Robust Inference of Principal Road Paths for Intelligent Transportation Systems

Agamennoni, Gabriel; Nieto, Juan; Nebot, E.

doi:10.1109/tits.2010.2069097

Cited by 94 publications

(73 citation statements)

References 23 publications

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“…However, many links are superfluous and are removed. A transition count matrix and transition probability matrix as in [9] are computed to reduce the complexity of the graph. A transition count matrix of size X ( is the total number of clusters) records the number of vehicle trajectories going from cluster to cluster .…”

Section: ) Linking Clustersmentioning

confidence: 99%

“…Incremental tracking insertion methods and intersection linking algorithms offer an advantage when no existing map information is available, which is often the case for heavy construction operations, as well as when the underlying environment has a frequently re-configured nature [13]- [16]. However, map inference for heavy construction operations is only applied in [9] from a safety perspective, with the aim of predicting and preventing collisions between vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Driving time and path generation for heavy construction sites from GPS traces

Jenelius

Koutsopoulos

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Section: ) Linking Clustersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Driving time and path generation for heavy construction sites from GPS traces

Jenelius

Koutsopoulos

2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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“…The principal curve is a "self-consistent" smooth curve which shows a nonlinear summary of a set of given data points [14]. It has been applied to various fields of research including road geometry estimation [2,3]. "Self-consistent" means that any point on the principal curve coincides with the average of the data points which were projected to that curve, and, therefore, that the curve passes through the middle of the data cloud.…”

Section: Road Geometry As Shown Inmentioning

confidence: 99%

“…One of the most popular issues with trajectorybased analysis is estimation of road geometry based on a large number of vehicle trajectories [1][2][3][4]. The trajectory of vehicles has also been used to analyze vehicle accidents and the behavior of drivers.…”

Section: Introductionmentioning

confidence: 99%

Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks

Lee

Kim

Hahn

2014

International Journal of Distributed Sensor Networks

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As mobile devices came into wide use, it became practical to collect travel data in personal logs. Many studies have been conducted to extract meaningful information from this trend. In this study, we present a system for monitoring road-geometry and crash-risk estimation, based on trajectories created using a smartphone-aided sensor network. The proposed system consists of a number of node vehicles with smartphone applications for GPS data collection and a map server which aggregates the collected GPS trajectories and estimates road conditions. In order to estimate road geometry and crash risk information, the trajectories were segmented and categorized into groups according to their headings. Based on the processed trajectories, the geometry of the road section was estimated using the principal curve method. The crash risk of the road section was estimated from the constructed road geometry and the density map of the trajectories. Our system was evaluated using bicycle trajectories collected from segregated bicycle tracks in Seoul, Korea. Constructed geometry and crash-risk information of the track was compared with real track geometry and crash data. As a result, the estimated road geometry showed over 74% similarity and the calculated crash risk (61%) matched the real crash data.

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“…A road network is a system of interconnecting lines that represent the interconnecting roads in a given area [1][2][3]. Traditionally, the road networks are constructed from geographic surveying through devices, such as telescopes and sextants.…”

Section: Introductionmentioning

confidence: 99%

Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm

Xie

Liao

Aghajan

et al. 2016

IJGI

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Abstract:Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users' turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score.

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Robust Inference of Principal Road Paths for Intelligent Transportation Systems

Cited by 94 publications

References 23 publications

Driving time and path generation for heavy construction sites from GPS traces

Driving time and path generation for heavy construction sites from GPS traces

Trajectory-Based Road-Geometry and Crash-Risk Estimation with Smartphone-Assisted Sensor Networks

Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm

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