Scooters Arrived Without a Manual. Here's the Operating Guide.
Shared e-scooters are notoriously hard to run efficiently. Operators deploy a fixed number of vehicles and hope for the best — even though demand swings wildly by season, day of week, weather, and local events. Too many scooters clog sidewalks; too few mean missed revenue and unhappy riders.
The challenge is especially acute for new deployments: a city launching scooters has no historical data of its own. This paper tackles that problem directly by asking: can we train a machine learning model on one city's data and use it to predict demand in another?
The answer, using Austin (source) and Louisville (target), is yes — with the right techniques.
From Raw Data to Daily Fleet Predictions
The researchers built a pipeline that takes four types of freely available data — scooter trips, weather, census demographics, and built environment info from OpenStreetMap — and combines them to predict the single most operationally useful number: trips per vehicle per day (fleet utilization).
Predicting utilization rate (rather than raw trip count) is smart design. It controls for fleet size differences between cities, avoids the supply-demand chicken-and-egg problem, and directly informs how many vehicles to deploy on any given day.
Transfer learning
The key technical challenge is called covariate shift: Austin and Louisville have different demand scales, fleet sizes, and rider populations. A model trained on Austin data would naively underestimate Louisville's higher utilization rates. The two-step fix — sample normalization plus label differencing — elegantly aligns the distributions without needing to retrain.
Four Algorithms Walk Into Louisville. One Wins.
Four machine learning models were tested, ranging from classical statistics to cutting-edge deep learning. The results challenge a common assumption: more complexity doesn't always win.
LSTM — the deep learning champion often used for time-series prediction — came in last. The authors explain: for tabular data with mixed static and dynamic features, decision-tree-based models like LightGBM consistently outperform neural networks. This is a well-known pattern in data science competitions, but worth emphasizing for practitioners excited about deep learning.
with sample normalization
+ label differencing
Neither transfer strategy alone was enough. Label differencing without normalization didn't help; normalization without differencing didn't help either. Only combining both reduced the cross-city generalization error — by 15.9% on the best model.
Yesterday's Demand Is the Best Predictor of Tomorrow's.
The LightGBM model ranks its features by how often it splits on them. The results confirm intuition — but also reveal some surprises about what doesn't matter as much as you'd think.
The dominance of time-series features (67%) reflects a fundamental property of urban mobility: tomorrow looks a lot like today, and a lot like last week. The top individual predictor is yesterday's demand (6.6%), followed by elapsed days since service launch (6.3%) — a proxy for the service maturity effect where early users behave differently than regular users.
Removing time-series features caused a 43% jump in prediction error. Removing built environment or sociodemographic features each caused less than 2% degradation — yet both still matter for spatial prediction accuracy.
The practical takeaway: you don't need a massive feature set to build a working demand predictor. You need the last 30 days of trips, the temperature forecast, and basic census data. All of it is free.
Downtown Belongs to Weekends. Campuses Own Weekdays.
Both Austin and Louisville exhibit the same spatial split that the companion study of five cities found. University areas — UT Austin, University of Louisville — dominate weekday demand at all hours. Downtown entertainment districts flip to dominate on weekends and early mornings.
The models' spatial error analysis reveals something important: prediction errors concentrate around downtown and university zones. These high-demand areas are also where the model understimates peaks — because they're also where unpredictable events (festivals, games, concerts) spike demand beyond what historical patterns suggest.
Different Urban Structures, Same Spatial Logic
Austin and Louisville have completely different city layouts and sizes — yet their scooter demand is spatially concentrated in the same types of zones (educational and entertainment hubs). This suggests the framework can generalize broadly.
Seasonal Synchrony Across Cities
Both cities show demand increasing through spring and summer, dropping from October, and hitting lows in January — despite different climates. The scaled demand trends are nearly identical once fleet size differences are controlled.
Under One Trip Per Vehicle Per Day — and That's a Problem
The median fleet utilization in both cities is below 1 trip/vehicle/day. Most scooters sit idle most of the time. The authors argue fleet sizes should be dynamically adjusted — ideally daily — to match predicted demand, not held constant.
A Practical Tool, Not Just a Research Exercise
The paper is explicit about practical applicability. The entire methodology uses publicly available data sources that any city or operator can access: open city trip portals, census.gov, openstreetmap.org, and visualcrossing.com. No proprietary data required.
Deploy New Cities Without Historical Data
The transfer learning approach means a city launching scooters for the first time can borrow demand patterns from a similar city. Only ~3 months of pilot data is needed in the target city before the model adapts.
Dynamic Fleet Sizing by Season and Forecast
Instead of deploying a fixed 1,200 or 15,000 scooters year-round, operators can use daily utilization predictions to right-size fleets. Fewer idle scooters means less sidewalk clutter, lower redistribution costs, and improved sustainability metrics.
Special Events Need Special Planning
Austin's SXSW festival drove 5–6× normal demand — an extreme outlier the model struggled with. Event calendars should be integrated as explicit features in future model versions, with dedicated redistribution protocols.
Open Data Publication Is a Policy Tool
The study would be impossible without cities publishing trip data. The authors explicitly call for more cities to follow suit — not just for research, but because transparency creates accountability and improves service quality.
The Full Technical Story
The paper contains the complete model specifications, all coefficient tables, spatial error maps, and full feature engineering procedures. Everything reproducible with open data.
Read the Full Paper →