Increased congestion during peak morning and afternoon periods in urban areas is increasing logistics costs. In addition, environmental, social, and political pressures to limit the impacts associated with CO2 emissions are mounting rapidly. A key challenge for transportation agencies and businesses is to improve the efficiency of urban freight and commercial vehicle movements while ensuring environmental quality, livable communities, and economic growth. However, research and policy efforts to analyze and quantify the impacts of congestion and freight public policies on CO2 emissions are hindered by the complexities of vehicle routing problems with time-dependent travel times and the lack of network-wide congestion data. This research focuses on the analysis of CO2 emissions for different levels of congestion and time-definitive customer demands. Travel time data from an extensive archive of freeway sensors, time-dependent vehicle routing algorithms, and problems-instances with different types of binding constraints are used to analyze the impacts of congestion on commercial vehicle emissions. Results from the case study indicate that the impacts of congestion or speed limits on commercial vehicle emissions are significant but difficult to predict since it is shown that it is possible to construct instances where total route distance or duration increases but emissions decrease. Public agencies should carefully study the implications of policies that regulate depot locations and travel speeds as they may have unintended negative consequences in terms of CO2 emissions. © 2010 Elsevier Ltd.
https://doi.org/10.1016/j.trc.2010.11.002Cite as:
@article{Figliozzi_2011,
doi = {10.1016/j.trc.2010.11.002},
url = {https://doi.org/10.1016%2Fj.trc.2010.11.002},
year = 2011,
month = {aug},
publisher = {Elsevier {BV}},
volume = {19},
number = {5},
pages = {766--778},
author = {Miguel Andres Figliozzi},
title = {The impacts of congestion on time-definitive urban freight distribution networks {CO}2 emission levels: Results from a case study in Portland, Oregon},
journal = {Transportation Research Part C: Emerging Technologies}
}