Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows
This paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high densit...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/4629792 |
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| author | Rafał Kucharski Arkadiusz Drabicki |
| author_facet | Rafał Kucharski Arkadiusz Drabicki |
| author_sort | Rafał Kucharski |
| collection | DOAJ |
| description | This paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high density of traffic flow). We employ the well-known hydrodynamic relation of fundamental diagram to derive the so-called quasi-density from measured time-mean speeds and flows. This allows formulating the VDF estimation problem with a speed being monotonically decreasing function of quasi-density with a shape resembling the typical VDF like BPR. This way we can use the actually observed speeds and propose the macroscopic VDF realistically reproducing actual speeds also for hypercritical conditions. The proposed method is illustrated with half-year measurements from the induction loop system in city of Warsaw, which measured traffic flows and instantaneous speeds of over 5 million vehicles. Although the proposed method does not overcome the fundamental limitations of static macroscopic traffic models, which cannot represent dynamic traffic phenomena like queue, spillback, wave propagation, capacity drop, and so forth, we managed to improve the VDF goodness-of-fit from R2 of 27% to 72% most importantly also for hypercritical conditions. Thanks to this traffic congestion in macroscopic traffic models can be reproduced more realistically in line with empirical observations. |
| format | Article |
| id | doaj-art-dac414374621492aa685f5b6a8e526f5 |
| 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-dac414374621492aa685f5b6a8e526f52025-02-03T06:42:29ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/46297924629792Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and FlowsRafał Kucharski0Arkadiusz Drabicki1Department of Transportation Systems, Cracow University of Technology, Ul. Warszawska 24, 31-155 Kraków, PolandDepartment of Transportation Systems, Cracow University of Technology, Ul. Warszawska 24, 31-155 Kraków, PolandThis paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high density of traffic flow). We employ the well-known hydrodynamic relation of fundamental diagram to derive the so-called quasi-density from measured time-mean speeds and flows. This allows formulating the VDF estimation problem with a speed being monotonically decreasing function of quasi-density with a shape resembling the typical VDF like BPR. This way we can use the actually observed speeds and propose the macroscopic VDF realistically reproducing actual speeds also for hypercritical conditions. The proposed method is illustrated with half-year measurements from the induction loop system in city of Warsaw, which measured traffic flows and instantaneous speeds of over 5 million vehicles. Although the proposed method does not overcome the fundamental limitations of static macroscopic traffic models, which cannot represent dynamic traffic phenomena like queue, spillback, wave propagation, capacity drop, and so forth, we managed to improve the VDF goodness-of-fit from R2 of 27% to 72% most importantly also for hypercritical conditions. Thanks to this traffic congestion in macroscopic traffic models can be reproduced more realistically in line with empirical observations.http://dx.doi.org/10.1155/2017/4629792 |
| spellingShingle | Rafał Kucharski Arkadiusz Drabicki Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows Journal of Advanced Transportation |
| title | Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows |
| title_full | Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows |
| title_fullStr | Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows |
| title_full_unstemmed | Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows |
| title_short | Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows |
| title_sort | estimating macroscopic volume delay functions with the traffic density derived from measured speeds and flows |
| url | http://dx.doi.org/10.1155/2017/4629792 |
| work_keys_str_mv | AT rafałkucharski estimatingmacroscopicvolumedelayfunctionswiththetrafficdensityderivedfrommeasuredspeedsandflows AT arkadiuszdrabicki estimatingmacroscopicvolumedelayfunctionswiththetrafficdensityderivedfrommeasuredspeedsandflows |