Sensor layout strategy and sensitivity analysis for macroscopic traffic flow parameter acquisition

Li, Haijian; Qin, Lingqiao; Chang, Xin; Rong, Jian; Jia, Limin

doi:10.1049/iet-its.2016.0297

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…Link capacity: 7800 O: [3,5] D: [9,10] OD demand: (3,9,7500), (3,10,7300), (5,9,1000), (5,10,1200) Based on the OD demand, a traffic network that prefers to use the "upper half" is constructed. As the network has a symmetrical structure, the importance of the upper half is higher than that of the lower half.…”

Section: Link Travel Time: 10mentioning

confidence: 99%

“…There are two paths from node 5 to nodes 9 and 10, where c BW (r) is 0.1. In order to cover more important paths, links (6,7), (5,9), and (5,10) are selected for the detector layout, in order to cover more travel routes. Obviously, this is an optimal solution under the current conditions.…”

Section: Definition 3 Path Centrality C Bw (R)mentioning

confidence: 99%

“…When T = 18, the traffic was in the evening peak period, and the traffic demand in links 5,14,31,23,19,7,37,34,28,49, and 56 increased. Due to different degrees of congestion, the average travel time of these links increased.…”

mentioning

confidence: 99%

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Optimization of Traffic Detector Layout Based on Complex Network Theory

Wang

2020

Sustainability

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With the recent development of traffic networks, traffic detector layout has become very complicated, due to the complexity of traffic network structures and states. Thus, this paper presents an optimal method for traffic detector layout based on network centrality using complex network theory. It mainly depends on the topology of the traffic network, and does not depend on pre-conditions (e.g., OD (Origin Destination)) traffic, path traffic, prior matrix, and so on) or consider route-choosing behavior too much. Considering the travel time, OD demand, observation demand of urban managers, dynamic characteristic of the traffic network, detector failure, and so on, an optimization model for traffic detector layout is established, which is called the Traffic Network Centrality Model (TNCM). Numerical experiments are conducted, based on data from the Sioux Falls network, which demonstrate that the model has a strong practical value. TNCM is not only helpful in reducing the traffic detector layout cost, but also improves the monitoring revenue of the traffic network in complex scenarios, which offers a promising way of thinking about the optimization of traffic detector layout schemes.

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Section: Link Travel Time: 10mentioning

confidence: 99%

Section: Definition 3 Path Centrality C Bw (R)mentioning

confidence: 99%

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Optimization of Traffic Detector Layout Based on Complex Network Theory

Wang

2020

Sustainability

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“…The placement of directional sensors has been extensively studied. This is often formulated as a mathematical programming problem, with the objective of maximizing FoV coverage under a cost constraint or minimizing cost under a coverage constraint [16,17]. Coverage is a measure describing the sensor's ability to detect events within the region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the performance and optimizing the placement of roadside sensors for cooperative vehicle‐infrastructure systems

Wang

Zhao

et al. 2022

IET Intelligent Trans Sys

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A cooperative vehicle-infrastructure system (CVIS) can provide perception information beyond the visual range for autonomous vehicles via roadside directional sensors, such as cameras, millimeter-wave radar, and lidar. The performance of roadside perception strongly depends on sensor placement, where physical occlusion between vehicles is inevitable. This paper proposes an occlusion degree model (ODM) to describe the dynamic occlusion between sensors, obstacles, and targets in a three-dimensional space. The ODM is then integrated with microscopic traffic simulation to study the impacts of traffic density, vehicle model composition, and sensor configurations on vehicle detection and tracking performances. Based on the simulation, a multifactor regression model with a logistic growth curve is established to quantify the performance of roadside sensors with different factors, where the evaluation metrics are rigorously designed. Finally, an optimization model of roadside sensor placement with non-linear constraints is formulated to reduce the construction cost under a coverage constraint and improve the perception accuracy within budget limitations. A case study of a highway scenario shows that the performance of the proposed approach improves by 5.63%, 6.55%, and 8.01%, respectively, under the worst possible condition with accuracy requirements of 0.97, 0.96, and 0.95 compared with the conventional placement scheme. The study provides a map of the performance of roadside perception with different influencing factors and guides the optimal roadside sensor placement for a CVIS.

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“…Given their importance, the fault data detection of traffic detectors is always a research focus and has attracted the attention of numerous scholars for years [11]. At present, the research of traffic detector fault data recognition mainly focuses on data fault identification based on a traffic flow three-parameter rule [12], data fault identification based on statistical analysis and data fault identification based on artificial intelligence [13]. Xu [14], by analyzing the influence of inner relationship of data acquisition interval and three traffic flow parameters, then designed a four-step data sieving method.…”

Section: Introductionmentioning

confidence: 99%

Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA

Zhang²,

Zhang

et al. 2019

Applied Sciences

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To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a poor capability for multiscale of fault data, PCA (principal component analysis) is adopted for traffic fault data identification. This paper proposes the fault data detection models based on the PCA model, MSPCA (multiscale principal component analysis) model and improved MSPCA model, respectively. In order to improve the recognition rate of traffic detectors’ fault data, the improved MSPCA model combines the wavelet packet energy analysis and PCA to achieve traffic detector data fault identification. On the basis of traditional MSPCA, wavelet packet multi-scale decomposition is used to get detailed information, and principal component analysis models are established on different scale matrices, and fault data are separated by wavelet packet energy difference. Through case analysis, the feasibility verification of traffic flow data identification method is carried out. The results show that the improved method proposed in this paper is effective for identifying traffic fault data.

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Sensor layout strategy and sensitivity analysis for macroscopic traffic flow parameter acquisition

Cited by 10 publications

References 36 publications

Optimization of Traffic Detector Layout Based on Complex Network Theory

Optimization of Traffic Detector Layout Based on Complex Network Theory

Quantifying the performance and optimizing the placement of roadside sensors for cooperative vehicle‐infrastructure systems

Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA

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