Optimization Of Aggregation Level Of Floating Car Data For Estimation Of Fuel Consumption And Emissions

Floating car traffic data collection technology has become one of the most important development directions for collecting traffic information due to its advantages of short construction cycle, real-time characteristics, and broad covering range.Besides its extensive applications in real-time estimation of traffic flow parameters, floating car data has begun to be appliedin estimating fuel consumption and emissions of road traffic. One important issue to be determined in using floating car technology to estimate fuel consumption and emissions is how to define a reasonable time interval (termed as "Aggregation Level" in this thesis) for data aggregation is an important and problematic issue.The aggregation level affects directly the accuracy of estimation, the cost of communication, and the load of data processing.Therefore, it is of great significance to developing optimal aggregation levels that fall within the accepted accuracy for reducing the cost of communication and improving the operational efficiency of the system.In this background, this thesis is intended to study the optimization issue of the aggregation level of floating car speed data for calculating the emission and fuel consumption, addressing the existing problem in determining the aggregation level by means of subjective experience.The objective is to improve the operational efficiency of the system as well asto meet the required estimation accuracy, effectively supporting the evaluation of the effectiveness of transportation strategies on fuel consumption and emissions reductions. First, an experimental plan is designed and implemented to collect floating car data on expressways in Beijing. Then, a stability index is proposed by considering the theory of VSP distribution. It is validated that the application of raw second-by-second floating data to the study of vehicle operating modes on roadsprovides the highest reliability, through examining the stability of VSP distributionsat different aggregation levels. Subsequently, the thesis establishes a system of comprehensive indexes, including the information-loss index, temporal- and spatial- complexity index, and relative error index, and determines3-minutes as the optimal aggregation level of floating car speed data in Beijing by analyzing the differences in VSP distributions and emission and fuel consumption estimations between aggregated versus raw data, as well as the difference in the usage of computer resources at different aggregation levels.Furthermore, the optimal aggregation level is used to aggregate the floating car speed data on expresswaysin Beijing in 2006 and 2010 and the resulting VSP distributions are compared. It is found that there is a consistency in vehicle operating modes and regularities for vehicles on expressways in Beijing in 2006 and 2010.Finally, the optimal aggregation level and vehicle operating modes derived from this research are applied in a case study.The case study uses3-minute interval to aggregate the real floating car speed data and analyzestheir vehicle operating modes. By combining the emission rates in MOVES, the emissions and fuel consumption are calculated and compared by using the real-world measured operating modes, operating modes derived from the proposed aggregation level, and MOVES model. It is found that results from the aggregated floating car data is closer to real data with relative errors of 8.5%,25.6%,0.86%, and 22.2% for fuel consumption, NOx, CO, and HC emissionsrespectively, which are59%,17%,22.6%, and 22.6% lower than those by using the MOVES model.The case study demonstrates the reliability and accuracy in estimating fuel consumption and emissions for expressways in Beijingby using the proposed optimal aggregation level.