Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Tsapakis, Ioannis; Schneider, William H.; Nichols, Andrew P; Haworth, James

doi:10.1002/atr.1186

Cited by 5 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both criteria are expressed as a percentage of the actual realization, which makes them proper to use when reporting the model accuracy and is easy to interpret. They are also widely used in the literature .…”

Section: Resultsmentioning

confidence: 99%

“…We forecast for one‐period, two‐period, three‐period and four‐period ahead. Tsapakis et al mentioned that the hourly time‐of‐day factors are more important than the average daily traffic in the assignment process. Therefore, we also focus on small time slots more than average daily traffic.…”

Section: Resultsmentioning

confidence: 99%

“…They developed a stochastic equilibrium formulation to analyze different cases including deterministic travel state, emergent event, peak travel, and completely stochastic state. Tsapakis proposed the traditional functional classification, a discriminant analysis and a weighted coefficient of variation to assign the short‐term counts to seasonal adjustment factor for the total volume and the directional specific volumes of a site. The total volume includes travel volume in two directions, while the directional volume analysis is also included.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They found the unobserved component model to be a better approach because it diagnoses the trend and seasonal components over time and is flexible to changes on omitted variables and consumer preferences. Most of transportation departments in the USA use the Drusch traditional method to estimate the annual average daily traffic ]. This model uses the traffic data collected continuously by automatic traffic recorders located on major roads.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Estimation of the annual average daily traffic is also interesting for transportation experts and departments because the future knowledge leads to better performance . Trip generation is the first step of urban transportation modeling systems to do travel forecasts in metropolitan regions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems

Anvari

Tuna

Çancı

et al. 2015

J of Advced Transportation

View full text Add to dashboard Cite

SUMMARYEfficient management of public transportation systems is one of the most important requirements in rapidly urbanizing world. Forecasting the demand for transportation is critical in planning and scheduling efficient operations by transportation systems managers. In this paper, a time series forecasting framework based on Box-Jenkins method is developed for public transportation systems. We present a framework that is comprehensive, automated, accurate, and fast. Moreover, it is applicable to any time series forecasting problem regardless of the application sector. It substitutes the human judgment with a combination of statistical tests, simplifies the time-consuming model selection part with enumeration, and it applies a number of comprehensive tests to select an accurate model. We implemented all steps of the proposed framework in MATLAB as a comprehensive forecasting tool. We tested our model on real passenger traffic data from Istanbul Metro. The numerical tests show the proposed framework is very effective and gives higher accuracy than the other models that have been used in many studies in the literature.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems

Anvari

Tuna

Çancı

et al. 2015

J of Advced Transportation

View full text Add to dashboard Cite

show abstract

Estimating vehicle miles traveled (VMT) in urban areas using regression kriging

Kim

Park

Heo

et al. 2016

J of Advced Transportation

View full text Add to dashboard Cite

The recent increase in demand for performance-driven and outcome-based transportation planning makes accurate and reliable performance measures essential. Vehicle miles traveled (VMT), the total miles traveled by all vehicles on roadways, has been utilized widely as a proxy for traffic impact assessment, vehicle emissions, gasoline consumption, and crashes. Accordingly, a number of studies estimate VMT using diverse data sources. This study estimates VMT in the urban area of Bucheon, South Korea, by predicting the annual average daily traffic for unmeasured locations using spatial interpolation techniques (i.e., regression kriging and linear regression). The predictive performance of this method is compared with that of the existing Highway Performance Monitoring System (HPMS) method. The results show that regression kriging could provide more accurate VMT estimates than the HPMS method and linear regression, especially with a small sample size. additional control variables. Pathomsiri et al. [16] develop an econometric model for estimating VMT in multivehicle households and apply it to the 2001 National Household Transportation Survey. Erlbaum [17] applied the fuel-sales-based method to estimate VMT. White [18] and Greene [19] used odometer reading data to estimate annual VMT by driver. However, the previously mentioned methods, based on household activity surveys, fuel sales, and odometer reading data, are too resource-intensive and costly to perform regularly. Also, these methods reflect personal travel and do not reflect the total number of vehicles on the road.Traffic-count-based VMT estimation methods are currently the most commonly performed and preferred method because they are based on actual data for vehicle movement [20]. The predictive accuracy of traffic-count-based methods depends on the quality and coverage of traffic count data, given that the length of all road network sections is known. Thus, it can be said that if traffic counts were available for all roads in a network, the VMT estimate would be the most accurate measure of vehicle movement. Traffic counts are, however, only available at segments of road networks where there is a count station because of the relatively high cost of the stations. For urban areas, traffic count data are collected less frequently than in other areas because of the complexity of measurement [21]. For this reason, the Highway Performance Monitoring System (HPMS) method of VMT estimation, a representative traffic-count-based method performed in the USA, simply extrapolates the VMT of a sample section into other sections, so long as the other sections are in the same strata of traffic volume group and road functional system [20]. Despite the advantages of being a relatively simple and quick procedure, the HPMS method has been criticized by its accuracy and sample size demand needed to achieve the required precision level. Moreover, stratification of sampling by traffic volume group requires knowledge of the traffic count information on all roads [14]. A number of rec...

show abstract

Exploring vehicle probe data as a resource to enhance network-wide traffic volume estimates

et al. 2022

View full text Add to dashboard Cite

Annual average daily traffic (AADT) is a fundamental input for numerous civil engineering applications, yet generating reliable estimates of AADT at a network-wide level poses challenges. This article explores the potential use of vehicle probe data to enhance conventional traffic monitoring practice for generating network-wide estimates of AADT by exploring relationships between site-specific traffic volume data and vehicle probe data collected in Manitoba, Canada. The analysis revealed that mean travel speed cannot be used to predict traffic volumes on Manitoba highways, since the mean travel speed did not deviate from the free-flow speed regardless of the volume measured. The quantity of probe data observations showed moderate correlation with traffic volume at some sites (R-squared up to 0.65), but these correlations were stronger (R-squared up to 0.9) when considering trucks only. These findings suggest that probe data could be used to estimate truck volumes at certain locations.

show abstract

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Cited by 5 publications

References 22 publications

Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems

Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems

Estimating vehicle miles traveled (VMT) in urban areas using regression kriging

Exploring vehicle probe data as a resource to enhance network-wide traffic volume estimates

Contact Info

Product

Resources

About