Network sensor location problem for flow observability and Origin-Destination estimation with consideration of sensor failure
| dc.contributor.advisor | Kattan, Lina | |
| dc.contributor.author | Salari, Mostafa | |
| dc.contributor.committeemember | Dann, Markus R. | |
| dc.contributor.committeemember | Gentili, Monica | |
| dc.contributor.committeemember | Ferguson, Robert Daniel | |
| dc.contributor.committeemember | Waters, Nigel M. | |
| dc.date | 2020-06 | |
| dc.date.accessioned | 2019-11-01T18:30:20Z | |
| dc.date.available | 2019-11-01T18:30:20Z | |
| dc.date.issued | 2019-10-31 | |
| dc.description.abstract | The network sensor location problem (NSLP) addresses the location of traffic sensors to observe/estimate the link, route or OD flows in a traffic network. While counting sensors such as loop detectors still have an extensive application for traffic monitoring purposes, they suffer from a considerable rate of failure. In this study, I focus on two well-known problems in the NSLP known as the full link flow observability problem and the origin-destination estimation problem while considering the failure of sensors. The full link flow observability problem is to identify the minimum set of traffic sensors to be installed in links in a road traffic network. The sensors are used to both monitor the flow of observed links and to provide flow information for the link flow inference of unobserved links. Unavoidably, the traffic sensors deployed in a traffic network are subject to failure which leads to missing the link flow observation of observed links as well as the inability to infer the link flow of unobserved links. This study aims to identify the minimum set of links in a traffic network to be instrumented with two different types of counting sensors (basic and advanced sensors) to reach full link flow observability while minimizing the effect of sensor failure on the link flow inference of unobserved links. Mathematically, I formulate two objective functions including min-max and min-sum functions. The first function attempts to minimize the maximum effect of sensor failure on the link flow inference of unobserved links while the second one minimizes the expected number of unobserved links where the flow cannot be inferred due to the failure of sensors. I select the genetic algorithm (GA) as a well-known heuristic to solve the proposed optimization model. The results recommend minimizing the number of sensors required for the link flow inference of each unobserved link as well as installing advanced sensors on links involved in the link flow inference of multiple unobserved links. I also develop a new objective function to reflect that links in a traffic network can be either minor or major roads with different levels of importance. The results suggest installing more advanced sensors on the major roads as well as minimizing the number of major roads included in the set of unobserved links. Concerning the availability of route flow information in a network, I consider the effect of this information on evaluating the sensor deployment in a network. To maintain full link flow observability of a traffic network if any sensor fails, I study the location and type of additional sensors introduced as redundant sensors, which are more than the minimum required for full link flow observability. Finally, I discuss the applicability of the proposed model for the partial observability problem in which the full link flow observability conditions are not satisfied. In addition to the link flow observability problem, this study also focuses on the OD estimation problem considering the failure of sensors. The OD estimation problem is to find the location of the minimum number of sensors to estimate the flow of OD pairs in a traffic network. Traffic sensors can observe the summation of OD demand flows traversing a link and through OD estimation techniques such as maximum entropy, I can estimate the OD demand flows. Contrary to the flow observability problem, the failure of a sensor, does not necessarily lead to missing the chance of estimating the OD demand of one or more OD pairs but can affect the OD demand flow information gain from OD demands. In this study, I identify the location of counting sensors aiming to minimize the possible adverse effect of sensor failure on the OD estimation process. The input data required for the OD estimation may consist of the prior information of the OD trips that can be used to make the OD trip estimation as close as possible to the actual vehicular trips generated between each OD in the road network. However, the sensors, similar to other measurement apparatus, are subject to failure and this failure can affect the reliability of the OD trip information especially under congested traffic conditions. In this paper, I address the sensor location problem (NSLP) to identify the most reliable location set of sensors in a road traffic network with consideration of the possibility of sensors failure. I introduced two objective functions including maximization of expected OD demand flow information gain on both observed link and each OD pair. I then employed the weighted sums method (WSM) and an ε-constraint to incorporate these two objective functions. With respect to the available budget constraint, different types of sensors are considered to identify different location sets of sensors with different levels of reliability for the OD estimation. The results applied to different road traffic networks indicate the improvement in the reliability of information obtained from the selected sensor location sets. | en_US |
| dc.identifier.citation | Salari, M. (2019). Network sensor location problem for flow observability and Origin-Destination estimation with consideration of sensor failure (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/37228 | |
| dc.identifier.uri | http://hdl.handle.net/1880/111190 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Network sensor location problem | en_US |
| dc.subject | OD estimation | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Traffic information gain | en_US |
| dc.subject | Sensor failure | en_US |
| dc.subject | Link flow observability | en_US |
| dc.subject.classification | Education--Mathematics | en_US |
| dc.subject.classification | Sociology--Transportation | en_US |
| dc.subject.classification | Engineering--Civil | en_US |
| dc.title | Network sensor location problem for flow observability and Origin-Destination estimation with consideration of sensor failure | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Engineering – Civil | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | en_US |