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Dynamic travel time prediction using data clustering and genetic programming
Affiliation:1. Department of Electrical and Computer Engineering, 3500 Transportation Research Plaza, Blacksburg, VA 24061, United States;2. Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 3500 Transportation Research Plaza, Blacksburg, VA 24061, United States;1. Universidade da Coruña, CITIC, Databases Lab., A Coruña, Spain;2. Universidad de Concepción, Computer Science Department, Concepción, Chile;3. Millennium Institute for Foundational Research on Data, Santiago, Chile;1. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Puebla 72840, Mexico;2. INFOTEC – Centro de Investigación e Innovación, en Tecnologías de la Información y Comunicación, Cátedras CONACyT, Aguascalientes, Mexico;1. Tree-Lab, Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Blvd. Industrial y Ave. ITR Tijuana S/N, Mesa de Otay, Tijuana, BC 22500, Mexico;2. Facultad de Ingeniería, Universidad Autónoma del Carmen, Ciudad del Carmen, Campeche 24180, Mexico;3. Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes por Periférico Norte, Apdo. Postal 150 Cordemex, Mérida, Yucatán, Mexico
Abstract:The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modeling and predicting the expected, lower, and upper bounds of dynamic travel times along freeways. The models obtained from the genetic programming approach are algebraic expressions that provide insights into the spatiotemporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. Our algorithm is tested on a 37-mile freeway section encompassing several bottlenecks. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm and the historical average averaged over seven weekdays (p-value <0.0001). Specifically, the proposed algorithm achieves more than a 25% and 76% reduction in the prediction error over the instantaneous and historical average, respectively on congested days. When bagging is used in addition to the genetic programming, the results show that the mean width of the travel time interval is less than 5 min for the 60–80 min trip.
Keywords:Travel time prediction  Clustering  Genetic programming  Sampling with replacement  Probe data
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