Mohamadreza Farzaneh

Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations

Standard multinomial logit (MNL) and mixed logit (MXL) models are developed to estimate the degree of influence that bicyclist, driver, motor vehicle, geometric, environmental, and crash type characteristics have on bicyclist injury severity, classified as property damage only, possible, nonincapacitating or severe (i.e., incapacitating or fatal) injury. This study is based on 10,029 bicycleinvolved crashes that occurred in the State of Ohio from 2002 to 2008. Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates. Consequently, separate models are developed to independently assess the impacts of various factors on the degree of bicyclist injury severity resulting from crashes at intersection and non-intersection locations. Several covariates are found to have similar impacts on injury severity at both intersection and non-intersection locations. Conversely, six variables were found to significantly influence injury severity at intersection locations but not non-intersection locations while four variables influenced bicyclist injury severity only at non-intersection locations. In crashes occurring at intersection locations, the likelihood of severe bicyclist injury increases by 14.8 percent if the bicyclist is not wearing a helmet, 82.2 percent if the motorist is under the influence of alcohol, 141.3 percent if the crash-involved motor vehicle is a van, 40.6 percent if the motor vehicle strikes the side of the bicycle, and 182.6 percent if the crash occurs on a horizontal curve with a grade. Results from non-intersection locations show the likelihood of severe injuries increases by 374.5 percent if the bicyclist is under the influence of drugs, 150.1 percent if the motorist is under the influence of alcohol, 53.5 percent if the motor vehicle strikes the side of the bicycle and 99.9 percent if the crash-involved motor vehicle is a heavy-duty truck.

Inclement Weather Impacts on Freeway Traffic Stream Behavior

The research reported in this paper quantifies the impact of inclement weather (precipitation and visibility) on traffic stream behavior and key traffic stream parameters, including free-flow speed, speed at capacity, capacity, and jam density. The analysis is conducted using weather data (precipitation and visibility) and loop detector data (speed, flow, and density) obtained from the Baltimore, Maryland; Minneapolis–Saint Paul, Minnesota; and Seattle, Washington, areas in the United States. The precipitation data included intensities up to 1.6 and 0.33 cm/h for rain and water equivalent of snow intensity, respectively. The paper demonstrates that the traffic stream jam density is not affected by weather conditions. Snow results in larger reductions in traffic stream free-flow speed and capacity when compared with rain. Reductions in roadway capacity are not affected by the precipitation intensity except in the case of snow. Reductions in free-flow speed and speed at capacity increase as the rain and snow intensities increase. Finally, the paper also develops free-flow speed, speed-at-capacity, and capacity weather adjustment factors that are multiplied by the base clear-condition variables to compute inclement weather parameters. These adjustment factors vary as a function of the precipitation type, precipitation intensity, and visibility level. It is intended that these adjustment factors be incorporated into the Highway Capacity Manual.

Development of fuel and emission models for high speed heavy duty trucks, light duty trucks, and light duty vehicles

The current state-of-practice emission modeling tools, namely: MOBILE, EMFAC, the Comprehensive Modal Emission Model (CMEM), and VT-Micro model do not provide reliable emission estimates for high speeds greater than 80 mph since the models do not have supporting data at these high speeds. Consequently, the research presented in this paper gathers field data and develops models for the estimation of fuel consumption, carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOX), hydrocarbon (HC), and particulate matter (PM) emissions at high speeds. A total of nine vehicles including three semi-trucks, three pick-up trucks, and three passenger cars are tested on a nine-mile test track in Pecos, Texas. The fuel consumption and emission rates are measured using two portable emission measurement systems. Models are developed using these data, producing minimum errors for fuel consumption, CO2, NO2, HC, and PM emissions. Alternatively, the NO and NOX emission models produce the highest errors with the least degree of correlation. The study demonstrates that the newly constructed models overcome the shortcomings of the state-of-practice models and can be utilized to evaluate the environmental impacts of high speed vehicles.

