Abstract
Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.
Highlights
Along with economic and populations grow, the number of cars has increased dramatically, causing a series of problems such as traffic congestion, traffic accidents, and environmental pollution [1,2,3]
Through the harmonious and close cooperation of people, vehicles, and roads, Intelligent Transportation System (ITS) can improve the efficiency of transportation, ease traffic congestion, improve road network capacity, reduce traffic accidents, lower energy consumption, and decrease environmental pollution
The result showed that accuracy of the model was significantly improved under the condition of high variance
Summary
Along with economic and populations grow, the number of cars has increased dramatically, causing a series of problems such as traffic congestion, traffic accidents, and environmental pollution [1,2,3]. The result showed that accuracy of the model was significantly improved under the condition of high variance These models need large amounts of computation, the high accuracy drives scholars to shift their research focus on artificial intelligence technology method. A wide range of models has been developed for travel time estimation These models have their own advantages, the number of independent variables selected is limited, and the influence of traffic flow parameters on travel time has not been thoroughly considered. The development of traffic information acquisition technology (such as data of GPS trajectories) has provided us with a large amount of traffic data, which offer an opportunity to develop a more accurate travel time estimation based on data mining.
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