To date, GPS tracking data for minibus taxis has only been captured at a sampling frequency of once per minute. This is the first GPS tracking data captured on a per-second (1 Hz) basis. Minibus taxi paratransit vehicles in South Africa are notorious for their aggressive driving behaviour characterised by rapid acceleration/deceleration events, which can have a large effect on vehicle energy consumption. Infrequent sampling cannot capture these micro-mobility patterns, thus missing out on their effect on vehicle energy consumption (kWh/km). We hypothesised that to construct high fidelity estimates of vehicle energy consumption, higher resolution data that captures several samples per movement would be needed. Estimating the energy consumption of an electric equivalent (EV) to an internal combustion engine (ICE) vehicle is requisite for stakeholders to plan an effective transition to an EV fleet. Energy consumption was calculated following the kinetic model outline in "The bumpy ride to electrification: High fidelity energy consumption estimates for minibus taxi paratransit vehicles in South Africa".

Six tracking devices were used to record GPS data to an SD card at a frequency of 1Hz. The six recording devices are based on the Arduino platform and powered from alkaline battery packs. The device can therefore operate independently of any other device during tests. The acquired data is separately processed after the completion of data recording. Data captured is initiated with the press of a button, and terminated once the vehicle reached the destination. Each recorded trip creates an isolated file. This allows for different routes to be separately investigated and compared to other recordings made on the same route.

There are 62 raw trip files, all found in the attached 'raw data' folder under the corresponding route and time of day in which they were captured. The raw data includes date, time, velocity, elevation, latitude, longitude, heading, number of satellites connected, and signal quality. Data was recorded on three routes, in both directions, for a total of six distinct routes. Each route had trips recorded in the morning (before 11:30AM) , afternoon (11:30AM-4PM) and evening (after 4PM).

The processed data is available in the 'Processed Data' folder. In addition to the raw data, these processed data files include the displacement between observations, calculated using Geopy's geodesic package, and the estimated energy provided by the vehicle's battery for propulsion, braking, and offload work. The python code for the kinetic model can be found in the attached GitHub link https://github.com/ChullEPG/Bumpy-Ride.

Future research can use this data to develop standard driving cycles for paratransit vehicles, and to improve the validity of micro-traffic simulators that are used to simulate per-second paratransit vehicle drive cycles between minutely waypoints.

Here you find an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected during trips conducted by three drivers driving the same vehicle in Austria.

The dataset contains 20Hz sampled CAN bus data from a passenger vehicle, e.g. WheelSpeed FL (speed of the front left wheel), SteerAngle (steering wheel angle), Role, Pitch, and accelerometer values per direction.

GPS data from the vehicle (see signals 'Latitude_Vehicle' and 'Longitude_Vehicle' in h5 group 'Math') and GPS data from the IMU device (see signals 'Latitude_IMU', 'Longitude_IMU' and 'Time_IMU' in h5 group 'Math') are included. However, as it had to be exported with single-precision, we lost some precision for those GPS values.

For data analysis we use R and R Studio (https://www.rstudio.com/) and the library h5.

e.g. check file with R code:

library(h5)

f <- h5file("file path/20181113_Driver1_Trip1.hdf")

summary(f["CAN/Yawrate1"][,])

summary(f["Math/Latitude_IMU"][,])

h5close(f)

GPS Data (Geolocation) date and time: • Latitude and longitude • Direction of travel (heading) • Speed

Engine Fault Data (Sample of Data Elements): • Vehicle active (driving, idling, stopped) • Odometer • Fuel level (percentage) • Driver seat belt status • Engine operational time

Historical Street Level Context: • Historical Road Level Data and classification at time of event – Understand driving behavior with historical street level detail Trip Data

• Aggregated data elements of vehicle trip (from ignition on to ignition off): • Trip begin and end date and time • Start and End latitude and longitude • Idling Time, Drive Time, Stopped Time • Distance Traveled

Accelerometer Data (Driver Behavior) Acceleration, Braking, and Cornering Events:
• Latitude and longitude of event • Date and Time of Event • G-Force values (x-axis, y-axis)

3.8 M data points collected for driver behavior: More than half of the vehicles monitored are urban based light duty vehicles. • 50% LCV, 30% HCV, 15% MCV

Floating car DatA in an area of the Gulou Zone in Fuzhou, China. The area is the core center of Fuzhou and is densely populated. Floating car trajectories were collected from May 5th, 2018 and the data contain 6,402,027 GPS sample points of 7073 taxes (Dataset, 2020). The trajectories comprise from 10s to 60s sample points with an average sample rate of 20s.

