Geometric modelling of highways using global positioning system (GPS) data and spline approximation

Castro, María; Iglesias, L.; Rodríguez-Solano, Roberto; Sánchez, Javier Solana

doi:10.1016/j.trc.2006.06.004

Cited by 69 publications

(40 citation statements)

References 7 publications

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“…Choi and Sung (2006) corroborated this result since they found that the estimate of the clothoid scale parameter had a higher error range when compared with the geometric characteristics of tangents and circular arcs. Castro et al (2006) compared the two curvature diagrams obtained from the highway alignment defined by parametric cubic smoothing splines and project alignment, and found that the maximum error in the definition of the roadway alignment was equal to 1 m, a value that they considered satisfactory for topographical representation and highway applications. Imran et al (2006) also collected data using a differential GPS surveying method along a 25 km section of a two-lane rural highway in eastern Ontario.…”

Section: Related Workmentioning

confidence: 99%

Mobile mapping systems and spatial data collection strategies assessment in the identification of horizontal alignment of highways

Marinelli

Bassani

Piras

et al. 2017

Transportation Research Part C: Emerging Technologies

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The horizontal alignment of existing highways may be identified by using several terrestrial or aerial geomatics technologies. Such technologies involve different levels of precision and accuracy; hence, different results can be expected. At present, there are no comparisons available between the solutions resulting from the use of different technologies and data sources for the same road alignment.In this investigation, a number of terrestrial mobile mapping techniques and data collection strategies were evaluated. The centerline of a 3.6 km section of a highway was used to estimate radii, centers of curvature and orientation of tangents. Two statistical fitting methods were used to back-calculate these parameters, and the results were then compared with as-built alignment data.Terrestrial images from a mobile mapping vehicle were used to determine the centerline, which was also estimated as the average line of the carriageway and pavement edges, and as the average line of the two driving trajectories. Positions were surveyed using low-cost sensors (an integrated GPS-IMU platform, HD webcam). For comparison purposes, aerial orthophotos and a GNSS (high-cost) receiver were used simultaneously. Although the GPS-IMU data and estimated trajectories provided results comparable to those of the GNSS receiver, the use of georeferenced images proved less accurate. The results and comments in the paper should be of use to survey practitioners when they need to select an acquisition methodology appropriate to the desired level of accuracy and in line with budget constraints.

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Section: Related Workmentioning

confidence: 99%

Mobile mapping systems and spatial data collection strategies assessment in the identification of horizontal alignment of highways

Marinelli

Bassani

Piras

et al. 2017

Transportation Research Part C: Emerging Technologies

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show abstract

“…The method of least squares was used to deduce design elements of horizontal alignment [21], ψ-S curves were used in models to determinate line segments, circular arcs and clothoidal arcs that form a complex [22], clothoids and splines where used to represent transition curves [23,24].…”

Section: Related Workmentioning

confidence: 99%

Automatic Horizontal Road Design Information Extraction from Georeferenced Polygonals: A Brazilian Federal Highway Network Study

Coelho¹,

Borges²,

Borges³

et al. 2015

JCEA

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Road geometric design data are a vital input for diverse transportation studies. This information is usually obtained from the road design project. However, these are not always available and the as-built course of the road may diverge considerably from its projected one, rendering subsequent studies inaccurate or impossible. Moreover, the systematic acquisition of this data for the entire road network of a country or even a state represents a very challenging and laborious task. This study's goal was the extraction of geometric design data for the paved segments of the Brazilian federal highway network, containing more than 47,000 km of highways. It presents the details of the method's adoption process, the particularities of its application to the dataset and the obtained geometric design information. Additionally, it provides a first overview of the Brazilian federal highway network composition (curves and tangents) and geometry.

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“…Besides convenience, reliability and security should also be guaranteed by Pervasive Urban Sensing (PUS). As a result, organizations and researches are creating and developing services such as digital map construction and update [6][7][8][9], traffic light optimization [1,2], traffic flow detection [10], and so forth. Digital map construction and update are the foundation for most other services.…”

Section: Introductionmentioning

confidence: 99%

Pervasive Urban Sensing with Large-Scale Mobile Probe Vehicles

Zhu

Liu

Yin

2013

International Journal of Distributed Sensor Networks

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With the advance of embedded sensing devices, Pervasive Urban Sensing (PUS) with probe vehicles is becoming increasingly practical. A probe vehicle is equipped with onboard sensing devices that detect urban information as the probe vehicle drive across the road network. For example, GPS sensors can detect real-time vehicle status including instant speed and physical position. PUS can provide the general public valuable urban sensing information, such as frequently updated digital maps, and real-time traffic light states. In this paper, we first present the framework of Pervasive Urban Sensing with probe vehicles. Next, we present two cases of urban sensing with probe vehicles. As case one, we discuss the design of a sensing algorithm for detecting the instant state of traffic lights. As case two, we discuss the design of sensing algorithms for recognizing roads by using the vehicular footprints. Some preliminary results of these two cases of urban sensing are presented and discussed.

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Geometric modelling of highways using global positioning system (GPS) data and spline approximation

Cited by 69 publications

References 7 publications

Mobile mapping systems and spatial data collection strategies assessment in the identification of horizontal alignment of highways

Mobile mapping systems and spatial data collection strategies assessment in the identification of horizontal alignment of highways

Automatic Horizontal Road Design Information Extraction from Georeferenced Polygonals: A Brazilian Federal Highway Network Study

Pervasive Urban Sensing with Large-Scale Mobile Probe Vehicles

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