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AI predicts accident hot-spots from satellite imagery and GPS data

Researchers from MIT and the Qatar Center for Artificial Intelligence have developed a machine learning system that analyzes high-resolution satellite imagery, GPS coordinates and historical crash data in order to map potential accident-prone sections in road networks, successfully predicting accident ‘hot spots’ where no other data or previous methods would indicate them.Middle right, predictive accident…

Researchers from MIT and the Qatar Center for Artificial Intelligence have developed a machine learning system that analyzes high-resolution satellite imagery, GPS coordinates and historical crash data in order to map potential accident-prone sections in road networks, successfully predicting accident ‘hot spots’ where no other data or previous methods would indicate them.

Middle right, predictive accident hot-spots emerge from collating three sources of data. Areas highlighted in circles are 'high risk' predictions that actually have no historical accident history. Source: https://openaccess.thecvf.com/content/ICCV2021/papers/He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper.pdf

Middle right, predictive accident hot-spots emerge from collating three sources of data. Areas highlighted in circles are ‘high risk’ predictions that actually have no historical accident history. Source: https://openaccess.thecvf.com/content/ICCV2021/papers/He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper.pdf

The system offers bold predictions for areas in a road network that are likely to become accident black-spots, even where those areas have zero history of accidents. Testing the system over data covering four years, the researchers found that their predictions for these ‘no history’ potential accident hazard zones were borne out by events in subsequent years.

The new paper is called Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories. The authors predict uses for the new architecture beyond accident prediction, hypothesizing that it could be applied to 911 emergency risk maps or systems to predict the likelihood for demand for taxis and ride-share providers.

Prior similar efforts have attempted to create similar incident-predictors from low-resolution maps with high bias, or else to leverage accident frequency as a key, which led to high-variance, inaccurate predictions. Instead, the new project, which covers four major US cities totaling 7,488 square kilometers, outperforms these earlier schemes by collating more diverse forms of data.

Sparse Data

The problem the researchers face is sparse data – very high volumes of accidents will inevitably be noticed and addressed without the need for machine analytics, but more subtly dangerous correlations are difficult to identify.

Previous accident prediction systems center on Monte Carlo estimation of historical accident data, and can provide no effective prediction mechanism where this data is lacking. Therefore the new research studies road network sections with similar traffic patterns, similar visual appearance and similar structure, inferring a disposition to accidents based on these characteristics.

It’s a ‘shot in the dark’ that seems to have unearthed fundamental accident indicators, which could be utilized in the design of new road networks.

Kernel Density Estimation (KDE) has been used to highlight historical traffic accident hot-spots, failing to predict future accident locations. In the upper left image we see where KDE has predicted accidents in the blue box region, versus where the accidents generally localized (adjacent). Bottom right, a comparison of KDE prediction failure to the accurate prediction (blue box) of the MIT system.

Kernel Density Estimation (KDE) has been used to highlight historical traffic accident hot-spots, failing to predict future accident locations. In the upper left image we see where KDE has predicted accidents in the blue box region, versus where the accidents generally localized (adjacent). Bottom right, a comparison of KDE prediction failure to the accurate prediction (blue box) of the MIT system.

The authors note that GPS trajectory data offers information on the flow, speed and density of traffic, while satellite imagery of the area adds information about lane disposition, and the number of lanes, as well as the existence of a hard shoulder and the presence of pedestrians.

Contributing author Amin Sadeghi, from Qatar Computing Research Institute (QCRI) commented “Our model can generalize from one city to another by combining multiple clues from seemingly unrelated data sources. This is a step toward general AI, because our model can predict crash maps in uncharted territories.” and continued “The model can be used to infer a useful crash map even in the absence of historical crash data, which could translate to positive use for city planning and policymaking by comparing imaginary scenarios”.

The architecture of the traffic prediction system generates an accident risk map at a 5-meter resolution, which the authors state is critical to distinguish different risks between freeway and adjacent residential roads.

The architecture of the traffic prediction system generates an accident risk map at a 5-meter resolution, which the authors state is critical to distinguish different risks between freeway and adjacent residential roads.

The project was evaluated on crashes and lateral data covering a period between 2017-18. Predictions were then made for 2019 and 2020, with several ‘high risk’ locations emerging even in the absence of any historical data that would normally predict this.

Achieving Useful Generalization

Overfitting is a critical risk in a system fueled by sparse data, even where, as in this case, there are two additional sources of supporting data. Where an incidence is low, excessive assumptions can be drawn from too few examples, leading to an algorithm that is expecting a very particular, narrow band of possible circumstances, and which will fail to identify broader probabilities.

Therefore, in training the model the researchers randomly ‘dropped out’ each input source as a 20% probability, so that areas with less (or no) accident data can be considered as the model trains towards generalization, and so that parallel data sources can act as a representative proxy for missing information for any particular study of an intersection or section of road.

Evaluation

The model was tested on a dataset comprising nearly 7,500km of urban area in Boston, Los Angeles, Chicago and NYC. The dataset was organized in the form of 1,872 2kmx2km tiles, each containing satellite imagery from MapBox, with road segmentation masked via data from OpenStreetMap. Both the base imagery and the segmentation maps have a resolution of 0.625 meters.

The GPS data comes in the form of a proprietary dataset collected between 2015-17 over the four cities, totaling 7.6 million kilometers of GPS trajectories at a 1-second sampling

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