*1.3. Impact*

Thus, the presented work has a grea<sup>t</sup> impact by enhancing the usability of cross-section-based vehicle detectors by enabling them to acquire microscopic traffic data based only on unsynchronized records of detected timestamps and respective speeds. As many of the cross-section-based sensors do not require recording images or videos, using this method enables the acquisition of vehicle trajectories without any privacy issues. The method also greatly reduces the financial, organizational, and maintenance effort for camera-based acquisition with the ability of filling interim gaps between covered road sections, thus leading to less sensors required to cover a given length. With the proposed method, simple and cost-effective sensors can be used for a detailed safety analysis. If such sensors are deployed along highway exit lanes, this method can be adopted to derive parameters such as individual deceleration rates along the exit lane and to support decision making in regards to appropriate traffic harmonization methods. Furthermore, surrogate safety indicators such as space headways, time-to-collision, or minimal deceleration rates to avoid a crash can be derived, to better understand the evolution of these potential conflicts over time. In addition to presenting the basic principles of the method, we will also analyze the limitations of the method with respect to traffic volume, cross-section distance and detection accuracy.

The remainder of the paper is organized as follows: Section 2 presents the general methodology of the vehicle matching (Section 2.1) and trajectory derivation (Section 2.2). Section 3.1 shows how the underlying data for our experiments have been recorded, while Sections 3.2 and 3.3 present the experimental validation based on synthetic and real data. In Section 4, we draw the conclusions and discuss potential future research based on our approach.
