Anti-Theft Vehicle Tracking System Using GPS and Location Prediction

A vehicle tracking system is very useful for tracking the movement of a vehicle from any location at any time. An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of popular technology that combines a smartphone with an Arduino UNO. This easy to make and inexpensive compared to others. The designed in vehicle device works using Global Positioning System (GPS) and Global System for Mobile Communication(GSM) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle those positions is to be determined and tracked in real time. A vehicle tracking system is an electronic device installed in a vehicle to enable the owner or a third party to track the vehicle's location. This paper proposed to design a vehicle tracking system that works using GPS and GSM technology, which would be the cheapest source of vehicle tracking and it would work as anti-theft system. It is an embedded system which is used for tracking and positioning of any vehicle by using Global Positioning System (GPS) and Global system for mobile communication (GSM). An Arduino UNO is used to control the GPS receiver and GSM module. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time interval. The GSM module is used to transmit and update the vehicle location to a database. This paper gives minute by minute update about vehicle location by sending SMS through GSM modem. This SMS contain latitude and longitude of the location of vehicle. Arduino UNO gets the coordinates from GPS modem and then it sends this information to user in text SMS. GSM modem is used to send this information via SMS sent to the owner of the vehicle. Location is displayed on LCD. And then Google map displays location and name of the place on cell phone. Thus, user able to continuously monitor a moving vehicle on demand using smartphone and determine the estimated distance and time for the vehicle to arrive at a given destination.

Evaluating traffic networks is crucial for administration of roadway systems, to better address congestion, safety, and air quality issues around the globe. However, challenges in implementation abound, including major investment costs, large and dynamic data streams and a need for real-time response. Recently developed Global Positioning System (GPS) data loggers are a promising tool for traffic monitoring, thanks to their low cost, ready availability on smartphones, and ability to simultaneously track many travelers and vehicles, relative to expensive, built-in traffic GPS. GPS data from many travelers provides real-time details of traffic conditions and can improve active traffic management using various big-data analytics. We demonstrate how to couple such GPS data to estimate relative roadway speeds in order to improve system management. By analyzing real-time traffic surveillance software with high data coupling and concurrent processing, a new coupling method for real-time traffic evaluation is proposed. Experimental results show efficient coupling of all available GPS data with road condition can improve traffic state estimation accuracy. This new method may increase matching accuracy by more than 1 m in vehicle position. Over 98% of GPS data can be successfully matched to service routes when the low-cost GPS devices are used to detect real-time traffic conditions. The results of traffic network evaluation could well serve as a driving assistant for connected and autonomous vehicles (C/AVs) and other traffic operations.

Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

In the globalization era, the vehicle tracking system is important and it is needed in the fleet asset management system. This management system has a significant role in good logistics distribution in order to measure mileage of truck traveling. Therefore, the existence of the global positioning system (GPS), information system, and network technology can help to monitor the fleet system. GPS devices, from anywhere and anytime, send JSON information by POST method in HTTP to the information system server through internet connection. Furthermore, the stakeholders, the customer and the company of fleet service, can measure the real distance which is gathered from GPS. Contribution of this research is to find the best distance measurement gathered by micro-segmentation technique and summarization of each segment by Haversine formula. Micro-segmentation in this research is performed by modification of how often data sent to the server. Furthermore, we use RMS value that shows correlation, which is obtained by comparing our system result to the real odometer, Google Maps, and GPS data measurement. The obtained RMS of our system exceeds 0.9005 which compared to GPS data. From the result we obtain most optimal repeat duration for sending data by tuning duration with value of every 30 seconds.

The 2002 Denali fault, Alaska, earthquake ( M w 7.9) caused one or more components of most broadband seismometers in western Canada to clip, yet did not trigger strong-motion instruments, thus leaving a substantial gap in the seismic record of this event. However, the large-amplitude surface waves generated by this event were well recorded by the Global Positioning System (GPS) in the same region, out to epicentral distances of more than 3000 km. In this article, we explore the capabilities of GPS seismology, specifically how the relative strengths of GPS and seismic data sets can be exploited in order to more effectively study earthquake source characteristics and wave-propagation effects. High-rate (1-Hz) GPS data from 23 stations throughout western North America have been analyzed to derive displacement waveforms for this event, and the impact of instrumentation (GPS receiver model) and error-reduction strategy (modified sidereal and spatial filtering) on the noise characteristics of displacement time series at each GPS site was assessed. After applying error-reduction methods to GPS displacements, the final average noise floors of 0.5 cm in the horizontal and 1.5 cm in the vertical indicate that large dynamic displacements are observable by GPS. We validate the GPS displacements by comparing broadband seismic recordings (integrated to displacement) with GPS recordings for four effectively colocated sets of instruments. We show excellent agreement between the unclipped seismic and GPS recordings of the surface waves from the Denali earthquake over the period range of 10–50 sec and for ground displacements exceeding about 1.0 cm. Thus, a large GPS displacement data set is now available for western North America, an area where records of this event were previously missing or incomplete. The final GPS seismograms are archived at Incorporated Research Institutions for Seismology (IRIS) for public use in future studies of the 2002 Denali earthquake.

