Patricia S Hu

Transferability of Nationwide Personal Transportation Survey Data to Regional and Local Scales

The development of a system for using Nationwide Personal Transportation Survey (NPTS) data to estimate regional or local travel behavior—vehicle and person trips and miles of travel—is detailed. This system can be used by state or municipal transportation planners. The census tracts were classified into groups, or clusters, that tend to be homogeneous for individual travel behavior. These census tract clusters were based on household income, employment rate, number of household vehicles, and area type (urban, suburban, or rural). NPTS data were used to estimate driving characteristics for each of the clusters derived in the classification step. How well the goal of estimating regional or local travel characteristics was achieved by using standards computed from an independent survey from Baton Rouge, Louisiana, and by using independent data from three add-on components of the NPTS itself was assessed. Estimates computed from the NPTS data by using the census tract cluster method were compared with estimates computed from the standards and with estimates computed from the NPTS data by using competing methods in which households were classified by size of the metropolitan statistical area, census division, or census region. It was found that in most cases the census tract clustering method predicted travel better than the other methods, with small sample sizes generally being the cause when the census tract method was not the best.

Variability in traffic monitoring data. Final summary report

For highway maintenance and planning purposes, it is desirable to characterize each road segment by its traffic flow [such as the annual average daily traffic (AADT) and the AADT for each vehicle class], by the weight distribution of vehicles that travel on its roads [such as the annual average daily equivalent single axle loadings (ESAL) and the annual average daily weight per vehicle for each vehicle class]. As with almost any data collection effort, the monitoring data suffer from errors from many sources. This report summarizes results of a two year empirical research effort, which was sponsored by the Federal highway Administration, (i) to study and characterize the variability in the traffic data (volume, classification, and weight) from the continuously monitored road segments, and (ii) to study the extent to which this variability is transferred to, and affects the precision of the data produced form the road segments which are monitored only one or two days each year. The ultimate hope is not only that states will eventually be able to publish an estimate of a characteristic such as AADT for each road segment, but also that each estimate will be accompanied by a statement of how good the estimate is in terms of the estimated variability or precision which will likely be experienced as a coefficient of variation (i.e., the quotient of a standard deviation and a mean). This report provides highlights of research reported in five working papers.

TRAFFIC COUNT ESTIMATES FOR SHORT-TERM TRAFFIC MONITORING SITES: SIMULATION STUDY

Traffic characteristics, such as the annual average daily traffic (AADT) and the AADT for each vehicle class, are essential for highway maintenance and planning. In practice, selected road segments are monitored continuously every day of the year to identify their traffic characteristics. A sample of the remaining road segments is monitored for 1 or 2 d each year, and the resulting data are adjusted (by using factors based on data collected from the continuously monitored road segments) to produce estimates of annual average daily traffic characteristics. A simulation study empirically considered how the precision of an estimate from a continuously monitored site compares with the precision of an estimate from a short-term monitored site. The original estimates of traffic characteristics (i.e., AADT and AADT by vehicle class) treating the site as a continuously monitored site are on average quite close to, but smaller than, the simulated estimates treating the site as a short-term monitored site. The original estimates (continuous monitoring) appear to be more precise, on average, than the simulated estimates (short-term monitoring). This decrease in precision typically occurs for vehicle classes that account for less than 1 percent of the daily traffic volume, suggesting that these less-common vehicle classes could be combined to achieve reliable AADT estimates.

MIXED GENERALIZED LINEAR MODEL FOR ESTIMATING HOUSEHOLD TRIP PRODUCTION

An innovative modeling framework to estimate household trip rates using 1995 Nationwide Personal Transportation Survey data is presented. A generalized linear model with a mixture of negative binomial probability distribution functions was developed on the basis of characteristics observed from the empirical distribution of household daily trips. This model provides a more flexible framework and a better model specification for analyzing household-specific trip production behavior. Compared with traditional least squares-based regression models, the parameter estimates from the proposed model are more efficient. Although the mean accuracies from the two modeling approaches are comparable, the mixed generalized linear model is more robust in identifying outliers due to its unsymmetric prediction bounds derived from more correct model specification.

FUEL USED FOR OFF-ROAD RECREATION: A REASSESSMENT OF THE FUEL USE MODEL

The Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) established a National Recreational Trails Funding Program and the National Recreational Trails Trust Fund. ISTEA required that certain tax revenue generated from the sales of motor fuel used for off-road recreation be transferred from the Highway Trust Funds to the Trails Trust Fund for recreational trail and facility improvements. In order to apportion the Trails Trust Fund to individual States equitably, the Federal Highway Administration (FHWA) asked the Oak Ridge National Laboratory (ORNL) in 1993 to estimate the amount of motor fuel used for off-road recreation in the State level by different vehicle types. A modification of the methodology developed by ORNL has been used to apportion funds to the States since that time.

Statistical Data Filtering and Aggregation to Hour Totals of Intelligent Transportation System 30-s and 5-min Vehicle Counts

For traffic analysts who rely on traditional traffic monitoring data from state and local transportation departments, Intelligent Transportation System (ITS) count, speed, and occupancy data are a largely untapped resource. As with most data, however, ITS data are not immune to outliers, missing values, and other anomalies, which can be time-consuming and costly in data analysis. Furthermore, because ITS data are recorded at intervals as short as 20 s, the tasks of archiving and disseminating them are substantial. Therefore, before ITS data are applied in traditional traffic analysis problems, and before archiving and disseminating them, their fitness for traditional uses should be demonstrated, and methods should be established for ensuring their fitness. Traffic counts from ITS installations in Orlando, Florida, and Long Island, New York were examined. Methods were developed for (a) filtering out data values beyond credible limits and runs of the same value too long to be credible and (b) dealing with zeros, which, ambiguously, indicate either missing values or actual zero counts. Because hour totals are often the focus in traditional traffic analyses, methods were also developed for aggregating the remaining counts to estimates of hour totals. Adjustments were made for the joint effect of missing values and trends within hours. Application of the methods to the Florida and Long Island ITS traffic counts and comparisons with hour counts from state departments of transportation demonstrated the feasibility of using ITS counts in traditional traffic analyses.