Field Evaluation of Carbon Dioxide Emissions at High Speeds

This study has two main objectives. First, the impact of vehicle cruise speed on its carbon dioxide (CO2) emission rates at speeds up to 95 mph is evaluated with field data gathered under real-world driving conditions. Second, the cycle-based CO2 emission rates for a broad range of average cycle speeds are investigated. A portable emission measurement system unit was used to measure tailpipe emissions of three light-duty gasoline vehicles and three Class 2b diesel vehicles. The vehicles were tested on a speed track 9 mi high in Pecos, Texas, while the driver followed predeveloped drive patterns. The drive patterns were used to ensure good coverage of various steady-state and transitional operational modes of the vehicles. The results show that the CO2 emissions of gasoline vehicles follow a sharply increasing pattern between 40 mph and 70 mph. At speeds higher than 70 mph, the vehicles fuel economy tends to decrease at a small rate or to stay flat. The Class 2b diesel vehicles, in contrast, show a monotonically increasing trend for speeds higher than 40 mph. The second-by-second emission data were used to develop a series of instantaneous emission models capable of providing accurate emissions at each speed and acceleration rate. The models were applied to a series of representative drive cycles to provide distance-based average CO2 emission rates. The results indicate that the gasoline vehicles have a minimal CO2 emission interval between 55 and 65 mph. Diesel vehicles, however, seem to reach their minimum CO2 production at 55 mph and the trend increases on both sides of this optimal speed.

Characterization of On-Road Emissions of Compressed Natural Gas and Diesel Refuse Trucks

Portable emission measurement systems were used to perform on-road emissions testing of compressed natural gas (CNG) and diesel refuse trucks to determine whether replacing diesel-fueled refuse trucks with CNG-fueled trucks would reduce emissions and fuel consumption. Two types of on-road testing were conducted: one while performing actual garbage collection in a service area (in-service testing) and the other following predeveloped duty cycles (duty cycle testing). Carbon dioxide (CO2), gaseous pollutants [oxides of nitrogen (NOx), carbon monoxide (CO), and hydrocarbons (HC)], and particulate matter (PM) emissions as well as carbon-based fuel consumption from refuse trucks during both types of testing were measured and analyzed. The analyzed results showed that CNG refuse trucks generally produced about 20% lower CO2 emissions and significantly lower NOx emissions compared with diesel trucks. However, CNG trucks emitted more CO and HC than diesel trucks. Almost all measured HC and PM emissions from diesel trucks were negligible. Measured PM emissions from CNG trucks were also negligible. Detailed tests and analyzed results are presented in this paper.

Greenhouse Gas Emissions and Urban Congestion: Incorporation of Carbon Dioxide Emissions and Associated Fuel Consumption into Texas A&M Transportation Institute Urban Mobility Report

The Texas A&M Transportation Institutes Urban Mobility Report (UMR) is acknowledged to be the most authoritative source of information about traffic congestion and its possible solutions. As policy makers from the local to national levels devise strategies to reduce greenhouse gas (GHG) emissions, the level of interest in the environmental impact of urban congestion has increased. To this end, the researchers developed and applied a methodology to determine carbon dioxide (CO2) emissions caused by congestion for inclusion in the UMR. The methodology also estimated fuel consumption on the basis of the CO2 emissions estimates. The researchers developed a five-step methodology with data from three primary data sources: (a) FHWAs Highway Performance Monitoring System, (b) INRIX traffic speed data, and (c) the U.S. Environmental Protection Agencys Motor Vehicle Emissions Simulator model. Results were intuitive and reasonable when emission rates (pounds of CO2 per mile) were compared with the emissions inventories in selected cities. The researchers incorporated the new methodology for all urban areas into the 2012 UMR and plan to include the same measures in future releases of the report. The researchers reported that, in 2011, 56 billion pounds of additional CO2 were produced in all 498 urban areas during congestion only; this amount equated to 2.9 billion gallons of wasted fuel. The amount of CO2 produced under free-flow conditions (i.e., absent congestion) was 1.8 trillion pounds in 2011 in all 498 urban areas.

Optimal Deployment of Emission Reduction Technologies for Large Fleets

In states that have serious air quality problems, such as Texas and California, public fleet managers are under pressure to reduce emissions of their fleets. This paper presents an improved optimization framework for determining the most cost-effective assignment of emission reduction technologies to the vehicles in a large fleet and applies it in a much larger setting than previously done in the literature (in relation to fleet size, geographical area, and numbers of pollutants and technologies). Although the improved framework is applicable to any large setting, this study uses the fleet of the Texas Department of Transportation (DOT) as one example. An explanation of the three components of the framework (i.e., fleet data preprocessing, emissions rate estimation, and the binary integer programming optimization model) is followed by the presentation of two scenarios in which the framework is applied to the whole Texas DOT fleet. The resulting optimal deployment of nine technologies and five pollutants is reported. The improved framework uses weighting schemes for counties and pollutants that allow the optimization model itself to choose the best budget distribution strategy from an arbitrary number of possibilities acceptable to the decision maker. In contrast, previous frameworks could consider only an extremely small number of budget distribution strategies for counties.