Coverage and Mobile Sensor Placement for Vehicles on Predetermined Routes: A Greedy Heuristic Approach

Ali, Junade; Dyo, Vladimir

doi:10.5220/0006469800830088

Cited by 17 publications

(21 citation statements)

References 10 publications

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“…An example of a street section is a portion of street between two consecutive corners. A similar definition can be found in the work by Ali and Dyo [ 9 ].…”

Section: Coverage Modelmentioning

confidence: 84%

“…It returns the most likely path followed by a vehicle according to the input coordinates, in the form of new coordinates, adjusted to the topology of the roads. The work by Ali and Dyo uses a similar approach to obtain the set of streets that can be covered by each bus route [ 9 ]. The difference between both methods is explained in Section 5 .…”

Section: Case Studymentioning

confidence: 99%

“…The previous work by Ali and Dyo considers the coverage of a bus-based sensor network for road surface inspection [ 9 ]. Their work presents a coverage model based on the concept of a street segment, defined as a part of a street between two adjacent intersections.…”

Section: Related Workmentioning

confidence: 99%

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On the Coverage of Bus-Based Mobile Sensing

Caminha

Couto

Costa

et al. 2018

Sensors

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A cost-effective approach to gather information in a smart city is to embed sensors in vehicles such as buses. To understand the limitations and opportunities of this model, it is fundamental to investigate the spatial coverage of such a network, especially in the case where only a subset of the buses have a sensing device embedded. In this paper, we propose a model to select the right subset of buses that maximizes the coverage of the city. We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among other findings, we observe that the fleet of buses covers approximately 5655 km of streets (approximately 47% of the streets) and show that it is possible to cover 94% of the same streets if only 18% of buses have sensing capabilities embedded.

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“…An example of a street section is a portion of street between two consecutive corners. A similar definition can be found in the work by Ali and Dyo [ 9 ].…”

Section: Coverage Modelmentioning

confidence: 84%

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

On the Coverage of Bus-Based Mobile Sensing

Caminha

Couto

Costa

et al. 2018

Sensors

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show abstract

“…Time-frequency analysis was used such as the Discrete Wavelet Transform to estimate gravel roads ride quality, detect the location and the severity of surface potholes [75], or the Gabor transform to estimate road roughness condition in combination with image processing for PD [76]. In [77], a greedy heuristic approach for an optimal mobile sensor placement maximises the total length of the road inspected by sensors. Frequency filter, speed filter and small peaks filter were used to develop the vertical acceleration impulse that corresponds to a "high-energy event" on the road surface in UNIquALroad [78,79].…”

Section: Signal Processingmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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“…Available articles regarding response-based methods to measure road roughness condition were collected mainly from "Scopus" database [77] and partially from "Google Scholar" [78]. Articles of focus are those published by international journals and high-quality conferences.…”

Section: State-of-the-art Methods For Road Irregularity Estimation (Literature Review)mentioning

confidence: 99%

Road infrastructure design towards passenger ride comfort for autonomous public transport

Van¹,

Ron²

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The rapid developments of smart technology and the vision to smart cities have led to parallel developments of various innovative transport means. Among them, road-based autonomous public transport (APT) is being planned, designed and developed worldwide such as the Dynamic Autonomous Road Transit (DART) in the city of Singapore. Innovative vehicle configuration and operation in the APT shall demand new requirements for design and construction of the infrastructure concerning the ride comfort of onboard passengers. This study aims at supporting the required update of guidelines for the design of roadway (e.g. horizontal alignment design), for vehicle speed recommendation along existing infrastructure and for pavement maintenance concerning passenger ride comfort in which DART is considered as a case study for APT application. Bottom-up and top-down approaches are used, in which the former method uses existing bus vehicles and network as a base-line reference while the latter method is based on APT's vehicle specification and operation.At first, this study starts with a comprehensive review of ride comfort thresholds, recent approaches to evaluate passenger ride comfort onboard and to assess road surface irregularity, where research gaps are pointed out for further study. The interaction between passenger-vehicle-pavement is studied through numerical analysis. Given the similarities between regular bus and APT, the former system has been investigated and detailed bus mathematical models are constructed and validated to evaluate the pavement quality that affects passenger ride comfort. Based on the comparison of different bus models regarding their simulation complexity and performance, a refined Bus Ride Index (BRI) is proposed as a key achievement of this research study. At the same time, regression relationships are established between road geometrical design (e.g. horizontal curve radius) and passenger perceptions at different postures onboard the bus. Herein, experimental study with analytical analysis is carried out to evaluate principal factors (e.g. lateral acceleration, lateral jerk and duration of turning movement) affecting passenger-vehicle-road interaction and ride discomfort based on existing bus fleet and road network.Finally, the DART concept is examined from different perspectives such as network analysis, traffic operation and road infrastructure design.The findings indicate that research on geometry design and road maintenance based on passenger ride comfort for APT is important to support the new vehicle concept. In the urban context, while road geometry concerning the horizontal curve is contributing to the upper range (uncomfortable, very uncomfortable to extremely uncomfortable), road roughness is the principal factor affecting passenger ride quality in the lower range of discomfort (not uncomfortable, a little uncomfortable, fairly uncomfortable to uncomfortable). The developed BRI offers a faster, inexpensive, convenient and more suitable method for evaluating bus lane roughness regarding p...

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Coverage and Mobile Sensor Placement for Vehicles on Predetermined Routes: A Greedy Heuristic Approach

Cited by 17 publications

References 10 publications

On the Coverage of Bus-Based Mobile Sensing

On the Coverage of Bus-Based Mobile Sensing

Response-based methods to measure road surface irregularity: a state-of-the-art review

Road infrastructure design towards passenger ride comfort for autonomous public transport

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