Traffic Detection Datasets

<p><em>Public (anonymized) road traffic detection datasets from Huawei Munich&nbsp;Research Center.</em></p> <p>Traffic detection datasets from a variety of traffic sensors (i.e. induction loops). The data is useful for traffic indexing, dominant flows detection, forecasting traffic patterns, and adjusting stop-light control parameters, i.e. cycle length, offset and split times.</p> <p>There are three datasets:</p> <ul> <li><strong>RM </strong>is a real-life dataset collected in the period of March 2020 from traffic detectors in an area of a Chinese city. The detectors were located in 6 intersections monitoring the traffic on 44 road edges. Each detector was collecting data every 1 second for all the road edges in its radius. In total, 2894174 detections were collected, from 337089 different vehicles.</li> <li><strong>COM </strong>is a synthetic dataset that generated in the same road network as the real-life data RM and RD, with the difference that we included detectors in the 2 intersections where the real scenario didn&rsquo;t have. Then, we generated equally random trips over the road network. In total we generated 18910 detections from 7500 vehicles.</li> <li><strong>GRID </strong> was created using the SUMO Simulation of Urban Mobility [2]. We randomly generated trips on a 10𝑥10 intersections grid road network using a utility from SUMO that equally generates trips over the road network. Then, running the simulation and using TraCI Traffic Control Interface library [3] we read the simulation data and collect the detections. The simulation collected data include 1806141 detections from 135618 different vehicles.</li> </ul> <p>The datasets were used in the Querying Top-k Dominant Traffic Flows on Large Urban Road Networks [1] paper.</p> <p>[1] Stella Maropaki, Paolo Sottovia, and Stefano Bortoli. 2021. Querying Top-k Dominant Traffic Flows on Large Urban Road Networks. In 2021 24th International Conference on Extending Database Technology (EDBT).</p>