Using Seasonal and Day-of-Week Factoring to Improve Estimates of Truck Vehicle Miles Traveled

Weinblatt, Herbert

doi:10.3141/1522-01

Cited by 7 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This procedure will identify the most appropriate buffer zone size and model for a particular type of roadway. In order to reduce the prediction error and maximize the correlation between the prediction variables and the predicted truck volumes, the dataset was clustered into six subsets (Table 2) according to the functional class (FC) of the roadway (Weinblatt 1996).…”

Section: Case Studymentioning

confidence: 99%

Truck Volume Estimation via Linear Regression Under Limited Data

Boilé¹,

Golias²

2010

J Transp Res Forum

View full text Add to dashboard Cite

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models.

show abstract

Section: Case Studymentioning

confidence: 99%

Truck Volume Estimation via Linear Regression Under Limited Data

Boilé¹,

Golias²

2010

J Transp Res Forum

View full text Add to dashboard Cite

show abstract

“…Resource constraints, however, make it impractical for the collection of traffic count data on all sections of interest. Hence, data is collected continuously at a limited number of count locations while other locations are counted only at infrequent intervals such as once every 3 years for relatively short durations usually 24 or 48 hours [2]. To account for the temporal variations in short-duration traffic counts, data from sites that are counted continuously are used to develop adjustment factors for factoring short-duration counts to estimates of annual average daily traffic (AADT).…”

Section: Traffic Count Based Methodsmentioning

confidence: 99%

“…The U.S. economy thrives significantly on freight transportation, which takes place mostly by means of trucks [9]. Estimates of truck VMT are therefore necessary to understand the importance of trucks to the nation's economy and to evaluate the costs and benefits of potential changes in truck regulation [2]. Trucks have characteristics that differ from passenger vehicles.…”

Section: Need For Researchmentioning

confidence: 99%

“…week rather than factors that only reflect variations in total traffic that exhibits much less day-of-week variation [2]. In addition, he reports that the "Scheme F" classification algorithm used by most automatic vehicle classifiers (AVCs) produces significant undercounts of the number of six-tire vehicles.…”

Section: Other Research Findingsmentioning

confidence: 99%

“…Local governments and Environmental Protection Agencies (EPAs) use statewide VMT for air quality analysis (vehicle emissions estimation) to determine compliance with the Clean Air Act Amendments (CAAA) of 1990. VMT estimates by vehicle class are required to derive accident rates by vehicle class, compare accident rates across classes, and to allocate highway costs across vehicle classes [2]. For VMTrelated revenue, estimates of VMT by vehicle class is required for producing estimates of revenue forecasts for proposed new taxes, for equity analyses to determine tax payments by vehicle class; and for evasion analysis to determine revenue that should be collected for comparison with actual revenue collected [7].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Reducing uncertainty in the estimation of heavy truck Vehicle Miles Traveled (VMT)

Lamptey¹

View full text Add to dashboard Cite

Use of n-Fold Cross-Validation to Evaluate Three Methods to Calculate Heavy Truck Annual Average Daily Traffic and Vehicle Miles Traveled

Hallmark

Souleyrette

Lamptey

2007

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.

show abstract

Using Seasonal and Day-of-Week Factoring to Improve Estimates of Truck Vehicle Miles Traveled

Cited by 7 publications

References 0 publications

Truck Volume Estimation via Linear Regression Under Limited Data

Truck Volume Estimation via Linear Regression Under Limited Data

Reducing uncertainty in the estimation of heavy truck Vehicle Miles Traveled (VMT)

Use of n-Fold Cross-Validation to Evaluate Three Methods to Calculate Heavy Truck Annual Average Daily Traffic and Vehicle Miles Traveled

Contact Info

Product

Resources

About