Completed Projects / Project 03

PROJECT 03 · GPS NAVIGATION & FLEET TRACKING

Low-Cost GPS-Based Navigation and Tracking System

Design and Implementation of a GPS-Based Navigation and Fleet Tracking Platform for Intelligent Transportation, Autonomous Vehicles, and Drones.

Developed a cost-effective GPS-based navigation and vehicle-tracking platform that combines real-time tracking, offline trip recording, interactive geospatial visualization, online fleet monitoring and comprehensive trip analysis.

Vehicles record their positions during daily operations; trips are then replayed and analyzed on a local server without continuous internet connectivity, or remotely through an online version in a web browser. Speed, distance, stops, driver behavior and estimated fuel consumption come together in one fleet-management workflow.

Open Live Demo Explore the System Watch Demonstrations
  • GPS Navigation
  • Raspberry Pi 4
  • Offline Fleet Tracking
  • Trip Playback
  • Geospatial Visualization
  • Fuel Analysis

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Trip playback screenshot: a red car marker moves along a green recorded route on a street map. An overlay shows vehicle speed, distance travelled, estimated fuel used, trip duration and time; a control bar at the top adjusts playback position, playback speed and the speed limit.
Project software Recorded trip replayed on the digital map, with live speed, distance, estimated fuel and duration.
Hardware
GPS receiver + Raspberry Pi 4 Model B with local storage
Operating modes
Offline recording, local or online server, real-time tracking
Analysis
Playback, speed violations, stops, distance, duration, fuel estimate
Field use
Five-unit fleet trial, followed by a request for 30 units

How it is built

System Architecture and Hardware

The system uses a GPS receiver, a Raspberry Pi 4 Model B single-board computer and local storage to record vehicle movements. During a trip the GPS receiver supplies geographic coordinates and time information, and the system records the data locally for later processing.

Data path from the vehicle to the analyst

Implemented system

  1. GPS Receiver

    Geographic coordinates and time

  2. Raspberry Pi 4 Model B

    On-vehicle computer that records the trip

  3. Local GPS Data Storage

    Positions and timestamps kept on the device

  4. Data Transfer

    When the vehicle returns, or as required

  5. Offline or Online Server

    Local serverOnline server

  6. Map Visualization & Trip Analysis

    Playback, reports, speed, stops, distance, fuel

System architecture overview: GPS module and antenna feed an embedded processing unit that parses GPS data, calculates the trajectory and logs data to a local data store; data is then transferred to a ground-station interface for offload, trip playback and fleet analysis. Key design principle: no real-time internet dependency.
Project illustration Architecture summary: recording on the vehicle, analysis at the ground station, no dependency on a live internet link.

Recording now, analyzing later

Recorded trips can later be transferred to a company server for playback, reporting and analysis. Depending on operational requirements, the platform uses a local offline server, an online server, or both.

The GPS tracking unit: a compact black rectangular enclosure with a raised 'GPS Tracking' emblem on the lid and a small status light on the front edge.
Tracking unit The GPS Tracking Version 01 enclosure installed in vehicles.

GPS Tracking Version 01

GPS Tracking Version 01 — Offline Fleet Tracking and Analysis System

The offline system works like a vehicle black box. Instead of continuously transmitting GPS data over mobile networks, it records the journey locally. When the vehicle returns to the company, the recorded data is transferred to a local server or an online server, according to the customer's requirements.

When local recording is the better choice

  • Mobile-network coverage is poor or unavailable.
  • The company wants to reduce recurring SIM and cloud-service costs.
  • Vehicles travel across borders or through remote areas.
  • The organization prefers to keep vehicle-location data within its own infrastructure.
  • Historical trips must be reviewed after a vehicle returns.
  • Driver behavior and speed violations need to be investigated.
Illustration: trip data is copied from the device's local storage card to a laptop, then reviewed in dedicated software on a desktop screen for trip playback, visualization and fleet analysis.
Project illustration Trip data moves from the device's local store to the company computer for fleet-level analysis.

Offline workflow

On the vehicle At the company Analysis & reporting
  1. On the vehicle

    Collect

    GPS coordinates and timestamps are collected during vehicle operation.

  2. On the vehicle

    Store locally

    The recorded data is kept on the unit, with no live connection required.

  3. At the company

    Transfer

    The data is transferred to the company's local or online server.

  4. At the company

    Select vehicle & trip

    The analyst chooses the company, vehicle, date and time window to review.

  5. Analysis

    Replay the journey

    The recorded trip is replayed on an interactive digital map.

  6. Analysis

    Analyze the trip

    Speed, distance, stops, route history and other trip indicators are reviewed.

