Trajectory Analysis for Driving

Krumm, John

doi:10.1007/978-1-4614-1629-6_7

Cited by 15 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O "Grupo 2 " corresponde a 49 trajetórias com precisão média (2 ≤ PDOP < 3). O "Grupo 3 "é formado por 38 trajetórias com baixa precisão (3 ≤ PDOP) 6 .…”

Section: Estimação Dos Parâmetros Do Modelounclassified

“…Portanto, faz-se necessária uma etapa de pré-processamento dos dados, localizada entre a coleta e aplicação. Neste cenário, uma importante técnica de pré-processamentoé o map-matching [6]. Os algoritmos map-matching utilizam como entrada, dados de um sistema de posicionamento, como GPS, e dados de um mapa digital de ruas.…”

Section: Introductionunclassified

“…PDOP representa a precisão instantânea da posição fornecida pelo GPS. Menor valor indica maior precisão 6. http://wiki.openstreetmap.org/wiki/Accuracy_of_GPS_data…”

unclassified

See 2 more Smart Citations

Modelo Logı́stico para Map-Matching Online de Trajetória de Veı́culos

Fonseca-Galindo¹,

Neto²,

Castro³

et al. 2018

Proceedings XIII Brazilian Congress on Computational Inteligence

View full text Add to dashboard Cite

“…O "Grupo 2 " corresponde a 49 trajetórias com precisão média (2 ≤ PDOP < 3). O "Grupo 3 "é formado por 38 trajetórias com baixa precisão (3 ≤ PDOP) 6 .…”

Section: Estimação Dos Parâmetros Do Modelounclassified

Section: Introductionunclassified

See 1 more Smart Citation

Modelo Logı́stico para Map-Matching Online de Trajetória de Veı́culos

Fonseca-Galindo¹,

Neto²,

Castro³

et al. 2018

Proceedings XIII Brazilian Congress on Computational Inteligence

View full text Add to dashboard Cite

“…As opposed to linear interpolation used in [5], map-based interpolation has very higher accuracy in estimated positions and estimated turning angles and speeds. This method implicitly does mapmatching [12], [20] to reduce GPS noise as well.…”

Section: A Trajectory Pre-processingmentioning

confidence: 99%

Measuring Similarity Between Mobility Models and Real World Motion Trajectories

Barroudi

Harwood

Karunasekera

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…Based on the analysis, they detected flaws in the urban plan for Beijing. Krumm discusses the use of the trajectories in order to do map matching, target location prediction, and route recommendation. Gidófalvi and Saqib proposed an empirical study based on trajectories from approximately 170 million GPS readings collected within a year from 1500 taxis and 400 trucks in the streets of Stockholm.…”

Section: Introductionmentioning

confidence: 99%

A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories

Teknomo

Fernandez

2012

J of Advced Transportation

View full text Add to dashboard Cite

This paper shows the relationship between flow, generalized origin-destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra-like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. is to catch the same agent identification at two locations. The two locations should be considered as source and sink along the cordon area set by the study. Because of such difficulties and hassle in the OD survey, many researchers have proposed to estimate the OD matrix on the basis of traffic count in predetermined links. The whole technology to do the OD survey has been based on traffic counting the agents who pass certain predetermined locations of the observers.Recent technology on tracking is catching up, and the product of tracking an agent's position is a set of trajectories. With improvements in technology, it is now possible to collect actual individual trajectories on a network using existing tracking devices such as video [4], GPS [5], and mobile or wireless devices [6]. Advanced methods that combine multiple sensor information have even been developed for more precise automatic location systems [7,8], and this type of information has enabled better analysis of various aspects of transit systems [5,9].In contrast to counting, tracking technologies do not require the observers to be in stationary locations. The agent itself is still associated with an identification number, but the agent can now generate location data at certain predetermined periodic time interval. The tracking devices can be video cameras, RFID devices, GPS systems, and possibly many other new devices. In fact, by using (microscopic) simulation, it is also possible to produce estimated trajectories based on the OD matrix and the network graph.With trajectories coming up as a new product, the obvious question is how to link these trajectories together with the other two existing products-OD matrix and traffic flow. The set of trajectories has not come up in the earlier literature of transportation planning because gathering trajectories itself requires the maturity of the tracking technology (e.g., cell phone with GPS) that, until recently, has not been widely utilized by the public.Even with the recent availability of tracking technology, the standardization has not reached the point where there is agreement to share data among the tracking data holders. The lack of full trajectory data on real traffic, however, does not mean that we cannot produce, at least theoretically, a link between the trajectory as input data and the OD matrix and traffi...

show abstract

Trajectory Analysis for Driving

Cited by 15 publications

References 23 publications

Modelo Logı́stico para Map-Matching Online de Trajetória de Veı́culos

Modelo Logı́stico para Map-Matching Online de Trajetória de Veı́culos

Measuring Similarity Between Mobility Models and Real World Motion Trajectories

A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories

Contact Info

Product

Resources

About