Traffic clustering and online traffic prediction in vehicle networks: A social influence perspective

Zhang, Bowu; Xing, Kai; Cheng, Xiuzhen; Huang, Liusheng; Bie, Rongfang

doi:10.1109/infcom.2012.6195790

Cited by 21 publications

(2 citation statements)

References 25 publications

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“…The first one is their use for improving the navigation of robots in shared spaces (Bennewitz, Burgard and Cielniak, 2003; Bennewitz, Burgard and Thrun, 2003; Bennewitz et al, 2002, 2002b, 2005; Freitas et al, 2004; Fulgenzi et al, 2008, 2009; Sasaki et al, 2010; Sehestedt et al, 2010; Tanzmeister et al, 2014; Thompson et al, 2009). The second one is for improved motion prediction (Bowu et al, 2012; Sung et al, 2012; Vasquez and Fraichard, 2004; Vasquez et al, 2006, 2009; Zhi et al, 2020). A detailed discussion covering the applications of trajectory maps is presented in Section 6.…”

Section: Surveymentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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Section: Surveymentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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“…The authors have proposed a model-based temporal association scheme and novel pre-processing and post-processing operations which together with AP make a quite successful method for vehicle detection and on road traffic surveillance. Zhang et al (2012) proposed an instant traffic-clustering algorithm using AP to find points on road having similar traffic pattern. The authors found the algorithm to be suitable in predicting the traffic pattern and for finding the influence of traffic pattern at one point to that at another point.…”

Section: Introductionmentioning

confidence: 99%

Affinity Propagation Clustering in Defining Level of Service Criteria of Urban Streets

Bhuyan

Mohapatra

2014

Transport

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Defining Level Of Service (LOS) criteria is very important as this is the first step of LOS analysis but this is not well defined in Indian context. The analysis followed in India is basically adaptation of Highway Capacity Manual (HCM 2000) methodology which is more suitable for developed countries having homogenous traffic pattern. An attempt has been made in this study to define LOS criteria of urban streets for developing countries having heterogeneous traffic flow condition. Defining LOS is basically a classification problem and to solve it Affinity Propagation (AP), a very recently developed cluster algorithm is used. Inventory details and the required speed data are collected from five major street corridors of Greater Mumbai Region in India through the application of Trimble GeoXT Global Positioning System (GPS) receiver. Six validation parameters are used on Free Flow Speed (FFS) data to find the optimal number of clusters, which is required for the classification of street segments into number of classes. After that speed data collected during both peak and off-peak hours are averaged over street segments and clustered into six groups to get the speed ranges of different LOS categories. Using validation parameters, considering the physical and surrounding environmental characteristics it is found that street segments can be classified into four classes in Indian context as mentioned in Highway Capacity Manual 2000. However, the FFS range for urban street class IV (urban design category) is significantly lower because of varying road geometric characteristics. The speed ranges of LOS categories under urban street classes are proportionately lower to that values mentioned in HCM 2000 because of highly heterogeneous traffic flow on urban Indian roads. The travel speed data collection procedure using GPS is simple and accurate. In addition, AP clustering is highly efficient in terms of time saving and provides a very accurate solution to classification problems. Hence, both GPS and AP techniques can be applied in other countries to define the speed ranges of LOS categories considering the local conditions.

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