The transportation sector is one of the most important components of modern society, playing a central role in economic development, social connectivity, and overall quality of life. As cities continue to grow and mobility demands intensify, the challenges and opportunities within the transportation sector can no longer be overlooked.

To achieve this, municipalities and transit authorities are increasingly turning to data collected from various traffic participants, leveraging emerging technologies and data-driven approaches to gain deeper insights into road traffic dynamics. This information enables the development of services and policies that support smoother, safer, and more environmentally responsible transportation systems.

Among these technologies, trajectory data has emerged as a powerful resource for traffic analytics. However, its widespread adoption also introduces significant challenges. First, the massive volume of data generated imposes substantial communication, processing, and storage costs. Second, transforming raw trajectory data into actionable insights remains a complex task requiring interdisciplinary expertise. This thesis addresses these two key challenges.

To tackle the data collection problem, we propose two complementary strategies aimed at reducing the volume of position data without compromising its relevance. On the client side, we investigate traditional sampling methods based on the core features of commercial GNSS devices -- time, distance, speed, and heading. We evaluate these methods using a range of generic and application-specific metrics, and based on the findings, propose hybrid strategies that mitigate their weaknesses while enhancing their strengths. On the server side, we introduce a novel sampling approach that leverages server-side intelligence and historical GNSS data to selectively retrieve data from regions or contexts of high analytical interest.

We address the traffic analytics challenge through three distinct contributions. First, we quantify the impact of maintaining a steady driving speed on vehicle energy consumption. We analyze varying levels of speed fluctuations, measure their effect on consumption, and identify optimal fluctuation levels as well as the conditions under which steady-speed driving is most beneficial. Second, we assess the influence of roundabouts on energy consumption using a large-scale dataset comprising over 1,000 roundabouts with diverse characteristics and layouts. Finally, we present a comprehensive framework that leverages trajectory data to automatically identify and monitor junctions within a road network. As one of the most complextasks in traffic management, our framework simplifies the monitoring process and enables rapid detection of roundabouts and signalized intersections that require immediate attention.

Together, the five papers that compose this thesis make significant contributions to the fields of geographic information systems and urban mobility, advancing current knowledge on data collection, analysis, and application of trajectory data for more intelligent, sustainable, and efficient transportation systems.

All interested parties are welcome.