Shauna Hallmark

ACCURACY ISSUES WITH ROUTE CHOICE DATA COLLECTION BY USING GLOBAL POSITIONING SYSTEM

Advancements in global positioning system (GPS) technology now make GPS route choice data collection for travel diary studies and other transportation applications a reality. Opportunities abound for increased quantities of data, for improved quality of data, and for new data elements that were once considered too burdensome or expensive to capture. For example, automated travel diaries can electronically capture trip purpose, origin and destination location names, and driver and passenger names at the push of a button. An accompanying GPS receiver can accurately capture origin and destination locations, departure and arrival times, as well as trip lengths and travel routes. This wealth of data can be used to validate or calibrate travel demand models, for in-vehicle information systems analysis, and for modeling mobile source emissions across a given network. These data collection and processing advancements do have their costs, however. In fact, care and caution should be exercised when GPS technologies are selected and used to collect route choice data. The focus of this paper is on the accuracy issues related to route choice data collection and processing using GPS technology. Vendor specifications, observation techniques, data collection procedures, data postprocessing, and the importance of using a reliable and accurate geographic information system (GIS) database are examined in detail. Critical issues in the calculation of GPS accuracy are reviewed. Finally, recent experience in Atlanta is reported, and recommendations designed to reduce the introduction of error into automated route choice data collection are provided.

Modeling regional mobile source emissions in a geographic information system framework

Suburban sprawl, population growth, and automobile dependency contribute directly to air pollution problems in US metropolitan areas. As metropolitan regions attempt to mitigate these problems, they are faced with the difficult task of balancing the mobility needs of a growing population and economy, while simultaneously lowering or maintaining levels of ambient pollutants. Although ambient air quality can be directly monitored, predicting the amount and fraction of the mobile source components presents special challenges. A modeling framework that can correlate spatial and temporal emission-specific vehicle activities is required for the complex photochemical models used to predict pollutant concentrations. This paper discusses the GIS-based modeling approach called the Mobile Emission Assessment System for Urban and Regional Evaluation (MEASURE). MEASURE provides researchers and planners with a means of assessing motor vehicle emission reduction strategies. Estimates of spatially resolved fleet composition and activity are combined with activity-specific emission rates to predict engine start and running exhaust emissions. Engine start emissions are estimated using aggregate zonal information. Running exhaust emissions are predicted using road segment specific information and aggregate zonal information. The paper discusses the benefits and challenges related to mobile source emissions modeling in a GIS framework and identifies future GIS mobile emissions modeling research needs.

CHARACTERIZING ON-ROAD VARIABLES THAT AFFECT PASSENGER VEHICLE MODAL OPERATION

Abstract The current research direction in transportation-related air-quality modeling is towards development and implementation of modal emissions models that correlate emission rates to specific ranges of activity. This paper describes a methodology to identify roadway characteristics at signalized intersections which affect the fraction of vehicle activity spend in specific operating modes where modal emission rate models indicate elevated emissions occur to improve vehicle activity inputs to modal emissions models. Field studies using laser guns were conducted on-road collecting second-by-second activity for individual vehicles at signal-controlled intersections and roadway segments. Hierarchical tree-based regression analysis was used to identify on-road geometric and operational characteristics that influenced the fractions of vehicle activity spent in specific modes. Results indicated that queue position, grade, downstream and upstream per-lane hourly volume, distance to the nearest downstream signalized intersection, percent heavy vehicles, and posted link speed limit were the most statistically significant variables.

Assessing Impacts of Improved Signal Timing as a Transportation Control Measure Using an Activity-Specific Modeling Approach

Metropolitan areas in nonattainment for transportation-related air pollutants rely on transportation control measures (TCMs) to reduce emissions and meet clean-air goals. However, since traditional transportationrelated air quality models use emission rates based on average speeds, only TCMs that either increase or decrease vehicle activity to speeds in which emissions are lower show reductions in output of air pollutants. In recent years, transportation air quality analysis has shifted to an activityspecific modeling approach that correlates emissions to vehicle operating mode. With an activity-specific approach, the emission reduction potential of a TCM can be evaluated by its ability to decrease time spent in modes in which emissions are disproportionately elevated. Signal timing improvements are attractive TCMs for traffic flow improvement. However, with traditional modeling, they may greatly decrease extremes in modal activity yet not show significant emission reduction if only moderate changes in average speed are realized. The benefits of using activityspecific modeling for evaluation of improved signal coordination as a TCM are described. A brief overview of the development of an activityspecific carbon monoxide emission rate model is provided, data collection for on-road vehicle activity estimates is briefly outlined, and a comparison of traditional emission modeling versus activity-specific modeling is provided to estimate the air quality benefits from improved coordination at a study intersection. Results for the study intersection indicate that more significant reductions in carbon monoxide emissions are realized using an activity-specific approach than with traditional methods.

