Sara Khoeini

Traffic Monitoring of Motorcycles During Special Events Using Video Detection

Because of a recent federal initiative, states are required to submit motorcycle vehicle miles traveled data to the FHWA. Data are needed to obtain better counts of motorcycles to evaluate their impact on crashes and traffic flow, but there is concern about the quality of data. Many states have identified problems with automatic traffic recorders accounting for motorcycle traffic. Existing sensors exhibit difficulties counting motorcycles that travel side by side or close behind each other, they have difficulty distinguishing larger motorcycles from passenger vehicles, and magnetic counters in particular do not sense motorcycles that do not pass over or travel close enough to sensors. Alternatively, some states conduct manual classification counts, which are labor intensive and lead to sparse data. Because classification counts are frequently conducted during the week, they do not capture weekend numbers. This paper evaluates a video-based traffic monitoring system that was developed at Clemson University and can classify vehicles, including motorcycles. The processor uses vehicle tracking, rather than virtual detection, to collect vehicle count, speed, and classification data. An algorithm calculates a motorcycles length, width, and height through a series of frames. The system is evaluated by using traffic data for more than 2,000 motorcycles, collected at two locations in Myrtle Beach, South Carolina, during a motorcycle rally. The difference between actual and system motorcycle counts ranged from 0.6% to just over 6%, depending on direction and location. The difference for all vehicles ranged from 0.25% to 3.6%. While the system successfully classifies motorcycles traveling in close pairs and in small groups, it experienced difficulty in cases of severe occlusion.

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.

I-85 High-Occupancy Toll Lane’s Impact on Commuter Bus and Vanpool Occupancy in Atlanta, Georgia

In October 2011, Atlanta, Georgia, opened its first high-occupancy toll (HOT) lanes, which were converted from high-occupancy vehicle (HOV) lanes. In partnership with the Georgia Department of Transportation, the Georgia Institute of Technology established a research team to assess changes in vehicle throughput, vehicle occupancy, and passenger throughput associated with the I-85 HOV-to-HOT lane conversion. For the assessment of these measures, commuter bus ridership, which carries a significant portion of ridership, could not be collected through the applied efforts to collect field data. Moreover, the effects of ridership and vehicle throughput on vanpools, which also use the managed lanes, are unknown. The purpose of this research was to estimate the change in vehicle and person throughput of alternative modes before and after the HOV-to-HOT lane conversion. The results indicate that person throughput remained relatively stable for commuter buses, even with an increase in vehicle throughput. The vehicle throughput of vanpools was not substantial and increased slightly after the conversion. The commuter bus results were unexpected, as ridership was expected to increase because of the related travel time saving and reliability. Behavioral research is needed to understand the underlying effects of ridership to separate the underlying effects from external factors such as gas prices, travel times, employment, and others.

Socioeconomic Assessment: Conversion of I-85 High-Occupancy Vehicle to High-Occupancy Toll in Atlanta, Georgia

High-occupancy toll (HOT) lanes use dynamic pricing to manage demand for lane use and to maintain acceptable traffic flow and traffic speeds. However, minimum occupancy and toll payment requirements raise potential socioeconomic questions concerning disparate negative effects across demographic groups. The objective of this study was to improve the knowledge about socioeconomic aspects of congestion pricing by using the I-85 high-occupancy vehicle (HOV) to HOT conversion in Atlanta, Georgia, as a case study. To evaluate the effects across user groups, more than 1.5 million license plate records were collected during a 2–year period, 1 year before and 1 year after the conversion. Analyses compared collected records with state motor vehicle registration databases to identify the vehicles and link census block group level and marketing household level socioeconomic attributes. In addition, results of a 2-day travel diary survey conducted by Volpe 6 months before and 6 months after the conversion were assessed. This study used all three sources of data in parallel to undertake a socioeconomic evaluation of the HOV-to-HOT conversion. Whereas previous studies were built on only one data source with significantly smaller sample sizes, the use of three distinctive sources of socioeconomic data and an exceptionally large sample size advanced the understanding of the potential socioeconomic effects of managed lanes. Furthermore, with the noted advantages and disadvantages of these data sources, this study provided valuable insight for general demographic analysts.

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.