Towards Vehicle Emission Estimation from Smartphone Sensors

Lehmann, Anders; Gross, Allan

doi:10.1109/mdm.2017.29

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…This will help in curtailing the air pollution and waiting time for vehicles at traffic signals. 36,37,38 Multi modal transport system including use of electricity in plying trains for the commuters of the city and international practice for Metro rail systems.…”

Section: Resultsmentioning

confidence: 99%

RETRACTED: Environmental and health hazards of fuel adulteration and its detection, safeguards

Babu

Krishna

Mani

et al. 2020

International Journal of Electrical Engineering & Education

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In day to day the usage of the petroleum products are increasing. The utility can be foreseen in the fields of transportation, energy generation, domestic etc. These traders are en cashing the usage to their profits. This is causing health hazards so also the environmental pollution. This has long impact on the ecosystem. The blending of fuels with other similar fuels can detect with the conventional distillation process so also the present computational techniques.

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Section: Resultsmentioning

confidence: 99%

RETRACTED: Environmental and health hazards of fuel adulteration and its detection, safeguards

Babu

Krishna

Mani

et al. 2020

International Journal of Electrical Engineering & Education

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show abstract

“…Figure 6 presents the decision matrix for the review of the state-of-the-art related to the polluting emissions monitoring algorithms. Most used techniques proposed as alternatives to compute vehicle emissions are fuzzy logic approaches [ 47 ], neural networks [ 49 , 50 ], statistical methods [ 48 ], deep learning techniques [ 72 ] and phenomenological models [ 12 , 73 ]. In this case, inertial and GPS signals are used from smartphones and fuel injection and vehicle acceleration data from vehicles’ ECU.…”

Section: Resultsmentioning

confidence: 99%

“…Some of these approaches use real vehicles for non-risk maneuvers or validated simulation platforms for effective risk tests [ 9 ]. Phenomenological algorithms are used to calculate energy expenditure [ 10 , 11 ], or emissions [ 12 ]. Statistical algorithms and modeling have also been used when calculating driving events and energy consumption [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Campos-Ferreira

Lozoya-Santos

Tudón-Martínez

et al. 2023

Sensors

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This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

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“…In road transport, emissions and other externalities have been assessed in only a limited but growing set of applications (3%). Emissions have been in certain cases metered and analyzed at a finer scale than before: ML approaches range from downscaling national transportation emissions to the street level (Alam, Duffy, Hyde, & McNabola, 2018), to estimating of vehicle emissions from smartphone GPS traces (Lehmann & Gross, 2017), and analyzing of the emissions associated with the current German fleet development (Krause, Small, Haas, & Jaeger, 2016).…”

Section: Vehicles Efficiencymentioning

confidence: 99%

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont

Creutzig

2021

Sustainable Cities and Society

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Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision. climate change mitigation a distant goal, but also making comparisons between local-scale studies difficult (Lamb, Callaghan, Creutzig, Khosla, & Minx, 2018). The IPCC's AR5 reports knowledge gaps on urban climate action (IPCC, 2014): there is too little understanding of the magnitude of the emissions reductions from altering urban form, and emissions savings from integrated infrastructure and land use planning. New analyses are required both to understand relationships (

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Towards Vehicle Emission Estimation from Smartphone Sensors

Cited by 5 publications

References 15 publications

RETRACTED: Environmental and health hazards of fuel adulteration and its detection, safeguards

RETRACTED: Environmental and health hazards of fuel adulteration and its detection, safeguards

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Machine learning for geographically differentiated climate change mitigation in urban areas

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