Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

Geetla, Tejswaroop; Batta, Rajan; Blatt, Alan; Flanigan, Marie; Majka, Kevin

doi:10.1016/j.trc.2014.02.024

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…In general, the number of counting sensors determines the scale of the survey, and their location determines the amount of information that can be collected. Therefore, the problem of traffic count location based on OD estimation is mainly concerned with the following two aspects: 1) Minimizing the cost of data collection, such as by minimizing the number of sensors or minimizing the total cost of their locations (Zanguia et al, 2015;; 2) Maximizing the amount of information that can be obtained under the condition of limiting the number of sensors (Park and Haghani, 2015;Geetla et al, 2014). In short, an optimal traffic count location scheme must consider the limited resources available for data collection and collect as much traffic information as possible to improve the estimation accuracy of the OD matrix.…”

Section: Literature Reviewmentioning

confidence: 99%

Bi-objective traffic count location model for mean and covariance of origin–destination estimation

Sun

Shao

Shen

et al. 2021

Expert Systems with Applications

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Section: Literature Reviewmentioning

confidence: 99%

Bi-objective traffic count location model for mean and covariance of origin–destination estimation

Sun

Shao

Shen

et al. 2021

Expert Systems with Applications

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“…Sensors 2024, 24, 961 2 of 13 Photoelectric sensors can be categorized into three types based on the measurement context: planar photoelectric sensors, full-range photoelectric sensors, and combined sensors [6][7][8]. Planar-type photoelectric sensors are noted for their high measurement accuracy and simple mechanical structure, giving them the lowest design difficulty and the widest range of applications [9].…”

Section: Introductionmentioning

confidence: 99%

Omnidirectional Sensor Design for Distributed Laser Measurement Systems

Liu,

Zhi

et al. 2024

Sensors

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Distributed laser measurement systems, widely used in high-end equipment such as airplanes, ships, and other manufacturing fields, face challenges in large spatial measurements due to laser plane obstructions and weak intersections. This paper introduces a novel omnidirectional sensor with enhanced adaptability to complex environments and improved measurement accuracy. Initially, an integrated omnidirectional measurement model is established, followed by the analysis of the optical path of the front-end detector, and the design of a signal-conditioning circuit for the photoelectric conversion of the front-end laser signal, Subsequently, a circuit testing platform is established to validate the detection functionality, and the corresponding results indicate that the symmetry of the output waveform is under 10 ns, the response time is under 100 ns, and the maximum detection distance is 22 m. Further, experimental results demonstrate the superiority of omnidirectional sensors over planar ones in complex environments, successfully receiving 360° laser signals. The positional accuracy of the common point to be measured on the top of the omnidirectional sensor is confirmed to exceed 0.05 mm, and the accuracy of the angle of attitude exceeds 0.04°. Using the laser tracker, the measurement accuracy of the system is verified to be better than 0.3 mm. When rotating in the horizontal and pitch directions, the measurement accuracy is better than 0.35 mm and 0.47 mm, respectively, fulfilling the sub-millimeter precision requirement and expanding the application scope of distributed laser measurement systems.

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“…These are called directional sensors because they have a specific direction of operation. Unlike the omnidirectional sensor [9,10], the coverage of a directional sensor network (DSN) is not only related to the detection radius of the sensor but the angle of view, working direction, and line of sight (LoS) properties. A sector area is commonly used to model the FoV of a directional sensor in two-dimensional space.…”

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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Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

Cited by 20 publications

References 15 publications

Bi-objective traffic count location model for mean and covariance of origin–destination estimation

Bi-objective traffic count location model for mean and covariance of origin–destination estimation

Omnidirectional Sensor Design for Distributed Laser Measurement Systems

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

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