This study estimates the effectiveness of a vehicle miles travelled (VMT) tax in controlling mobile-source emissions of particulate matter (PM2.5) in a non-attainment area located in northern Utah. Using a recently updated household-level dataset, the study finds no evidence of an endogenous relationship between choice of vehicle type and VMT. VMT elasticities are also estimated with respect to cost per mile that are in some cases larger in magnitude than those reported in previous studies. Based on vehicle emissions tests performed by the Houston Advanced Research Center, the study estimates the reduction in particulate emissions that would occur with two different sets of VMT tax rates. Principal findings are that a VMT tax rate of $0.003 per passenger car mile and $0.01 per light-duty truck mile (resulting in a mean annual tax burden of $128 per household in the first year) would reduce annual particulate emissions by between 7% and 11%, depending upon the degree of heterogeneity in household driving behaviour. Assuming constant elasticity, this means that at tax rates of $0.006 and $0.02 per mile for passenger cars and light-duty trucks, respectively (resulting in double the mean annual tax burden), annual particulate emissions would be reduced by between 12% and 23%. Both the advantages and limitations of the VMT tax are discussed. 相似文献
Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.
Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.
Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.
Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed. 相似文献