TY - JOUR
T1 - Modeling the effect of sensor failure on the location of counting sensors for origin-destination (OD) estimation
AU - Salari, Mostafa
AU - Kattan, Lina
AU - Lam, William H.K.
AU - Esfeh, Mohammad Ansari
AU - Fu, Hao
N1 - Funding Information:
This work is financially supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery and Discovery Accelerator Supplement grants (RGPIN/03923-2014, RGPAS/00099-2020), Alberta Innovate Strategic Research Projects on Integrated Urban Mobility (G2018000894), and NSERC CREATE on Integrated Infrastructure for Sustainable Cities (CREATE/511060-2018). It is also jointly supported by a Postgraduate Studentship of the Hong Kong Polytechnic University, together with grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 152628/16E) and from the Dean’s Reserve Committee of the Hong Kong Polytechnic University (Project No. ZVSA).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/11
Y1 - 2021/11
N2 - The network sensor location problem (NSLP) for origin–destination (OD) estimation identifies the optimal locations for sensors to estimate the vehicular flow of OD pairs in a road network. Like other measurement apparatuses, these sensors are subject to failure, which can affect the reliability of the OD estimations. In this paper, we propose a novel model that allows us to solve the NSLP for OD demand estimation by identifying the most reliable locations to install sets of sensors with consideration for a nonhomogeneous Poisson process to account for time-dependent sensor failure. The proposed model does not rely on the assumption that true OD demand information is known. We introduce two separate objective functions to minimize the maximum possible information loss (MPIL) associated with OD demand on sensor-equipped links and OD pairs during the lifetimes of the sensors. Both objective functions are formulated to incorporate the possibility of sensor failure into the calculated OD demands. We use stochastic user equilibrium (SUE) to address the stochasticity of traffic route selection. We then employ the weighted sums method (WSM) and an ε-constraint to incorporate the objective functions into an integrated formulation. Two sensor types with different time-dependent failure rates are considered to identify the optimal locations for sets of sensors for OD demand estimation purposes while addressing the available budget constraints. We also address the problem of scheduled/routine maintenance of existing sensors by introducing an additional sensor deployment phase that focuses on maintaining the reliability of information by repairing or replacing failed sensors, installing additional sensors or a combination of both. The numerical results from the proposed model demonstrate how the deployment of more advanced sensors with lower failure rates can effectively improve the reliability of the information obtained from sensors. We also evaluate the use of different weights for the WSM's objective functions to explore alternative combinations of sensor configurations. The introduction of additional sensors to a network shows that the decision between repairing failed sensors and installing new sensors is highly dependent on the available budget and the failed sensors’ locations.
AB - The network sensor location problem (NSLP) for origin–destination (OD) estimation identifies the optimal locations for sensors to estimate the vehicular flow of OD pairs in a road network. Like other measurement apparatuses, these sensors are subject to failure, which can affect the reliability of the OD estimations. In this paper, we propose a novel model that allows us to solve the NSLP for OD demand estimation by identifying the most reliable locations to install sets of sensors with consideration for a nonhomogeneous Poisson process to account for time-dependent sensor failure. The proposed model does not rely on the assumption that true OD demand information is known. We introduce two separate objective functions to minimize the maximum possible information loss (MPIL) associated with OD demand on sensor-equipped links and OD pairs during the lifetimes of the sensors. Both objective functions are formulated to incorporate the possibility of sensor failure into the calculated OD demands. We use stochastic user equilibrium (SUE) to address the stochasticity of traffic route selection. We then employ the weighted sums method (WSM) and an ε-constraint to incorporate the objective functions into an integrated formulation. Two sensor types with different time-dependent failure rates are considered to identify the optimal locations for sets of sensors for OD demand estimation purposes while addressing the available budget constraints. We also address the problem of scheduled/routine maintenance of existing sensors by introducing an additional sensor deployment phase that focuses on maintaining the reliability of information by repairing or replacing failed sensors, installing additional sensors or a combination of both. The numerical results from the proposed model demonstrate how the deployment of more advanced sensors with lower failure rates can effectively improve the reliability of the information obtained from sensors. We also evaluate the use of different weights for the WSM's objective functions to explore alternative combinations of sensor configurations. The introduction of additional sensors to a network shows that the decision between repairing failed sensors and installing new sensors is highly dependent on the available budget and the failed sensors’ locations.
KW - Genetic algorithm
KW - Network sensor location problem
KW - Nonhomogeneous Poisson process
KW - OD demand information loss
KW - OD demand reliability
KW - Origin-Destination estimation
KW - Sensor failure
UR - http://www.scopus.com/inward/record.url?scp=85114945458&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2021.103367
DO - 10.1016/j.trc.2021.103367
M3 - Journal article
AN - SCOPUS:85114945458
VL - 132
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
SN - 0968-090X
M1 - 103367
ER -