Snapshot img

Car GPS Navigation System Market Size 2024-2028

The car GPS navigation system market size is forecast to increase by USD 9.75 billion at a CAGR of 10.03% between 2023 and 2028.

The market is experiencing significant growth due to the advancements In the automotive industry. With the increasing demand for connected and autonomous vehicles, the integration of GPS navigation systems is becoming essential. Furthermore, the investment in satellite deployment is driving market growth, enabling real-time traffic updates and accurate location tracking.
However, design complexity and technological challenges pose significant hurdles to market growth. Manufacturers must balance the need for advanced features with affordability and user-friendliness to meet consumer demands. Incorporating voice recognition and augmented reality technologies can enhance the user experience and differentiate offerings in a competitive market. Overall, the market is expected to witness steady growth In the coming years, driven by technological advancements and evolving consumer preferences.

What will be the Car GPS Navigation System Market Size During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced in-vehicle technology. These electronic devices, which integrate GPS receivers, computer mapping tools, voice recognition, and real-time traffic updates, have become essential navigation tools for drivers. The integration of voice instructions and satellite link enables hands-free use, enhancing safety while reducing driver distraction. Moreover, the addition of automobile safety features such as emergency assistance and accurate location tracking further bolsters the market's appeal. The integration of banks, gas stations, hospitals, restaurants, and other points of interest into the system enhances convenience for users.
Moreover, ride-hailing services and environmental concerns, including vehicle emissions, are also driving the market's growth. Navigation system gadgets are increasingly being integrated into dashboards, with driver alarms and vehicle tracking capabilities becoming standard features. The market is expected to continue growing as technology advances and consumer demand for real-time traffic information and improved driving experiences increases.

How is this Car GPS Navigation System Industry segmented and which is the largest segment?

The car GPS navigation system industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Software and services
  Hardware


Geography

  Europe

    Germany
    France


  North America

    US


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Component Insights

The software and services segment is estimated to witness significant growth during the forecast period.

Car GPS navigation systems utilize GPS satellites and cellular networks to pinpoint the location and direction of vehicles or assets. TomTom's cloud-native automotive solution offers fast routing, search, and maps, even without an internet connection. Voice assistance technology is increasingly integrated into navigation systems, enhancing user experience and minimizing distractions. Up-to-date maps, lane assistance, traffic congestion alerts, and speed limit warnings are essential safety features. IoT and cloud services ensure map accuracy and real-time traffic updates. User-friendly interfaces and smart connected cars offer integrated navigation solutions, while safety features like warning signals, sirens, and lights provide additional security. GPS data and natural language processing enable efficient and convenient navigation.

Get a glance at the Car GPS Navigation System Industry report of share of various segments Request Free Sample

The software and services segment was valued at USD 7.66 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 32% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The European automotive industry, home to major manufacturers like Volkswagen, BMW, AUDI AG (Audi), and Ford Motor Co., is witnessing significant investment In the production of commercial, electric, and hybrid vehicles. In line with this trend, Ford announced a USD180 million expansion to boost EV power unit production by 70% at its northern England engine facility. Similarly, BMW unveiled plans to invest approximately USD300 milli

This is an example research data dataset for the automotive demonstrator within the "AEGIS - Advanced Big Data Value Chain for Public Safety and Personal Security" big data project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732189. The time series data has been collected by using a BeagleBone single plate computer which has been developed at VIF to collect data for driving analytics. The BeagleBoard can be connected to the OBD2 interface of a vehicle to capture data from CAN bus and has been additionally equipped with further sensors (GPS, gyroscope, acceleration). The data in this research dataset was collected during 35 different trips conducted by one driver driving one vehicle in the Graz area in Austria.