New York Household Travel Patterns: A Comparison Analysis

In 1969, the U. S. Department of Transportation began collecting detailed data on personal travel to address various transportation planning issues. These issues range from assessing transportation investment programs to developing new technologies to alleviate congestion. This 1969 survey was the birth of the Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990 and 1995. Longer-distance travel was collected in 1977 and 1995. In 2001, the survey was renamed to the National Household Travel Survey (NHTS) and collected both daily and longer-distance trips in one survey. In addition to the number of sample households that the national NPTS/NHTS survey allotted to New York State (NYS), the state procured an additional sample of households in both the 1995 and 2001 surveys. In the 1995 survey, NYS procured an addition sample of more than 9,000 households, increasing the final NY NPTS sample size to a total of 11,004 households. Again in 2001, NYS procured 12,000 additional sample households, increasing the final New York NHTS sample size to a total of 13,423 households with usable data. These additional sample households allowed NYS to address transportation planning issues pertinent to geographic areas significantly smaller than for what the national NPTS and NHTS data are intended. Specifically, these larger sample sizes enable detailed analysis of twelve individual Metropolitan Planning Organizations (MPOs). Furthermore, they allowed NYS to address trends in travel behavior over time. In this report, travel data for the entire NYS were compared to those of the rest of the country with respect to personal travel behavior and key travel determinants. The influence of New York City (NYC) data on the comparisons of the state of New York to the rest of the country was also examined. Moreover, the analysis examined the relationship between population density and travel patterns, and the similarities and differences among New York MPOs. The 1995 and 2001 survey data make it possible to examine and identify travel trends over time. This report does not address, however, the causes of the differences and/or trends.

Variability in continuous traffic monitoring data

Each state in the United States can be viewed as a universe of road segments. For each road segment in each state, it is desired to know various traffic characteristics based on count data, classification count data, and weigh-in-motion data. These data are absolutely essential for highway design, maintenance, safety, and planning. Given no cost constraints, each road segment would be continuously monitored every day of the year. However, in practice, a few road segments are monitored continuously every day of the year to produce annual characteristics of traffic flow. The remaining road segments are monitored for one or two days each year, and this resulting data are `adjusted` (using factors based on data collected from the continuously monitored road segments) to produce estimates of annual characteristics. With this general approach, each state strives to provide estimates of annual characteristics for each road segment within its jurisdiction. In 1985, the Federal Highway Administration (FHWA) published the Traffic Monitoring Guide to assist states in achieving this end. As with almost any data collection effort, the monitoring data suffers from errors from many sources. In this paper, we report some empirical findings in a research project sponsored by the FHWA. This research project studied the variability in the traffic data from the continuously monitored road segments from state(s) and, the extent to which this variability is transferred to and affects the precision of the data produced from the road segments which are monitored only one or two days each year. The ultimate hope is that states will eventually be able to not only publish an estimate of a characteristic such as Average Annual Daily Traffic (AADT) for each road segment, but also that each estimate will be accompanied by a statement expressing how good the estimate is in terms of its estimated variability or precision, which will likely be expressed as a coefficient of variation.

A study of interstate motor carrier vehicle miles of travel

Abstract This article summarizes the evaluation results of six data sources in terms of their ability to estimate the number of commercial trucks operating in interstate commerce and their vehicle miles of travel by carrier type and by state. The six data sources were: (a) Truck Inventory and Use Survey of the U.S. Bureau of the Census; (b) Nationwide Truck Activity and Commodity Survey of the U.S. Bureau of the Census; (c) National Truck Trip Information Survey of the University of Michigan Transportation Research Institute; (d) Highway Performance Monitoring System of the Federal Highway Administration, U.S. Department of Transportation; (e) International Registration Plan of the American Association of Motor Vehicle Administrators; and (f) State fuel tax reports from each individual state and the International Fuel Tax Agreement. Evaluation results concluded that none of the data sources by themselves were capable of providing reliable estimates at the state level. Although several attempts were made to combine the strengths of different data sources so that reliable estimates could be generated, none of them were successful. Data inconsistency and incompatibility contributed primarily to the failures. Although several of the six data sources by themselves could provide estimates at the national level, each had limitations. As a result of these findings, two cost-effective methodologies were proposed to estimate the number of commercial trucks operating in interstate commerce and their vehicle miles of travel by carrier type. Neither method required collecting additional data.

Road transport energy conservation in Costa Rica

Transportation is typically the largest petroleum-using sector in less developed countries and is generally dominated by road transport. Despite this fact, energy planning and conservation assistance have concentrated on other sectors, partly because of a lack of experience with transport-energy conservation in less developed countries. Studies of short-term energy conservation in taxi and bus fleets under actual operating conditions in San Jose, Costa Rica, have shown that efficiency improvements on the order of 10% are obtainable. Training in fuel-efficient driving strategies reduced taxi-fuel use by 14% and bus-fuel use by 3% on the average. Implementation of fuel-efficient maintenance practices reduced bus-fuel consumption by an average of 6%. Radial tires were ineffective and possibly counter-productive in saving fuel. Because of the complex experimental design, the results were not readily perceived by vehicle operators. Future demonstrations should concentrate on showing monetary savings to vehicle operators.