City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms

Wilkowski, Artur; Mykhalevych, Ihor; Luckner, Marcin

doi:10.1007/978-3-030-13273-6_31

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…• Supporting maintenance (Adamson et al 2005;Killeen et al 2019;Hermann et al 2020). • Vehicle tracking (Barbosa et al 2017;Wilkowski et al 2020).…”

Section: Applications In Public Transportmentioning

confidence: 99%

Artificial intelligence for improving public transport: a mapping study

Jevinger,

Zhao,

Persson

et al. 2023

Public Transp

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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.

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“…• Supporting maintenance (Adamson et al 2005;Killeen et al 2019;Hermann et al 2020). • Vehicle tracking (Barbosa et al 2017;Wilkowski et al 2020).…”

Section: Applications In Public Transportmentioning

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

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“…The researchers focused on city traffic controlling with the use of proposed methods and algorithms effective for analyzing visual data from transportation monitoring systems [19]. In order to maximize the implementation, public transportation vehicles need to be recognized and mapped with the camera.…”

Section: A Computer Visionmentioning

confidence: 99%

Computer Vision on a Parking Management and Vehicle Inventory System

Africa¹

2020

IJETER

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Over time, scientists and engineers have developed technology that has improved everyday living. For every invention, they have found to reinvent it and create a better version of it. An example would be parking lots. From the usual time-in-time-out system; window cashier to parking spot back to window cashier, the hassle of this cycle was reduced through the use of license plate recognition which this study tackled. License plate identification technology has become a useful factor in different issues like parking management, vehicle owner recognition, etc. Computer vision has provided a means to reduce manual labor in parking lots by replacing them with sensors and scanners which would take over. This would be beneficial to the company as this technology would require minimal maintenance. Through image processing and enhancement, the license plates of these cars could be taken account of in real-time and this data could be used to generate the parking bills given the time-in and a time-out of the vehicle. This paper aimed to tackle the problems of car management and to further simplify the parking system.

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“…Cao used image data, language data, and spatiotemporal data on Sina Weibo, and applied the computer vision deep learning algorithm, the Bossen natural semantic algorithm, and GIS spatial analysis to accurately depict city images based on the three dimensions of image structure, image type, and emotional assessment [29]. Wilkowski et al attempted to relate city form to human perception by introducing computer vision and machine learning and trained an image classification model to more accurately identify the five types of image elements [30]. These research methods provide multifaceted perspectives and technical support for understanding urban spatial images.…”

Section: Introduction 1research Background and Progressmentioning

confidence: 99%

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

Zhou,

Li,

et al. 2024

Land

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This study proposes a two-dimensional analytical framework based on urban spatial form and spatial service perspectives, utilizing data on buildings and points of interest (POIs). It integrates fishnet analysis, kernel density analysis, the categorization of POI functionalities, and mixture calculations to enhance our understanding of urban spatial form and function. Taking the main urban area of Zhengzhou as an example, this study identifies image elements that can describe urban spatial characteristics through the results of two-dimensional analysis and enriches the city image in the form of a portrait. The experimental findings demonstrate that the elements of the annular layer, functional landmarks, ring line boundaries, and special districts can fully convey the spatial picture of Zhengzhou City. The performance of the four types of image elements has a high degree of matching with the content of the urban spatial planning of Zhengzhou City, which can effectively identify the urban multi-center structure and development pattern. This paper explores and tests the development status of the city from a new perspective, which can provide an effective reference for the future planning and sustainable development of the city.

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City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms

Cited by 4 publications

References 9 publications

Artificial intelligence for improving public transport: a mapping study

Artificial intelligence for improving public transport: a mapping study

Computer Vision on a Parking Management and Vehicle Inventory System

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

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