Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes
Travel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide trav...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/2415312 |
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| _version_ | 1849308558346158080 |
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| author | Jiangfeng Wang Chao Wang Jiarun Lv Zhiqi Zhang Cuicui Li |
| author_facet | Jiangfeng Wang Chao Wang Jiarun Lv Zhiqi Zhang Cuicui Li |
| author_sort | Jiangfeng Wang |
| collection | DOAJ |
| description | Travel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide travelers with more timely and accurate travel information, which can further improve the travel efficiency of road network. Five CV characteristics indexes are selected as explanatory variables including the Congestion Level (CL), Penetration Rate (PR), Compliance Rate (CR), release Delay Time (DT), and Following Rate (FR). Based on the five explanatory variables, a TTR model is proposed using the multilogistic regression method, and the prediction accuracy and the impact of characteristics indexes on TTR are analyzed using a CV guidance scenario. The simulation results indicate that 80% of the RMSE is concentrated within the interval of 0 to 0.0412. The correlation analysis of characteristics indexes shows that the influence of CL, PR, CR, and DT on the TTR is significant. PR and CR have a positive effect on TTR, and the average improvement rate is about 77.03% and 73.20% with the increase of PR and CR, respectively, while CL and DT have a negative effect on TTR, and TTR decreases by 31.21% with the increase of DT from 0 to 180 s. |
| format | Article |
| id | doaj-art-04223336672b454d8b211ca2bdece668 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-04223336672b454d8b211ca2bdece6682025-08-20T03:54:25ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/24153122415312Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics IndexesJiangfeng Wang0Chao Wang1Jiarun Lv2Zhiqi Zhang3Cuicui Li4MOE Key Laboratory for Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaTravel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide travelers with more timely and accurate travel information, which can further improve the travel efficiency of road network. Five CV characteristics indexes are selected as explanatory variables including the Congestion Level (CL), Penetration Rate (PR), Compliance Rate (CR), release Delay Time (DT), and Following Rate (FR). Based on the five explanatory variables, a TTR model is proposed using the multilogistic regression method, and the prediction accuracy and the impact of characteristics indexes on TTR are analyzed using a CV guidance scenario. The simulation results indicate that 80% of the RMSE is concentrated within the interval of 0 to 0.0412. The correlation analysis of characteristics indexes shows that the influence of CL, PR, CR, and DT on the TTR is significant. PR and CR have a positive effect on TTR, and the average improvement rate is about 77.03% and 73.20% with the increase of PR and CR, respectively, while CL and DT have a negative effect on TTR, and TTR decreases by 31.21% with the increase of DT from 0 to 180 s.http://dx.doi.org/10.1155/2017/2415312 |
| spellingShingle | Jiangfeng Wang Chao Wang Jiarun Lv Zhiqi Zhang Cuicui Li Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes Journal of Advanced Transportation |
| title | Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes |
| title_full | Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes |
| title_fullStr | Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes |
| title_full_unstemmed | Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes |
| title_short | Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes |
| title_sort | modeling travel time reliability of road network considering connected vehicle guidance characteristics indexes |
| url | http://dx.doi.org/10.1155/2017/2415312 |
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