kaif-gh / osmtrafficestimation

Traffic Estimation Operator for OpenStreetMap based on Open Traffic Cameras

Traffic Estimation for OpenStreetMap using Open Traffic Cameras

Displaying near-real time traffic state information is a useful feature of digital navigation maps. However, most commercial providers rely on privacy compromising measures such as deriving location information from cellphones to estimate traffic. The lack of an open-source traffic estimation method using open data platforms is a bottleneck for building sophisticated navigation services on top of OpenStreetMap (OSM). We propose a deep learning-based Complex Event Processing (CEP) method that relies on publicly available video camera streams for traffic state estimation. The proposed framework performs near-real time object detection and object property extraction across camera clusters in parallel to derive multiple measures related to traffic with the results visualized on OpenStreetMap. The estimation of object properties (e.g. speed, count, direction) provides multi-dimensional data that can be leveraged to create metrics and visualization for congestion beyond commonly used density based measures. For example, we couple both flow and count measures during interpolation by considering each vehicle as a sample point and their speed as weight. We demonstrate multidimensional traffic metrics (e.g. flow rate, congestion estimation) over OSM by processing 22 traffic cameras from London. The system achieves a near realtime performance of 1.42 seconds median latency and an average F-score of 0.80.

Usage

The above operator takes vehicle count, vehicle speed and list of cameras (Lat. and Long.) as input and use OSMNX library to plot traffic related parameters over OSM. For the easy usage, I have replaced the Cmaera vehicle count and speed with a random generator function. Please replace the needed values at the given location.

In [111]
        # for now generating dummy data
        # this will be replaced with the values coming from object detector function
        cam1_vehicle_count = random.randint(1,10)
        cam2_vehicle_count = random.randint(1,10)
        #print(cam1_vehicle_count)
        # replace this with speed calculation functiom
        cam1_veh_speed_list = generate_random_speed(cam1_vehicle_count)
        cam2_veh_speed_list = generate_random_speed(cam2_vehicle_count)

Dataset

1: Camera location file is updated in repo.

2: Camera Link {Note: Partial dataset currently available}

Camera Data: https://nuigalwayie-my.sharepoint.com/:f:/g/personal/p_yadav1_nuigalway_ie/EmTkcDesdf5AnNZb5aEH_2kBZgfkg_Y-oV0t_3Y8NuDgAg?e=C82KNP

References

Conference	References
GIScience 2020	Yadav, Piyush, Dipto Sarkar, Dhaval Salwala, and Edward Curry. “Complex Event Processing based Near Real-Time Traffic Prediction Framework for OpenStreetMap using Open Traffic Cameras”. In 11th International Conference on Geographic Information Systems (GIScience 2020), 2020.
IEEE Big Data 2019	Yadav, Piyush, Edward Curry. "VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams". In IEEE Big Data, Los Angeles 2019

Contact

Email [email protected]