{"id":3718,"date":"2015-04-01T06:07:19","date_gmt":"2015-04-01T06:07:19","guid":{"rendered":"http:\/\/revoscience.com\/en\/?p=3718"},"modified":"2015-04-01T06:07:19","modified_gmt":"2015-04-01T06:07:19","slug":"better-traffic-signals-can-cut-greenhouse-gas-emissions","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/better-traffic-signals-can-cut-greenhouse-gas-emissions\/","title":{"rendered":"Better traffic signals can cut greenhouse gas emissions"},"content":{"rendered":"

Analysis shows that smarter programming of stoplights could improve efficiency of urban traffic.<\/strong> <\/em><\/span><\/p>\n

\"Image:<\/a>
Image: Jose-Luis Olivares\/MIT<\/figcaption><\/figure>\n

CAMBRIDGE, Mass– Sitting in traffic during rush hour is not just frustrating for drivers; it also adds unnecessary greenhouse gas emissions to the atmosphere.<\/span><\/p>\n

Now a study by researchers at MIT could lead to better ways of programming a city\u2019s stoplights to reduce delays, improve efficiency, and reduce emissions.<\/span><\/p>\n

The new findings are reported in a pair of papers by assistant professor of civil and environmental engineering Carolina Osorio and alumna Kanchana Nanduri SM \u201913, published in the journals\u00a0Transportation Science<\/em>\u00a0and\u00a0Transportation Research<\/em>: Part B. In these papers, the researchers describe a method of combining vehicle-level data with less precise \u2014 but more comprehensive \u2014 city-level data on traffic patterns to produce better information than current systems provide.<\/span><\/p>\n

\u201cWhat we do,\u201d Osorio says, \u201cis develop algorithms that allow major transportation agencies to use high-resolution models of traffic to solve optimization problems.\u201d Typically, such timing determinations are set to optimize travel times along selected major arteries, but are not sophisticated enough to take into account the complex interactions among all streets in a city. In addition, current models do not assess the mix of vehicles on the road at a given time \u2014 so they can\u2019t predict how changes in traffic flow may affect overall fuel use and emissions.<\/span><\/p>\n

For their test case, Osorio and Nanduri used simulations of traffic in the Swiss city of Lausanne, simulating the behavior of thousands of vehicles per day, each with specific characteristics and activities. The model even accounts for how driving behavior may change from day to day: For example, changes in signal patterns that make a given route slower may cause people to choose alternative routes on subsequent days.<\/span><\/p>\n

While existing programs can simulate both city-scale and driver-scale traffic behavior, integrating the two has been a problem. The MIT team found ways of reducing the amount of detail sufficiently to make the computations practical, while still retaining enough specifics to make useful predictions and recommendations.<\/span><\/p>\n

\u201cWith such complicated models, we had been lacking algorithms to show how to use the models to decide how to change patterns of traffic lights,\u201d Osorio says. \u201cWe came up with a solution that would lead to improved travel times across the entire city.\u201d In the case of Lausanne, this entailed modeling 17 key intersections and 12,000 vehicles.<\/span><\/p>\n

In addition to optimizing travel times, the new model incorporates specific information about fuel consumption and emissions for vehicles from motorcycles to buses, reflecting the actual mix seen in the city\u2019s traffic. \u201cThe data needs to be very detailed, not just about the vehicle fleet in general, but the fleet at a given time,\u201d Osorio says. \u201cBased on that detailed information, we can come up with traffic plans that produce greater efficiency at the city scale in a way that\u2019s practical for city agencies to use.\u201d<\/span><\/p>\n

In short, Osorio says, \u201cWe take complex data and couple that with less-detailed data [to create] computer-friendly solutions that combine the two kinds of data to come up with practical solutions.\u201d<\/span><\/p>\n

Osorio adds, \u201cAgencies are now being asked, whenever they propose changes, to estimate what impact that will have environmentally.\u201d Currently, such evaluations need to be made after the fact, through actual measurements, but with these new software tools, she says, \u201cWe can put the environmental factors in the loop in designing the plan.\u201d<\/span><\/p>\n

The team now is working on a project in Manhattan, among other locales, to test the potential of the system for large-scale signal control.<\/span><\/p>\n

In addition to timing traffic lights, in the future such simulations could also be used to optimize other planning decisions, such as picking the best locations for car- or bike-sharing centers, Osorio says.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Analysis shows that smarter programming of stoplights could improve efficiency of urban traffic. CAMBRIDGE, Mass– Sitting in traffic during rush hour is not just frustrating for drivers; it also adds unnecessary greenhouse gas emissions to the atmosphere. Now a study by researchers at MIT could lead to better ways of programming a city\u2019s stoplights to […]<\/p>\n","protected":false},"author":6,"featured_media":3719,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15,14],"tags":[],"class_list":["post-3718","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-environment","category-innovation"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01-150x150.jpg",150,150,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01-300x200.jpg",300,200,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",600,400,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",600,400,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",540,360,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",95,63,false],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",639,426,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/04\/MIT-PredictTraf-01.jpg",150,100,false]},"author_info":{"info":["Amrita Tuladhar"]},"category_info":"Environment<\/a> Innovation<\/a>","tag_info":"Innovation","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/3718","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=3718"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/3718\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/3719"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=3718"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=3718"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=3718"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}