• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.259-260
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• 2023

The rapid urbanization and modernization observed in countries like Myanmar have led to significant concerns regarding traffic congestion, especially in urban areas. This study focuses on the analysis and revitalization of urban transport in selected areas of Myanmar. The core of urban transportation planning lies in travel forecasting, which employs models to predict future traffic patterns and guide decisions related to road capacity, transit services, and land use policies. Travel demand modeling involves a series of mathematical models that simulate traveler behavior and decision-making within a transportation system, including highways, transit options, and policies. The paper offers an overview of the traditional four-step transportation modeling system, utilizing a simplified transport network in the context of Mandalay City, Myanmar.

• Journal of Korean Society of Transportation
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• v.34 no.5
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• pp.476-488
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• 2016

This study reviewed factors and causes that affect on reliability and accuracy of transportation demand forecasting. In general, the causes of forecasting errors come from variety and irregularity of trip behaviors, data limitation, data aggregation and model simplification. Theoretical understanding about the inevitable errors will be helpful for reasonable decision making for practical transportation policies. The study especially focused on traffic assignment with the KTDB data, and described the factors and causes of errors by classifying six categories such as (1) errors in input data, (2) errors due to spacial aggregation and representation method of network, (3) errors from representing values for variations of traffic patterns, (4) errors from simplification of traffic flow model, and (5) errors from aggregation of route choice behavior.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.2
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• pp.45-55
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• 1986

This study proposes the explanatory indices of urban traffic for the purpose of solving the ambiguity of selection of the explanatory variables, which always raises problems in case of the travel-demand forecasting in the urban transportation planning, and develops optimal urban traffic generation models. The multiple regression models for objective traffic generation are developed by using the proposed explanatory inidces. Objective variables that can be explained by one explanatory variable are modified into simple regression type (Y=bX) in order to ensure the nonnegativity of traffic generation. Similarities are noted in the generaton characteristics of generated traffic from homogeneous land-use activity. Objective variables that can not be explained by multiple variable, such as trip attraction of school and trip generation of social-recreation, are classified by the characteristics of each zone. And traffic generation forecasting models are built as homogeneous zone group, the validity of each model being tested by a statistical method. It is desired that the forecasting precision is in improved by easy and simple method. Accordingly, trip generation rates are calculated from each land-use activity, and trip generation rates for practical application are proposed by considering their stability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.145-154
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• 2012

This paper analyzed the characteristics of traffic accidents in all tunnels on nationwide freeways and selected some various independent variables related to accident occurrence in tunnels. The study aims to develop reliable accident forecasting models using the various dependent variables such as the number of accident (no.), no./km, and no./MVK. Finally, reliable multiple linear regression models were proposed in this paper. This study tested the validity verification of developed models through statistics such as $R^2$, F values, multicollinearity, residual analysis. The paper selected the accident forecasting models considering the characteristics of tunnel accidents and two models were finally proposed according to two groups of tunnel length. In the selected models, natural logarithm of ln(no./MVK) is used for the dependent variable and AADT, vertical slope, and tunnel hight are used for the independent variables. The reliability of two models was proved by the comparison analysis between field data and estimating data using RMSE and MAE. These models may be not only effective in evaluating tunnel safety under design and planning phases of tunnel but also useful to reduce traffic accidents in tunnels and to manage the traffic flow of tunnel.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1990.10a
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• pp.329-338
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• 1990

• Transportation Technology and Policy
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• v.7 no.5
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• pp.125-132
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• 2010

• Journal of Navigation and Port Research
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• v.32 no.1
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• pp.81-88
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• 2008

The forecast of a container traffic has been very important for port plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate that effectiveness can differ according to the characteristics of ports.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.259-260
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• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.