Exact and approximate route set generation for resilient partial observability in sensor location problems

Rinaldi, Marco; Viti, Francesco

doi:10.1016/j.trb.2017.08.007

Cited by 22 publications

(6 citation statements)

References 42 publications

(77 reference statements)

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“…A fully observable system is one in which all the states are observable given the available measurements from the sensors. Different observability problems considered in this category are link flow observability [12]- [16], route flow observability [17], [18] and OD flow observailibty [19]- [21]. The general idea is to determine a sensor placement configuration that ensures full observability of the system.…”

Section: Motivation and Paper Contributionsmentioning

confidence: 99%

Where Should Traffic Sensors Be Placed on Highways?

Nugroho¹,

Vishnoi²,

Taha³

et al. 2021

Preprint

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This paper investigates the practical engineering problem of traffic sensors placement on stretched highways with ramps. Since it is virtually impossible to install bulky traffic sensors on each highway segment, it is crucial to find placements that result in optimized network-wide, traffic observability. Consequently, this results in accurate traffic density estimates on segments where sensors are not installed. The substantial contribution of this paper is the utilization of control-theoretic observability analysis-jointly with integer programming-to determine traffic sensor locations based on the nonlinear dynamics and parameters of traffic networks. In particular, the celebrated asymmetric cell transmission model is used to guide the placement strategy jointly with observability analysis of nonlinear dynamic systems through Gramians. Thorough numerical case studies are presented to corroborate the proposed theoretical methods and various computational research questions are posed and addressed. The presented approach can also be extended to other models of traffic dynamics.

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Section: Motivation and Paper Contributionsmentioning

confidence: 99%

Where Should Traffic Sensors Be Placed on Highways?

Nugroho¹,

Vishnoi²,

Taha³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The discussion of resilience of the sensor networks, nevertheless, has been sparse in this research stream. One relevant study is from Rinaldi and Viti [45]. They developed a novel methodology to determine sensor layout for the optimal trade-off solution between the number of sensors needed and the overall resilience of the sensor set.…”

Section: Resilience In Sensor Systemsmentioning

confidence: 99%

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Tang

Wan

Nochta

et al. 2020

IJGI

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Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.

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“…In addition to link flow observability, some studies (16)(17)(18)(19)(20)(21) have also dealt with the problem of OD trip and path flow observability/estimation based on plate scanning and link observations. Considering path redundancy, Rinaldi et al (22) proposed the solution algorithms for large-scale networks. Based on the Graph theory (2, 23), Information theory (24,25), and Nullspace metric (26,27) related to topological networks the NSLP solutions were optimized.…”

Section: Network Sensor Location Problemmentioning

confidence: 99%

Optimal Time Interval for Investigating Prior Information in Network Sensor Location Problem

Xie

Shao

2020

Transportation Research Record: Journal of the Transportation R

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Based on various types of prior information, the network sensor location problem (NSLP) optimizes the locations of sensors in a network, providing comprehensive traffic flow information with a minimum number of sensors. However, few studies have considered the accuracy of prior information, and it is obvious that prior information inconsistent with reality will affect the results of NSLP. Generally, prior information can be obtained from other flow information. When solving NSLP, prior information requires additional or specialized field flow investigations to ensure its accuracy. In general, short-term prior information is adopted to infer the flow of information of interest. The purpose of this study is, therefore, to select the optimal time interval for investigating prior information. This study analyzed the types and characteristics of prior information, and proposed a mathematical method based on Pareto Optimality to identify the optimal time interval for investigating prior information, which can not only ensure the accuracy of prior information, but also reduce the investigation cost. Taking the turning ratio as an example, the traffic flow collected by sensors located at nine intersections on Zhongshan North Road in Shanghai, China was analyzed, and the turning ratios were calculated. Using the proposed method, the optimal time interval for investigating the turning ratio was determined to be 20 to 25 min. In addition, the most representative time intervals were identified as 9:00 to 10:00 a.m. It is recommended that investigating prior information in the early morning and in the morning and evening peaks be avoided.

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Exact and approximate route set generation for resilient partial observability in sensor location problems

Cited by 22 publications

References 42 publications

Where Should Traffic Sensors Be Placed on Highways?

Where Should Traffic Sensors Be Placed on Highways?

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Optimal Time Interval for Investigating Prior Information in Network Sensor Location Problem

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