Correlation between automotive CO, HC, NO, and PM emission factors from on-road remote sensing: implications for inspection and maintenance programs

Carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NO) emission factors (EFs) are measured with a commercial vehicle emissions remote sensing system (VERSS) during a large-scale vehicle exhaust emissions study in Las Vegas. Particulate matter (PM) EFs are simultaneously measured for individual vehicles with a newly developed PM-VERSS based on ultraviolet backscatter light detection and ranging (Lidar). The effectiveness of CO and HC EFs as proxy for NO and PM EFs for spark-ignition vehicles is evaluated. Poor correlations were found between EFs for pollutants on an individual vehicle basis indicating that high EFs for one or more pollutants cannot be used as a predictor of high EFs for other pollutants. Stronger functional relationships became evident after averaging the EF data in bins based on rank-order of a single pollutant EF. Low overlap between the highest 10% emitters for CO, HC, NO, and PM was found. These results imply that for an effective reduction of the four pollutants, inspection and maintenance (I/M) programs, including clean screening, should measure all four pollutants individually. Fleet average CO and HC concentrations determined by gaseous VERSS were compared with fleet average CO and HC concentrations measured at low-idle and at high-idle during local I/M tests for spark-ignition vehicles. The fleet average CO concentrations measured by I/M tests at either idle were about half of those measured by remote sensing. The fleet average high-idle HC concentration measured by I/M tests was about half of that measured by VERSS while low-idle I/M and VERSS HC average concentrations were in better agreement. For a typical vehicle trip, most of the fuel is burned during non-idle conditions. I/M measurements collected during idling conditions may not be a good indicator of a vehicle’s potential to be a high emitter. VERSS measurements, when the vehicle is under a load, should more effectively identify high emitting vehicles that have a large contribution to the mobile emissions inventory.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

Predicting queue variability to enable analysis of overload risk

ABSTRACT

Predicting the risk of traffic demands and delays exceeding critical limits at road junctions, airports, hospitals, etc., requires knowing how both mean and variance of queue size vary over time. Microscopic simulation can explore variability but is computationally demanding and gives only sample results. A computationally efficient approximation to the mean is used in many modelling tools, but only empirical extensions for variance in particular situations have been available. The paper derives theoretical formulae for time-dependent and equilibrium variance, believed to be novel and to apply generally to queues covered by the Pollaczek–Khinchin mean formula, and offering possible structural insights. These are applied in an extended approximation giving mutually consistent mean and variance estimates with improved accuracy. Tests on oversaturated peak demand cases are compared with Markov probabilistic simulation, demonstrating accuracy (R^2?>?0.99) for typical random, priority-like (M/M/1) and traffic-signal-like (M/D/1) queues. Implications for risk analysis, planning and policy are considered.     

Disentangling the role of cars and transit in employment and labor earnings

We examine the relationship between transportation access on the one hand and individuals’ employment and labor earnings on the other. We improve on existing studies by bringing a large national panel data set to bear on this question, attempting to disentangle the mechanisms by which individuals improve their economic standing and, finally, comparing the economic benefits to the direct costs of car ownership. To do this, we use nine waves from the Panel Study of Income Dynamics from 1999 to 2015. We find that access to a car is a strong predictor of future economic benefit for individuals, and that at very high levels of transit access, carless individuals can also fare equally well. Access to an automobile is strongly associated with employment, job retention, and earning more money over time. Though having a car is associated with economic benefits, owning and operating a car is expensive; yet, our findings suggest that the benefits may outweigh the costs for most people living outside neighborhoods with truly excellent transit service.

     

Economic value of fish,wildlife, and recreation in Michigan's coastal wetlands

On the basis of annual expenditures by recreational participants and standard values of recreation days, as well as estimated sport and commercial harvests, the gross annual return from Michigan's 105,855 acres of coastal wetlands has been estimated. Analysis of fish, wildlife, and recreational values revealed that in 1977 these wetland areas generated an economic value of $489.69 per wetland acre, for a direct annual gross of $51.8 million. Specifically, these average return values per wetland acre/year, in descending order, are sport fishing ($286), nonconsumptive recreation ($138.24), waterfowl hunting ($31.23), trapping of furbearers ($30.44), and commercial fishing ($3.78). These gross annual return values were calculated in an effort to sensitize the general public to the diversity and possible magnitude of selected wetland functions.     

Design and analysis of control strategies for pedestrian flows

Transportation - Exploiting the full potential of pedestrian infrastructure is becoming critical in many environments which cannot be easily expanded to cope with the increasing pedestrian demand....     

Car today,gone tomorrow: The ephemeral car in low-income,immigrant and minority families

Most transportation research in the United States uses cross-sectional, “snapshot” data to understand levels of car access. Might this cross-sectional approach mask considerable variation over time and within households? We use a panel dataset, the Panel Study of Income Dynamics (PSID), for the years 1999–2011 to test this question. We find that for most families, being “carless” is a temporary condition. While 13 % of families in the US are carless in any given year, only 5 % of families are carless for all seven waves of data we examine in the PSID. We also find that poor families, immigrants, and people of color (particularly, blacks) are considerably more likely to transition into and out car ownership frequently and are less likely to have a car in any survey year than are non-poor families, the US-born, and whites.