scholarly journals Real-Time Prediction of Fuel Consumption Based on Digital Map API

2019 ◽  
Vol 9 (7) ◽  
pp. 1369 ◽  
Author(s):  
Qi Zhao ◽  
Qi Chen ◽  
Li Wang
Keyword(s):  
Fuel Consumption ◽  
Application Programming Interface ◽  
Specific Power ◽  
Traffic Condition ◽  
Digital Map ◽  
Digital Maps ◽  
Forecasting Error ◽  
Trip Time ◽  
Application Programming ◽  
Consumption Model

At present, digital maps can estimate the travel time of each trip’s route but cannot offer a fuel consumption estimation at the same time. In this paper, we develop a fuel consumption model based on the Vehicle Specific Power (VSP) distribution, which can connect the traffic condition prediction with the fuel consumption model to predict fuel consumption. First, the traffic condition forecasting and the trip time of each route can be obtained through the digital map Application Programming Interface (API). Secondly, the users need to provide the engine displacement of their vehicles to match the fuel consumption model. Then, the fuel consumption prediction application based on Android is developed to forecast the fuel consumption by using traffic prediction data. Finally, the fuel consumption provided by the On-Board Diagnostic (OBD) data is used to verify the proposed application, and the forecasting error is less than 20%.

scholarly journals Development of Hybrid Vehicle Energy Consumption Model for Transportation Applications—Part II: Traction Force-Speed Based Energy Consumption Modeling

2019 ◽  
Vol 10 (2) ◽  
pp. 22 ◽  
Author(s):  
Siriorn Pitanuwat ◽  
Hirofumi Aoki ◽  
Satoru IIzuka ◽  
Takayuki Morikawa
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Traction Force ◽  
Hybrid Vehicles ◽  
Specific Power ◽  
Mode Transition ◽  
Hybrid Powertrain ◽  
Modeling Methods ◽  
Consumption Model ◽  
Energy Consumption Modeling

In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.

scholarly journals Implementasi Algoritma Dijkstra Dalam Penentuan Jalur Terpendek Di Yogyakarta Menggunakan GPS Dan Qt Geolocation

2013 ◽  
Vol 8 (2) ◽  
Author(s):  
Blasius Neri Puspika ◽  
Antonius Rachmat Chrismanto ◽  
Erick Kurniawan
Keyword(s):  
Information Technology ◽  
Mobile Devices ◽  
Shortest Path ◽  
Application Programming Interface ◽  
Google Maps ◽  
Digital Map ◽  
Dijkstra's Algorithm ◽  
Application Programming ◽  
Programming Interface ◽  
Gps Device

With the development of information technology, the map is no longer in the form of sheet or book. Currently there is a digital map services already invested in mobile devices. Google Maps is one of the leading providers of online digital map which can be accessed using the Application Programming Interface (API) is available using tools such as Qt. Qt is a C + + framework which provides a library to get the location using a GPS device to the library QtGeolocation. By implementing Dijkstra's algorithm, the problem of determining the shortest path towards a desired location of a user's location can be overcome. This study discusses how to implement the algorithm to find the shortest path Djikstra in Yogyakarta-based mobile devices.

scholarly journals Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles

2018 ◽  
Vol 2672 (25) ◽  
pp. 139-149 ◽  
Author(s):  
Lei Zhu ◽  
Jacob R. Holden ◽  
Jeffrey D. Gonder
Keyword(s):  
Fuel Consumption ◽  
Large Scale ◽  
Hybrid Electric Vehicles ◽  
Application Programming Interface ◽  
Fuel Saving ◽  
Data Set ◽  
Global Positioning ◽  
Application Programming ◽  
Road Grade ◽  
Programming Interface

The green-routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation application programming interface (API), route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). Navigation APIs, such as Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin–destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed model. This paper envisions two kinds of applications: (1) identifying alternate routes that save fuel, and (2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey global positioning system trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and travel time due to choosing different routes is also examined for both powertrains.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

Is JUUL marketing to kids on social media? Analysis of JUUL Twitter Followers� Age (Preprint)

2018 ◽  
Author(s):  
Annice Kim ◽  
Robert Chew ◽  
Michael Wenger ◽  
Margaret Cress ◽  
Thomas Bukowski ◽  
...  
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Media Analysis ◽  
Prediction Algorithm ◽  
Social Media Networks ◽  
Verification Methods ◽  
Twitter Account ◽  
Application Programming ◽  
Age Verification ◽  
Age Category

BACKGROUND JUUL is an electronic nicotine delivery system (ENDS) resembling a USB device that has become rapidly popular among youth. Recent studies suggest that social media may be contributing to its popularity. JUUL company claims their products are targeted for adult current smokers but recent surveillance suggests youth may be exposed to JUUL products online. To date, there has been little attention on restricting youth exposure to age restricted products on social media. OBJECTIVE The objective of this study was to utilize a computational age prediction algorithm to determine the extent to which underage youth are being exposed to JUUL’s marketing practices on Twitter. METHODS We examined all of @JUULvapor’s Twitter followers in April 2018. For followers with a public account, we obtained their metadata and last 200 tweets using the Twitter application programming interface. We ran a series of classification models to predict whether the account following @JUULvapor was an underage youth or an adult. RESULTS Out of 9,077 individuals following @JUULvapor Twitter account, a three-age category model predicted that 44.9% are 13 to 17 years old (N=4,078), 43.6% are 18 to 24 years old (N=3,957), and 11.5% are 25 years old or older (N=1,042); and a two-age category model predicted that 80.6% (N=7,313) are under 21 years old. CONCLUSIONS Despite a disclaimer that followers must be of legal age to purchase tobacco products, the majority of JUUL followers on Twitter are under age. This suggests that ENDS brands and social media networks need to implement more stringent age-verification methods to protect youth from age-restricted content.

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