Dataset Information

Encapsulating urban traffic rhythms into road networks.

ABSTRACT: Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a proof of concept, when we lowered the speed limit or increased the capacity of road segments in the targeted road clusters, we found that both the number of congested roads and extra travel time were effectively reduced. In addition, the proposed modeling framework provided new insights on the optimization of transport efficiency in any infrastructure network with a specific supply and demand distribution.

SUBMITTER: Wang J

PROVIDER: S-EPMC3929915 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Encapsulating urban traffic rhythms into road networks.

Wang Junjie J Wei Dong D He Kun K Gong Hang H Wang Pu P

Scientific reports 20140220

Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a ...[more]

PMID: 24553203

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Project description:Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48-60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (∼2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020-30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019-02/05/2019). NO2, O3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from -24.9% in Milan to -59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOCs-limited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of ∼13-17% counterbalanced by increases up to ∼9%. Higher household heating usage (+16-19% in March), also driven by colder weather conditions than 2019 (-0.2 to -0.8 °C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health.

Dataset Information

Encapsulating urban traffic rhythms into road networks.

Publications

Encapsulating urban traffic rhythms into road networks.

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