This dataset is used for the classification of traffic intersection regulations using (car) GPS trajectory data. The coverage is mostly a large part of the city of Hannover, Germany.

The overview of the GPS trajectory dataset is given in the below figure: https://data.uni-hannover.de/dataset/379ed322-a9ea-48f3-bc13-2f5ea3174470/resource/ecfbc059-9ca3-4cbc-8774-de091f0fbcd6/download/hannover_traj.png" alt="Overview">

When the trajectory dataset is combined with the related intersection ground-truth information (available at: https://doi.org/10.25835/cqg0x1el): https://data.uni-hannover.de/dataset/379ed322-a9ea-48f3-bc13-2f5ea3174470/resource/bc832922-aedd-4bac-86b3-5d4a753bfecc/download/hannover_rules_traj.png" alt="Combined">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Hannover, Germany. The acquisition period was from December 2017 to March 2019 by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

According to Cognitive Market Research, the global Car GPS Navigation System Market size will be USD 18241.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 7296.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 5472.36 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 4195.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 912.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 364.82 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.
The Passenger Cars held the highest Car GPS Navigation System Market revenue share in 2024.

Market Dynamics of Car GPS Navigation System Market

Key Drivers for Car GPS Navigation System Market

Increasing adoption of advanced driver-assistance systems (ADAS) integrating GPS navigation for enhanced driving safety and convenience.

The car GPS navigation system market is significantly driven by the increasing adoption of advanced driver-assistance systems (ADAS) that integrate GPS navigation for enhanced driving safety and convenience. Modern vehicles are increasingly equipped with ADAS features that include GPS navigation as a core component, providing drivers with real-time directions, traffic updates, and route optimization. These integrated systems enhance driving safety by offering features such as lane-keeping assistance, adaptive cruise control, and collision avoidance, all of which rely on accurate GPS data. As consumers and manufacturers prioritize safety and convenience, the demand for advanced navigation systems embedded within ADAS continues to grow. This trend is fueling the expansion of the car GPS navigation system market, as it has become an essential element of modern automotive technology.

Rising demand for real-time traffic updates and navigation features driving growth in GPS navigation systems.

The growth of the car GPS navigation system market is being driven by the rising demand for real-time traffic updates and advanced navigation features. Consumers increasingly expect their navigation systems to provide accurate, up-to-date information on traffic conditions, road closures, and route optimization to enhance their driving experience. Real-time traffic updates help drivers avoid congested routes, reduce travel time, and improve overall convenience. Advanced navigation features, such as turn-by-turn directions, dynamic rerouting, and integration with mobile apps, further contribute to the appeal of GPS systems. As the need for efficient and informed travel continues to grow, the demand for GPS navigation systems with real-time capabilities is boosting market growth.

Restraint Factor for the Car GPS Navigation System Market

Emergence of smartphone navigation apps providing free or low-cost alternatives to dedicated car GPS systems.

The car GPS navigation system market faces a restraint due to the emergence of smartphone navigation apps that provide free or low-cost alternatives to dedicated car GPS systems. With the widespread availability of smartphones and advanced navigation apps, many consumers now prefer using their mobile devices for navigation rather than investing in separate GPS units. These apps offer a range of features, including real-time traffic updates, voice-guided directions, and route planning, often at no additional cost or for a minimal subscription fee. The convenience and affordability of smartphone navigation apps pose a competitive challenge to traditional GPS systems, potentially limiting their market growth as consumers opt for more cost-effective and integrated solutions.

Impact of Covid-19 on the Car GPS Navigation System Market

The COVID-19 pandemic had a mixed impact on the Car GPS Navigation System market. Initially, the market faced challenges due to reduced travel, lo...

This dataset is used for the classification of traffic intersections regulation rules using car GPS trajectories. In order to apply supervised classification methods, the ground-truth information is also contained in this dataset. The regulators are annotated based on the intersection arms at each intersection.

