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A network sensor location procedure accounting for o–d matrix estimate variability

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  • Simonelli, Fulvio
  • Marzano, Vittorio
  • Papola, Andrea
  • Vitiello, Iolanda

Abstract

The paper illustrates an innovative and theoretically founded methodology for solving the network sensor location problem (NSLP), explicitly accounting for the variability of the o–d matrix estimate. The proposed approach is based on a specific measure, termed synthetic dispersion measure (SDM), related to the trace of the covariance matrix of the posterior demand estimate conditional upon a set of sensor locations. Under the mild assumption of multivariate normal distribution for the prior demand estimate, the proposed SDM does not depend on the specific values of the counted flows – unknown in the planning stage – but just on the locations of such sensors. From a practical standpoint, a stepwise algorithm is implemented for calculating the proposed measure given a set of link counts, which avoids matrix inversion. In addition, a sequential heuristic algorithm is presented for the application of the proposed NSLP to real contexts. The methodology also allows a formal budget allocation problem to be set between surveys and counts in the planning stage, in order to maximize the overall quality of the demand estimation process.

Suggested Citation

  • Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.
    • Handle: RePEc:eee:transb:v:46:y:2012:i:10:p:1624-1638
    DOI: 10.1016/j.trb.2012.08.007
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    3. Lo, Hong K. & Chen, Anthony & Castillo, Enrique, 2016. "Robust network sensor location for complete link flow observability under uncertaintyAuthor-Name: Xu, Xiangdong," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 1-20.
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    5. Cantelmo, Guido & Viti, Francesco & Cipriani, Ernesto & Nigro, Marialisa, 2018. "A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 303-320.
    6. Zhu, Ning & Fu, Chenyi & Zhang, Xuanyi & Ma, Shoufeng, 2022. "A network sensor location problem for link flow observability and estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 428-448.
    7. He, Sheng-xue, 2013. "A graphical approach to identify sensor locations for link flow inference," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 65-76.
    8. Fu, Chenyi & Zhu, Ning & Ma, Shoufeng, 2017. "A stochastic program approach for path reconstruction oriented sensor location model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 210-237.
    9. Viti, Francesco & Rinaldi, Marco & Corman, Francesco & Tampère, Chris M.J., 2014. "Assessing partial observability in network sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 65-89.
    10. Bagloee, Saeed Asadi & Sarvi, Majid & Wolshon, Brian & Dixit, Vinayak, 2017. "Identifying critical disruption scenarios and a global robustness index tailored to real life road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 60-81.

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