  7. Reporting

    Report

    A comprehensive trip report is generated and can be printed or saved as PDF.

Inside the software

Interactive Trip Playback and Vehicle Analysis

The playback application turns a stored trip back into motion on a digital map, so an analyst can see where the vehicle went, how fast it travelled, where it stopped and whether it exceeded the configured limit.

  • Interactive digital-map visualization
  • Smooth map zooming and navigation
  • Vehicle trajectory visualization
  • Playback of historical trips
  • Adjustable playback speed
  • Vehicle speed and position over time
  • Configurable speed limit for the trip review
  • Detection of speed-limit violations
  • Identification of stops and stopping duration
  • Total trip distance and duration
  • Review of route and driver behavior

From trip selection to printed report

Screenshots of the playback software, in the order an analyst uses it. Select any image to enlarge it.

  1. Car Tracking playback-configuration window: company and car name, playback date with the number of available records, start and end time, speed limit, playback speed, fuel rate in litres per 100 km and screen resolution, with Load and Start Playback buttons.
    STEP 01 Configure the playback Choose the company, vehicle, date and time window; the tool reports how many records are available. Speed limit, playback speed and fuel rate are set before starting.
  2. Trip playback on a street map: the vehicle marker follows its recorded route while an overlay shows speed, distance, estimated fuel and trip duration, and the coordinates under the cursor are shown in the corner.
    STEP 02 Replay the trip on the map The vehicle follows its recorded trajectory with running speed, distance, estimated fuel and duration. The control bar scrubs through the trip and changes playback speed and speed limit.
  3. Trip report window in Arabic: playback period, company, vehicle, fuel consumption, trip time and trip distance, followed by a table of speed-limit violations with speed, limit, distance, start and end time and duration, and a table of stops with their durations. Buttons to close, save as PDF and print.
    STEP 03 Review the trip report Trip summary (Arabic interface) with distance, duration and estimated fuel, followed by every speed-limit violation and every stop with its duration.
  4. Printable trip report page: trip summary table, speed-violations table and stops table, with the number of records and the configured speed limit in the footer.
    STEP 04 Print or save as PDF The same report laid out for printing or saving as a PDF document.

What the analysis looks for

Explanatory illustrations; the values shown in them are examples, not measured results.

  • Illustrative speed-profile chart: vehicle speed plotted against trip distance with a dotted posted-speed-limit line; the segment above the limit is shaded and labelled as a violation event.
    Illustration Speed profile against a posted limit: the segment above the line is reported as a violation.
  • Illustration titled Monitoring Driver Safety and Behavior: a map with a route segment highlighted as a speed violation inside a speed zone, beside notes on speed profiles and violations and on reconstructing vehicle position over time.
    Illustration Speed violations and position-over-time playback support driver-behavior review.

Web platform

Online GPS-Based Vehicle Tracking and Fleet Analysis

I developed an online GPS-based vehicle tracking and fleet-analysis platform designed for companies operating multiple vehicles that need reliable monitoring, historical analysis, and actionable operational insights.

The system processes GPS latitude, longitude, and time information to reconstruct vehicle trajectories and calculate movement-related indicators.

Through a web-based interface, authorized users can access the platform from desktop computers, tablets, and mobile phones. The system supports remote vehicle monitoring, route visualization, trip-history analysis, and reporting.

The platform is designed to complement the offline version, giving organizations the flexibility to choose online monitoring, local-server processing, or a combination of both.

Key analysis capabilities

  • Vehicle location and route history
  • Vehicle speed and movement analysis
  • Speed-limit violation detection
  • Stops and stopping duration
  • Total trip distance
  • Trip duration and timestamps
  • Fuel-consumption estimation
  • Driver-behavior and driving-performance analysis
  • Vehicle utilization and operational performance
  • Comprehensive trip and vehicle reports

Fleet-level analysis

Project illustrations of the analysis views; the figures in them are examples. This portfolio page does not connect to live vehicle telemetry.

  • Illustration titled Analytics for Fleet Efficiency and Performance: a tablet showing a trip performance report with route, total distance, duration, idle time and estimated fuel, next to notes on distance and trip duration, stopping and parking events, and estimated fuel consumption.
    Illustration Per-trip performance: distance, duration, idle time and estimated fuel.
  • Illustration titled Fleet Analysis: data from trucks, vans and cars is aggregated into one dashboard so managers can compare drivers and vehicles, find inefficiencies in routes and schedules, and set safety and efficiency benchmarks.
    Illustration Aggregating trips from every vehicle to compare drivers, routes and vehicles.

