Traffic forecasting utilizes a variety of methods, including:
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Four-step models: Models like the urban transportation planning (UTP) model are used in this approach. It involves steps such as trip generation, trip distribution, mode selection, and route assignment to predict future traffic based on existing data and trends.
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Neural networks: These are frequently employed to forecast traffic flow, speed data, and travel times using historical data and predictable characteristics of congested traffic flow.
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Machine Learning: Techniques involving machine learning, inclusive of deep learning algorithms, are utilized to process vast amounts of data for traffic prediction. This aids in managing vehicle movement and mitigating congestion.
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Statistical methods: These techniques assist in identifying traffic patterns at various scales such as daily fluctuations, weekly trends, and seasonal changes to predict traffic flow and levels of congestion.
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Heuristic-based models: These models formulate rules based on statistical analysis of past data to predict short-term and long-term traffic demand.
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Modal split models: These models calculate the percentage of trips made using different modes of transport and assign trips to specific routes following user equilibrium principles.
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Growth rate methods: Another method used in traffic forecasting is predicting external trips using growth rates of trip generation/attraction and vehicle trips.
These methodologies amalgamate historical data analysis, statistical techniques, machine learning algorithms, and growth rate forecasts to accurately predict future traffic patterns for diverse applications in transportation planning and management.
