K-Means Clustering Method to Classify Freeway Traffic Flow Patterns

Silgu, Mehmet Ali; Çelikoğlu, Hilmi Berk

doi:10.5505/pajes.2014.36449

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…The economic geography has used the classic view in which a system is considered as a simplified version of a machine structure based on homogeneous parts, linear relationships among them, and unique basins of attractions. In this approach, distances are measured by a straight line between two points, even though there are other options to estimate it based on arrays—for example, Manhattan and Chebyshev formulations (Fujita et al, 2001, Celikoglu and Silgu, 2016, Silgu and Çelikoğlu, 2014). Following the spatial analysis notation (Smith and Longley, 2015), we defined the simplest interaction measure,

W_{i j}

, from location i to j , with coordinates

false(x_{i}, y_{i} false)

and

false(x_{j}, y_{j} false)

respectively, as a function of the Euclidean distance:

W_{i j} = f false(S_{i j} false) = f false(d_{i j} false)

d_{i j} = \sqrt{{false(x_{j} - x_{i} false)}^{2} + {false(y_{j} - y_{i} false)}^{2}}

where

S_{i j}

is the attribute of physical proximity; it is also known as a transport friction in spatial interaction models (Rodrigue, 2013).…”

Section: Methodsmentioning

confidence: 99%

Determination of the complexity of distance weights in Mexican city systems

Lugo

2017

Heliyon

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This study tests distance weights based on the economic geography assumption of straight lines and the complex networks approach of empirical road segments in the Mexican system of cities to determine the best distance specification. We generated network graphs by using geospatial data and computed weights by measuring shortest paths, thereby characterizing their probability distributions and comparing them with spatial null models. Findings show that distributions are sufficiently different and are associated with asymmetrical beta distributions. Straight lines over- and underestimated distances compared to the empirical data, and they showed compatibility with random models. Therefore, accurate distance weights depend on the type of the network specification.

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W_{i j}

, from location i to j , with coordinates

false(x_{i}, y_{i} false)

and

false(x_{j}, y_{j} false)

respectively, as a function of the Euclidean distance:

W_{i j} = f false(S_{i j} false) = f false(d_{i j} false)

d_{i j} = \sqrt{{false(x_{j} - x_{i} false)}^{2} + {false(y_{j} - y_{i} false)}^{2}}

where

S_{i j}

is the attribute of physical proximity; it is also known as a transport friction in spatial interaction models (Rodrigue, 2013).…”

Section: Methodsmentioning

confidence: 99%

Determination of the complexity of distance weights in Mexican city systems

Lugo

2017

Heliyon

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“…The distribution of the angles shown in Figure 9 clearly indicates that the direction followed by the majority of pedestrians was the one from the station or subway entrances toward the square, with higher numerosity observed during the morning hours. However, to better understand and visualize the prevalent direction followed by pedestrians during the morning and evening hours, data clustering was carried out using the well-known K-mean methodology [35] to cluster the whole datasets of speeds and directions estimated during the observation period. The results (Figure 10 and Table 1) show that speed and direction could be grouped into two main categories.…”

Section: Speed and Directionmentioning

confidence: 99%

Pedestrian Flows Characterization and Estimation with Computer Vision Techniques

et al. 2023

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This work describes a straightforward implementation of detecting and tracking pedestrian walking across a public square using computer vision. The methodology consists of the use of the well-known YOLOv3 algorithm over videos recorded during different days of the week. The chosen location was the Piazza Duca d’Aosta in the city of Milan, Italy, in front of the main Centrale railway station, an access point for the subway. Several analyses have been carried out to investigate macroscopic parameters of pedestrian dynamics such as densities, speeds, and main directions followed by pedestrians, as well as testing strengths and weaknesses of computer-vision algorithms for pedestrian detection. The developed system was able to represent spatial densities and speeds of pedestrians along temporal profiles. Considering the whole observation period, the mean value of the Voronoi density was about 0.035 person/m2 with a standard deviation of about 0.014 person/m2. On the other hand, two main speed clusters were identified during morning/evening hours. The largest number of pedestrians with an average speed of about 0.77 m/s was observed along the exit direction of the subway entrances during both morning and evening hours. The second relevant group of pedestrians was observed walking in the opposite direction with an average speed of about 0.65 m/s. The analyses generated initial insights into the future development of a decision-support system to help with the management and control of pedestrian dynamics.

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