Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches
Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/5148085 |
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| _version_ | 1832552363901059072 |
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| author | Wenwen Qin Meiping Yun |
| author_facet | Wenwen Qin Meiping Yun |
| author_sort | Wenwen Qin |
| collection | DOAJ |
| description | Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have multimodal distributions that are associated with the underlying traffic states. In this case, the study develops a Bayesian approach based on particle filter framework for link TTD estimation using real-time and historical travel time observations from FCD. First, link travel times are classified by different traffic states according to the levels of vehicle delays. Then, a state-transition function is represented as a Transition Probability Matrix of the Markov chain between upstream and current links with historical observations. Using the state-transition function, an importance distribution is constructed as the summation of historical link TTDs conditional on states weighted by the current link state probabilities. Further, a sampling strategy is developed to address the sparsity problem of observations by selecting the particles with larger weights in terms of the importance distribution and a Gaussian likelihood function. Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles. The proposed approach is evaluated using real-world FCD. The results indicate that the proposed approach provides good accurate estimations, which are very close to the empirical distributions. In addition, the approach with different percentage of floating cars is tested. The results are encouraging, even when multimodal distributions and very few or no observations exist. |
| format | Article |
| id | doaj-art-6e240ff58db04ba1b117f1baf88f2ffb |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-6e240ff58db04ba1b117f1baf88f2ffb2025-02-03T05:58:54ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/51480855148085Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian ApproachesWenwen Qin0Meiping Yun1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Highway, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Highway, Shanghai 201804, ChinaDespite the wide application of Floating Car Data (FCD) in urban link travel time and congestion estimation, the sparsity of observations from a low penetration rate of GPS-equipped floating cars make it difficult to estimate travel time distribution (TTD), especially when the travel times may have multimodal distributions that are associated with the underlying traffic states. In this case, the study develops a Bayesian approach based on particle filter framework for link TTD estimation using real-time and historical travel time observations from FCD. First, link travel times are classified by different traffic states according to the levels of vehicle delays. Then, a state-transition function is represented as a Transition Probability Matrix of the Markov chain between upstream and current links with historical observations. Using the state-transition function, an importance distribution is constructed as the summation of historical link TTDs conditional on states weighted by the current link state probabilities. Further, a sampling strategy is developed to address the sparsity problem of observations by selecting the particles with larger weights in terms of the importance distribution and a Gaussian likelihood function. Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles. The proposed approach is evaluated using real-world FCD. The results indicate that the proposed approach provides good accurate estimations, which are very close to the empirical distributions. In addition, the approach with different percentage of floating cars is tested. The results are encouraging, even when multimodal distributions and very few or no observations exist.http://dx.doi.org/10.1155/2018/5148085 |
| spellingShingle | Wenwen Qin Meiping Yun Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches Journal of Advanced Transportation |
| title | Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches |
| title_full | Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches |
| title_fullStr | Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches |
| title_full_unstemmed | Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches |
| title_short | Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches |
| title_sort | estimation of urban link travel time distribution using markov chains and bayesian approaches |
| url | http://dx.doi.org/10.1155/2018/5148085 |
| work_keys_str_mv | AT wenwenqin estimationofurbanlinktraveltimedistributionusingmarkovchainsandbayesianapproaches AT meipingyun estimationofurbanlinktraveltimedistributionusingmarkovchainsandbayesianapproaches |