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제안 방법
- Some studies to censor outliers contaminating probe TTs on signalized rural highways were also performed by the author(2016). However, all the studies performed previously are only suitable for aggregated data, so it is necessary to develop a new method for a probe system where TT information is generated in the unit of an individual probe vehicle.
- Because of low probe sampling, direct use of raw individual probe TTs cause significant fluctuations, resulting in high error in TT information. To alleviate high fluctuations in individual probe TTs, KF, moving average, and Loess techniques were applied to smooth the individual TTs and compared their performances to select the optimal technique. Since no outlier treatment methods suitable for individual probe-based TT information have been found, an outlier removal algorithm applicable to individual probe TTs is also developed.
- A multi-lane highway, as shown in [Fig. 1], in the vicinity of Seoul, South Korea, was selected to apply the proposed methodology. The four-lane roadway section spans approximately 3 km in a mountainous terrain with one intersection and one interchange, indicating a typical multi-lane highway according to the Korea Highway Capacity Manual (KHCM).
- Recently, a question about TT information at the study site has arisen: could individual probe data be used, instead of 5-min aggregated data, to provide real-time TT information? Could this possibly decrease the time-lag, especially on the short roadway section (3-min driving distance during free flow) on which this study is based? To obtain an answer to the research question, this study initiated the development of a new scheme to use individual probe TTs for real-time TT information. After comparing the accuracy of TT information from individual probe and 5-min aggregated data, a hybrid model that resulted in the lowest error was chosen for the new scheme.
- Recently, a question about TT information at the study site has arisen: could individual probe data be used, instead of 5-min aggregated data, to provide real-time TT information? Could this possibly decrease the time-lag, especially on the short roadway section (3-min driving distance during free flow) on which this study is based? To obtain an answer to the research question, this study initiated the development of a new scheme to use individual probe TTs for real-time TT information. After comparing the accuracy of TT information from individual probe and 5-min aggregated data, a hybrid model that resulted in the lowest error was chosen for the new scheme. This study could be highly regarded in that no previous studies have been found that use individual probe TTs to produce real-time TT information.
- To mitigate the high variability, applying smoothing techniques to raw probe TTs was necessary. In this study, three widely-recognized smoothing techniques―Kalman filter, moving average, and Loess―were explored and the technique that exhibits the lowest error was chosen for use in generating individual probe-based TT information.
- TT information from each scheme―individual probe TTs smoothed by KF and 5-min aggregated TTs―was evaluated using baseline TTs obtained by probes that pass the start point while providing the TT information from each scheme. The baseline TTs for the two schemes were therefore different from each other.
- The fundamental logic behind the proposed scheme is to maximize the benefit and minimize the detriment of each method for generating TT information. That is, under normal (or uncongested) conditions, 5-min aggregated data are used; under congested conditions, individual probe data smoothed by KF are used directly for real-time TT information.
- So simple 5-min aggregated data are used in many TT systems with high TT variability; they, of course, cause substantial errors in real-time TT information. Recently, a research question has arisen: could individual probe TTs be used as real-time TT information? Could this possibly reduce the time lag in TT information compared to the use of 5-min aggregated data, which is the current state of practice? In this study, a thorough investigation of TT information errors from the two types of TT information generation schemes―individual probe and 5-min aggregated data―was conducted. Subsequently, a hybrid method that selectively uses individual probe and 5-min aggregated data with a judgment logic was proposed.
- The improvement was also proven to be significant by t-statistics. The findings of this study can be applied in practice to real-world systems that are compelled to use simple 5-min aggregated data due to their high TT variability, which prevents the effective application of any kind of prediction technique. The next step of the research would be to expand the temporal data as well as to transfer the proposed scheme to other sites to verify its robustness.
데이터처리
- The proposed hybrid scheme and the two benchmark methods―5-min aggregated TTs and individual probe TTs smoothed by KF―were evaluated using two evaluation indices: mean absolute percentage error (MAPE) and root relative square error (RRSE). The MAPE, given by Equation 5 is officially specified for use as an evaluation index for traffic data in Korea, according to the Intelligent Transport Systems performance evaluation guidelines (MOLIT, 2010).
- To statistically verify the improvement, statistical t-tests were performed. Consequently, the differences in errors were proven to be significant by paired t-tests, as shown in [Table 6], with t-statistics higher than the critical t-statistic of 1.
- The proposed method was evaluated with real-world data, and the real-time TT information errors diminished by 9–18% in comparison with the benchmark method of using 5-min aggregated data. The improvement was also proven to be significant by t-statistics. The findings of this study can be applied in practice to real-world systems that are compelled to use simple 5-min aggregated data due to their high TT variability, which prevents the effective application of any kind of prediction technique.
이론/모형
- A method that filters out probe TTs that lie outside a predefined confidence interval determined based on the Greenshield’s traffic flow model was invented by Boxel et al.
- In this study, a confidence interval concept based on the central limit theorem is employed to determine the validity window of individual probe TTs. Here, as the TTs of the study site follow a log-normal distribution rather than a normal distribution (see [Table 2] and [Fig.
- Initially developed by Kalman in 1960, Kalman filter has been broadly applied to smooth and predict variables observed in a time sequence (Kim, 2010). In this study, the KF algorithm (see below) constructed by Chien (20) for use in processing probe TT data was adopted. It should be noted that this algorithm was used for smoothing probe TTs in this study, although it has been used for predicting TTs in other studies (Chien, 2003; Jang, 2013).
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