing is structurally more profitable than immediate sizing.
A service sized for its final objective wastes capacity during the learning phase. A service sized for initial demand, with rapid extension capacity activated when the waves come, optimizes the cost per user and the program's profitability. This principle holds for many public infrastructures whose usage grows in waves rather than linearly.
GET STARTED
Preparing a long-horizon usage transformation policy?
Long-horizon usage transformation policies, shared mobility, residential energy transition, remote work adoption, dietary change, public service transformation, share common mechanics with this case. Progression in waves rather than linear, the weight of external signal events, the value of scalable sizing, the change of communication register. Every policy is singular, but the analytical levers are transposable.
The dynamic agents scope with you the parameters of a simulation adapted to your situation, ahead of the decision. From initial brief to first deliverable, allow 20 to 30 minutes, depending on the case's complexity and the breadth of the populations to model.