Comparison of Speed-Acceleration Profiles from Field Data with NETSIM Output for Modal Air Quality Analysis of Signalized Intersections

New vehicle modal emissions rate models will assess emissions as a function of specific operating mode or engine load surrogates. These new models require that vehicle activity be input by fraction of time spent in different operating modes. However, the ability to realistically model onroad modal vehicle activity currently limits the implementation of these models. Few data on how vehicles operate in a real-world setting exist. Simulation models offer attractive advantages for modal modeling. They are readily available and generally can be used with both simple and detailed data input. Simulation models were developed to model the impacts of signal timing, incidents, or design features on traffic flow and perform well for these applications. However, simulation models, such as CORSIM, use theoretical profiles of vehicle acceleration and speed relationships that have not been validated in the field. To determine the feasibility of using simulation models to predict on-road speedacceleration profiles and to identify potential problems in their use as such, a study intersection was modeled in NETSIM, and the simulation output was compared with data collected from field studies of signalized intersections. Analyses of the simulation output and field data indicate that NETSIM does not adequately simulate instantaneous modal vehicle activity. NETSIM intersection activity shows higher fractions of hard accelerations [≥ 9.7+ km/h/s (6 mph/s)] than are demonstrated by field data for the study intersection. For midblock, the results indicate that field data demonstrate a much greater distribution of speeds and accelerations than the distribution modeled by NETSIM.

An empirical analysis of farm vehicle crash injury severities on Iowa's public road system

Farm vehicle crashes are a major safety concern for farmers as well as all other users of the public road system in agricultural states. Using data on farm vehicle crashes that occurred on Iowas public roads between 2004 and 2006, we estimate a multinomial logit model to identify crash-, farm vehicle-, and driver-specific factors that determine farm vehicle crash injury severity outcomes. Estimation findings indicate that there are crash patterns (rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median) and conditions (obstructed vision and crash in rural area; dry road, dark lighting, speed limit 55 mph or higher, and harvesting season), as well as farm vehicle and driver-contributing characteristics (old farm vehicle, young farm vehicle driver), where targeted intervention can help reduce the severity of crash outcomes. Determining these contributing factors and their effect is the first step to identifying countermeasures and safety strategies in a bid to improve transportation safety for all users on the public road system in Iowa as well as other agricultural states.

Roadway Lighting Shows Safety Benefits at Rural Intersections

Intersection-related crashes account for approximately 31% of fatal crashes in Minnesota and roughly 37% of those occurred at night, dusk, or dawn. Nationally, intersection-related fatal crashes accounted for 21% of all fatal crashes and 40% of those crashes occurred during dark, dusk, or dawn light conditions while only 25–33% of the vehicle miles traveled occur at night. Furthermore, Minnesota experienced 70% of its fatal crashes in rural areas, as compared to 58% nationally. As a result, rural intersections at night are at higher risk for fatal crashes than other locations in Minnesota. This research was part of Research Rep. No. MN/RC-2006-35, “Safety impacts of street lighting at isolated rural intersections—Part II.” This research evaluated the effectiveness of roadway lighting in reducing nighttime crashes at isolated rural intersections. A before-and-after study was used to evaluate the impact of lighting at 33 intersections with 3 years of before data and 3 years of after data. A Poisson regressi...

On-Road Evaluation of Emission Impacts of Roundabouts

Reduced emissions and improved air quality are typically attributed to the use of roundabouts rather than traditional stop or signal control. Roundabouts are expected to reduce emissions as a result of reduced delays and stops. However, roundabouts slow all vehicles to speeds at which emissions may be higher, while signals stop and delay only a portion of vehicles. Roundabouts may also increase the amount of acceleration and deceleration for all vehicles. Because emissions are correlated to these modal events, their impacts should be considered in the evaluation of roundabouts. An on-road assessment of the emission impacts of two roundabouts compared with other types of traffic control was conducted with a vehicle instrumented with a portable emissions monitor. Emissions were measured along two corridors in Woodbury, Minnesota, with two drivers. The corridor at Bailey Road had two four-way stop-controlled intersections, one roundabout, and one signal. The corridor at Lake Road had one four-way stop, one roundabout, and one signal. Intersection spacing and traffic parameters were similar along each corridor. Emissions were compared for sections 750 ft upstream and 750 ft downstream of each intersection. Traffic conditions during data collection along both corridors were uncongested. Average emission rate by driver was compared for the three types of traffic control. Results suggested that emissions were highly dependent on driver behavior. The roundabouts did not necessarily have lower emissions than traditional stop or signal control.

Evaluation of Iowa's graduated driver's licensing program

The effectiveness of Iowas graduated drivers licensing (GDL) program was evaluated for a 4-year period before and after implementation in 1999. Since some changes had occurred in the crash reporting format, changes in crash rates for younger drivers were compared to those for 35-44-year-old drivers (middle-age group of drivers) who were used as a control group. After implementation of GDL, the 14-, 16- and 17-year-old age groups experienced a greater decrease in crash rate than the middle-age control group while 15-year-old experienced a smaller decrease. This suggests that the crash rate for 15-year-old drivers may actually have increased when downward trends were adjusted for. Iowas GDL program allows holders of the instruction permit to travel unaccompanied to and from school and school-endorsed activities after obtaining a minor school license. Fifteen-year-old with minor school licenses account for up to 26.7% of 15-year-old license holders yet represent up to 74.8% of 15-year-old drivers involved in crashes (depending on the year) from 1998 to 2004. As a result, 15-year-old drivers with minor school licenses are involved in 7.2-8.9 times more crashes, are 7.7 times more likely to have one or more sanctions, and are 4.8 times more likely to receive one or more moving convictions than their peers with a regular instruction permit. This help may explain why 15-year-old drivers did not seem to benefit from implementation of the GDL program in Iowa.

Investigation of Violation Reduction at Intersection Approaches with Automated Red Light Running Enforcement Cameras in Clive, Iowa, Using a Cross-Sectional Analysis

Red light running causes more than 100,000 crashes and 1,000 fatalities annually and results in an estimated economic loss of over