Vetri Elango

Georgia’s Commute Atlanta Value Pricing Program: Recruitment Methods and Travel Diary Response Rates

The Commute Atlanta program is an instrumented vehicle research program funded by the FHWA Value Pricing Program and the Georgia Department of Transportation. A major objective for the multiyear program is to assess the effects of converting fixed automotive costs into variable driving costs. The main research hypothesis is that given a per mile pricing system, participants will modify their driving patterns in an effort to reduce their total costs, pocketing the savings. The Commute Atlanta project includes the parallel collection of instrumented vehicle data, household sociodemographic surveys, 2-day travel diaries, and employer commute options surveys. The research team will monitor the changes in driving patterns and will use statistical analyses of household characteristics, vehicle travel, and relevant employer survey data to examine the relationships between the incentives offered and subsequent travel behavior changes. This paper focuses on the recruitment methods and travel diary response rates for the 2-day diary surveys conducted in February and March 2004. As in other instrumented vehicle studies, researchers collected data that allow the comparison of reported diary travel with monitored vehicle travel. However, this paper focuses on a new type of comparison. Because the households had been recruited into the study 8 months before the diary study and their vehicles were transmitting activity data, the research team could examine whether there were differences in household vehicle activity between that 77% of households that completed the diary data collection and the 23% that did not. The differences were significant at both the high and low ends of the travel-reporting spectrum and may have some major implications for evolving household travel survey methods.

Day-to-Day Travel Variability in the Commute Atlanta, Georgia, Study

Traditional travel diary surveys collect 1 or 2 days of travel data from participant households. Although useful in determining the overall average travel behavior of a regional population, cross-sectional travel diary surveys provide little insight into intrahousehold and intraperson trip variability. Longitudinal surveys are generally preferred for examining travel variability. The objective of the research is to examine the intrahousehold travel variability in the Commute Atlanta, Georgia, study, a Global Positioning System–based instrumented-vehicle monitoring study that collected vehicle trips from approximately 500 vehicles in 260 representative households. The research effort uses 2004 travel data collected for the Commute Atlanta study, in which the average variability or deviation in the number of trips by a household was 3 trips/day. Demographic variables (e.g., household size, household income, vehicle ownership, number of children, number of workers, and number of students) significantly affect the day-to-day variability in the total number of household trips per day. The variability due to seasonal effects is controlled by separately analyzing travel data during specific months in spring, summer, and fall. Results indicate that demographic variables have a significant effect on the day-to-day variability of the household number of trips when the variability associated with seasonal effects is excluded. Vehicles identified by participants as being used always or occasionally for business or commercial purposes have travel patterns different from those of other vehicles, and their presence in the sample will significantly bias analytical results in the analysis of longitudinal data. Commercial use vehicles are excluded from travel variability analysis, and the argument is made that households with such vehicles must be treated as an independent sample in future travel diary data collection and longitudinal studies.

Idle Monitoring, Real-Time Intervention, and Emission Reductions from Cobb County, Georgia, School Buses

Georgia Institute of Technology researchers developed an idle detection and warning notification system that features Global Positioning System–based real-time tracking and a web-based user interface. Four hundred and eighty buses in the Cobb County (Georgia) School District were equipped with the idle detection system, and the research team provided bus dispatchers with a web-based system to track vehicle activity and provide notification of idle events exceeding 5 min. The idle detection and warning notification system can differentiate idling with engine on from key-on events with engine off, an important capability that sets it apart from previous systems that only detected key-on events. Idle reductions were monitored, and emissions and fuel savings were evaluated with the Environmental Protection Agencys MOVES (Motor Vehicle Emission Simulator) model. The idle reduction that resulted from implementing the system was statistically significant—more than 6 min of idle reduction per bus per day. Greater idle reduction could be achieved with more stringent implementation of the system. The anti-idle program reduced total annual emissions of criteria pollutants (oxides of nitrogen, particulate matter, and carbon monoxide) by 1.82 tons and annual emissions of carbon dioxide by 53.3 tons. Implementation throughout the school district would conserve 6,400 gal of diesel fuel. Approximately 41,100 children riding the buses or attending schools served by the buses were positively affected by the idle reduction system.

