Traffic Forecasting and Decision making of investment and construction of Tourism Highway under the Background of Artificial intelligence

Abstract : Passenger traffic fore casting and decision-making of investment and construction of the tourism highway under the background of artificial intelligence is studied in this paper. As an important information asset, big data is expected to provide people with comprehensive, accurate, and real-time business insights and decision guidance. The value of big data lies in its decision-making usefulness, and the knowledge contained in it can be discovered through analysis and mining can provide decision support for various practical applications. Forecasting is based on past and present conditions to then make predictive judgments about future development. This paper used the prediction model to construct the framework of decision making. The experimental results have proven the ineffectiveness.
 The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimizer (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets.
 Transportation problems become a challenge when the system and users’ behavior is too difficult to model and predict the travel patterns.
 This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.
 ? This is important to overcome the issue of a continuously rising demand with limited road supply. This includes better utilization of accurate prediction and detection models aiming to better forecast traffic volume, traffic conditions, and incidents. ? Applications of AI aiming to improve public transport is also discussed. It is due to the fact that public transportation is regarded as a sustainable mode of mobility.

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