A power-based vehicle fuel consumption model,entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model(VT-CPFM)that was developed in an earlier publication is validated against in-field fuel consumpt...A power-based vehicle fuel consumption model,entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model(VT-CPFM)that was developed in an earlier publication is validated against in-field fuel consumption measurements.The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 0.96.More importantly,both estimates and measurements produce very similar behavioral changes depending on engine load conditions.The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data.展开更多
The paper presents the results of a field experiment that was designed to compare manual driving,conventional cruise control(CCC)driving,and Eco-cruise control(ECC)driving with regard to fuel economy.The field experim...The paper presents the results of a field experiment that was designed to compare manual driving,conventional cruise control(CCC)driving,and Eco-cruise control(ECC)driving with regard to fuel economy.The field experiment was conducted on five test vehicles along a section of Interstate 81 that was comprised of±4%uphill and downhill grade sections.Using an Onboard Diagnostic II reader,instantaneous fuel consumption rates and other driving parameters were collected with and without the CCC system enabled.The collected data were compared with regard to fuel economy,throttle control,and travel time.The results demonstrate that CCC enhances vehicle fuel economy by 3.3 percent on average relative to manual driving,however this difference was not found to be statistically significant at a 5 percent significance level.The results demonstrate that CCC driving is more efficient on downhill versus uphill sections.In addition,the study demonstrates that an ECC system can produce fuel savings ranging between 8 and 16 percent with increases in travel times ranging between 3 and 6 percent.These benefits appear to be largest for heavier vehicles(SUVs).展开更多
The research identifies the steady-state car-following model parameters within state-of-thepractice traffic simulation software that require calibration to reflect inclement weather and roadway conditions.The research...The research identifies the steady-state car-following model parameters within state-of-thepractice traffic simulation software that require calibration to reflect inclement weather and roadway conditions.The research then develops procedures for calibrating non-steady state carfollowing models to capture inclement weather impacts and applies the procedures to the INTEGRATION software on a sample network.The results demonstrate that the introduction of rain precipitation results in a 5%reduction in light-duty vehicle speeds and a 3%reduction in heavy-duty vehicle speeds.An increase in the rain intensity further reduces light-duty vehicle and heavy-duty truck speeds resulting in a maximum reduction of 9.5%and 5.5%at the maximum rain intensity of 1.5 cm/h,respectively.The results also demonstrate that the impact of rain on traffic stream speed increases with the level of congestion and is more significant than speed differences attributed to various traffic operational improvements and thus should be accounted for in the analysis of alternatives.In the case of snow precipitation,the speed reductions are much more significant(in the range of 55%).Furthermore,the speed reductions are minimally impacted by the snow precipitation intensity.The study further demonstrates that precipitation intensity has no impact on the relative merit of various scenarios(i.e.the ranking of the scenario results are consistent across the various rain intensity levels).This finding is important given that it demonstrates that a recommendation on the optimal scenario is not impacted by the weather conditions that are considered in the analysis.展开更多
文摘A power-based vehicle fuel consumption model,entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model(VT-CPFM)that was developed in an earlier publication is validated against in-field fuel consumption measurements.The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 0.96.More importantly,both estimates and measurements produce very similar behavioral changes depending on engine load conditions.The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data.
基金sponsored by the Tran LIVE University Transportation Center and the Mid-Atlantic University Transportation Center.
文摘The paper presents the results of a field experiment that was designed to compare manual driving,conventional cruise control(CCC)driving,and Eco-cruise control(ECC)driving with regard to fuel economy.The field experiment was conducted on five test vehicles along a section of Interstate 81 that was comprised of±4%uphill and downhill grade sections.Using an Onboard Diagnostic II reader,instantaneous fuel consumption rates and other driving parameters were collected with and without the CCC system enabled.The collected data were compared with regard to fuel economy,throttle control,and travel time.The results demonstrate that CCC enhances vehicle fuel economy by 3.3 percent on average relative to manual driving,however this difference was not found to be statistically significant at a 5 percent significance level.The results demonstrate that CCC driving is more efficient on downhill versus uphill sections.In addition,the study demonstrates that an ECC system can produce fuel savings ranging between 8 and 16 percent with increases in travel times ranging between 3 and 6 percent.These benefits appear to be largest for heavier vehicles(SUVs).
文摘The research identifies the steady-state car-following model parameters within state-of-thepractice traffic simulation software that require calibration to reflect inclement weather and roadway conditions.The research then develops procedures for calibrating non-steady state carfollowing models to capture inclement weather impacts and applies the procedures to the INTEGRATION software on a sample network.The results demonstrate that the introduction of rain precipitation results in a 5%reduction in light-duty vehicle speeds and a 3%reduction in heavy-duty vehicle speeds.An increase in the rain intensity further reduces light-duty vehicle and heavy-duty truck speeds resulting in a maximum reduction of 9.5%and 5.5%at the maximum rain intensity of 1.5 cm/h,respectively.The results also demonstrate that the impact of rain on traffic stream speed increases with the level of congestion and is more significant than speed differences attributed to various traffic operational improvements and thus should be accounted for in the analysis of alternatives.In the case of snow precipitation,the speed reductions are much more significant(in the range of 55%).Furthermore,the speed reductions are minimally impacted by the snow precipitation intensity.The study further demonstrates that precipitation intensity has no impact on the relative merit of various scenarios(i.e.the ranking of the scenario results are consistent across the various rain intensity levels).This finding is important given that it demonstrates that a recommendation on the optimal scenario is not impacted by the weather conditions that are considered in the analysis.