An overview of the dataset's trajectories can be seen in the subsequent figure: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c5fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png" alt="Overview of the dataset containing the trajectories collected in Edessa.">

An overview of the related intersection regulator ground-truth rules can be seen in the following figure: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c8d75ea3-83ed-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png" alt="">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Edessa, Greece. The acquisition period was from March 2018 to September 2018 (6 month) by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second. Additionally to the GPS trajectories, the ground-truth regulator rules of the traffic intersections were annotated via street view images.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

Dublin Bus GPS sample data (Insight Project) from DCC. Published by Dublin City Council. Available under the license cc-by (CC-BY-4.0).Bus GPS Data Dublin Bus GPS data across Dublin City, from Dublin City Council'traffic control, in csv format. Each datapoint (row in the CSV file) has the following entries:''Timestamp micro since 1970 01 01 00:00:00 GMT'Line ID'Direction'Journey Pattern ID'Time Frame (The start date of the production time table - in Dublin the production time table starts at 6am and ends at 3am)'Vehicle Journey ID (A given run on the journey pattern)'Operator (Bus operator, not the driver)'Congestion [0=no,1=yes]'Lon WGS84'Lat WGS84'Delay (seconds, negative if bus is ahead of schedule)'Block ID (a section ID of the journey pattern)'Vehicle ID'Stop ID'At Stop [0=no,1=yes]...

Context

Location data for Swedish taxi cars during October and November 2018

Content

GPS coordinates for taxi cars sampled appoximatley once per minute.

Columns: - Date and time - Latitude - Longitude - Vehicle ID

Acknowledgements

Courtesy of Nimling AB

Inspiration

Initially used to identify appropriate locations for building charging infrastructure for taxis (see associated notepad). Any support on improving the accuracy of this estimate is greatly appriciated. Should you have other ideas, please let me know.

Dash-Cam GPS Combo Market Outlook

According to the latest research conducted in 2025, the global Dash-Cam GPS Combo market size was valued at USD 4.1 billion in 2024, with a robust compound annual growth rate (CAGR) of 11.2% expected over the forecast period. By 2033, the market is projected to reach a size of USD 11.8 billion, reflecting the rapidly growing adoption of integrated dash-cam and GPS solutions across various sectors. This significant growth is primarily driven by increasing concerns over vehicular safety, the rising need for precise vehicle tracking, and stringent regulatory mandates related to road safety and surveillance.

One of the most prominent growth drivers for the Dash-Cam GPS Combo market is the surge in demand for advanced driver-assistance systems (ADAS) and next-generation telematics. As road safety becomes a paramount concern for both individuals and organizations, the integration of dash-cams with GPS technology offers a dual advantage: real-time video evidence and accurate location tracking. Insurance companies are increasingly incentivizing the use of these devices by offering policy discounts, recognizing their role in reducing fraudulent claims and expediting accident investigations. The proliferation of ride-sharing and delivery services has also contributed to the market's expansion, as these businesses seek to protect their assets and drivers while improving operational transparency.

Technological advancements have further fueled the adoption of Dash-Cam GPS Combo devices. The evolution of high-definition video recording, cloud connectivity, AI-based incident detection, and mobile app integration has transformed these devices from simple recording tools into comprehensive vehicle monitoring systems. The growing consumer preference for smart, connected vehicles—especially in urban environments—has accelerated the uptake of these solutions. Additionally, the decreasing cost of hardware components and the availability of user-friendly interfaces have made Dash-Cam GPS Combos accessible to a broader audience, including small fleet operators and individual vehicle owners.

Regulatory frameworks and government initiatives aimed at enhancing road safety and accountability have provided a strong impetus to market growth. In several regions, authorities are mandating the installation of dash-cams and GPS trackers in commercial and public transportation vehicles to curb reckless driving, monitor compliance, and ensure rapid emergency response. The implementation of such policies, coupled with the increasing incidence of road accidents and vehicle thefts, is compelling both commercial and government entities to invest in advanced surveillance and tracking solutions. As a result, the market is witnessing heightened demand from sectors such as law enforcement, logistics, and public transportation.

