Fine-grained analysis on fuel-consumption and emission from vehicles trace. (1st December 2018)

• Record Type: Journal Article
• Authors: Kan, Zihan; Tang, Luliang; Kwan, Mei-Po; Ren, Chang; Liu, Dong; Pei, Tao; Liu, Yu; Deng, Min; Li, Qingquan
• Is Part Of: Journal of cleaner production. Volume 203(2018)
• Page Start: 340
• Page End: 352
• Publication Date: 2018-12-01
• Subjects: Fuel consumption -- Emissions -- Big data -- Activity analysis -- GPS trace -- CMEM; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.08.222
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Traffic-related fuel consumption and emissions pose a severe problem with adverse impact on human health and urban sustainability. GPS trajectory data can provide useful insights into the quantities and distributions of fuel consumption and emissions. Previous research has primarily focused on understanding the spatiotemporal distributions of fuel consumption and emissions with GPS trajectory data, but has not paid adequate attention to estimation accuracy. Thus, this study proposes a method that estimates vehicular fuel consumption and emissions at a fine-grained level based on analysis of vehicles' mobile activities, stationary activities with engine-on, and stationary activities with engine-off. Using the analytical framework of space-time paths in time geography, this study first builds space-time paths of individual vehicles, extracts moving parameters and analyzes the activities from each space-time path segment (STPS). Based on the activity analysis, we then estimate fuel consumption and emissions using a microscopic model (CMEM), and distinguish between the cold-start phases and the hot phases in the space-time paths. In the case study, the fuel consumption and emissions for individual trajectories and a road network were estimated and analyzed. The distribution of activity-related fuel consumption was also explored. The effectiveness of the proposed methodology is illustrated using three datasets that were collected from vehicles with various types of engines, with estimation accuracy of over 90%. Highlights: Using space-time paths in time geography, individual vehicle activities are analyzed by mobile and stationary activities. Cold- and hot-start phases are distinguished in space-time paths, before emission estimation using CMEM based on activity. The fuel consumption and emissions for individual trajectories and a road network were estimated and analyzed. The distribution of activity-related fuel consumption was also explored.