Acquiring global positioning system (GPS) data can help researchers understand the relationship between older adult’s location/activity and their health, but existing GPS data collection modes suffer from issues with precision, consistency, and acceptability. Some limitations of current GPS mobile apps are satellite service disruption, power saving mode interference, miscalibration of device’s compass, and user-error during data collection which result in missing or inaccurate data. We present a novel multilingual GPS mobile app and live administrative dashboard to address these limitations. The app performs triangulation between GPS satellite data, nearest cellular tower, and WiFi router location data which are processed through a unique GPS algorithm with a mean error of only 1.5 meters. Since GPS data results in coordinates commonly reverse geo-coded to place-based datasets, the accuracy is critical in determining place type or other characteristics. The app is additionally designed with redundancy and fault tolerance in mind to run continuously in the background of a user’s smartphone, so there is no data loss from accidentally closing the application or restarting the phone. Moreover, this new application has a flexible sampling rate ranging from 1 second to 20 minutes. Sampling frequency flexibility can adjust the battery life and reduce the need to charge as frequently. Last, application programming interface (API) integrations allow for live data stream, which can improve monitoring and troubleshooting in the field. For research with older adult populations, these features can not only improve the accuracy of collected data but also increase the acceptability of collecting GPS data.

Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package

The capabilities of Ashtech XII for differential GPS (Global Positioning System) are discussed. Differential algorithms and communication protocols to exchange differential information between receivers are included in the internal software of the receiver. Hence, there is no need for external computers to provide the protocol link. The all-in-view capability of the receivers makes them suitable for reference stations as well as remotes. Integration of ASHTECH XII with the vehicle tracking system (VTS) is presented. Differential GPS messages are transferred via the VTS communication link. The integration of the VTS and differential GPS has resulted in an accurate vehicle tracking and positioning system called differential GPS/VTS.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Mobility-based exposure assessment of air pollution has been proposed as a potentially more valid approach than home-based assessments. However, methodological uncertainties in operationalizing mobility-based assessment may still increase inaccuracies in estimating exposures. It remains unclear whether using short-term mobility data and yearly average air pollution concentrations is reliable for estimating personal air pollution exposure. This study aimed to assess variability in exposure estimates modeled by short- and long-term global positioning system (GPS) data and air pollution maps with yearly and monthly temporal resolutions. We tracked 428 participants for a short period (14days) with a GPS device and for a long period (several months) with a smartphone application. Exposure estimates of nitrogen dioxide, ozone, and fine particulate matter (PM10 and PM2.5) were computed based on GPS data, air pollution maps, and temporal and indoor/outdoor adjustments. The concordance correlation coefficient (CCC) indicated excellent agreement (0.85-0.99) between exposure estimates based on short- and long-term GPS data from smartphones but ranged from moderate to excellent (0.57-0.99) when comparing exposure estimates based on data from different devices. Agreement between yearly and monthly map-based estimates was poor to moderate without temporal adjustment (CCC: 0-0.63) but excellent after temporal adjustment (CCC: 0.92-1.0). The findings suggest that using short-term (i.e., 7 or 14days) GPS data and yearly average air pollution concentrations in mobility-based assessments can well represent long-term mobility and yearly averages for determining long-term exposures. However, GPS data collected via dedicated devices and smartphones may identify distinct indoor/outdoor patterns, affecting the indoor/outdoor adjustments of exposure estimates. Additionally, careful selection of using yearly or monthly maps is advised for assessing exposures within specific short periods.

A vehicle tracking system assists public transportation users in their movements by providing real-time information on the locations of vehicles in transit. Public transportation in parts of developing nations, especially Nigeria is ineffective. The system is chaotic and frustrating, especially at peak traffic periods. In a bid to address the problem, a vehicle tracking system was developed as a component of an Advanced Public Transportation System to improve commuting in an urban arterial. The developed system is based on wireless technologies of the Global Positioning System (GPS) and Global System for Mobile Communication module. It records and displays real-time vehicle location using a GPS-based greedy forwarding algorithm, computes route distance information using distance-time based algorithm and radar range sensor (RRS). A pseudo-range mathematical model using the Haversine formula was adopted in determining the accurate position of an object during signal transmission from GPS satellites to the receiver message module. The minimum inversion matrix method was used for the GPS-based geometric dilution of precision (GDoP) selection of satellite approximation and distance. Atmega328P controller chip was used as the logical control unit for processing activities in the tracking system and programming in Arduino IDE using C-language. The system was deployed to a university transportation system in Nigeria: a journey to and from the Bosso and Gidan Kwano Campuses route in the Federal University of Technology, Minna. The vehicle tracking system was tested with 11 tracked satellite and minor dilution error (PDoP error = 1.9, HDoP=0.9, and VDOP=1.7) was recorded. The system is efficient and accurate in distance and time information display with a minor delay. The system would enhance fleet management schemes for urban arterial and can be adopted universally.

Travel mode imputation using GPS and accelerometer data from a multi-day travel survey

Defining the source region of the Indian Ocean Tsunami from GPS, altimeters, tide gauges and tsunami models

Abstract— Vehicle tracking system using GPS and GPRS integration googlemap provide information that is maximized with the technology of GPS (Global Positioning System) receiver which can indicate the position of the vehicle with the map and the ability of the appointment of direction and position coordinates (x, y or latitude, longitude) textually and visual at any location. Vehicle tracking system is built using equipment GIS (Geographic Information System) and dedicated to smartphones which support Global Positioning System (GPS), as well as portable computers, using General Packet Radio Service (GPRS) as a connection to the internet. The hardware used is a smartphone that supports GPS with supporting tools used are GoogleMaps, Notepad + +, NetBeans IDE 6.5.1, Mysql, Java (J2ME), PHP, JavaScript. The results of this system in the form of tracking system capable of monitoring the movement of vehicles on an ongoing position by utilizing GPS and GPRS as the sender of the wireless data and Internet connections. Keywords— GIS, GPS, GPRS, googlemaps, J2ME,

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.