One of the best and – hear me out – fairest and most equitable ways of achieving this ambitious goal is through real-time dynamic pricing<\/strong>.<\/p>\n Here\u2019s how it works: when drivers approach a congested stretch of highway with a managed lane, they have the option to pay a fee to travel in the faster lane or stay in lanes with heavier traffic.<\/p>\n Of course, if too many folks want to select the paid option there’s a risk that demand can exceed a lane\u2019s capacity, causing it to quickly become congested.<\/p>\n To resolve this classic economics problem, you can either reduce demand by prohibiting certain vehicles from choosing the express lanes<\/strong> or keep the lanes open to all drivers while dampening demand by increasing the price,<\/strong> so that drivers can make their decision based on their urgency depending on each specific trip purpose.<\/p>\n We believe managed lanes provide the best and fairest option of a fast and reliable trip when they are open to all drivers. Our team is committed to developing efficient and smart tolling algorithms that work with innovative policies like discounts for HOVs and free rides for transit and vanpools<\/strong>, which inventive more efficient travel.<\/p>\n Over the years, managed lane operators like Cintra have relied on parametric algorithms to set prices. These algorithms are easy to build, understand, and explain. Typically, they use a fixed number of factors to adjust express lane prices<\/strong> up or down when conditions exceed certain thresholds. These models measure things like speed, density, and traffic flow rates to inform price changes that keep the managed lanes moving.<\/p>\n But after years of studying driver behavior on our managed lanes, Cintra\u2019s Traffic & Analytics team began to realize this approach was not getting great results every time.<\/p>\n We saw what behavioral economists know well–that customer decision-making was occasionally a little erratic and irrational. Our parametric algorithms alone could not always strike the balance between supply and demand.<\/p>\n Instead of starting from scratch, we decided to build on the existing model by adding a second machine learning model.<\/strong> Our existing algorithm was already automatically changing prices without human intervention, so it was an easy choice to develop a complementary algorithm.<\/p>\n We have named this complimentary algorithm Real-Time Propensity Factor<\/strong> (RTPF) because it looks for unusual shifts in driver propensity to choose the express managed lane and reacts in real-time by adjusting the toll up or down<\/strong> by a computed factor.<\/p>\n Machine learning provides a few distinct advantages for pricing. Notably, machine learning models are very good at identifying patterns as they can consider hundreds or thousands of variables at one time compared to a few dozen for a parametric algorithm.<\/p>\n Typical machine learning techniques include, at increasing levels of complexity: classic regression models, decision trees, random forests, clustering analysis, all the way to neural networks in which data can be fed in video, audio formats etc, and the models learn to identify and group objects and extract lessons from their behavior.<\/p>\n A good machine learning model can find the historical correlation between congestion in a one part of the roadway and changes in driver decision-making in another. It looks out for these curiosities and can step in by either raising or lowering the price in a more sophisticated way than the parametric algorithm.<\/p>\n At key moments RTPF can take over, manage the unique situation for a bit, and then hand back control to the parametric algorithm.<\/strong> This allows us to incorporate machine learning in a manner that produces a very transparent, explainable pricing model.<\/p>\n If you had to draw academic battle lines around pricing a scarce asset, experts would generally suggest designing your algorithm for revenue maximization or for throughput optimization. But taken to their extremes, neither philosophy produces a good outcome<\/strong> for an express managed lane.<\/p>\n Doggedly pursuing revenue optimization might lead you to price express lanes so high that only 1% of drivers would ever choose them. The express lanes would be under-utilized, and you would not be providing the community with improved mobility, but you would certainly be achieving your objective.<\/p>\n Throughput maximization seems like a reasonable alternative.<\/strong> You would price low enough to ensure the lanes were filled to their capacity at every moment.<\/p>\n But we have all experienced a crowded, fast-moving freeway suddenly becoming a congested, snarled mess. The same thing can happen on an express lane. Trying to fit those last few hundred cars on the express lane can produce unstable traffic flow that can quickly evolve into congestion which can take hours to untangle.<\/p>\n With our dual-algorithm approach and RTPF, we have a third option, which seeks to ensure the lanes are fast but flowing even during the most congested rush hours.<\/strong> This balance seeks to ensure optimal reliability and performance from-day-to-day.<\/p>\n We are currently using RTPF on the LBJ, NTE, and 35W TEXpress lanes<\/strong> in Dallas-Ft. Worth. Our intention is to roll this out on all four of our dynamically priced facilities across Cintra concessions in the US.<\/p>\n The journey doesn\u2019t end here. We continue to improve our model by letting it consider more factors, take the reins a bit more and enhance its explainability.<\/p>\n The dual-algorithm approach of an explainable parametric algorithm with a little machine learning sprinkled on top is a pragmatic, sensible way of bringing machine learning into the practice of priced infrastructure.<\/p>\n Through this blog, we\u2019re introducing this innovation across Ferrovial and to you. Innovation is central to our growth and development as a company.<\/strong> We\u2019re lucky to work with expert colleagues who understand how their area works – that expertise should never be discounted for the sake of a machine learning model.<\/p>\n We believe a dual-algorithm approach could be used more across Ferrovial. You can have it both ways: leverage your expertise to build a strong foundation and then allow machine learning to extend your thinking.<\/p>\n We expect both the customer and the company to benefit through RTPF, as trips on managed lanes become more reliable and efficiently priced. Thanks to revenue sharing agreements with the states we operate in, departments of transportation can benefit directly, too.<\/p>\n We are proud of this technical achievement and the direct improvement it can have for Cintra, our customers and the communities they support.<\/p>\n An article by John Brady<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Cintra does all it can to make trips on our roads a more reliable experience. We want to give folks a better option, so they don\u2019t end up in endless gridlock like you see on the highways around Manhattan and LA. As cities look to increase mobility and reduce congestion, many choose to add highway […]<\/p>\n","protected":false},"featured_media":42283,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"nivel-1":[6273],"nivel-2":[4747,6306,4746,5760,4804,6305],"nivel-3":[4848],"nivel-4":[5750,5746],"nivel-5":[5638,5648,5640],"topic":[7296,7300,7295],"coauthors":[6650],"class_list":["post-42209","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","nivel-1-highways","nivel-2-artificial-intelligence","nivel-2-dynamic-pricing","nivel-2-innovation","nivel-2-managed-lanes-en","nivel-2-road-traffic","nivel-2-technology","nivel-3-united-states","nivel-4-dallas-en","nivel-4-texas-en","nivel-5-cintra-en","nivel-5-lbj-texpress-en","nivel-5-north-tarrant-express-en","topic-construction-and-infrastructure","topic-technology-and-innovation","topic-transportation"],"acf":[],"yoast_head":"\nBuilding on our Parametric Model<\/h2>\n
Introducing RTPF<\/h2>\n
A third way: throughput, revenue or performance?<\/h2>\n
Rolling it out to four (or more) express lanes<\/h2>\n
![\"LBJ](\"https:\/\/static.ferrovial.com\/wp-content\/uploads\/sites\/3\/2022\/01\/17143326\/lbj-28-carriles-02-1280x720-1-e1642430518589.jpeg\")
<\/p>\nTake five<\/h2>\n