Transportation Systems: Monitoring, Control, and Security

Timotheou, Stelios; Panayiotou, Christos G.

doi:10.1007/978-3-662-44160-2_5

Cited by 6 publications

(7 citation statements)

References 99 publications

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“…For equal process and measurement noise variance both approaches perform equally well, FTS is slightly better than FAS when the measurement noise variance is large than the process noise variance, while FAS is the clear winner when the process noise variance is larger than the measurement noise variance, achieving two times the performance of FTS. Notice also that FAS achieves performance almost equal to the case with no sensor faults for all scenarios considered 1 . Similar behaviour is observed for an additive sensor fault at ILD3 of varying magnitude 2 .…”

Section: Simulation Resultsmentioning

confidence: 82%

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Fault-Adaptive Traffic Density Estimation for the Asymmetric Cell Transmission Model

Timotheou¹,

Panayiotou²,

Polycarpou³

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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The often faulty nature of measurement sensors hinders reliable traffic state estimation, affecting in this way various transportation operations such as traffic control and in-car navigation. This work proposes a systematic, modelbased, online and network-wide approach to achieve robust and good quality state estimation in the presence of sensor faults. The approach is comprised of three stages aiming at 1) identifying the level of faulty behavior of each sensor using a novel fault-tolerant optimization algorithm, 2) isolating faults, and 3) improving state estimation performance by adaptively compensating sensor faults and resolving the state estimation problem. The approach is examined in the context of the Asymmetric Cell Transmission Model for freeway traffic density estimation. Simulation results demonstrate the effectiveness of the proposed fault-adaptive approach, yielding estimation performance very close to the one obtained with healthy measurements, irrespective of the fault magnitude and type.

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Section: Simulation Resultsmentioning

confidence: 82%

“…Traffic state estimation (TSE) is an important topic in transportation engineering that enables a wealth of applications related to the monitoring and control of intelligent transportation systems (ITS) [1]. However, TSE is a challenging task for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Fault-Adaptive Traffic Density Estimation for the Asymmetric Cell Transmission Model

Timotheou¹,

Panayiotou²,

Polycarpou³

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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“…3(b), illustrates that the model and measurement noise variance also have an important role in achieving fault-tolerance. Notice that when the process variance is large the performance of MHE-FTS- 1 Let ρ i,t be the true traffic density at sensor i. A multiplicative sensor fault of value α results in measurement z i,t = α(ρ i,t + v i,t ).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Traffic state estimation (TSE) is an important topic in transportation engineering that enables a wealth of applications related to the monitoring and control of intelligent transportation systems (ITS) [1]. In advanced traveler information systems, accurate knowledge of the traffic state (e.g.…”

Section: Introductionmentioning

confidence: 99%

Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

Timotheou¹,

Panayiotou²,

Polycarpou³

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-Traffic state estimation is an important problem with significant applications in advanced traveler information systems, transportation management and traffic control. Nonetheless, the often faulty nature of measurement sensors, especially inductive loop detectors, hinders reliable state estimation. This work proposes a systematic, model-based, networkwide, moving-horizon approach for fault-tolerant traffic state estimation. By exploiting information redundancy and fault sparsity, it achieves reliable estimation and simultaneously detects, isolates and corrects measurements from periods of faulty sensor behavior. The approach is examined in relation to the Asymmetric Cell Transmission Model, a popular and powerful macroscopic first-order traffic flow model. In the absence of any faults, the proposed approach achieves similar results with other state-of-the-art estimation approaches while it can achieve better estimation performance when some sensors are faulty. It is further demonstrated that the developed approach can successfully handle multiple faults of different types.

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“…Traffic monitoring has been one of the major tools used for transportation planning, decision-making and the implementation of various control strategies [1]. Traffic monitoring tasks include traffic state estimation (TSE) [2], origindestination (OD) matrix estimation [3], travel-time [4], and queue estimation [5] and traffic state and demand prediction [6].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

Sofokleous,

Englezou,

Savva

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Monitoring the traffic state of the road network is very important for a plethora of reasons, such as the prevention of traffic congestion and the development of estimation and control policies. In order to efficiently obtain high-quality information on traffic, the sensors must be installed at optimal locations in the road network under study. This problem is known as the Network Sensor Location Problem (NSLP). In this work, a multi-objective NSLP is proposed for the installation of a pre-defined number of sensors to maximise (i) the covered traffic flow volume and (ii) the minimum distance between candidate links for sensor installation, while taking into account pre-installed sensors. We reformulate the problem into a single-objective mixed-integer linear program (MILP) that yields the optimal sensor locations. In addition, we propose four low-complexity heuristics for the solution of the problem. The performance of the proposed algorithms is evaluated for the traffic network of the Republic of Cyprus under reallife conditions and traffic data. Results show that the four low-complexity approaches yield a different trade-off between execution speed and solution quality.

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Transportation Systems: Monitoring, Control, and Security

Cited by 6 publications

References 99 publications

Fault-Adaptive Traffic Density Estimation for the Asymmetric Cell Transmission Model

Fault-Adaptive Traffic Density Estimation for the Asymmetric Cell Transmission Model

Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

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