Regionally, the Asia Pacific market stands out as the fastest-growing, owing to rapid urbanization, burgeoning automotive sales, and rising awareness regarding vehicle safety technologies. North America continues to dominate in terms of market share, driven by early adoption, robust regulatory support, and a strong presence of technology providers. Meanwhile, Europe is experiencing steady growth, supported by stringent safety norms and a mature automotive ecosystem. Latin America and the Middle East & Africa are emerging markets, with increasing investments in smart transportation infrastructure and growing fleet management needs. Overall, the global Dash-Cam GPS Combo market is poised for sustained expansion, underpinned by technological innovation, regulatory support, and evolving consumer expectations.

Product Type Analysis

The Product Type segment in the Dash-Cam GPS Combo market is broadly categorized into Single Channel, Dual Channel, and Multi-Channel devices. Single channel dash-cam GPS combos, which record footage from the front of the vehicle, remain popular among individual consumers due to their affordability and ease of installati

The dataset contains 20Hz sampled CAN bus data from a passenger vehicle, e.g. WheelSpeed FL (speed of the front left wheel), SteerAngle (steering wheel angle), Role, Pitch, and accelerometer values per direction. Due to an export error, all GPS data is currently 0, but we are currently looking for a solution and will update the record as soon as possible.

This dataset is a subset of a much larger data collection and used for the analysis of speed- and time-profiles of trajectories crossing different selected intersections. Resulting findings can be used for the intersection categorization according to traffic regulation types.

The six selected intersections (A - F) and the crossing trajectory samples (green) can be seen in the subsequent figure: https://data.uni-hannover.de/dataset/1a97c0df-4659-43bd-99e9-d2573d31f5cd/resource/0571e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png" alt="Intersections">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Hannover, Germany. The acquisition period was from December 2017 to March 2019 by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

We strongly recommend that you read this content on the project page on GitHub, by clicking here

Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence

This project aims to develop solutions for vehicular perception through inertial sensor signals and Artificial Intelligence models. Vehicular perception comprises exteroception and proprioception. Exteroception aims to understand the environment outside the vehicle, recognizing the road features on which it travels. These features include transient events in the form of anomalies and obstacles, such as potholes, cracks, speed bumps, etc.; and persistent events, such as surface type, conservation condition, and the road surface quality. On the other hand, proprioception aims to understand vehicular movements to identify their own behavior. These identifications can also be transient in the form of driving events, such as lane change, braking, skidding, aquaplaning, turning right or left; and persistent, as a safe or dangerous driving behavior profile. This situational information (perceptions) has wide applicability in Intelligent Transport Systems (ITS) such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles.

For the development of this project, we collect nine datasets using GPS, camera, inertial sensors (accelerometers and gyroscopes), magnetometer, and temperature sensor. These data were produced with contextual variations including three different vehicles, driven by three different drivers, traveling through three different environments. To recognize and classify the vehicular perception patterns, we have developed several models based on Artificial Intelligence, among Classical Machine Learning and Deep Learning approaches. Below we describe the datasets produced, models developed and the results obtained, together with published scientific papers and source-codes.

Table of Contents

• Vehicular Perception Research
• Passive Vehicular Sensors Dataset
• Data Classes
• Best Models
• Aplications
• How To Cite
• License

Vehicular Perception Research

The project is active and we are currently developing new models for new perception pattern recognition. Below are described the research progress, in chronological order of research development. At the Research Gate you can also find the published scientific papers and request a full text for free.

Research in English

Vehicular Perception Based on Inertial Sensing: a Systematic Review

In this paper, we describe the state-of-the-art vehicle perception produced through inertial sensors and Artificial Intelligence techniques. Through a literature review, we compiled the data extracted from the selected studies and described each paper in detail and chronological order of publication. Access here

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

In this paper, we present a structured literature mapping of the state-of-the-art vehicle perception produced through inertial sensors and Artificial Intelligence techniques. We describe a structured, approach, and technologies-oriented panorama of this field. Access here

[Road Surface Type Classification Based on Inertial Sensors and Machine Learning: A Comparison Between Cla...

