Cost-Effective Reporting of Travel on Local Roads

Blume, Kelly; Lombard, Marais; Quayle, Shaun; Worth, Phill; Zegeer, John

doi:10.1177/0361198105191700101

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…The stratification is generally based on one or more attributes like the functional class type (say, urban or rural local road) [44]. Agencies collect traffic volume data at selected locations in each stratum and consider that as a representative of all the roads within the stratum [46], [47], [48], [49].…”

Section: Literature Reviewmentioning

confidence: 99%

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One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation

Mathew,

Pulugurtha,

Bhure

et al. 2023

IEEE Access

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The focus of this research is to develop a robust model for accurately estimating link-level annual average daily traffic (AADT) of all the local functionally classified roads. The capabilities of onedimensional convolutional neural network (1D-CNN), a deep learning architecture, and the domain knowledge pertaining to local road travel characteristics were combined to estimate local road AADT. The AADT based on traffic counts collected at 12,899 stations on local roads in North Carolina during 2014, 2015, and 2016 were considered for model training, testing, and validation. A total of eight existing state-ofthe-art statistical, geospatial, and selected other machine learning models were compared with the 1D-CNN model to estimate local road AADT. These include ordinary least square (OLS) regression, geographically weighted regression (GWR), ordinary kriging, natural neighbor (NN) interpolation, inverse distance weighting (IDW), backpropagation artificial neural network (BP-ANN), random forest (RF), and support vector machine (SVM). The model development and test results showed that the 1D-CNN model performed better than the other considered models. The architecture of the 1D-CNN model can learn the intricate patterns in the local road AADT. The outputs from the methodological framework proposed in this research help practitioners perform safety evaluation, planning and implementing infrastructure improvements, fund allocation and prioritization, air quality estimates, and meeting Highway Safety Improvement Program (HSIP) reporting requirements.INDEX TERMS Annual average daily traffic, AADT, convolutional neural network, deep learning, local road, low volume road, machine learning.

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Section: Literature Reviewmentioning

confidence: 99%

“…Blume et al [46] proposed a methodology using census data and random sampling to estimate local road AADT. Their methodology includes dividing and categorizing the study area into regions based on population density, job density, and road density.…”

Section: Literature Reviewmentioning

confidence: 99%

One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation

Mathew,

Pulugurtha,

Bhure

et al. 2023

IEEE Access

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Traffic volume prediction on low-volume roadways: a Cubist approach

Das¹

2020

Transportation Planning and Technology

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Improving Stratification Procedures and Accuracy of Annual Average Daily Traffic (AADT) Estimates for Non-Federal Aid-System (NFAS) Roads

Tsapakis¹,

Das²,

Anderson³

et al. 2021

Transportation Research Record: Journal of the Transportation R

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The 2016 safety Final Rule requires states to have access to annual average daily traffic (AADT) for all public paved roads, including non-Federal aid-system (NFAS) roads. The latter account approximately for 75% of the total roadway mileage in the country making it difficult for agencies to collect traffic data on these roads. Many agencies use stratified sampling procedures to develop default AADT estimates for uncounted segments; however, there is limited guidance and information about how to stratify the network effectively. The goal of this paper is to enhance transportation agencies’ ability to improve existing stratification schemes, design new schemes, and ultimately develop more accurate AADT estimates for NFAS roads. The paper presents the results from five pilot studies that validated and compared the performance of current, updated, and new (traditional and decision-tree-based) schemes using readily available data. According to the results, the median absolute percent error of existing AADT estimates, developed by state agencies, ranged between 71% and 120%. Updating these schemes using recent counts resulted in an AADT accuracy improvement of 25%. The best-performing schemes were developed using DTs that improved the AADT accuracy of existing schemes by 41%. Overall, having more strata and very homogenous strata is better than having fewer strata and more samples within each stratum. The analysis revealed that a key to selecting an effective scheme is to determine a critical point, beyond which creating more strata improves the AADT accuracy marginally but increases the required sample size exponentially.

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Cost-Effective Reporting of Travel on Local Roads

Cited by 3 publications

References 1 publication

One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation

One-Dimensional Convolutional Neural Network Model for Local Road Annual Average Daily Traffic Estimation

Traffic volume prediction on low-volume roadways: a Cubist approach

Improving Stratification Procedures and Accuracy of Annual Average Daily Traffic (AADT) Estimates for Non-Federal Aid-System (NFAS) Roads

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