As traffic volumes in cities around the world are constantly growing we are faced with the challenge to track and control car movements in a more detailed and intelligent way to beat the traffic. Real-time information on traffic including automotive sensors and crowd-sourced data feeds are an interesting new source of data. However, to utilize this data to its full extent and turn it into valuable information, intelligent methods for analyzing and predicting traffic are needed.
Pivotal’s Data Science Team has developed several innovative methods to analyze this traffic flow information harvested from real-time and in-car data sources including GPS. These methods by themselves are highly useful for predicting future traffic conditions and dissecting traffic data. We will describe how we created these algorithms and show different interesting results from their application. This example demonstrates how deeper insights into a problem can be found by combining different machine learning methods.
The methods developed by our team enable more intelligent routing systems through a more detailed velocity prediction based on a number of influencing factors. This is highly valuable for planning routes to far-away destinations and also useful on inner-city routes where traffic can be influenced by a lot of different factors. However, we recently tapped another valuable source of data that could enrich traffic prediction models even further.
Local transport authorities already make a lot of traffic and travel disruption freely available. These reports form the basis of traffic updates across a wide range of media. Currently however the reports are limited to acknowledging the start of a disruption, and then providing updates as the situation develops. In the smart city of the future these disruption reports will also predict the duration and severity of the disruptions, enabling route guidance systems to make better decisions.
We will also demonstrate a traffic disruption model that can predict the duration of recently begun incidents, learning the distinct traffic and disruption patterns of a major global city. The disruption prediction model incorporates historical traffic count data, previous incident reports and local weather conditions and uses an interesting variety of machine learning methods running on a massively parallel analytics database system.
We will conclude by outlining how the crowd-sourced real-time data could be matched to traffic disruption and open government data, to push the envelope in traffic analysis and prediction even further.
Presented at Strata Santa Clara 2014 Conference: Making Data Work
Ian Huston (Pivotal), Alexander Kagoshima (Pivotal), Noelle Sio (Pivotal)
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