Improved AADT Estimation by Combining Information in Image- and Ground-Based Traffic Data

Jiang, Zhuojun; McCord, Mark R.; Goel, Prem K.

doi:10.1061/(asce)0733-947x(2006)132:7(523)

Cited by 25 publications

(15 citation statements)

References 3 publications

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“…The prediction error is defined as ‫݊݅ݐܿ݅݀݁ݎܲ‬ ‫ݎݎݎܧ‬ ൌ ‫݀݁ݐܿ݅݀݁ݎ‪ȁ‬‬ ‫݁ݑ݈ܽݒ‬ െ ‫݁ݑݎݐ‬ ‫‪݁ȁ‬ݑ݈ܽݒ‬ ‫݁ݑݎݐ‬ ‫݁ݑ݈ܽݒ‬ (7) In the above formula, we let the predicted value be 0 if it is negative. By doing this procedure until the preset accuracy is reached by the L1 norm of the output difference between the current step and the last step, or until the total iteration number reaches a preset limit, say 500 or 1000 times, we can get the median prediction error for different percentiles.…”

Section: Comparisonmentioning

confidence: 99%

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Efficient local AADT estimation via SCAD variable selection based on regression models

Yang

Wang

Bao

2011

2011 Chinese Control and Decision Conference (CCDC)

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A bstract-In transportation networks, Annual Average Daily Traffic (AADT) estimation is very important to decision making, planning, air quality analysis, etc. Regression method may be the most popular one for estimating AADT on non-counters roads. Most literatures focus on how to collect different groups of predicting variables, and select significant variables by t-test and F-test. However, there is no theory on the validity of these multiple selecting steps. Furthermore, variables they collected for high functional class roads may be not suitable for the estimation of local AADT because of lacking counters. This paper focuses on the estimation and variable selection for the local AADT using different groups of variables. The variable selection by smoothly clipped absolute deviation penalty (SCAD) procedure is proposed. It can select significant variables and estimate unknown regression coefficients simultaneously at one step. The estimation algorithm and the tuning parameters selection are presented. The used data is from Mecklenburg County of North Carolina in 2007 for demonstration. The proposed method shows that our selection procedure is valid and it further improves the local AADT estimation by incorporating satellite information. The proposed method outperforms some other regression method when it is applied to local AADT estimation.

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Section: Comparisonmentioning

confidence: 99%

“…Jiang, McCord and Goel (2006) [7] proposed to utilize weighted information of both imaged-based and ground-based traffic data. Eom, Park, Heo and Hunstiger (2006) [2] took into account both spatial trend (mean) and spatial correlation using the spatial regression model.…”

Section: Introductionmentioning

confidence: 99%

Efficient local AADT estimation via SCAD variable selection based on regression models

Yang

Wang

Bao

2011

2011 Chinese Control and Decision Conference (CCDC)

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“…The results showed an improvement ranging from 10% to 20% over the conventional estimation of the seasonal factors. More recently, Jiang, McCord, and Goel (2006) combined imagery (satellites and air photos) and ground-based data to improve AADT estimates for coverage segments.…”

Section: Current-year Aadt Estimation Studiesmentioning

confidence: 99%

AADT prediction using support vector regression with data-dependent parameters

Castro-Neto

Jeong

et al. 2009

Expert Systems with Applications

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“…Yang and Davis (2002) used Bayesian techniques for calculating classified mean daily traffic and also analysed the effect of how long a short duration traffic count took. McCord et al (2003) proposed an alternative methodology in which AADT was estimated from a single image of a road segment and, based on this model, Jiang et al (2006) proposed a different approach that exploited existing imagery of highway segments and earlier year coverage counts.…”

Section: Introductionmentioning

confidence: 99%

Estimation of annual average daily traffic with optimal adjustment factors

Alonso¹,

Ibeas²,

Moura³

et al. 2015

Proceedings of the ICE - Transport

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This study aimed to estimate the annual average daily traffic in inter-urban networks determining the best correlation (affinity) between the short period traffic counts and permanent traffic counters. A bi-level optimisation problem is proposed in which an agent in an upper level prefixes the affinities between short period traffic counts and permanent traffic counters stations and looks to minimise the annual average daily traffic calculation error while, in a lower level, an origin-destination (O-D) trip matrix estimation problem from traffic counts is solved. The proposed model is tested over the well-known Sioux-Falls network and applied to a real case of Cantabria (Spain) regional road network. The importance of determining appropriate affinity and the effect of localisation of permanent traffic counters stations are discussed.

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Improved AADT Estimation by Combining Information in Image- and Ground-Based Traffic Data

Cited by 25 publications

References 3 publications

Efficient local AADT estimation via SCAD variable selection based on regression models

Efficient local AADT estimation via SCAD variable selection based on regression models

AADT prediction using support vector regression with data-dependent parameters

Estimation of annual average daily traffic with optimal adjustment factors

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