Telematics for Sustainable Mobility

The University of California, Berkeley's Data Science Discovery capstone project explored the potential of telematics data to drive innovations in urban mobility and sustainability. The project focused on analyzing a detailed dataset capturing vehicle and driver behaviors, aiming to assess the feasibility of transitioning fleet vehicles to electric vehicles (EVs). Over the semester, a team of five students worked to evaluate energy savings and efficiency improvements achievable through such transitions, laying the groundwork for future research in sustainable urban mobility.

The project began with an in-depth data collection and preprocessing phase. The team gathered telematics data from fleet vehicles, which included information on routes, speeds, fuel consumption, and other relevant metrics. Despite challenges with data granularity, the team successfully cleaned and organized the dataset to ensure accurate analysis. This foundational work was crucial in enabling the team to derive meaningful insights and identify patterns that could inform their recommendations.

In the analysis phase, the team used various data science techniques to evaluate the potential benefits of transitioning to EVs. They applied clustering algorithms to segment the dataset and identify optimal routes for electric vehicles. By examining factors such as trip length, frequency, and energy consumption, the team assessed which segments of the fleet would benefit most from electrification. Visualizations, including maps and elbow plots, were created to illustrate traffic patterns, frequently traveled paths, and the clustering results, providing a clear picture of the current state of fleet operations.

The findings revealed significant opportunities for improving energy efficiency and reducing emissions through the adoption of electric vehicles. However, the study also highlighted the complexity of modeling real-world travel dynamics and the need to consider additional factors such as route choices and unexpected traffic conditions. The team recommended further research to refine the models and incorporate real-time data, which could enhance the accuracy of predictions and support more effective decision-making.

Overall, the project underscored the importance of telematics data in driving sustainable urban mobility solutions. By leveraging detailed vehicle and driver behavior data, the team demonstrated how data science can inform strategic decisions and promote environmental sustainability. The insights gained from this capstone project provide a valuable foundation for future research and development efforts aimed at optimizing fleet transitions to electric vehicles, ultimately contributing to greener and more efficient urban transportation systems.