Estimating the Effects of Traffic Congestion on Fuel Consumption and Vehicle Emissions Based on Acceleration Noise

Abstract

While significant progress has occurred in quantifying congestion costs within the sphere of traffic engineering, little progress has been made to incorporate such costs into network-level highway evaluation systems. The model presented in this paper takes a middle ground approach between the highly detailed microsimulation approach that is most appealing to the traffic engineer and the traditional highway development engineers’ approach of ignoring traffic congestion. The approach adopted is based on modeling of acceleration noise, defined as the standard deviation of accelerations. During periods of high traffic congestion, there is a greater variability in speed, resulting in higher acceleration noise levels. Data collection and analysis have been undertaken on highways in Auckland (New Zealand), Kuala Lumpur (Malaysia), and Bangkok (Thailand). This updated approach (relative to the original HDM-4 research in 1995) has been integrated with the International Study of Highway Development and Management Tools in order to provide an updated model HDM-4 (Highway Development and Management version 4). Fuel consumption predictions were tested both with and without the impact of a simulated acceleration noise level. For the latter of the two (i.e., a given drive cycle) the predictions were within 0.25%. For a generated drive cycle, the results show a consistent underprediction of some 25%. It is believed that this underprediction is largely due to the assumption of the acceleration noise data conforming to a normal distribution. While not prescribed in detail within this paper, vehicle emission models were also developed as part of the work with the results of tests presented. Vehicle emissions of carbon dioxide and hydrocarbons were within the range of the seven vehicles observed. Carbon monoxide and oxides of nitrogen were grossly underpredicted and were below the minimum observed values. These latter results are thought to be caused by the fuel model not predicting rich operating conditions during periods of high acceleration. The model presented, even with the preceding limitations, still has wide application in improving the prediction of vehicles operating on highways in congested conditions. In particular, the model presented provides a much improved predictive ability over those of traditional speed-flow approaches, where errors as high as 200% for passenger cars (higher for trucks) are observed. This makes the approach highly appropriate for inclusion into highway evaluation procedures such as HDM-4. The patterns of fuel consumption and emissions show the appropriate changes in relation to traffic congestion. Furthermore, the model framework readily lends itself to enhancement via adoption of new submodels.