Abstract
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.
Highlights
It is undoubtable that advanced traffic control and management systems, such as intelligent transportation systems (ITS), are indispensable tools for modern cities
This study proposed a non-parametric model which utilized the spatiotemporal relationship between vehicles in the target link, and took advantage of the spatiotemporal relationship between vehicles in the target link and vehicles in the crossing direction
This study aims to maintain the accuracy of the proposed model at the same level as that of the k-nearest neighbors (kNN)-based and the particle filtering (PF)-based models, so some parameters in the proposed model were set as the same value as those in the two comparison models in advance
Summary
It is undoubtable that advanced traffic control and management systems, such as intelligent transportation systems (ITS), are indispensable tools for modern cities. A timely, reliable, and safe transportation system that can predict traffic conditions is required [1]. Many researchers have focused on the route travel time, but Shi et al [3] argued that the route travel time can be formulated as the sum of the link travel times. Using this formulation, the distinct travel time delays due to traffic signals and different turning behaviors can be well captured. It is more flexible to use the link travel time because it is difficult to predict the travel demand and the routes that travelers may take. Elhenawy et al [4] pointed out Sensors 2020, 20, 265; doi:10.3390/s20010265 www.mdpi.com/journal/sensors
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