Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems

Mastalerz, Marcin W.; Malinowski, A.; Kwiatkowski, Sławomir; Śniegula, Anna; Wieczorek, Bartosz

doi:10.1016/j.procs.2020.09.009

Cited by 11 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…and the other about automatic control of backup of surveillance videos (Cui et al 2020) and one study classified as Prediction being a case of information messaging (Genser et al 2020), and two classified as Current status estimate, being a case of Covid 19 detection (Liu and Huang 2020) and one being a case of smart fare collection (Mastalerz et al 2020). AI appears rather evenly distributed between the classes, although prediction is the most commonly used mechanism, whereas the more advanced tasks of planning/scheduling/resource allocation are the least common.…”

Section: Mechanism For Achieving Benefitmentioning

confidence: 99%

“…The challenges that actually have been identified mainly concern data availability and quality. For many AI methods, in particular ML, large datasets, often from different sources, are needed to get good results, and they may require substantial efforts to collect and be difficult to store (Mastalerz et al 2020;Nguyen et al 2021;Reddy et al 2016;Roulland et al 2014;Tran et al 2020).…”

Section: Challenges Of Using Aimentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence for improving public transport: a mapping study

Jevinger,

Zhao,

Persson

et al. 2023

Public Transp

View full text Add to dashboard Cite

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

show abstract

Section: Mechanism For Achieving Benefitmentioning

confidence: 99%

Section: Challenges Of Using Aimentioning

confidence: 99%

Artificial intelligence for improving public transport: a mapping study

Jevinger,

Zhao,

Persson

et al. 2023

Public Transp

View full text Add to dashboard Cite

show abstract

“…Association: After the baggage is placed in the baggage tray, the system will identify the information marked by the RFID of the tray, and conduct real-time ReID identification and positioning of passengers. The concept of integrated passenger identification model based on machine learning technology [12] is applied to reduce or eliminate the risk related to the misclassification of smart phone users as measuring passengers. If the passenger identification is successful at this time, call the service to associate the RFID information of the baggage tray, the basis of the person and the identification information; If ReID recognition fails, cooperative face recognition is performed.…”

Section: B) Baggage Rfid Identification and Person-baggagementioning

confidence: 99%

Design of civil aviation security check passenger identification system based on residual convolution network

Zhang

Liang

Ambati

2022

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

INTRODUCTION: A civil aviation security check passenger identification system based on residual convolution network is designed to improve the efficiency of airport passenger security check service. OBJECTIVES: The system uses the basic resource layer to provide communication and configuration services, collects the basic information of passengers, the images of passengers' faces and whole body, and the images of baggage security X-ray machine through the data layer, and stores the collected results in the unstructured database; METHODS: The image processing module of the business service layer calls the data in the database, and takes the STM32F103VBT6 microprocessor as the image processing control chip to complete the image data processing. The person, baggage, X-ray machine image and passenger basic information are associated through the person, baggage and X-ray machine information binding service module, and the association results are uploaded to the person and certificates integration unit of the client application layer. RESULTS: The face recognition module identifies the passenger identity through the residual convolution network with the attention mechanism, and realizes the ReID identification of passengers and baggage and the association of people and baggage through the transmission control unit. CONCLUSION: The experimental results show that the system can accurately identify the identity of civil aviation security passengers, and the identification efficiency of security passengers can reach more than 27 frames per second.

show abstract

“…ML techniques are used in the traffic data analysis process, which is used here to identify the traffic flow patterns and classes based on previously recorded data and events. ML techniques increase the overall accuracy rate in the prediction and detection process, which helps to enhance the efficiency and effectiveness of the data analysis process [1,18]. The support vector machine approach is used in the traffic data analysis process, which helps to find out the actual cause and detail of traffic with the help of closed-circuit television cameras and sensors.…”

Section: Introductionmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

View full text Add to dashboard Cite

Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

show abstract

Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems

Cited by 11 publications

References 20 publications

Artificial intelligence for improving public transport: a mapping study

Artificial intelligence for improving public transport: a mapping study

Design of civil aviation security check passenger identification system based on residual convolution network

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Contact Info

Product

Resources

About