{"id":296,"date":"2016-04-13T11:56:36","date_gmt":"2016-04-13T10:56:36","guid":{"rendered":"https:\/\/www.discoverpassenger.com\/?p=296"},"modified":"2021-12-30T18:56:48","modified_gmt":"2021-12-30T18:56:48","slug":"studying-urban-traffic-control-systems-with-transport-for-london","status":"publish","type":"post","link":"https:\/\/www.masabi.com\/es\/news\/studying-urban-traffic-control-systems-with-transport-for-london\/","title":{"rendered":"Studying Urban Traffic Control systems with Transport for London"},"content":{"rendered":"<div class=\"block-\"><span style=\"font-weight: 400;\">One thing we do a lot of at Passenger is research and development. By looking at the bigger picture we can help our customers to understand how lots of different systems and data sources can work together to make their passengers lives easier. This strengthens the retail relationship and helps to build brand loyalty in the competitive environment that lies beyond London\u2019s city limits.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Urban Traffic Control systems (UTC) have been helping vehicles flow in cities for decades. For example, traffic lights in London are automatically adjusted with the help of <\/span><a href=\"http:\/\/www.scoot-utc.com\/\"><span style=\"font-weight: 400;\">SCOOT<\/span><\/a><span style=\"font-weight: 400;\"> system <\/span><span style=\"font-weight: 400;\">(Split Cycle Offset Optimisation Technique) <\/span><span style=\"font-weight: 400;\">and thousands of induction loop sensors on the roads. These sensors provide binary data every quarter of a second (1 if there is a vehicle on top, 0 otherwise), so each sensor ends up with 345,600 values every day. There are around 12,000 sensors in London, adding up to 4.1 billion data points every single day. And that\u2019s just for London!<\/span><\/p>\n<p style=\"text-align: center;\"><i><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-297 size-full\" src=\"https:\/\/www.discoverpassenger.com\/wp-content\/uploads\/2016\/04\/redlight-1.jpg\" alt=\"redlight-1\" width=\"416\" height=\"422\" \/>Inductive loop sensors help to monitor the traffic (<\/span><\/i><a href=\"http:\/\/auto.howstuffworks.com\/car-driving-safety\/safety-regulatory-devices\/red-light-camera2.htm\"><i><span style=\"font-weight: 400;\">pic source<\/span><\/i><\/a><i><span style=\"font-weight: 400;\">)<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">On 6th April, R&amp;D Engineer, Manuel, attended the <\/span><a href=\"http:\/\/blog.tfl.gov.uk\/2016\/03\/30\/traffic-data-hack-day-register-now\/\"><span style=\"font-weight: 400;\">Traffic Data Hack event<\/span><\/a><span style=\"font-weight: 400;\"> organised by <\/span><a href=\"http:\/\/www.tfl.gov.uk\/\"><span style=\"font-weight: 400;\">Transport for London<\/span><\/a><span style=\"font-weight: 400;\"> (TfL) and <\/span><a href=\"https:\/\/aws.amazon.com\/\"><span style=\"font-weight: 400;\">Amazon Web Services<\/span><\/a><span style=\"font-weight: 400;\"> (AWS), tackling the challenge of storing and processing huge amounts of data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">One of the technologies that TfL uses for storing this data is <\/span><a href=\"https:\/\/docs.aws.amazon.com\/redshift\/latest\/mgmt\/welcome.html\"><span style=\"font-weight: 400;\">Amazon Redshift<\/span><\/a><span style=\"font-weight: 400;\">, a petabyte-scale data warehouse service in the cloud based on PostgreSQL. The attendees were given the opportunity to query a small portion of this data during the hack event (3 months of a subset of sensors). Data processing is done following a workflow which includes <\/span><a href=\"https:\/\/aws.amazon.com\/elasticmapreduce\/\"><span style=\"font-weight: 400;\">Amazon EMR<\/span><\/a><span style=\"font-weight: 400;\"> (Elastic MapReduce) service.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Attendees of the hackathon were split into small groups to tackle different problems using any of these Amazon services. Manuel joined one of the data visualisation teams with members from TfL and <\/span><a href=\"http:\/\/www.jacobs.com\/\"><span style=\"font-weight: 400;\">Jacobs<\/span><\/a><span style=\"font-weight: 400;\">. One of the first challenges that they discovered was the complexity of the database. After brainstorming, the team decided to visualize the amount of traffic recorded by the sensors at different times of the day. They split their work to effectively create a pipeline of data selection, data preparation and data visualization.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-298 size-full\" src=\"https:\/\/www.discoverpassenger.com\/wp-content\/uploads\/2016\/04\/IMG_20160406_113718506.jpg\" alt=\"IMG_20160406_113718506\" width=\"1600\" height=\"900\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Raw data was extracted from the database by three periods of time (morning peak, inter-peak and evening peak) using SQL queries. Then, they aggregated the data to get the average amount of traffic. After that, sensors had to be matched with their corresponding geolocation that the team had to convert from ordnance survey coordinates to GPS coordinates. Finally, after creating a JSON file with all this information, Manuel and his team visualised each sensor in a map using Google Maps API in which each circle represents a sensor and how busy is that road in a particular time of the day (see picture below).<\/span><\/p>\n<p style=\"text-align: center;\"><i><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-299 size-full\" src=\"https:\/\/www.discoverpassenger.com\/wp-content\/uploads\/2016\/04\/160407123159.png\" alt=\"160407123159\" width=\"747\" height=\"624\" \/>Average traffic congestion in morning peak<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">Other solutions that were introduced by the other teams included a REST API that allows users to query data easily, a Machine Learning solution to predict traffic congestion and an anomaly detection approach to alert about possible accidents.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In 2015, <\/span><a href=\"https:\/\/tfl.gov.uk\/info-for\/media\/press-releases\/2015\/march\/-4-billion-road-modernisation-programme-gets-underway\"><span style=\"font-weight: 400;\">TfL invested \u00a34bn<\/span><\/a><span style=\"font-weight: 400;\"> to improve and modernise traffic and public transport. There is indeed a big interest among both citizens and governments to guide people\u2019s mobility and apply smart solutions every step of the way.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With such a huge quantity of data points adding up daily for London, the future of this data analysis will only get exponentially larger as the same sensors and data expand across the UK.<\/span><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Experimenting with data storage technology, Amazon Redshift, a petabyte-scale data warehouse service, and Amazon Elastic MapReduce at TfL&#8217;s April 2016 Traffic Data Hack.<\/p>\n","protected":false},"author":7,"featured_media":12544,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[70],"tags":[],"class_list":["post-296","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-passenger-team","passenger-team"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - 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