TY  - JOUR
PY  - 2022//
TI  - Reliable location of automatic vehicle identification sensors to recognize origin-destination demands considering sensor failure
JO  - Transportation research part C: emerging technologies
A1  - Sun, Weiwei
A1  - Shao, Hu
A1  - Wu, Ting
A1  - Shao, Feng
A1  - Fainman, Emily Zhu
SP  - e103551
EP  - e103551
VL  - 136
IS  - 
N2  - Locating automatic vehicle identification (AVI) sensors to recognize Origin-Destination (OD) demand has been attracting extensive attentions in academia and industry. Although most scholars determined OD demand after deriving unique route flow based on AVI sensors, this traditional method does not necessarily obtain the smallest number of sensors to ensure the uniqueness of OD demand. Moreover, the sensors can fail in reality, which results in missing some information of observed links, thus the uniqueness of OD demand cannot be guaranteed. In this paper, we propose a new AVI sensor location model considering sensor failures to ensure the uniqueness of OD demand directly, without determining route flows. Typically, given the observation order of AVI sensors, this method can minimize the number of sensors to determine OD demand uniquely, in the meanwhile satisfying certain reliability given sensors' failure. Moreover, under budget constraints, we develop a sensor location model to estimate OD demand under sensor failures, which maximizes information value of the differentiated OD pairs. Then we design several greedy heuristic algorithms to solve these two sensor location problems. Through numerical experiments, we show that the proposed models and algorithms can effectively determine the AVI sensor locations to recognize the OD demand and its uniqueness in the event of uncertain sensor failures.<p />  <p>Language: en</p>
LA  - en
SN  - 0968-090X
UR  - http://dx.doi.org/10.1016/j.trc.2021.103551
ID  - ref1
ER  -