Urban transportation systems and traffic management
A. Rasaizadi; A. Ardestani; S.E. Seyedabrishami
Abstract
BACKGROUND AND OBJECTIVES: One of the short-term strategies to manage the traffic and make a balance between travel supply and demand for the near future is the short-term prediction of traffic parameters and informing the passengers. Therefore passengers are more likely to avoid traveling during traffic ...
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BACKGROUND AND OBJECTIVES: One of the short-term strategies to manage the traffic and make a balance between travel supply and demand for the near future is the short-term prediction of traffic parameters and informing the passengers. Therefore passengers are more likely to avoid traveling during traffic peak hours. In this study, hourly average traffic speed and hourly traffic volume as two traffic parameters that indicate traffic state are predicted for Karaj-Chaloos road in Iran. METHODS: Since traffic data have large volume, machine learning-based models have more suitable performance than traditional models. However, it is not merely possible to discover the cause and effect relationships and the importance of features. In this study, after using the artificial neural network and K-nearest neighbor models to predict traffic parameters, to analyze the sensitivity of the results, the importance of used features is investigated. Also, the effect of passing the time over the accuracy of predictions has been examined. FINDINGS: According to the results, the highest accuracy of predicting hourly traffic volume and hourly average traffic speed is achieved by the K-nearest neighbor that is equal to 61% and 91%, respectively. CONCLUSION: Compared to the historical average as a benchmark model, a significant improvement in the accuracy of predictions has been obtained by the artificial neural network and K-nearest neighbor models.
Urban transportation systems and traffic management
A. Rasaizadi; M. Askari
Abstract
The modal split model is one of the steps of the classical four-step travel demand planning. Predictive, descriptive, and prescriptive modal split models are essential to make a balance between travel demand and supply. To calibrate these models, it is necessary to detect and employ influential independent ...
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The modal split model is one of the steps of the classical four-step travel demand planning. Predictive, descriptive, and prescriptive modal split models are essential to make a balance between travel demand and supply. To calibrate these models, it is necessary to detect and employ influential independent variables that are related to characteristics of travel modes, individual and family attributes, zones land use, etc. In previous studies, researchers used the household size, the number of children, and the number of employees as independent variables to show the role of family structure on the modal split. These variables cannot discriminate between different families with different structures. This paper uses the life cycle concept to categorize families based on their structures, and the effectiveness of these new variables on modal split models is examined. For this purpose, five types of family structures are considered that differences between them are based on the age of the family’s children. The Multinomial Logit model is used for mode choice modeling for different trip aims. The mode choice model has been calibrated using the origin-destination data of Qazvin-Iran. Results show the critical role of life cycle dummies in the mode choice models compared to household size, for work, educational, personal, and social- recreational trip aims. Life cycle variables are more active on the work trips mode choice model by estimating 14 significant coefficients, in a 90 percent level of significance. The number of life cycle significant coefficients is decreased to 3 for the shopping trips model.