The North America automotive navigation system market, valued at $7.74 billion in 2025, is projected to experience robust growth, driven by increasing vehicle production, rising consumer demand for advanced driver-assistance systems (ADAS), and the integration of navigation systems with infotainment features. The market's Compound Annual Growth Rate (CAGR) of 6.50% from 2025 to 2033 indicates a significant expansion, primarily fueled by the adoption of sophisticated navigation technologies such as real-time traffic updates, cloud-based mapping, and augmented reality overlays. The OEM segment is expected to dominate the market due to the increasing integration of navigation systems as standard features in new vehicles. However, the aftermarket segment is also poised for substantial growth driven by the rising popularity of aftermarket upgrades and retrofits in older vehicles. Passenger vehicles constitute a larger portion of the market compared to commercial vehicles, reflecting the higher demand for navigation in personal vehicles. Key players such as Denso, Bosch, and Harman are investing heavily in research and development to enhance navigation system capabilities, creating a competitive landscape marked by innovation. The integration of navigation with other vehicle functionalities, like voice control and smartphone connectivity, is anticipated to further propel market growth. The United States, being the largest automotive market in North America, is likely to contribute significantly to the region's overall market value. Growth within the North American market will be influenced by several factors. Technological advancements, such as the development of more accurate and detailed maps, enhanced user interfaces, and integration with other vehicle technologies, are critical drivers. However, challenges remain, such as increasing data costs associated with real-time map updates and the potential for cybersecurity vulnerabilities within connected navigation systems. Moreover, the increasing adoption of electric vehicles may create opportunities for specialized navigation systems optimized for EV-specific requirements, such as range planning and charging station location. Competitive pressures from established players and new entrants will continue to shape the market dynamics, leading to price competitiveness and ongoing innovation. The increasing demand for personalized and user-friendly navigation experiences will be key in shaping future growth. Recent developments include: In October 2023, Mapbox, Inc. introduced the MapGPT voice navigation system, that employs generative artificial intelligence (AI), as well as the Mapbox Autopilot Map position information system for self-driving vehicles. The MapGPT system incorporates real-time vehicle, destination, and environmental information into the company's location information service, delivering route and desired facility information., In September 2023, Qualcomm Technologies, Ltd. collaborated with Mercedes-Benz AG to offer the most advanced in-vehicle technologies and features to the new Mercedes-Benz E-Class Sedan in 2024. The newest Snapdragon Cockpit Platforms will help power the Mercedes-Benz E-Class User Experience multimedia system, which will operate on the MBUX Superscreen, to provide increased graphics and multimedia capability. MBUX's high-resolution widescreen device will also benefit from Snapdragon Cockpit Platforms' touchscreen control, navigational display, and augmented reality capabilities., In September 2023, BMW Group and Qualcomm Technologies, Inc. expanded their technological partnership to provide the most recent Snapdragon Digital Chassis Solution developments to power BMW Group's new cars. Through this partnership, the company will integrate more safety and comfort features in vehicles. In-car virtual assistance, natural language interactions between the vehicle and the driver, in-cabin monitoring for contextual safety, and accurate positioning for safer and smarter navigation are examples of these.. Key drivers for this market are: Growing Consumer Trend Towards In-dash Navigation System. Potential restraints include: Growing Consumer Trend Towards In-dash Navigation System. Notable trends are: Passenger Car Hold Major Market Share.

Snapshot img

GPS Market Size 2024-2028

The GPS market size is forecast to increase by USD 111.6 million, at a CAGR of 22.1% between 2023 and 2028.

