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Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment

1
Department of Civil Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
2
Institute of Innovation for Future Society, Nagoya University, Nagoya, Aichi 464-8603, Japan
3
Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya, Aichi 464-8603, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 265; https://doi.org/10.3390/s20010265
Received: 30 October 2019 / Revised: 3 December 2019 / Accepted: 27 December 2019 / Published: 2 January 2020
(This article belongs to the Section Intelligent Sensors)
Short-term travel time prediction is an important consideration in modern traffic control and management systems. As probe data technology has developed, research interest has moved from highways to urban roads. Most research has only focused on improving the prediction accuracy on urban roads because it is the key index of evaluating a model. However, the low penetration rate of probe vehicles at urban networks may result in the low coverage rate which restricts prediction models from practical applications. This research proposed a non-parametric model based on Bayes’ theorem and a resampling process to predict short-term urban link travel time, which can enhance the coverage rate while maintaining the prediction accuracy. The proposed model used data from vehicles in both the target link and its crossing direction, so its coverage rate can be expanded, especially when the data penetration rate is low. In addition, the utilization of relationships between vehicles in both directions can reflect the influence of signal timing. The proposed model was evaluated in a computer simulation to test its robustness and reliability under different data penetration rates. The results implied that the proposed model has a high coverage rate, demonstrating stable and acceptable performance at different penetration rates. View Full-Text
Keywords: urban travel time prediction; low penetration rate; coverage rate; vehicles in the crossing direction; short-term; probe vehicle data urban travel time prediction; low penetration rate; coverage rate; vehicles in the crossing direction; short-term; probe vehicle data
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MDPI and ACS Style

Tang, R.; Kanamori, R.; Yamamoto, T. Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment. Sensors 2020, 20, 265. https://doi.org/10.3390/s20010265

AMA Style

Tang R, Kanamori R, Yamamoto T. Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment. Sensors. 2020; 20(1):265. https://doi.org/10.3390/s20010265

Chicago/Turabian Style

Tang, Ruotian, Ryo Kanamori, and Toshiyuki Yamamoto. 2020. "Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment" Sensors 20, no. 1: 265. https://doi.org/10.3390/s20010265

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