Data Processing Techniques for Real-Time Traveler Information: Use ofDedicated Short-Range Communications Probes on Suburban Arterial

Background:As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, twoproblems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removaland travel-time prediction need to be applied. Methods:In this study, data processing methods for addressing the two issues are proposed. After investigating the characteristic of the travel times on thetest section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, arecurrent neural network, a class of deep learning where temporal sequence data are normally treated, was applied to predict travel times. Results:As a result of evaluation with ground-truth data obtained through test-car runs, the proposed methods showed enhanced performances withprediction errors lower than 13% on average compared to current practices. Conclusion:The suggested methods can make drivers to better arrange their trip schedules with real-time travel-time information with improved accuracy.