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Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design

Author

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  • Stefano de Luca

    (Department of Civil Engineering, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy)

  • Roberta Di Pace

    (Department of Civil Engineering, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy)

  • Silvio Memoli

    (Department of Mobility, Infrastructures and Public works, Municipality of Naples, piazza Municipio, 80133 Naples, Italy)

  • Luigi Pariota

    (Department of Civil, Architectural and Environmental Engineering, University of Naples “Federico II” via Claudio 21, 80125 Naples, Italy)

Abstract

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.

Suggested Citation

  • Stefano de Luca & Roberta Di Pace & Silvio Memoli & Luigi Pariota, 2020. "Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:726-:d:310634
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    References listed on IDEAS

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    Cited by:

    1. Farnaz Khaghani & Farrokh Jazizadeh, 2020. "mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    2. Di Pace, Roberta & Storani, Facundo & Guarnaccia, Claudio & de Luca, Stefano, 2023. "Signal setting design to reduce noise emissions in a connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).
    3. Barroso, Joana Maia Fernandes & Albuquerque-Oliveira, João Lucas & Oliveira-Neto, Francisco Moraes, 2020. "Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks," Journal of Transport Geography, Elsevier, vol. 89(C).
    4. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    5. Dan Su & Yi-Sheng Liu & Xin-Tong Li & Xiao-Yan Chen & Dong-Han Li, 2020. "Management Path of Concrete Beam Bridge in China from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 12(17), pages 1-22, September.
    6. Ji-Shuang Tan & Khalid Elbaz & Zhi-Feng Wang & Jack Shui Shen & Jun Chen, 2020. "Lessons Learnt from Bridge Collapse: A View of Sustainable Management," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    7. Wang, Jiawen & You, Lan & Hang, Jiayu & Zhao, Jing, 2023. "Pre-trip reservation enabled route guidance and signal control cooperative method for improving network throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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