Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models
Speed-volume-density relationship and capacity are key elements in modelling traffic operations, designing roadways, and evaluating facility performance. This paper uses a modified five-parameter logistic model to describe the speed-density relationship. The calibrated speed-density models show that...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/9614501 |
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| _version_ | 1850178665405480960 |
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| author | Chaoru Lu Jing Dong Anuj Sharma Tingting Huang Skylar Knickerbocker |
| author_facet | Chaoru Lu Jing Dong Anuj Sharma Tingting Huang Skylar Knickerbocker |
| author_sort | Chaoru Lu |
| collection | DOAJ |
| description | Speed-volume-density relationship and capacity are key elements in modelling traffic operations, designing roadways, and evaluating facility performance. This paper uses a modified five-parameter logistic model to describe the speed-density relationship. The calibrated speed-density models show that the stop-and-go speed (Vb) and shape parameters (θ1 and θ2) are similar for work zones and the nonwork zone site. Accordingly, an operational capacity prediction method is proposed. To demonstrate the effectiveness of the proposed method, the predicted operational capacities are compared with the field data, Highway Capacity Manual method, the output of WorkZoneQ software, and the ensemble tree approach under different work zone scenarios. Furthermore, a lifetime distribution prediction framework for stochastic capacity of work zones is proposed. The predicted lifetime distribution can well capture the tendency of the observed work zone capacities. |
| format | Article |
| id | doaj-art-c5b667680df74b368ba5db311fe1df19 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c5b667680df74b368ba5db311fe1df192025-08-20T02:18:39ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/96145019614501Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density ModelsChaoru Lu0Jing Dong1Anuj Sharma2Tingting Huang3Skylar Knickerbocker4Traffic Engineering Research Center, Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7033, NorwayDepartment of Civil, Construction and Environmental Engineering, Iowa State University, Ames, 50010, USADepartment of Civil, Construction and Environmental Engineering, Iowa State University, Ames, 50010, USADepartment of Civil, Construction and Environmental Engineering, Iowa State University, Ames, 50010, USADepartment of Civil, Construction and Environmental Engineering, Iowa State University, Ames, 50010, USASpeed-volume-density relationship and capacity are key elements in modelling traffic operations, designing roadways, and evaluating facility performance. This paper uses a modified five-parameter logistic model to describe the speed-density relationship. The calibrated speed-density models show that the stop-and-go speed (Vb) and shape parameters (θ1 and θ2) are similar for work zones and the nonwork zone site. Accordingly, an operational capacity prediction method is proposed. To demonstrate the effectiveness of the proposed method, the predicted operational capacities are compared with the field data, Highway Capacity Manual method, the output of WorkZoneQ software, and the ensemble tree approach under different work zone scenarios. Furthermore, a lifetime distribution prediction framework for stochastic capacity of work zones is proposed. The predicted lifetime distribution can well capture the tendency of the observed work zone capacities.http://dx.doi.org/10.1155/2018/9614501 |
| spellingShingle | Chaoru Lu Jing Dong Anuj Sharma Tingting Huang Skylar Knickerbocker Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models Journal of Advanced Transportation |
| title | Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models |
| title_full | Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models |
| title_fullStr | Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models |
| title_full_unstemmed | Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models |
| title_short | Predicting Freeway Work Zone Capacity Distribution Based on Logistic Speed-Density Models |
| title_sort | predicting freeway work zone capacity distribution based on logistic speed density models |
| url | http://dx.doi.org/10.1155/2018/9614501 |
| work_keys_str_mv | AT chaorulu predictingfreewayworkzonecapacitydistributionbasedonlogisticspeeddensitymodels AT jingdong predictingfreewayworkzonecapacitydistributionbasedonlogisticspeeddensitymodels AT anujsharma predictingfreewayworkzonecapacitydistributionbasedonlogisticspeeddensitymodels AT tingtinghuang predictingfreewayworkzonecapacitydistributionbasedonlogisticspeeddensitymodels AT skylarknickerbocker predictingfreewayworkzonecapacitydistributionbasedonlogisticspeeddensitymodels |