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Wednesday, October 27, 2021

Nw: AI predicts accident sizzling-spots from satellite tv for pc imagery and GPS data

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Researchers from MIT and the Qatar Center for Synthetic Intelligence enjoy developed a machine studying system that analyzes high-resolution satellite tv for pc imagery, GPS coordinates and historical crash data in divulge to procedure attainable accident-inclined sections in motorway networks, efficiently predicting accident ‘sizzling spots’ the build no other data or outdated methods would showcase them.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

Heart true, predictive accident sizzling-spots emerge from collating three sources of data. Areas highlighted in circles are ‘high threat’ predictions that in actuality enjoy no historical accident history. Source: https://openaccess.thecvf.com/affirm/ICCV2021/papers/He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper.pdf

The system provides heroic predictions for areas in a motorway network that are seemingly to change into accident murky-spots, even the build those areas enjoy zero history of accidents. Trying out the system over data covering four years, the researchers chanced on that their predictions for these ‘no history’ attainable accident hazard zones were borne out by events in subsequent years.

The contemporary paper is named Inferring high-resolution traffic accident threat maps per satellite tv for pc imagery and GPS trajectories. The authors predict makes command of for the contemporary architecture beyond accident prediction, hypothesizing that it will be utilized to 911 emergency threat maps or programs to predict the chance for request for taxis and dart-portion suppliers.

Prior same efforts enjoy attempted to create same incident-predictors from low-resolution maps with high bias, or else to leverage accident frequency as a key, which ended in high-variance, incorrect predictions. As a substitute, the contemporary mission, which covers four predominant US cities totaling 7,488 sq. kilometers, outperforms these earlier schemes by collating extra diverse kinds of data.

Sparse Data

The topic the researchers face is sparse data – very high volumes of accidents will inevitably be noticed and addressed without the need for machine analytics, however extra subtly unhealthy correlations are fascinating to title.

Previous accident prediction programs middle on Monte Carlo estimation of historical accident data, and can provide no efficient prediction mechanism the build this data is missing. Resulting from this fact the contemporary analysis reports motorway network sections with same traffic patterns, same visible appearance and same structure, inferring a disposition to accidents per these characteristics.

It’s a ‘shot at middle of the evening’ that appears to enjoy unearthed classic accident indicators, that is also utilized within the beget of contemporary motorway 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 venerable to highlight historical traffic accident sizzling-spots, failing to predict future accident areas. In the upper left image we look the build KDE has predicted accidents within the blue field space, versus the build the accidents in total localized (adjoining). Bottom true, a comparability of KDE prediction failure to the true prediction (blue field) of the MIT system.

The authors present that GPS trajectory data provides data on the float, tempo and density of traffic, whereas satellite tv for pc imagery of the inform provides data about lane disposition, and the series of lanes, as neatly as the existence of a exhausting shoulder and the presence of pedestrians.

Contributing creator Amin Sadeghi, from Qatar Computing Evaluate Institute (QCRI) commented “Our mannequin can generalize from one city to every other by combining multiple clues from apparently unrelated data sources. Right here is a step toward celebrated AI, because our mannequin can predict crash maps in uncharted territories.” and persevered “The mannequin can even be venerable to deduce a valuable crash procedure even within the absence of historical crash data, which might translate to particular command for city planning and policymaking by evaluating imaginary eventualities”.

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 threat procedure at a 5-meter resolution, which the authors inform is serious to distinguish assorted risks between runt-derive entry to motorway and adjoining residential roads.

The mission became evaluated on crashes and lateral data covering a duration between 2017-18. Predictions were then made for 2019 and 2020, with a total lot of ‘high threat’ areas rising even within the absence of any historical data that might per chance on the total predict this.

Achieving Helpful Generalization

Overfitting is a famous threat in a system fueled by sparse data, even the build, as in this case, there are two additional sources of supporting data. The build an incidence is low, low assumptions can even be drawn from too few examples, leading to an algorithm that is looking ahead to a in point of fact particular, narrow band of likely circumstances, and that also can fail to title broader potentialities.

Resulting from this fact, in coaching the mannequin the researchers randomly ‘dropped out’ every input provide as a 20% chance, so that areas with less (or no) accident data can even be regarded as as the mannequin trains towards generalization, and so that parallel data sources can act as a consultant proxy for missing data for any particular survey of an intersection or piece of motorway.


The mannequin became tested on a dataset comprising just about 7,500km of urban inform in Boston, Los Angeles, Chicago and NYC. The dataset became organized within the affect of 1,872 2kmx2km tiles, every containing satellite tv for pc imagery from MapBox, with motorway segmentation masked through data from OpenStreetMap. Each the gross imagery and the segmentation maps enjoy a resolution of 0.625 meters.

The GPS data comes within the affect of a proprietary dataset restful between 2015-17 over the four cities, totaling 7.6 million kilometers of GPS trajectories at a 1-2nd sampling price.

The mission also exploits 4.2 million records covering 2016-2020 within the US Accidents Dataset. Every file involves timestamps and other metadata.

The first two years of historical data were fed to the mannequin, and the final two years venerable for coaching and evaluate, enabling the researchers to construct the accuracy of the system over two years in a rapid time-body.

The system became tested with and without historical data, and became chanced on to efficiently include the underlying threat distribution all over all cases, particularly bettering on prior KDE-primarily based completely methods (look above).

Roads Forward

The authors contend that their system can even be utilized to other countries with runt architectural modification, even in areas the build accident data is no longer on hand. Additionally, the authors indicate their analysis as a likely adjunct to city planning beget for price spanking contemporary urban tendencies.

Lead creator Songtao He commented on the contemporary work:

“By taking pictures the underlying threat distribution that determines the chance of future crashes in any respect areas, and without any historical data, we are able to search out safer routes, enable auto insurance coverage companies to provide customized insurance coverage plans per driving trajectories of patrons, assist city planners beget safer roads, and even predict future crashes.”

Despite the true fact that the paper indicates that the code for the system has been launched on GitHub, the hyperlink to the code is no longer crammed with life, can’t currently be chanced on by a search, and presumably will be integrated in a later revision.

The analysis has attainable to be incorporated into celebrated consumer-stage GPS-primarily based completely traffic apps and route planners, per Songtao He:

“If folk can command the threat procedure to title doubtlessly high-threat motorway segments, they’ll consume action upfront to gash the threat of journeys they consume. Apps handle Waze and Apple Maps enjoy incident feature tools, however we’re attempting to derive earlier than the crashes — earlier than they happen,”


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