Analyzing the Accuracy of Historical Average for Urban Traffic Forecasting Using Google Maps

Rezzouqi, Hajar; Gryech, Ihsane; Sbihi, Nada; Ghogho, Mounir; Benbrahim, Houda

doi:10.1007/978-3-030-01054-6_79

Cited by 10 publications

(5 citation statements)

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“…Traffic-related data is extracted from Google Traffic, as described in [51], which periodically captures Google Traffic maps as images and then applies image processing to extract the level of congestion on the main roads of the city.…”

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confidence: 99%

“…For the traffic data set, data collection was performed using a novel method that consists of exploiting Google Traffic maps using image processing [51]. We first started by selecting the area of interest, which includes the roads close to the nomadic sensor placement (a distance of 2 m), and launched an automatic program that obtains screen captures of the traffic map each 5 min.…”

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confidence: 99%

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MoreAir: A Low-Cost Urban Air Pollution Monitoring System

Gryech

Ben-Aboud

Guermah

et al. 2020

Sensors

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MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.

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confidence: 99%

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confidence: 99%

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

Gryech

Ben-Aboud

Guermah

et al. 2020

Sensors

Self Cite

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“…The scarcity of traffic sensors on Moroccan roads makes measuring the traffic flow a difficult task. Thus, we use a method of urban traffic data collection which consists of exploiting Google Traffic maps using image processing [ 40 ]. Traffic information is extracted from Google Traffic maps, which estimates the level of congestion on the main roads of the city of Rabat, the number of vehicles, as well as the occupancy rate on a road.…”

Section: Moreair Data Setmentioning

confidence: 99%

An Exploration of Features Impacting Respiratory Diseases in Urban Areas

Gryech

Ghogho

Mahraoui

et al. 2022

IJERPH

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Air pollution exposure has become ubiquitous and is increasingly detrimental to human health. Small Particulate matter (PM) is one of the most harmful forms of air pollution. It can easily infiltrate the lungs and trigger several respiratory diseases, especially in vulnerable populations such as children and elderly people. In this work, we start by leveraging a retrospective study of 416 children suffering from respiratory diseases. The study revealed that asthma prevalence was the most common among several respiratory diseases, and that most patients suffering from those diseases live in areas of high traffic, noise, and greenness. This paved the way to the construction of the MOREAIR dataset by combining feature abstraction and micro-level scale data collection. Unlike existing data sets, MOREAIR is rich in context-specific components, as it includes 52 temporal or geographical features, in addition to air-quality measurements. The use of Random Forest uncovered the most important features for the understanding of air-quality distribution in Moroccan urban areas. By linking the medical data and the MOREAIR dataset, we observed that the patients included in the medical study come mostly from neighborhoods that are characterized by either high average or high variations of pollution levels.

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“…After that, the traffic data are cross-matched and validated using historical data found from several local transport departments and private data providers [29,30]. The use of realtime traffic data from "Google Maps" is upturning in the fields of transport geography, accessibility, route optimization, and traffic impact analysis [31][32][33], and the accuracy of the data had also been substantiated for different spatial and temporal dimensions [34]. This real-time traffic data was also used in this research to apprehend the journey speed.…”

Section: Conceptualizationmentioning

confidence: 99%

Effectiveness of containment strategies and local cognition to control vehicular traffic volume in Dhaka, Bangladesh during COVID-19 pandemic: Use of Google Map based real-time traffic data

et al. 2021

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Background To prevent the viral transmission from higher infected to lower infected area, controlling the vehicular traffic, consequently public movement on roads is crucial. Containment strategies and local cognition regarding pandemic might be helpful to control vehicular movement. This study aimed to ascertain the effectiveness of containment strategies and local cognition for controlling traffic volume during COVID-19 pandemic in Dhaka, Bangladesh. Method Six containment strategies were considered to explore their influence on traffic condition, including declaration of general holiday, closure of educational institution, deployment of force, restriction on religious gathering, closure of commercial activities, and closure of garments factories. Newspaper coverage and public concern about COVID-19 were considered as local cognition in this research. The month of Ramadan as a potential event was also taken into account considering it might have an impact on the overall situation. Average daily journey speed (ADJS) was calculated from real-time traffic data of Google Map to understand the vehicular traffic scenario of Dhaka. A multiple linear regression method was developed to comprehend the findings. Results The results showed that among the containment strategies, declaration of general holiday and closure of educational institutions could increase the ADJS significantly, thereby referring to less traffic movement. Besides, local cognition could not significantly affect the traffic condition, although the month of Ramadan could increase the ADJS significantly. Conclusion It is expected that these findings would provide new insights into decision-making and help to take appropriate strategies to tackle the future pandemic situation.

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Analyzing the Accuracy of Historical Average for Urban Traffic Forecasting Using Google Maps

Cited by 10 publications

References 5 publications

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

An Exploration of Features Impacting Respiratory Diseases in Urban Areas

Effectiveness of containment strategies and local cognition to control vehicular traffic volume in Dhaka, Bangladesh during COVID-19 pandemic: Use of Google Map based real-time traffic data

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