The identification of truck-related greenhouse gas emissions and critical impact factors in an urban logistics network

Trucking activities in urban logistics networks (ULNs) are a major source of greenhouse gas (GHG) emissions. Diversified logistics demand leads to a variety of truck trip purpose, to study truck related emissions by trip purpose is necessary. This study aims to analyze the characteristics of trucking activities in a ULN by trip purpose and to investigate the relationships between trucks' trip emissions and critical influential factors from a ULN perspective, with a particularly focuses on the effects of the Euclidean distance of a trip, the vehicle curb weight, as well as the population density at a trip's origin/destination (OD). By combining a large set of empirical GPS trucking data and analytical information of ULN properties from Shenzhen, China, an imputation matrix approach is first developed to classify the truck data, based on trip purposes. Then, the trucking characteristics in terms of each trip purpose are extracted from the processed data. The GHG emissions associated with the trucking activities are estimated using a variant of the comprehensive modal emissions model (CMEM). A multivariate regression analysis is conducted to independently and quantitatively identify whether the critical factors vary in terms of a trip's purpose and to establish how such factors impact on GHG emissions. These results suggesting that designing emission management measures should take such purposes into consideration. The significant of OD Euclidean distance and the vehicle curb weight may vary by trip purpose, while the OD population density could also be regarded as an underlying determinant of most trip purposes. The results can be extended to other cities with similar classifications of trip purposes in their ULNs, thereby providing a decision-support tool for governmental policies and regulations, the locations of logistics facilities, and operational plans for trucks.