کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
524817 | 868862 | 2016 | 20 صفحه PDF | دانلود رایگان |

• Dynamic Origin–Destination flows are a key input for traffic modelling and policy making.
• A benchmarking framework for comparison of OD estimation algorithms is proposed.
• Considerably different approaches in a variety of settings/conditions can be tested.
• The platform, available to interested parties upon request, is described and illustrated.
• Applications to both off-line/planning and on-line algorithms are presented.
Estimation/updating of Origin–Destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in on-line contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks – except from closed highway systems – thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both off-line/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sources.
Journal: Transportation Research Part C: Emerging Technologies - Volume 66, May 2016, Pages 79–98