start2park

start2park is a research-focused parking utility that records parking search behavior and turns that data into usable insights for drivers and planners. The app tracks when and where users look for parking, aggregates anonymized search times and routes, and demonstrates how those measurements can be used to predict how long a parking search will take for individual trips. Users who want to reduce time spent hunting for spots or support sustainable mobility can log searches, view demo recordings of data capture, and contribute to models that help cities and services optimize parking and traffic planning.

Key Features

⭐ Accurate recording of parking search times and routes to generate research-ready datasets.

⭐ Demo mode that lets you see how start2park captures usage data without sharing personal information.

⭐ Aggregated and anonymized datasets used to train predictive models for parking search time estimation.

⭐ Lightweight interface designed for straightforward logging of parking searches during everyday trips.

Advantages

✅ Contributes to research that informs smarter traffic and parking planning backed by statistical models.

✅ Gives individual users insight into how much time they spend searching for parking.

✅ Helps develop predictive tools that can reduce cruising and lower emissions in urban areas.

✅ Simple, low-overhead operation suitable for regular commuters and occasional drivers alike.

Disadvantages

❎ Requires regular participation to build a dataset large enough for reliable local predictions.

❎ Predictive accuracy depends on the amount and diversity of contributed data, so results can vary by area and time.

❎ Records precise search routes and times, so it needs location access and may raise privacy considerations for some users.

Development Team

The start2park app was developed as part of an mFUND research and development initiative funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI). The research team collected and aggregated parking search data across different locations and times to build statistical explanatory models and train predictive algorithms, aiming to better understand and forecast parking search behavior and support climate-friendly mobility planning.