Characterising pavement roughness at non-uniform speeds using connected vehicles

Bridgelall, Raj; Hough, Jill; Tolliver, Denver

doi:10.1080/10298436.2017.1366768

Cited by 16 publications

(13 citation statements)

References 11 publications

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“…The overall contributions are not only in the new methodologies but also detailed specification and recommendation for the design of bus/BRT lanes or APT roadway. In line with the analysis in [21,23,24,100] the research contributions in the aspect of road infrastructure design have supported the planning and finalisation of the DART concept in Singapore. Cities with a similar condition can also utilise these findings for their potential implementation of the autonomous public transport system.…”

Section: Discussionsupporting

confidence: 57%

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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Section: Discussionsupporting

confidence: 57%

Road infrastructure design towards passenger ride comfort for autonomous public transport

Van¹,

Ron²

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“…This can be done with the help of the future development of sensor technology. Lastly, the intensive on-going research on RIF and TWIT [95][96][97][98][99][100][101] as the alternatives for IRI in connected vehicle environment will be promising for large-scale implementation.…”

Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

“…Regarding new roughness index, a speed-independent road impact factor -RIF (individual vehicle) and its corresponding time-wavelength-intensity-transform -TWIT (vehicle groups) for connected vehicles were established using advanced signal processing in [94]. Further studies were conducted intensively to investigate and validate the RIF regarding sampling rate selection [95], localisation [96,97], RIF-IRI proportionality [98], deterioration forecasts in consideration of suspension parameter variances [99], stop-andgo conditions [100], and wavelength sensitivity [101].…”

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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“…However, its acquisition requires drawing a complete flow-density curve, which need collecting large amount of data in different flow scenarios. RIF [11] is calculated by the changes of speed and acceleration of many test vehicles in the road surface. ese changes are in the horizontal and vertical directions on the condition of nonuniform speed.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Holistic Road Damage and Analysis of Its Corresponding Traffic Characteristics

Guo

Huang

Zheng

et al. 2019

Advances in Civil Engineering

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Many pavement distress (PD) indexes are defined only from the perspective of the PD physical structure, which cannot describe the change of traffic efficiency. Some PD indexes are defined considering the influence of traffic efficiency, but it is unpractical to calculate them out due to their complex processes. We quantify the impact of the cohesive whole of PD points on the traffic flow and also define the corresponding PD index which is easy to compute. Firstly, the lane cell based on the idea of road discretization is introduced. Secondly, the PD degree for the lane cell is defined by the reduction of the average vehicle speed given the same flow condition. The cell whose PD degree is larger than 0 is defined as the PD cell. Then, considering (1) the ratio of PD cells, (2) PD cell distribution, and (3) the influence of the vehicle lane changing, we define the holistic road-damage degree (HRDD) as the PD index. At last, the relationship among HRDD, flow speed, and flow volume is analyzed through the simulated experiments. The results show that (1) the average speed is inversely proportional to HRDD, and the reduction of vehicle speed is more significant with the increase of the traffic flow input; (2) the inverse relation between road capacity and HRDD can be seen on the whole HRDD range. In another word, the proposed HRDD describes the change of traffic efficiency indeed.

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Characterising pavement roughness at non-uniform speeds using connected vehicles

Cited by 16 publications

References 11 publications

Road infrastructure design towards passenger ride comfort for autonomous public transport

Road infrastructure design towards passenger ride comfort for autonomous public transport

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

Measurement of Holistic Road Damage and Analysis of Its Corresponding Traffic Characteristics

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