Sensitivity of Commuters’ Demographic Characteristics to License Plate Data Collection Specifications: Case Study of I-85 High-Occupancy Vehicle to High-Occupancy Toll Lanes Conversion in Atlanta, Georgia

The demographic characteristics of commuters are significant determining factors in many transportation-related policy and planning decisions. A popular way of obtaining the demographic characteristics of roadway system commuters is through license plate studies. There is a concern, however, that data collection factors including time, day, and location of data collection can potentially affect the observed demographic characteristics of the collected samples. This study uses statistical tests to assess the sensitivity of observed demographic characteristics from license plate data collection to data collection parameters by using data collected in the I-85 high-occupancy vehicle corridor in the Atlanta, Georgia, metropolitan area.

MOBILE Matrix: Application of Georgia Statewide Multimodal Transportation Planning Tool for Rural Areas

The U.S. Environmental Protection Agencys MOBILE emission rate model is used to evaluate carbon monoxide, hydrocarbon, nitrogen oxide, and particulate matter impacts of major transportation projects. Using fleet characteristics, environmental conditions, and on-road operating conditions, MOBILE estimates fleet average emission rates. Coupling projected traffic volumes and physical roadway characteristics yields pollutant mass flux rates with which dispersion models predict downwind pollutant concentrations. Distance-flux relationships may also prove useful in pollutant exposure modeling to help quantify relative environmental risks of living or working near major roadways. Applying the MOBILE model requires the creation and execution of scenario-specific input files, which must be properly structured. Researchers developed the MOBILE-matrix model to facilitate more readily the use of MOBILE emission rates in transportation modeling. The matrix creates large emission rate lookup tables that can be applied to traffic in any facility or subregion. It performs all necessary model runs well before a future modeling need. The goal of this specific application is to integrate MOBILE-matrix capabilities into the multimodal transportation planning tool (MTPT) employed by the Georgia Department of Transportation so that it can be used to estimate emissions from rural road facilities throughout the state. Thousands of MOBILE6.2 runs were executed to generate database output files, which were post-processed to create MOBILE-matrix lookup tables for 159 Georgia counties. For each transportation link in the MTPT, emission rates are extracted from the lookup matrix by facility type, average speed, temperature, and so forth. The predicted emissions can then be used for a variety of transportation planning purposes.

Tablet-Based Traffic Counting Application Designed to Minimize Human Error

Basic traffic counts are among the key elements in transportation planning and forecasting. As emerging data collection technologies proliferate, the availability of traffic count data will expand by orders of magnitude. However, availability of data does not always guarantee data accuracy, and it is essential that observed data are compared with ground truth data. Little research or guidance is available that ensures the quality of ground truth data with which the count results of automated technologies can be compared. To address the issue of ground truth data based on manual counts, a manual traffic counting application was developed for an Android tablet. Unlike other manual count applications, this application allows data collectors to replay and toggle through the video in supervisory mode to review and correct counts made in the first pass. For system verification, the review function of the application was used to count and recount freeway traffic in videos from the Atlanta, Georgia, metropolitan area. Initial counts and reviewed counts were compared, and improvements in count accuracy were assessed. The results indicated the benefit of the review process and suggested that this application could minimize human error and provide more accurate ground truth traffic count data for use in transportation planning applications and for model verification.

Load-Based Life Cycle Greenhouse Gas Emissions Calculator for Transit Buses: An Atlanta, GA, Case Study

Using the Public Transit Greenhouse Gas (GHG) Emissions Management Calculator (hereafter the Calculator), this paper presents a case study of transit bus GHG emissions using Atlanta, Georgia, data. The Calculator, developed by Georgia Tech researchers, is the first load-based life cycle emissions model for transit buses. The modal modeling approach of the Calculator estimates emissions as an indirect function of engine load, which in turn is a function of transit service parameters such as driving cycle (idling and speed-acceleration profile), road grade, and passenger loading. Direct emissions are calculated based on the scaled tractive power (STP) operating mode bins employed in the Motor Vehicle Emissions Simulator (MOVES) model, and life cycle emissions are calculated using the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The case study compares life cycle greenhouse gas emissions of five vehicle technologies, conventional compression ignition, parallel hybrid electric, series hybrid electric, battery electric, and fuel-cell electric, in combination with three fuel types, conventional diesel, compressed natural gas (CNG), and 20% biodiesel. The comparisons are carried out for two public transit route types, e.g. an urban transit route vs. an express bus route. The Atlanta case study showcases the practice-ready capabilities of the GHG emissions calculator in assessing the differences in technology and fuel performances under different operating conditions. The results illustrate that the decision as to which bus technology-fuel combination produces the least greenhouse gas emissions is a function of location and route characteristics. The Calculator will support transit agencies in evaluating bus technologies for GHG emissions within the context of local conditions.

