Context classifier for position-based user association control in vehicular hotspots

Santos, Pedro M.; Kholkine, Leonid; Cardote, André; Aguiar, Ana

doi:10.1016/j.comcom.2018.03.004

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…On the other hand, the constraint cannot link between and , denoted by CL( , ), meaning that the two objects cannot be in the same cluster. (Santos et al, 2018), instance-level constraints have the following: 2. A cannot-link problem may occur.…”

Section: Deep Constrained Clusteringmentioning

confidence: 99%

Using the Deep Constrained Clustering Approach to Create a Business Profile

Latif

Sutarman

Damius

2022

SinkrOn

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Identification of customers in the business sector that really needs to be done as an evaluation of a business that is run so that it can continue to grow and be able to follow business developments in the same sector. The deep constraint clustering approach is used to cluster customers towards a business. In this study, a clustering of customers using rail mass transportation will be carried out. The results achieved are the formation of 6 clusters using trains be built. The result of research expected to be a consideration in improving services to the company

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Section: Deep Constrained Clusteringmentioning

confidence: 99%

Using the Deep Constrained Clustering Approach to Create a Business Profile

Latif

Sutarman

Damius

2022

SinkrOn

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“…Recent studies were carried out in these years. For example, references [13,40] evaluated if a mobile AP installed on the bus helps detecting the relative position of a user device. This is to identify if a user device is inside or outside the bus.…”

Section: Emerging Automatic Passenger Counting Systemsmentioning

confidence: 99%

“…Only recently, Wi-Fi has been used to count the number of active interfaces detected nearby Wi-Fi Access Points (APs) [13]. These systems are typically based on the identification of the Media Access Control (MAC) address that is associated with a Wi-Fi interface.…”

Section: Introductionmentioning

confidence: 99%

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

et al. 2020

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Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other.

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Empirical Study on the Accuracy and Precision of Automatic Passenger Counting in European Bus Services

Olivo¹,

Maternini²,

Barabino³

2019

TOTJ

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Background: An Automatic Passenger Counting system represents a powerful resource for an efficient operational planning of public transport companies, but it gives rise to several challenges such as accuracy and precision, which must be addressed in order to operate successfully. Objective: Unlike previous studies in the North American bus market, this paper evaluates the accuracy and precision of an infrared APC system in a European bus market. Methods: The accuracy is evaluated by considering: (i) the presence/absence of the error and its direction; (ii) the magnitude of the error disregarding the direction and (iii) some tests on the nature of the error. The precision is evaluated by direct and inverse regression models and some t-test on biases. Results: As for accuracy, a small average magnitude of the errors is observed. In addition, the APC accurately measures alighting passengers, while it presents a slight tendency to systematically undercount boarding passengers. As for precision, the amount of measurement error due to the APC system exists and, even if it is relatively contained, it is statistically significant for boarding and alighting passengers. Conclusion: Although one type of APC system is evaluated only on one bus, it seems quite accurate for recording alighting passengers, whereas a correction factor should be applied for boarding passengers.

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Context classifier for position-based user association control in vehicular hotspots

Cited by 3 publications

References 18 publications

Using the Deep Constrained Clustering Approach to Create a Business Profile

Using the Deep Constrained Clustering Approach to Create a Business Profile

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

Empirical Study on the Accuracy and Precision of Automatic Passenger Counting in European Bus Services

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