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原文传递 Signal Timing Optimization with Consideration of Environmental and Safety Impacts, Part A.

题名：	Signal Timing Optimization with Consideration of Environmental and Safety Impacts, Part A.
作者：	Hadi, M.; Elefteriadou, L.; Chen, X.; Wang, T.; Xiao, Y.
关键词：	Signal timing optimization, Environmental and safety impacts
摘要：	This study aims at developing models that relate pollutant emissions to macroscopic mobility measures, which can be estimated using macroscopic/mesoscopic analysis tools or can be measured using sensors in the real world. Such models can be used in signal optimization tools to allow the optimization of signal timings based on emission, combined with other measures. These models can also be used as part of sketch planning tools, analysis models, and real-world data analytical tools to allow for the assessment of environmental impacts of advanced transportation and demand management (ATDM) strategies. Two sets of emission estimation models were developed in this study, one based on microscopic simulation and one based on real-world trajectory data collected as part of the Federal Highway Administration Next Generation Simulation (NGSIM) program data. Both simulated and real-world trajectory data were input to the MOtor Vehicle Emission Simulator model (MOVES) operating mode distribution analysis procedure to calculate emissions. Macroscopic mobility measures were also extracted from these trajectory data and related to the emission outputs from MOVES using regression analyses. It is found that the significant factors in the regression models to estimate pollutant emissions are Vehicle-Miles Traveled (VMT), total vehicle delay, stop delays, and/or number of stops, depending on the estimated pollutants, when using simulation model trajectory data. The significant factors when using NGSIM trajectory data are VMT, average speed, and number of stops. The developed emission models were tested using a simulated network as well as real-world roadway sections. The results show that the NGSIM-data-based model perform relatively better than the simulation-based models. As an application of the developed emission estimation models, a method is further developed to extract macroscopic mobility measures from automated vehicle identification (AVI) or Automatic Vehicle Location (AVL) data, such as Inirx and Wi-Fi data, and use them as input to emission models to estimate the pollutant emissions at a signalized intersection. This approach can be used by transportation agencies to monitor environmental impacts based on real-world data in data analytic tools.
报告类型：	科技报告