Field testing

Real-Time GPS Tracking and Validation

A real-time version was implemented and tested on a moving car, using a GPS receiver connected to a Raspberry Pi. During operation the system calculated the vehicle's position, speed and travelled distance.

These results were compared with the car's dashboard speedometer and trip meter to evaluate the tracking calculations. The real-time results were also compared with those obtained from offline trip playback of the same kind of recorded data.

This is practical engineering validation against the instruments a driver already trusts, not a laboratory-grade accuracy certification.

Comparisons performed

  • Calculated speedGPS + Raspberry Pi, real time VS SpeedometerVehicle dashboard
  • Calculated distanceGPS + Raspberry Pi, real time VS Trip meterVehicle dashboard
  • Real-time resultsLive calculation during the drive VS Offline playbackPost-trip calculation

Qualitative summary of the tests; no numerical accuracy figures are claimed.

Why distance matters

Reliable distance calculation is the foundation for route analysis, vehicle-utilization reporting and fuel-consumption estimation. If distance is wrong, every indicator built on it inherits the error.

Fuel figures are estimates. Calculated fuel consumption is derived from distance and a configured fuel rate. Real fuel usage also depends on vehicle condition, engine characteristics, load, route, traffic and driving conditions.

Coordinates to map

Geospatial Processing and Accurate Map Visualization

GPS latitude and longitude describe positions on the curved surface of the Earth. Before they can be drawn on a two-dimensional map, they need appropriate coordinate processing.

The project uses custom geodetic transformation algorithms to convert geographic coordinates into map-compatible coordinates, accounting for the relevant geometric and projection effects.

The goal is that recorded trajectories are plotted consistently against the digital map, and that the displayed routes correspond as closely as practical to the underlying geographic positions. In the playback screenshot above, the route follows the road network and the latitude/longitude under the cursor is shown in the corner of the map.

Infographic titled From Raw GPS to Precision Maps: raw latitude and longitude input, geodetic transformation with custom algorithms, Earth-curvature compensation and 2D map projection output; a vehicle path aligned with the road network; and interactive map features such as live coordinate readout under the mouse cursor.
Project infographic Processing chain: raw coordinates → geodetic transformation → curvature handling → 2D map projection.

Case study

From GPS Tracking to Fleet Cost Analysis

A fuel-consumption question from a fleet owner, answered with validated distance data and a linear-regression model.

  1. GPS Data
  2. Distance Calculation
  3. Trip Analysis
  4. Fuel-Consumption Analysis
  5. Linear Regression
  6. Maintenance Investigation
  7. Fleet-Cost Optimization
What happened Analysis and indication Outcome
  1. Trial

    Five units, five cars

    A company owner deployed five GPS tracking units across five cars as a trial.

  2. The question

    How accurate is the fuel figure?

    After three days, the owner asked about the accuracy of the fuel-consumption calculations.

  3. Foundation

    Distance already validated

    The system's distance calculations had been evaluated on real trips against vehicle dashboard measurements.

  4. Observation

    Two similar vehicles differ

    The owner compared two similar vehicles under comparable conditions and observed a significant difference in fuel consumption.

  5. Analysis

    Linear regression

    Historical trip data and a machine-learning linear-regression model were used to study how operating conditions relate to fuel consumption.

  6. Indication

    Inspection warranted

    The analysis indicated substantially higher consumption for one vehicle, suggesting mechanical inspection and maintenance attention.

  7. Outcome

    30 units requested

    Impressed with the distance and fuel-analysis results, the owner requested 30 GPS Tracking Version 01 units for the fleet.

What the model does and does not show. The regression model supports analysis and decision-making: it flagged a vehicle whose consumption did not fit the pattern of comparable vehicles. It does not, by itself, establish the mechanical cause; that requires inspection of the vehicle. No fuel-saving percentage or model-accuracy figure is claimed here.

Infographic titled GPS Tracking Version 01, Transforming Fleet Data into Cost Savings: distance calculations validated against vehicle dashboards, machine-learning linear-regression fuel analysis, high versus normal fuel consumption prompting maintenance decisions, the expansion of the initial five-unit trial, and the motto Record, Analyze, Improve.
Project infographic From validated distance to fuel analysis, maintenance decisions and fleet expansion.
GPS Tracking Version 01 unit on a dark background: black enclosure with a raised 'GPS Tracking' emblem, a status light on the front and an antenna connector on the side.
Tracking unit GPS Tracking Version 01, the unit requested for the 30-vehicle fleet.

Planned development

Future Enhancement — GPS and IMU Data Fusion

Planned · not yet implemented This section describes future development. GPS/IMU fusion is not part of the current tracking platform.

