Editing Traffic congestion (section)

==Positive effects==
[[File:Silverdale Rd. - geograph.org.uk - 1047019.jpg|thumb|Houses in this street in [[Royal Tunbridge Wells]] were built when cars were few. With no provision for garages or off-street parking, on-street parking has formed a choke point likely to cause traffic congestion.]]
[[File:Tráfico en Madrid. Noviembre de 2017.jpg|thumb|A traffic jam in [[Madrid]]]]
Congestion has the benefit of encouraging motorists to retime their trips so that expensive road space is in full use for more hours per day. It may also encourage travellers to pick alternate modes with a lower environmental impact, such as public transport or bicycles.<ref>{{cite web|url=http://www.aptnsw.org.au/blunden83.html|title=Congestion - Friend or Foe? - W R Blunden 1983|access-date=October 5, 2014|archive-date=March 9, 2015|archive-url=https://web.archive.org/web/20150309160401/http://aptnsw.org.au/blunden83.html|url-status=live}}</ref>

It has been argued that traffic congestion, by reducing road speeds in cities, could reduce the frequency and severity of road crashes.<ref>{{cite web |url=http://www.ipart.nsw.gov.au/files/13687885-53fa-40c8-b33d-a246010695c4/Draft_Report__Maximum_fares_for_metropolitan_and_outer_metropolitan_buses_from_January_2014.pdf |title=Maximum fares for metropolitan and outer metropolitan buses from January 2014 - Draft Report, p. 37, citing work by LECG "Value of Sydney bus externalities and optimal Government subsidy - Final report", September 2009, p. 17 |access-date=July 12, 2018 |archive-date=May 25, 2017 |archive-url=https://web.archive.org/web/20170525171209/https://www.ipart.nsw.gov.au/files/13687885-53fa-40c8-b33d-a246010695c4/Draft_Report__Maximum_fares_for_metropolitan_and_outer_metropolitan_buses_from_January_2014.pdf |url-status=live }}</ref> More recent research suggests that a U-shaped curve exists between the number of accidents and the flow of traffic, implying that more accidents happen not only at high congestion levels, but also when there are very few vehicles on the road.<ref name="Kumar">{{cite journal  | last1 = Kumar  | first1 = Nishant  | last2 = Raubal | first2 = Martin | title = Applications of deep learning in congestion detection, prediction and alleviation: A survey  | journal = Transportation Research Part C: Emerging Technologies  | volume = 133  | pages = 103432  | year = 2021 | doi = 10.1016/j.trc.2021.103432  | hdl = 10230/42143  | s2cid = 240420107 | doi-access = free  | arxiv = 2102.09759 | bibcode = 2021TRPC..13303432K | hdl-access = free  }}</ref>