William L Eisele

Archived Intelligent Transportation System Data Quality: Preliminary Analyses of San Antonio TransGuide Data

Described are three data quality attributes that are considered relevant to intelligent transportation system (ITS) data archiving: suspect or erroneous data, missing data, and data accuracy. Preliminary analyses of loop detector data from the TransGuide system in San Antonio were performed to identify the nature and extent of these data quality concerns in typical archived ITS data. The findings of the analyses indicated that missing data were inevitable, accounting for about one in five of all possible data records. Error detection rules were developed to screen for suspect or erroneous data, which accounted for only 1 percent of all possible data records. Baseline testing of TransGuide detector accuracy showed mixed results; one location collected traffic volumes within 5 percent of ground truth, whereas traffic volumes at another location ranged from 12 to 38 percent of ground truth. It was concluded that data quality procedures will be essential for realizing the full potential of archived ITS data.

Estimating the Safety and Operational Impact of Raised Medians and Driveway Density: Experiences from Texas and Oklahoma Case Studies

This paper describes research sponsored by the Texas Department of Transportation to investigate the operational and safety impact of raised medians and driveway consolidation. Operational effects (travel time, speed, and delay) were investigated through microsimulation on three field test corridors and three theoretical corridors. Safety effects were investigated along 11 test corridors to estimate relationships between crash rates and access point densities as well as the presence of raised medians or two-way left-turn lanes (TWLTLs). The research demonstrates that access management effects are case specific and that microsimulation can assess these unique operational effects. For the case studies investigated, replacing a TWLTL with a raised median resulted in an increase in travel time on two test corridors and a decrease on one test corridor. Small increases in travel time were found with the theoretical corridors as well. The travel time differences are based on the traffic level and location and number of the raised median openings. When present, the relatively small increases in travel time, and subsequent speed and delay, appear to be outweighed by the reduction in the number of conflict points and increased safety. Detailed crash analysis on 11 test corridors indicated that as access point density increases, crash rates increase. This trend holds regardless of the median type. For test corridors in which crash data were investigated before and after the raised median installation, a reduction in the crash rate was always found. Finally, future research needs are identified, including the need to investigate operational and safety impact over a broader range of geometric conditions and longer corridors than investigated here.

Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.

Using Intelligent Transportation Systems Travel-Time Data for Multimodal Analyses and System Monitoring

Intelligent transportation systems (ITS) technologies and infrastructure are a potentially rich travel-time data source for travel-time mean and variance estimates. ITS data traditionally have been deployed and used in real time for passenger cars. How ITS data can be used for multimodal analyses and system monitoring is examined. The methodology used is applicable to any detector technology. Automatic vehicle identification (AVI) data were collected along a 3.2-km (2-mi) segment of US-290 in Houston, Texas. Simultaneous instrumented test vehicles collected travel-time data, and commercial-vehicle travel-time data were collected by video. The nonparametric loess statistical procedure was used to estimate the travel-time distribution properties as a function of time of day. The first application presented investigates how well link travel times from AVI replicate travel conditions for commercial vehicles. During congested conditions, average differences in travel-time estimates of 6.4 percent were found, whereas percent differences in coefficient of variation (reliability) were 14.7 percent. The research concludes that it may be reasonable to provide real-time traffic maps specifically for commercial vehicles. The second application investigates the accuracy of the AVI data for system monitoring. Estimated mean differences between AVI data and test vehicles were small (0.8 percent), whereas the ratio of the mean to the standard deviation (coefficient of variation) was relatively high (37.6 percent) during congested conditions. The AVI data source is found to provide a very cost-effective data collection method with which to estimate mean travel time while increasing confidence in the estimate.

DEVELOPMENT OF INTELLIGENT TRANSPORTATION SYSTEM DATA MANAGEMENT

The intelligent transportation system (ITS) components deployed in U.S. urban areas produce vast amounts of data. These ITS data often are used for real-time operations and then are discarded. Few transportation management centers have any mechanism for sharing the data resources among other transportation groups or agencies within the same jurisdiction. Meanwhile, transportation analysts and researchers often struggle to obtain accurate, reliable data about existing transportation performance and patterns. The development of an ITS data management system (referred to as ITS DataLink) that is used to store, access, analyze, and present data from the TransGuide center in San Antonio, Texas, is presented. Data outputs are both tabular and graphical. No user costs are associated with the system except for an Internet connection.

Conceptual Framework and Trucking Application for Estimating Impact of Congestion on Freight

Few analytical techniques fully incorporate freight aspects into transportation system monitoring, system evaluation, and project selection. Transportation investment decisions are frequently based on typical performance measures of travel time and delay for passenger travel, and little, if any, attempt is made to incorporate goods movement into such analysis. Research was performed by the Texas Transportation Institute for a better understanding of freight mobility and reliability issues. The research developed a conceptual framework to help transportation professionals communicate, visualize, and understand factors affecting freight mobility and reliability; a methodology with which to estimate congestion for the conceptual framework; and two applications of the methodology to truck freight (one in Austin, Texas, and one in Denver, Colorado). The conceptual framework visually incorporates the effects of geographic area, commodity type, and time period on freight mobility and reliability. This framework provides a method to communicate freight congestion, mobility, and reliability and can be expanded to include all freight modes (truck, rail, maritime, air, and pipeline). The conceptual framework and methodology can help transportation professionals to better communicate, understand, and make planning-level decisions based on the factors that affect freight mobility and reliability.