Integrating the GPS receiver with an Inertial Measurement Unit (IMU) could improve navigation continuity when GPS signals are temporarily unavailable or unreliable, for example in tunnels or dense urban environments.

The planned system would combine GPS measurements with inertial data to estimate vehicle motion and keep a position estimate during short periods of GPS interruption.

Inertial navigation errors accumulate over time. How good the estimate remains depends on the sensors, their calibration, the fusion algorithm and how long GPS is unavailable, which is why the approach targets short interruptions rather than replacing GPS.

Concept diagram: a GPS module providing global position at a low rate and an IMU module providing acceleration and angular velocity at a high rate both feed a sensor-fusion algorithm, which outputs a state estimate of position, velocity and orientation.
Concept GPS (absolute position) and IMU (motion between fixes) feeding one state estimator.

Planned development

Future Enhancement — Path Guidance for Autonomous Vehicles and Drones

A proposed extension that reuses the platform's positioning and map processing to give a navigation or control system guidance information along a predefined route.

Proposed approach Planned

  1. Define routes using geographic waypoints.
  2. Compare the estimated vehicle position with the predefined path.
  3. Use GPS and, where appropriate, IMU measurements to estimate motion.
  4. Calculate path-following deviations.
  5. Provide guidance information to a navigation or control system.

Potential applications include autonomous ground vehicles, intelligent transportation and unmanned aerial vehicles (UAVs), where a vehicle must follow a known path and report how far it is from it.

Concept map: a car at its start position at waypoint 1 with a desired trajectory drawn through waypoints 2, 3, 4 and 5 along city streets.
Concept A desired trajectory defined by geographic waypoints.
Concept infographic on path guidance with GPS-IMU sensor fusion: path definition from waypoints, GPS and IMU data fused by a state estimator, comparison of actual trajectory with the predefined path and position error over time, and applications in self-driving cars and drones.
Concept The planned guidance loop: waypoints, fused state estimate, deviation from the path.

Implemented today vs. proposed next

Implemented: GPS tracking platformRecording, offline and online trip analysis, playback, reports and real-time tracking on a car.
Proposed: path guidanceWaypoint following and deviation output for a navigation or control system. Not a self-driving system, autonomous flight controller, obstacle-avoidance system or safety-certified navigation system.

See it working

Project Demonstrations

Recorded walkthroughs of the GPS tracking system.

  • Video 1

    GPS Tracking System Demonstration

    Video demonstration will be available soon.

    Video 1 — GPS Tracking System Demonstration

    Project 3.mp4
  • Video 2

    GPS Tracking System Demonstration (Arabic)

    Video demonstration will be available soon.

    Video 2 — GPS Tracking System Demonstration (Arabic)

    Project 3_ar.mp4
  • Video 3

    Additional Project Demonstration

    Video demonstration will be available soon.

    Video 3 — Additional Project Demonstration

    last.mp4

Skills applied

Engineering Capabilities Demonstrated

  • GPS Navigation and Tracking

    • GPS data acquisition
    • Position, speed and distance calculations
    • Real-time tracking
    • Historical trip reconstruction

  • Embedded Systems

    • Raspberry Pi 4 Model B
    • GPS receiver integration
    • Local data recording and storage

  • Geospatial Systems

    • Geographic-coordinate processing
    • Geodetic transformations
    • Digital-map visualization
    • Vehicle trajectory rendering

  • Data Analysis

    • Trip playback and reporting
    • Speed-limit violation analysis
    • Stop detection
    • Fuel-consumption estimation
    • Linear regression for fleet analysis

  • Software and System Integration

    • Offline and online architectures
    • Database-backed tracking applications
    • Web-based fleet monitoring
    • Data-driven operational reporting

Where it fits

Practical Applications

Any organization that runs vehicles and needs to know where they went, how they were driven and what they cost can use the platform. The autonomous and UAV entries are research directions tied to the planned enhancements above.

  • Fleet management
  • Logistics and transportation
  • Oil and gas operations
  • Construction
  • Agriculture
  • Industrial operations
  • Vehicle rental
  • Bus and transportation services
  • Intelligent transportation
  • Autonomous vehicles (research)
  • UAV and drone navigation (research)

Record the Journey.Analyze the Journey.Improve the Fleet.

Offline operation where networks are weak, online monitoring where they are not, historical trip analysis, geospatial visualization and decision support for operations and maintenance, in one platform. The value lies in better-informed decisions, not in guaranteed savings.

Contact & Collaboration

Running a Fleet or Building Navigation Systems?

I welcome discussions about GPS tracking, fleet analytics, geospatial processing and navigation research for intelligent transportation, autonomous vehicles and drones.

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