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Integrated Variable Speed Limits and User Information Strategy

Author

Listed:
  • Ernesto Cipriani

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Lorenzo Giannantoni

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Livia Mannini

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

Abstract

This paper deals with the study of variable speed limits (VSLs) for traffic control and their integration with user information strategies. As few studies have addressed the integrated VSL and user information strategy, we focus on comparing the adoption of the latter with the VSL alone strategy application and the no-control case, highlighting the benefits the integration brings. The integrated strategy is able to smooth the severity of congestion, shifting its occurrence in a section of the mainstream mostly suited to vehicle accumulation. An application on a real network is carried out. The traffic congestion conditions along the real highway are simulated by means of Dynameq simulation software and the METANET macroscopic model. The VSLs are applied in a control area aiming to evaluate the potential and the limitations of the strategy on a real network as well as the integration of variable speed limits and user information strategies. Two different cases of road congestion caused by the presence of on-ramps are studied. Results show that the integration of the two strategies leads to a redistribution of flows, achieving a reduction in the total travel time spent in the network and an increase in the traveled distances, i.e., reducing the overall network time despite the increase in assigned flows. However, an integrated strategy requires adequate transportation supply and mainly crossing demand.

Suggested Citation

  • Ernesto Cipriani & Lorenzo Giannantoni & Livia Mannini, 2023. "Integrated Variable Speed Limits and User Information Strategy," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10954-:d:1192702
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    References listed on IDEAS

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    1. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
    2. Wang, Yibing & Papageorgiou, Markos & Messmer, Albert, 2008. "Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1340-1358, December.
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