Browse Publications Technical Papers 2019-24-0252

Estimation of Fuel Consumption and CO2 Emissions of Car Travel in Transportation Planning: the Lazio Region Case Study 2019-24-0252

The reduction of oil dependence and CO2 emissions have been included in the set of policy objectives by the European Union, according to the latest White Paper on transportation. Car travel is heavily dependent on oil, with minor exceptions represented by CNG (compressed natural gas) and all-electric vehicles. There is a tight relationship between CO2 emissions, almost unanimously recognized as main determinant of climate change, and fuel consumption. The paper provides a comparative analysis of two methods that can be used in transportation planning for the estimation of fuel consumption and CO2 emissions of car travel. The first method uses consumption and emission factors per vehicle-km travelled that are based on average network speed. The second method uses consumption and emission factors that are specific of the individual links of the network. In the second case, the link-specific average speed and flow that result from the assignment of the origin-destination travel demand matrix to the road network, subject to congestion, are the inputs of consumption and emission estimation. Link-specific travel times and flows, in a pre-specified time frame, are computed at equilibrium according to the Wardrop principle: flows are distributed over the network so that no user can reduce her route travel time by unilaterally changing route. Thus, at equilibrium the routes that have travel time higher than the minimum are not used, i.e. they have zero flow. From a mathematical point of view, the equilibrium conditions are the solution of a variational inequality. Travel times and flows at equilibrium are computed by solving an equivalent minimization problem. The analysis is carried out for the Lazio region. A geo-coded graph with (950 x 950) origin-destination pairs, 12000 links and 8500 nodes is implemented using Transcad GIS and transportation planning software. The reference scenario for both demand and supply is year 2017. The vehicle fleet composition is from 2016 data by ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). With the first method, consumption and emission factors for five car classes from data by ISPRA are used. With the second method, average speed-dependent consumption and emission factors for 106 car classes from the COPERT (COmputer Programme to compute Emissions from Road Transport) database, release 2018, by EEA (European Environment Agency), are used. The results are indicative of the magnitude of the bias that affect the first method compared with the more detailed second method.


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