Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data
This study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a histori...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6483 |
| _version_ | 1797480964304142336 |
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| author | Hyungjoo Kim Lanhang Ye |
| author_facet | Hyungjoo Kim Lanhang Ye |
| author_sort | Hyungjoo Kim |
| collection | DOAJ |
| description | This study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a historical data-based data-driven method according to the characteristics of travel time regularity. Due to the variability of travel times during peak periods, this paper adopts a mixture distribution of travel times in the Bayesian approach rather than traditional single distribution. The Gibbs sampling method with a burn-in period is used to generate a series of sampling sequences from an unknown joint posterior distribution for estimating the posterior distribution of the parameters. The proposed algorithm is tested using traffic data collected from the Korean freeway section from Giheung IC to Dongtan IC. Both MAPE and RMSE of the estimation results show that the proposed method has the smallest deviation from the ground truth travel time compared to the simple mean and moving average methods. Moreover, the proposed Bayesian estimation yields the smallest standard deviation of MAPE for all test days. The credible intervals for estimated travel times show that the proposed method provides good accuracy in estimating travel time reliability. |
| first_indexed | 2024-03-09T22:08:42Z |
| format | Article |
| id | doaj.art-afd3e7614c5c44608ff5fe5b065c0183 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:08:42Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-afd3e7614c5c44608ff5fe5b065c01832023-11-23T19:37:29ZengMDPI AGApplied Sciences2076-34172022-06-011213648310.3390/app12136483Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe DataHyungjoo Kim0Lanhang Ye1Intelligent Transportation System Laboratory, Advanced Institute of Convergence Technology, Suwon-si 16229, KoreaCollege of Engineering, Zhejiang Normal University, 688 Yingbin Road, Jinhua 321004, ChinaThis study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a historical data-based data-driven method according to the characteristics of travel time regularity. Due to the variability of travel times during peak periods, this paper adopts a mixture distribution of travel times in the Bayesian approach rather than traditional single distribution. The Gibbs sampling method with a burn-in period is used to generate a series of sampling sequences from an unknown joint posterior distribution for estimating the posterior distribution of the parameters. The proposed algorithm is tested using traffic data collected from the Korean freeway section from Giheung IC to Dongtan IC. Both MAPE and RMSE of the estimation results show that the proposed method has the smallest deviation from the ground truth travel time compared to the simple mean and moving average methods. Moreover, the proposed Bayesian estimation yields the smallest standard deviation of MAPE for all test days. The credible intervals for estimated travel times show that the proposed method provides good accuracy in estimating travel time reliability.https://www.mdpi.com/2076-3417/12/13/6483Bayesian mixture estimationlow-frequency probe datadata-driven methodindividual travel datacredible interval |
| spellingShingle | Hyungjoo Kim Lanhang Ye Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data Applied Sciences Bayesian mixture estimation low-frequency probe data data-driven method individual travel data credible interval |
| title | Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data |
| title_full | Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data |
| title_fullStr | Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data |
| title_full_unstemmed | Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data |
| title_short | Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data |
| title_sort | bayesian mixture model to estimate freeway travel time under low frequency probe data |
| topic | Bayesian mixture estimation low-frequency probe data data-driven method individual travel data credible interval |
| url | https://www.mdpi.com/2076-3417/12/13/6483 |
| work_keys_str_mv | AT hyungjookim bayesianmixturemodeltoestimatefreewaytraveltimeunderlowfrequencyprobedata AT lanhangye bayesianmixturemodeltoestimatefreewaytraveltimeunderlowfrequencyprobedata |