The Global Positioning System (GPS) market is experiencing significant growth, driven by increasing investment in satellite deployment and the rising demand for advanced GPS devices. These trends reflect the market's potential for innovation and expansion. However, connectivity issues with GPS pose a notable challenge. As satellite coverage can be disrupted by various factors, ensuring uninterrupted GPS service remains a critical concern. Companies must invest in robust technologies to mitigate these disruptions and maintain reliable connectivity. To capitalize on market opportunities and navigate challenges effectively, businesses should focus on developing advanced GPS solutions that address connectivity concerns while offering enhanced features and functionality.
By doing so, they can cater to the evolving needs of consumers and industries, positioning themselves as leaders in the dynamic the market. Despite this,the market is expected to continue its expansion, driven by technological advancements and growing applications across various industries, including automotive technologies.

What will be the Size of the GPS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free Sample

The Global Positioning System (GPS) market continues to evolve, with dynamic applications across various sectors. Weather alerts integrated with GPS navigation systems provide real-time information, enhancing safety and convenience for travelers. Head-up displays merge GPS navigation with vehicle data, projecting essential information onto the windshield for easy viewing. Aviation navigation relies on GPS for precise flight tracking and route planning, while autonomous vehicles leverage GPS for positioning and navigation. Automotive navigation systems offer turn-by-turn directions, real-time traffic updates, and subscription models. GNSS receivers provide positioning accuracy for asset tracking in industries like logistics and construction. Smart cities utilize GPS for efficient traffic management, emergency response, and field data collection.

Outdoor navigation systems cater to hikers and adventurers, while security protocols ensure location tracking and positioning accuracy for personal safety. Mapping technologies and navigation services are essential for marine navigation, precision agriculture, and geospatial data collection. Navigation software upgrades, antenna design improvements, and signal strength enhancements continue to drive market innovation. Positioning algorithms and lane guidance systems offer more accurate and efficient navigation solutions. Voice guidance and subscription models cater to diverse user preferences. Road closures and speed limit warnings help optimize travel routes, while satellite positioning and cloud-based services enable remote sensing and real-time data processing.

The ongoing development of GPS technologies and their integration into various industries ensure a continuously evolving market landscape.

How is this GPS Industry segmented?

The GPS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Logistics and transportation
  Construction and mining
  Others


Type

  Handheld GPS Devices
  Vehicle GPS Devices
  Personal GPS Devices
  Asset Tracking Devices
  Smartphone GPS


End-use Industry

  Automotive
  Transportation & Logistics
  Consumer Electronics
  Aerospace & Defense
  Agriculture
  Mining
  Construction
  Healthcare
  Retail & E-commerce


Technology

  GNSS
  A-GPS
  DR-GPS


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The logistics and transportation segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth as businesses increasingly prioritize the optimization of their supply chains. Weather alerts and real-time traffic updates ensure the timely delivery of perishable goods, such as food, maintaining their market value. In the e-commerce sector, GPS navigation systems and voice guidance facilitate on-time delivery, enhancing customer satisfaction. For industries dealing with valuable assets, such as jewelry or electronics, security protocols and location tracking through GPS technology safeguard against the

Automotive Navigation System Market Outlook

As per our latest research, the global automotive navigation system market size stood at USD 37.8 billion in 2024, with a robust annual growth trajectory. The market is expected to expand at a CAGR of 8.2% from 2025 to 2033, reaching a forecasted market size of USD 75.5 billion by 2033. This growth is primarily driven by the increasing integration of advanced navigation technologies in vehicles, rising consumer demand for connected car features, and the rapid proliferation of electric and autonomous vehicles globally.

A key growth factor for the automotive navigation system market is the accelerating adoption of connected vehicles and smart mobility solutions. As consumers increasingly expect seamless, real-time information and enhanced in-vehicle experiences, automakers are prioritizing the integration of sophisticated navigation systems that offer live traffic updates, predictive route planning, and location-based services. The evolution of telematics and the Internet of Things (IoT) ecosystem is further fueling this trend, enabling vehicles to communicate not only with each other but also with infrastructure and cloud-based platforms. This interconnected environment is creating a fertile ground for the expansion of automotive navigation systems, as both original equipment manufacturers (OEMs) and aftermarket players strive to differentiate their offerings through innovative navigation capabilities.

