Traffic congestion is one of the most critical problems facing modern cities. The Economist estimates that the costs related to traffic congestion in 2017 in the US, Germany and the UK were $461bn . What can governments do to solve this pressing issue?
Pittsburgh based Rapid-Flow Technologies, a spin-off from Carnegie Mellon University’s Robotics Institute, is utilizing machine learning to reduce traffic congestion. The company’s first product, Surtrac, offers a real-time traffic signal control system. Utilizing cameras placed at road intersections and machine learning, Surtrac optimizes the performance of traffic signals for the traffic that is actually on the road, leading to “less waiting, reduced congestion, shorter trips, less pollution, and happier drivers” . Exhibit 1 showcases the details of how Rapid-Flow’s technology works.
Results from Surtrac’s first implementation are promising. From 2012 to 2016, Surtrac was installed at 50 intersections across the city of Pittsburgh (See Exhibit 2 for a map of deployment). Substantial traffic improvements followed this pilot: the system reduced travel times by 26%, the number of stops by 31%, wait times at intersections by 41%, carbon emissions by 21%, and total and fatal crashes by 13%-36%.
Traffic Signal Management: A Perfect Fit for Machine Learning
Applying Harvard researcher Anastassia Fedyk’s framework , traffic signal management is a fantastic area for machine learning to provide value. Firstly, the task involves managing many interconnected variables changing so rapidly over the course of a day that “automated without learning” solutions, which are the systems that most traffic signal networks use today, end up being inefficient. Secondly, the problem involves predicting traffic patterns rather than inferring what causes traffic, and machine learning models perform well in these contexts. Thirdly, the amount of traffic data available is vast, quite complete and can be easily collected by hardware installation. Lastly, new data becomes available constantly, allowing the model to improve with time.
In this context, it’s not surprising that Rapid-Flow is not alone in the space: competitors include Vivacity, a UK based company installing similar sensors and sharing its machine learning generated recommendations with drivers . Additionally, governments are developing their own “AI enabled” smart lights solutions, such as Maryland’s Governor recently announced . Moreover, researchers are working on new machine learning models to manage traffic . In the future, big tech companies such as Google, Uber or Apple, which are developing self driving car technologies and own vast information involving maps and location, could decide to enter the market.
What’s Next for Rapid-Flow?
According to McKinsey, in machine learning “more data translates into more insights and higher accuracy because it exposes algorithms to more examples they can use to identify correct and reject incorrect answers” . This explains why Rapid-Flow is looking to expand fast: the company has recently deployed its technology across the cities of Kane, IL, Portland, ME, Quincy, MA, and Atlanta, GA. Collecting new data from different cities, regions and neighborhoods will allow its machine learning technology to perform better in diverse environments.
Moreover, learning from the pilot, the team has enabled Surtrac to consider all travelers, including pedestrians and cyclists, as it manages a traffic signal system. This is a key improvement, as the initial version of the software was focused on improving car traffic, and was thus biased against pedestrians and cyclists.
Thinking longer term, some of the key challenges that Rapid-Flow faces are error tolerance and scaling. Regarding error tolerance, Anastassia Fedyk warns us that when using machine learning “mistakes will happen, and they will happen most often in ways that you cannot anticipate”. Tesla and Uber both were involved in self driving car fatal accidents recently , and an accident where AI enabled traffic signals cost a life could be brutal for the future of Rapid-Flow. Therefore the company needs to be extremely careful with the safety checks it develops: security should never be compromised in the quest for better performance.
Secondly, in order to scale faster, Rapid-Flow should shift its focus mainly to developing its software service, and try to build hardware partnerships. Currently, the company installs its cameras, sensors and controllers in every city it operates in. By partnering with companies that have resources and expertise in deploying large scale hardware systems, such as telcos or utility companies, Rapid-Flow could grow its footprint much faster, and focus entirely on improving its algorithms with the new data and variables collected.
Finally, there are some important questions about Rapid-Flow’s future that remain uncertain. Firstly: should the company also be interacting directly with citizens by accessing their location and providing tailored route recommendations? Or should it continue to work exclusively with governments? Secondly: as the company grows, it will collect data of millions of citizens around the world. How can the company make sure citizen’s data is securely stored? What would the consequences of a hack or data leak be to the company’s future?
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Exibit 1: How Does Surtrac Work?
Exhibit 2: Surtrac’s Pittsburgh, PA Deployment Mapped
Exhibit 3: 2017 Estimated Economic Cost of Traffic Congestion
 – “The hidden cost of congestion”, The Economist, Febraury 28th 2018, https://www.economist.com/graphic-detail/2018/02/28/the-hidden-cost-of-congestion
 – “Surtrac Deployment at Urban Grid Networks in Pittsburgh Neighborhoods”, Rapid Flow Company Website, August 30th 2018, https://www.rapidflowtech.com/blog/surtrac-deployment-at-urban-grid-networks-in-pittsburgh-neighborhoods
 – Fedyk, Anastassia, “How to tell if machine learning can solve your business problem”, Harvard Business Review Digital Articles, November 15th 2016.
 – Williams, Henry, “Artificial Intelligence May Make Traffic Congestion a Thing of the Past”, The Wall Street Journal, June 16th 2018, https://www.wsj.com/articles/artificial-intelligence-may-make-traffic-congestion-a-thing-of-the-past-1530043151
 – Shaver, Katherine, “‘Smart’ traffic signals soon will change themselves in Maryland”, Washington Post, October 25th 2017, https://www.washingtonpost.com/local/trafficandcommuting/smart-traffic-signals-soon-will-change-themselves-in-maryland/2017/10/25/d4f57058-b9bf-11e7-9e58-e6288544af98_story.html?utm_term=.c805dd4fa25c
 Liu, Ying & Liu, Lei & Chen, Wei-Peng. “Intelligent traffic light control using distributed multi-agent Q learning”, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017.
 – Bughin, Jacques et al., “Artificial intelligence: The next digital frontier”, McKinsey Global Institute, June 2017, https://www.mckinsey.com/~/media/mckinsey/industries/advanced%20electronics/our%20insights/how%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/mgi-artificial-intelligence-discussion-paper.ashx
 – Wakabayashi, Daisuke, “Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam”, The New York Times, March 19th 2018, https://www.nytimes.com/2018/03/19/technology/uber-driverless-fatality.html
Traffic Jam Picture: By NOMAD – https://www.flickr.com/photos/lingaraj/2415084235/sizes/l/, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=4783150