{"id":2621,"date":"2025-01-27T09:41:02","date_gmt":"2025-01-27T08:41:02","guid":{"rendered":"https:\/\/wp.unil.ch\/iaunil\/untangling-urban-traffic-when-ai-observes-traffic-flows-understands-mobility-patterns-and-anticipates-them\/"},"modified":"2026-04-19T13:12:49","modified_gmt":"2026-04-19T11:12:49","slug":"untangling-urban-traffic-when-ai-observes-traffic-flows-understands-mobility-patterns-and-anticipates-them","status":"publish","type":"post","link":"https:\/\/wp.unil.ch\/iaunil\/en\/untangling-urban-traffic-when-ai-observes-traffic-flows-understands-mobility-patterns-and-anticipates-them\/","title":{"rendered":"Untangling urban traffic: when AI observes traffic flows, understands mobility patterns and anticipates"},"content":{"rendered":"\n

Original text published on https:\/\/wp.unil.ch\/geoblog\/2024\/03\/qui-se-deplace-ou-comment-et-quand-le-deep-learning-contribue-a-demeler-lecheveau-du-trafic-urbain\/<\/a><\/em><\/p>\n\n\n\n

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How can we better understand urban flows in a city in constant motion? Christian Kaiser, a specialist in computational geography at UNIL, uses artificial intelligence to analyze urban mobility based on real-time image data. Through deep learning, mobility behaviors (whether on foot, by bike, or by car) are precisely modeled, providing valuable tools for urban planning. Yet this innovation also raises ethical concerns, which Kaiser addresses carefully in his collaborations with the City of Lausanne and other academic partners. <\/p>\n<\/div>\n\n\n\n

To study traffic in a city or neighborhood, it\u2019s essential to identify the movements of its many users. To this end, Christian Kaiser deploys cameras at strategic urban locations that transmit live data directly to visualization software, without recording footage, thereby preserving data privacy. Using deep learning, he trains the software to recognize different moving objects such as pedestrians, cyclists, and vehicles. The goal is to achieve accurate object recognition, enabling the distinction of each entity and the analysis of its trajectory or travel time. <\/p>\n\n\n\n

This method has significantly improved the recognition capabilities of visualisation software. It is now possible to reliably recognise 90% of objects moving in traffic, compared to 70% initially. Individual movements can therefore be better determined, providing valuable information on the preferred routes or alternatives chosen by different users. In addition, the algorithms also make it possible to group similar movement behaviours (clustering), which are too complex to identify immediately due to the very high number of images to be interpreted. <\/p>\n\n\n\n

AI, a significant advance for digital visualisation tools and other developments to come<\/h3>\n\n\n\n

Although Christian Kaiser does not like the term artificial intelligence<\/em> (preferring statistical learning<\/em>), he notes the enormous progress made in the field of digital visualisation. \u2018We have moved from shapeless blocks to recognisable structures<\/em>.\u2019 In addition, the quantity and quality of available data has also improved. For example, it is now possible to discern cycle lanes on satellite images or determine the function of a building based on the comings and goings recorded in its surroundings. Looking ahead, Christian Kaiser sees potential for development in text recognition and the possibility of drawing maps based on written descriptions. <\/p>\n\n\n\n

What We Want to Do, What We Can Do, and What We\u2019re Allowed to Do<\/h3>\n\n\n\n

While the results achieved through machine learning are highly promising, caution is essential in its application. \u201cThere\u2019s what we want to do, what we can do, and what we\u2019re allowed to do. Legal frameworks clearly define rules that prevent intrusion into people\u2019s privacy, which is a very good thing.\u201d<\/em> In the context of urban research based on image recognition and visualization, it is crucial to rely on these frameworks and ensure they are strictly respected. <\/p>\n\n\n\n

Leveraging AI for Urban Mobility: Observation as a Planning Tool<\/h3>\n\n\n\n

Christian Kaiser contribue \u00e0 un projet financ\u00e9 par Interact<\/mark><\/a> en collaboration avec Patrick R\u00e9rat (professeur \u00e0 l\u2019IGD) et St\u00e9phane Bolognini (chef de section au service de mobilit\u00e9 de la Ville de Lausanne). Ce projet vise \u00e0 d\u00e9terminer les comportements des cyclistes, suite \u00e0 l\u2019introduction de la possibilit\u00e9 de tourner \u00e0 droite \u00e0 certains feux rouges. Plusieurs cam\u00e9ras dispos\u00e9es \u00e0 des carrefours avec et sans cette option pour analyser le comportement des cyclistes : utilisent-ils cette possibilit\u00e9 ou non ? Si oui comment le font-ils ? Si non quelles sont leur r\u00e9ticences \u00e0 le faire etc.. Dans le cadre de ce projet le deep-learning servira au traitement automatique des donn\u00e9es transmises par les cam\u00e9ras.<\/p>\n\n\n\n

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Dr. Christian Kaiser <\/strong> is a lecturer and researcher in computational geography. He uses artificial intelligence to analyse urban mobility flows in real time, in collaboration with the City of Lausanne, in order to improve urban planning and mobility policies. <\/strong><\/strong><\/p>\n\n\n\n

Faculty of Geosciences and Environment<\/strong><\/p>\n\n\n\n

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Identifying cyclists, tracking pedestrians, analyzing intersections\u2014Christian Kaiser leverages deep learning to map mobility patterns while safeguarding privacy.<\/p>\n","protected":false},"author":1002618,"featured_media":923,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[21],"tags":[],"class_list":{"0":"post-2621","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-research"},"_links":{"self":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts\/2621","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/users\/1002618"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/comments?post=2621"}],"version-history":[{"count":5,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts\/2621\/revisions"}],"predecessor-version":[{"id":3926,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/posts\/2621\/revisions\/3926"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/media\/923"}],"wp:attachment":[{"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/media?parent=2621"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/categories?post=2621"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.unil.ch\/iaunil\/en\/wp-json\/wp\/v2\/tags?post=2621"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}