{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:49:57Z","timestamp":1773910197691,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,2]],"date-time":"2017-08-02T00:00:00Z","timestamp":1501632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most existing network sensor location problem (NSLP) models are designed to identify the number of sensors with fixed costs and installation locations, and sensors are assumed to be installed permanently. However, sometimes sensors are carried by individuals to collect traffic data measurements manually at fixed locations. Hence, their duration of operation for which traffic data measurements are collected is limited, and their costs are not fixed as they are correlated with the duration of operation. This paper proposes a NSLP model that integrates optimal heterogeneous sensor deployment and operation strategies for the dynamic O-D demand estimates under budget constraints. The deployment strategy consists of the numbers of link and node sensors and their installation locations. The operation strategy includes sensors\u2019 start time and duration of operation, which has not been addressed in previous studies. An algorithm is developed to solve the proposed model. Numerical experiments performed on a network from a part of Chennai, India show that the proposed model can identify the optimal heterogeneous sensor deployment and operation strategies with the maximum dynamic O-D demand estimation accuracy.<\/jats:p>","DOI":"10.3390\/s17081767","type":"journal-article","created":{"date-parts":[[2017,8,2]],"date-time":"2017-08-02T10:05:25Z","timestamp":1501668325000},"page":"1767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Integrating Optimal Heterogeneous Sensor Deployment and Operation Strategies for Dynamic Origin-Destination Demand Estimation"],"prefix":"10.3390","volume":"17","author":[{"given":"Senlai","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Transportation, Nantong University, Se Yuan Road #9, Nantong 226019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuntao","family":"Guo","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering\/NEXTRANS Center, Purdue University, 3000 Kent Avenue, West Lafayette, IN 47906, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1002\/atr.1292","article-title":"A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data","volume":"49","author":"Lu","year":"2015","journal-title":"J. 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