Abstract
Background:As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, two problems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removal and 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 the test section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, a recurrent 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 with prediction 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.
Highlights
Real-time travel-time information is an essential element of modern traffic management systems
In Korea, freeways and major arterials are equipped with Dedicated Short-Range Communications (DSRC) scanners (Fig. 1)
The DSRC systems installed on suburban arterials on which many intersections and roadside stores exist inevitably generate substantial outliers due to intermediate stops at stores and/or gas stations, exit/entry maneuvers on the route, U-turns, driving illegally on the shoulder lane during congestion, and so on
Summary
Real-time travel-time information is an essential element of modern traffic management systems. DSRC scanners cannot identify the direction of a detected probe, so if a probe makes a round trip on the roadway section, the probe is detected twice by the scanner. In this case, the second detection generates an abnormally long travel time and should be classified as an outlier. As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. Two problems generally arise in probe-based systems: one is the outlier and the other is time lag. Methods for outlier removal and travel-time prediction need to be applied
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