Longitudinal Global Positioning System Travel Data and Breach of Privacy via Enhanced Spatial and Demographic Analysis

Longitudinal Global Positioning System (GPS) travel data provide a wealth of information related to travel behavior and on-road vehicle behavior that is very valuable to researchers. Sharing the data publicly allows researchers to explore the data and create new knowledge beyond the initial research objectives. However, if any data are to be used outside a secure server, the data must be processed in such a manner that ensures that the confidentiality of the data will not be breached. High-resolution GPS data (e.g., second-by-second speed and location information), when associated with the individual households or drivers, compromise privacy and have a significant potential to harm human subjects. This paper explores how data from the Commute Atlanta study in Georgia could be processed to make it useful to researchers while participants’ privacy is protected. The research developed and assessed methodologies designed to identify the individual participants home location from processed data and then tested analytical data sets for breach of privacy. The research effort found that the home location could be identified to within reasonably small neighborhoods; when the household demographic information was included in the data sets (which was necessary for researchers), exact households could be identified. Although some new data-processing approaches might be used to eliminate privacy concerns, until such systems are developed and proved to be unbreachable through rigorous analysis, the Georgia Institute of Technology team has determined that researchers should access the high-resolution data in controlled secure labs and that the data sets should not be made public without additional efforts to ensure that home locations cannot be identified when external data sources are leveraged in the analyses.

Collection, Screening, and Evaluation of Vehicle Occupancy Data

In 2008, the Georgia State Road and Tollway Authority and the Georgia Department of Transportation successfully applied to the U.S. Department of Transportation for seed funding under the Congestion Reduction Demonstration Program Grant to convert the congested I-85 high-occupancy-vehicle (HOV) lane to a high-occupancy-toll (HOT) lane. The facility was converted into a HOT lane and opened on October 1, 2011. This paper reports on the collection, processing, and filtering of the vehicle occupancy data and the factors that influenced vehicle occupancy along the I-85 corridor both before and after the conversion. The research effort used regression tree analysis techniques to identify data collector bias and then the effects of different factors such as season, data collection site, morning peak–evening peak, and lane type on vehicle occupancy. The study found that vehicle occupancy data on Monday were different from those data collected on Tuesdays, Wednesdays, and Thursdays. The study identified data collectors and sessions that had statistically different data from other sessions and filtered those data. The research effort then explored the factors affecting vehicle occupancy for the morning peak and the afternoon peak periods separately, given that related travel behavior characteristics were quite different. The research identified lane type (managed or general-purpose) as the most significant factor affecting occupancy, followed by pre- and postconversion of the HOV lane to HOT lane. The study also found that average vehicle occupancy decreased after conversion of the HOV lane. On the basis of the results of this study, the vehicle occupancy data were aggregated into the center stations, north of the GA-316 region and south of the I-285 region. The vehicle occupancy data are currently being applied to evaluate person throughput along the corridor.

Load-Based Life-Cycle Greenhouse Gas Emissions Calculator: Running Emissions Sensitivity Analysis

This paper describes a running emissions sensitivity analysis of the U.S. Environmental Protection Agencys (EPAs) MOVES2010b model at the project level through the implementation of the Public Transit Greenhouse Gas (GHG) Emissions Management Calculator. MOVES model running emissions for diesel transit buses were estimated for 17 heavy-duty transit bus driving cycles across 12 different locations and meteorological conditions. The preliminary results examine the estimated annual diesel transit bus NOX and CO2 emissions. With an understanding of the scaled tractive power operating mode bin distribution of a driving cycle and the MOVES model emission rates, the annual emissions can be analyzed and compared. The results obtained from this sensitivity analysis indicate the potential usefulness of the Public Transit GHG Emissions Management Calculator for use in evaluating emissions across driving cycles and environmental operating conditions in a comparative mode.