Annual Average Daily Traffic Estimation using Co-kriging

Ha, Jung-Ah; Heo, Tae‐Young; Oh, Sang-Hoon; Lim, Sanghun

doi:10.12815/kits.2013.12.1.1

J. Korea Inst. Intelligent. Transp. Syst.

2013

DOI: 10.12815/kits.2013.12.1.1

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Annual Average Daily Traffic Estimation using Co-kriging

Jung-Ah Ha¹,

Tae‐Young Heo²,

Sang-Hoon Oh³

et al.

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2020

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Cited by 2 publications

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Forecasting Future Traffic Trend by Short-Term Continuous Observation

Mendi¹,

Reddy²

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Annual Average Daily Traffic (AADT) is a key parameter to understand the traffic flow rates, traffic density and to design any highway. Generally, short period observed traffic data mainly depends on that season in which the traffic surveys were conducted, which may be high or low compared to the other seasons. So, the behavior of seasonal variation of traffic must be considered for the AADT analysis. These seasonal variations can be found out using the past recorded data of that selected location. But in the case of a location where the past annual traffic data is not available, an alternative method is required to calculate the seasonal variation of the traffic data. The present study deals with the analysis of seasonal variation factors to estimate the AADT from the fuel sale data collected from the nearby petrol stations at the traffic survey point. This work explains how Annual Average Daily Traffic (AADT) can be estimated from a week’s limited traffic data when there is a scarcity of automatic traffic data collecting systems.

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Forecasting Future Traffic Trend by Short-Term Continuous Observation

Mendi¹,

Reddy²

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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show abstract

Future Traffic Prediction from Short Period Traffic Data

Reddy¹,

Mendi²

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Prediction of future traffic is a complicated process which requires logical prediction methods and experience. The present study focused on the prediction of future traffic from the AADT data which is estimated from the one week continuous traffic data collected from the selected study area. State wise traffic contributions are estimated from the one-day number plate survey data and major states effecting the traffic in the study area are observed. The population data, vehicle registration data and economic data of the major states which are observed through number plate survey are used for the prediction of future traffic data. The vehicle growth rates were calculated using average vehicle registration growth rate method and nonlinear regression (power regression). Finally, the growth rates were decided according to the traffic design norms provided by NHAI. The year wise future traffic was predicted and presented in this paper from the year 2013 to 2040. The changes in Level of Service (LOS) of the NH 8A for four lane and six lane configuration is detailed for the future scenario.

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Annual Average Daily Traffic Estimation using Co-kriging

Cited by 2 publications

References 3 publications

Forecasting Future Traffic Trend by Short-Term Continuous Observation

Forecasting Future Traffic Trend by Short-Term Continuous Observation

Future Traffic Prediction from Short Period Traffic Data

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