Another significant driver is the tightening of government regulations and safety mandates, particularly in developed markets. Regulatory bodies across North America, Europe, and parts of Asia Pacific are increasingly emphasizing road safety and efficiency, which has led to the widespread adoption of navigation systems equipped with advanced driver assistance features. These systems are designed to provide critical information such as speed limits, hazardous zones, and optimal routes, thereby reducing the risk of accidents and enhancing overall driving safety. Moreover, the rise of smart city initiatives and intelligent transportation systems is pushing automakers to incorporate more advanced navigation solutions that can interact with urban infrastructure, further accelerating market growth.

The rapid electrification of the automotive sector and the emergence of autonomous vehicles are also pivotal in shaping the future of the automotive navigation system market. Electric vehicles (EVs) require specialized navigation solutions that can optimize routes based on charging station locations, battery range, and energy consumption, while autonomous vehicles depend on highly accurate, real-time mapping and navigation data for safe operation. The convergence of these trends is prompting significant investments in navigation software, hardware, and services, driving continuous innovation and expanding the addressable market for navigation system providers. As a result, the automotive navigation system market is poised for sustained growth, underpinned by the ongoing transformation of mobility and transportation paradigms.

Regionally, Asia Pacific is emerging as the dominant market, accounting for the largest share of global revenues in 2024. The region's leadership is underpinned by the rapid expansion of the automotive industry in China, India, Japan, and South Korea, coupled with strong consumer demand for technology-driven vehicles. North America and Europe follow closely, benefiting from high levels of vehicle connectivity, advanced infrastructure, and supportive regulatory frameworks. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, driven by rising vehicle penetration and increasing investments in smart transportation solutions. The regional dynamics underscore the global nature of the automotive navigation system market, with each geography presenting unique opportunities and challenges for market participants.

Product Type Analysis

The automotive navigation system market is segmented by product t

Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data.

https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing

This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.

In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.

• The last digit of each acceleration file: 1, 2, 3, 4, 5
• Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial

LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.

• The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8
• Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.
• x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.

The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).

The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.

Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: (1) within a region, the train always followed the same track and (2) there are no tunnels in them so the GPS data is relatively consistent.

To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.

A data descriptor paper with details of the data collection process was published.

Please cite as

Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. https://doi.org/10.1038/s41597-019-0148-9(2019)

Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, https://doi.org/10.5281/zenodo.1432702(2018).

For questions or suggestions please e-mail Jingxiao Liu

Global Navigation Satellite System (GNSS) Station unavco/gnss/MAGNET/CARS/4229/L0/00:00:15 Name: CARS Processing Level: L0 measurement_technique: gnss variable_measured: position creator:UNAVCO data_start_time:2005-10-07T06:00:30 data_stop_time:2017-01-03T07:00:15 GPS/GNSS instrumentation records broadcast signals from the GPS and other satellite constellation, and these raw data are converted into standard daily RINEX files suitable for processing. GPS/GNSS data are recorded at 15-s or 30-s intervals. Several hundred stations of the PBO network also supply downloaded or streamed 1-s data for archiving and distribution. In addition highrate data of 1 Hz or 5 Hz may be Custom Data Requested in association with an event such as a significant earthquake. For data of all rates UNAVCO translates to RINEX and quality checks the data using teqc. GAGE Analysis Centers process data for all 1100 sites in the PBO GPS/GNSS network and for other sites, including most of the sites in COCONet in the Caribbean region and an additional 500 sites distributed across North America, most of which are operated by other institutions. The final, processed products are SINEX solutions, position ti Web Service Link ['The hydrologic models are surface-loading displacement time series calculated at GAGE-processed sites from hydrological data. Soil moisture, snow-water equivalent from snowpack, and water stored in vegetation exert a load on the Earth's surface that is modeled to obtain displacements at GPS/GNSS sites. Outputs GPS crustal motion velocity field estimates. '] Web Service Link [ 'Results from daily GPS station position solutions are combined to generate long-term velocity estimate solutions of stations in IGS08 and NAM08 (North America fixed) reference frames. Station offsets due to earthquakes and equipment changes are estimated and low-quality outliers due to snow, for example, are removed from the velocity estimate solutions ']