LIFE-CYCLE GRAPHICAL REPRESENTATION OF MANAGED HIGH-OCCUPANCY VEHICLE LANE EVOLUTION

High-occupancy vehicle (HOV) lanes usually go through an evolution of stages in their life cycle. The typical evolution includes changes in demand levels from several modes including 2+ or 3+ carpools and vanpools, transit, and general-purpose vehicles. To ensure adequate usage, most facilities have started out with a designation of HOV2+. In some cases, over time, HOV2 volumes have exceeded the capacity of the facility, which has caused delays for transit vehicles. Therefore, there is an inevitable need for managing the hierarchy of facility users over time. A graphical tool is presented that indicates the life span of a managed HOV lane, and it can be applied to a variety of existing and planned managed HOV lane projects. The graphic was used in Colorado, Florida, and Texas in communicating the managed lane concept to transportation professionals. Further, the graphic was used to explain the historical operation of a managed HOV lane facility and the likely progression if current management policies remain in effect, based on experiences in similar facilities. Alternative management strategies can also be evaluated and compared with the graphical tool. The graphical representation of this managed HOV lane concept is anticipated to be valuable for transportation professionals in many areas (e.g., highway, tolling, and transit) in presenting and understanding operating scenarios for managed lanes over time and how they meet the goals of the facility. Applications of the life-cycle graphic to various facilities in the United States are also presented.

Developing a Total Peak Period Travel Time Performance Measure

Transportation performance measures based on travel time quantities satisfy a range of mobility purposes. The measures can show the effect of many transportation and land use solutions, and they are relatively easy to communicate to a range of audiences. The concept of total travel time has been discussed since the early 1950s, but because of data inaccessibility, the planning community has rarely used total travel time as a measure. For the initial implementation of the total peak period travel time measure in the Urban Mobility Report, data from the reports primary data sets were combined in a new way to estimate road users’ total travel time during the peak period. Data shortcomings were addressed with simplifying assumptions to create a calculation method that would offer a more refined value than would the use of raw or incomplete data. Total peak period travel time can provide additional explanatory power to a set of mobility performance measures and bridge the gap between traditional delay-based measurement and accessibility.

Safety and Economic Impacts of Converting Two-Way Frontage Roads to One-Way Operation

In some states (e.g., Texas), frontage roads have been a design solution for providing access along rural freeways and access-controlled principal arterials. In rural and lesser-developed urban areas, the frontage roads are usually operated as two-way facilities because of relatively long distances between interchanges. As areas become more urban and the adjacent land is developed, traffic volumes increase, and as interchange spacing decreases, it becomes desirable to convert the frontage roads to one-way operation. There is a need to objectify safety impacts of frontage road conversion, and business and property owners are often concerned with economic impacts related to access, business activity, and property values. Recognizing these needs and concerns, the Texas Department of Transportation contracted with the Texas A&M Transportation Institute to investigate the safety and economic impacts of converting two-way frontage roads to one way. Researchers investigated eight sites throughout Texas: both conversion sites and comparison sites that remained two way. Researchers developed 12 crash modification factors by crash severity and crash type for frontage road conversion. A sample application is provided in this paper. To assess economic impacts, researchers obtained parcel-level appraisal data and found overall increases in appraised values. Researchers surveyed business owners and managers and customers. Researchers found that business owners and managers are typically concerned with access, gross sales, and ramp and interchange locations and spacing. Finally, researchers identified lessons learned with the crash data for those performing safety analyses.

Developing and Applying Models for Estimating Arterial Corridor Travel Time Index for Transportation Planning in Small to Medium-Sized Communities

This paper describes research with the objective of developing and validating a corridor arterial model to estimate the travel time index (TTI) for mobility analysis. The TTI is the ratio of the travel rate (minutes per mile) during the peak period to the travel rate (minutes per mile) during the off-peak period. The models are most useful for sketch planning purposes in areas where monitoring infrastructure is not in place. Practitioners in small and medium-sized communities, particularly those on the developing fringe of such communities, will benefit from the use of the models for tracking mobility along roadways of interest and prioritizing roadway improvements. This paper describes the development and validation of two models to assist transportation professionals in estimating TTI in the arterial environment during light and moderate congestion conditions. A 2.6-mi major arterial corridor in College Station, Texas, was used to develop the models, and three study corridors in Virginia were used to validate the models. To address limitations of existing models, the models presented in this paper (a) consider driveway density, (b) are corridor-based, (c) are a function of generally available or easy-to-obtain independent variables, (d) are calibrated and validated with extensive field data, and (e) explain a relatively high degree of variability. The use of TTI also makes the models more transferable. For sketch-planning applications, the models are a function of relatively available or easy-to-estimate data including traffic volume, driveway density, signal green time relative to the cycle time, and signal coordination conditions.