Congestion in cities causes fuel consumption to rise sharply, partly because of the longer time it takes to reach a destination, partly because the rate of consumption is much higher at low speeds. Existing traffic control systems do not cope well with heavily congested situations; essentially they were designed to respond to changes in moving traffic, not to the buildup of stopped traffic; and they assume that the supply of road space remains fixed, whereas congestion brings about a reduction in supply. This paper reports on a project which is evaluating the effectiveness of an artificial intelligence based supervisor for urban traffic control systems which aims to reduce congestion and hence fuel consumption.
Using artificial intelligence to reduce high fuel consumption in congested cities
1996-01-01
5 pages
Article (Journal)
English
knowledge elicitation , computerised monitoring , knowledge based systems , artificial intelligence , CLAIRE , road traffic control , knowledge acquisition , urban traffic control , knowledge based system , road traffic , fuel consumption reduction , traffic engineering computing , SCOOT , intelligent control , traffic control , traffic congestion
Using Artificial Intelligence to Reduce High Fuel Consumption in Congested Cities
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