{"id":192,"date":"2012-12-26T16:00:53","date_gmt":"2012-12-26T16:00:53","guid":{"rendered":"http:\/\/transportsdufutur.ademe.fr\/?p=192"},"modified":"2015-07-21T16:35:46","modified_gmt":"2015-07-21T16:35:46","slug":"nous-pouvons-mieux-decider-si-nous-ameliorons-nos-connaissances-emd-suite","status":"publish","type":"post","link":"https:\/\/transportsdufutur.ademe.fr\/2012\/12\/nous-pouvons-mieux-decider-si-nous-ameliorons-nos-connaissances-emd-suite.html","title":{"rendered":"Nous pouvons mieux d\u00e9cider si nous am\u00e9liorons nos connaissances (EMD suite)"},"content":{"rendered":"

Dans un pr\u00e9c\u00e9dent article<\/a><\/strong>, il \u00e9tait indiqu\u00e9 que nous pouvions d\u00e8s aujourd'hui faire \u00e9voluer les Enqu\u00eates M\u00e9nages D\u00e9placements en utilisant les potentiels offerts par les outils num\u00e9riques dont le GPS et le t\u00e9l\u00e9phone. Cette \u00e9tude<\/a><\/strong> r\u00e9cente r\u00e9alis\u00e9e aux USA confirme cela et donne d\u00e9j\u00e0 quelques exemples int\u00e9ressants. Le rapport complet est disponible ici<\/a><\/strong>.<\/p>\n

Ainsi, il est d\u00e9montr\u00e9 que l'annulation ou le d\u00e9calage de 1% des trajets permettrait de r\u00e9duire les retards caus\u00e9s par la congestion que d'environ 3 pour cent. Mais l'annulation de 1% des trajets en les s\u00e9lectionnant avec soin permettrait de r\u00e9duire la congestion dans une r\u00e9gion m\u00e9tropolitaine de pr\u00e8s de 18%.<\/p>\n

\"BayCongestion350\"<\/a> <\/p>\n

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\u201cThis is a preliminary study that demonstrates that not all drivers are contributing uniformly to congestion,\u201d said coauthor Alexandre Bayen, an associate professor of electrical engineering and computer science and of civil and environmental engineering at UC Berkeley. \u201cReaching out to everybody to change their time or mode of commute is thus not necessarily as efficient as reaching out to those in a particular geographic area who contribute most to bottlenecks.\u201d<\/p>\n

He noted, however, that while the researchers\u2019 computer modeling suggests that this strategy works, it remains for policy makers to decide whether it is practical or desirable to target specific geographic areas.<\/p>\n

San Francisco and Boston<\/strong><\/p>\n

The study was performed in Boston and San Francisco, two cities with radically different commute patterns. In Boston, the freeways spread radially outward to the suburbs, with concentric rings of freeways intersecting them like a spider web. In San Francisco, the freeways encircle San Francisco Bay and are connected by six bridges.<\/p>\n

Lead researcher Marta Gonz\u00e1lez, an assistant professor of civil and environmental engineering at MIT, and former MIT postdoc Pu Wang, now a professor at Central South University, used three weeks of cellphone data to obtain information about anonymous drivers\u2019 routes and the estimated traffic volume and speed on those routes both in Boston and the San Francisco Bay Area. They inferred a driver\u2019s home neighborhood from the regularity of the route traveled and from the locations of cell towers that handled calls made between 9 p.m. and 6 a.m. They combined this with information about population densities and the location and capacity of roads in the networks of these two metropolitan areas to determine which neighborhoods are the largest sources of drivers on each road segment, and which roads these drivers use to connect from home to highways and other major roadways.<\/p>\n

To double-check this method, Bayen and graduate student Timothy Hunter used a different set of data obtained from GPS sensors in taxis in the San Francisco area to compute taxis\u2019 speed based on travel time from one location to another. From that speed of travel, they then determined congestion levels.<\/p>\n

The results produced by both methods show good agreement, Bayen said.<\/p>\n

\u201cOne of the novelties of this work is that it used two very different sources of cellphone data to complete the work: cell tower data, which is relatively inaccurate for positioning, but provides good information about people\u2019s approximate location and routing, and GPS data, which provides very accurate positioning information, useful for precise congestion level assessment,\u201d he said.<\/p>\n

In the San Francisco area, for example, they found that canceling trips by drivers from Dublin, Hayward, San Jose, San Rafael and parts of San Ramon would cut 14 percent from the travel time of other drivers.<\/p>\n

\u201cThis has an analogy in many other flows in networks,\u201d Gonz\u00e1lez said. \u201cBeing able to detect and then release the congestion in the most affected arteries improves the functioning of the entire coronary system<\/strong>.\u201d<\/p>\n

Because the new methodology requires only three types of data \u2014 population density, topological information about a road network and cellphone data \u2014 it can be used for almost any urban area.<\/p>\n

\u201cIn many cities in the developing world, traffic congestion is a major problem, and travel surveys don\u2019t exist,\u201d Gonz\u00e1lez said. \u201cSo the detailed methodology we developed for using cellphone data to accurately characterize road network use could help traffic managers control congestion and allow planners to create road networks that fit a population\u2019s needs.\u201d<\/p>\n

Katja Schechtner, head of the Dynamic Transportation Systems group at the Austrian Institute of Technology and a visiting scholar at the MIT Media Lab, is a co-author on the Scientific Reports<\/em> paper with Gonz\u00e1lez, Wang, Bayen and Hunter.<\/p>\n

The study was funded by grants from the New England University Transportation Center, the NEC Corporation Fund, the Solomon Buchsbaum Research Fund and the National Natural Science Foundation of China. Wang received funding from the Shenghua Scholar Program of Central South University.<\/p>\n

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