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Bus emissions have become one of the important contributing factors in urban environmental pollution due to the frequent use of heavy-duty diesel engines in the day-time. Local bus driving cycles have a significant influence on bus emissions under the different traffic conditions. This study investigated the operation mode distributions and emission characteristics for urban buses based on localized MOtor Vehicle Emission Simulator (MOVES) using sparse Global Position System (GPS) data in Shanghai, China. Sparse GPS data from forty-three buses were prepared, and then bus trajectories were reconstructed to calculate local bus driving cycles, including model description, model calibration, and trajectory reconstruction. MOVES localization was conducted for emission estimation mainly focusing on the bus emission inventory comparison between US and China. Bus emission factors were estimated based on the localized MOVES from the aspect of different driving conditions. Results show that with the increase in average traveling speed, the proportion of idling operation mode showed a decreasing trend. Four typical vehicle operation mode distributions were identified with different average speeds to show the impact of traffic conditions. Bus emission factors first rapidly decreased and then slowly declined towards some minimum values. Bus lanes exhibited emission reduction benefits under serious traffic congestion. The findings of this study have great importance for transportation operation management and policy-making to reduce bus emissions, as well as improving air quality.

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In China, urban bus energy consumption is an increasing concern due to system expansion and poor energy efficiency due to frequent stopping and starting by buses. This study develops a mesoscopic bus energy consumption model based on the U.S. Environment Protection Agency’s Motor Vehicle Emission Simulator (MOVES). To localize MOVES, link operating mode distribution is calculated by bus GPS data collected from nine routes in Shanghai, China. A comparison of bus fuel economy between the U.S.A. and China is conducted to determine the model years in U.S.A. and China which have similar fuel consumption performance for buses with a certain weight. After MOVES localization, link energy consumption factors are estimated, and then the impacts of average speed, vehicle stops, acceleration, and road facility on link energy consumption factors are explored. Based on this exploration of influential variables, this study develops link-level bus energy consumption factor look-up tables for a variety of bus types. Model validation indicates that using link-level indicators to estimate bus energy consumption can achieve acceptable accuracy, and that the link type classification method can influence the accuracy of the mesoscopic bus energy consumption model. This study is useful to estimate bus energy consumption when instantaneous speed data is unavailable. This study also explores the extended application of MOVES by offering a procedure for applying MOVES to develop a bus energy consumption model in regions beyond the U.S.A.

The environmental benefits of zero-emission vehicles (ZEVs) are affected by both consumer adoption and usage patterns. While numerous studies examine consumers’ stated or revealed preferences for ZEV adoption, ZEV usage patterns have received less attention. Based on the 2019 California vehicle survey data, this paper analyzes the annual mileage of three ZEV types: battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV). Results show that ZEVs are driven as much as or more than internal combustion engine vehicles (ICEVs). Furthermore, focusing on households with one ZEV and one or more ICEVs, factors that influence household electric vehicle miles traveled (eVMT) are explored using multiple linear regression models. Greater battery range, home charging capability (regardless of charger type), and provision of special electricity rates for home charging are found to be positively correlated with the eVMT of PHEV households. The eVMT of BEV households is positively associated with Level 2 home charging capability, solar panel installation, access to workplace DC fast charging, and access to public Level 2 and DC fast charging stations. The number of routinely-used public hydrogen refueling stations is associated with higher FCEV household eVMT. Lastly, when high-occupancy vehicle lane access is rated as extremely important in the ZEV purchase decision, greater eVMT is found for both BEV and FCEV households, but not for PHEV households. Results of this study inform policies to encourage eVMT over vehicle miles traveled by ICEV in a household, achieving greater environmental benefits from ZEVs.

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