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- W601044329 abstract "The research presented in this paper develops an agent-based modeling approach to predict experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision-making system, which predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level output – a predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 was used to test the proposed method. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-practice and state-of-art methods (instantaneous travel time, historical average and K-Nearest Neighbor), and maintains less than a 9% prediction error for trip departures up to 60 minutes into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, no offline training is required for the proposed approach making it easily transferrable to other locations and the fast computation time ensures the proposed method can be used in real-time applications." @default.
- W601044329 created "2016-06-24" @default.
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- W601044329 date "2014-01-01" @default.
- W601044329 modified "2023-09-26" @default.
- W601044329 title "Agent-Based Modeling Approach to Predict Experienced Travel Times" @default.
- W601044329 hasPublicationYear "2014" @default.
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