As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

The road safety app market, currently valued at $239 million in 2025, is experiencing robust growth, projected to expand significantly over the next decade. A compound annual growth rate (CAGR) of 8.4% indicates a substantial market expansion, driven by several key factors. Increasing smartphone penetration, coupled with rising public awareness regarding road safety and the demand for convenient, data-driven solutions, are primary drivers. Furthermore, advancements in GPS technology, the integration of features like speed monitoring, driver behavior analysis, and emergency assistance functionalities are enhancing app capabilities and user appeal. The market segmentation reveals a robust presence across both enterprise and personal applications, with iOS and Android platforms catering to diverse user preferences. Competitive landscape analysis shows a mix of established players like Google Maps and Waze alongside specialized road safety apps such as SafetyCulture and Life360, indicating a dynamic market with opportunities for innovation. The market's growth isn't without challenges; data privacy concerns and the need for continuous app updates to maintain accuracy and relevance present ongoing hurdles. Future growth hinges on the successful integration of advanced technologies such as AI and machine learning for improved accident prediction and prevention, as well as fostering stronger collaborations between app developers, governments, and road safety organizations to further promote adoption and efficacy. The forecast period (2025-2033) presents lucrative opportunities for companies to capitalize on the rising demand for sophisticated road safety solutions. Strategic partnerships, technological advancements, and aggressive marketing strategies will be crucial for success. Expansion into emerging markets and the development of personalized safety features tailored to specific demographics are key areas for innovation. The continued integration of telematics data and real-time traffic information will significantly enhance the utility of these apps, attracting both individual users and organizations committed to improving fleet safety. Addressing concerns about data privacy and security through transparent data handling practices will be essential for building trust and expanding market penetration. The long-term outlook for the road safety app market remains exceptionally positive, fueled by technological innovation and growing global awareness of road safety.

Here are a few use cases for this project:

1. Smart Parking Assistance: Utilize the Tarik-Tyane-annotations model to develop an application that assists drivers in locating available parking spots, including valid and invalid spots like handicapped and fire hydrant zones, in real-time, making the parking experience more convenient and efficient.

2. Traffic Management: Integrate the model into city traffic management systems to monitor and manage parking spaces in high traffic areas. By identifying improper parking, such as in fire hydrant zones, bus stops, or handicapped spots, authorities can enforce parking regulations proactively.

3. Urban Planning and Analysis: Use the Tarik-Tyane-annotations model to analyze public parking data, identifying patterns and trends related to parking spots' utilization. This can help city planners make informed decisions on the allocation and distribution of parking spaces, optimizing infrastructure for future growth.

4. Navigation App Integration: Enhance navigation applications like Google Maps or Waze with the model's parking spot information, allowing users to find not only available parking spaces nearby but also information on rules and restrictions in real-time, avoiding fines or inconveniences.

5. Emergency Response: Equip emergency response vehicles, such as fire trucks and ambulances, with the Tarik-Tyane-annotations model to identify fire hydrant locations or restricted parking areas quickly. This can help emergency services to navigate congested areas and ensure unblocked access to critical infrastructure during emergencies.

Here are a few use cases for this project:

1. Road Safety Improvement: Government road maintenance departments or highway authorities can use this model to proactively identify and fix road anomalies, thus dramatically improving road safety and comfort for all road users.

2. Autonomous Vehicles: This model could be integrated into the systems of self-driving cars. It would allow these vehicles to accurately detect road anomalies in real-time and navigate around them appropriately, ensuring a safer and smoother journey.

3. Ride-Share Companies: Companies like Uber or Lyft could use this model to gather data on the condition of roads used by their drivers, and then prioritize routes with fewer road anomalies for the comfort and safety of their passengers.

4. Dynamic Navigation and Mapping Apps: Real-time road anomalies detection could be used to update navigation apps like Google Maps or Waze. This would provide real-time alerts about road conditions to users and suggest alternative routes to avoid problematic areas.

5. Infrastructure Maintenance: Urban planners and city maintenance departments could use this model as a tool to monitor urban infrastructure. It would assist in identifying areas requiring maintenance promptly, thus efficiently planning their repair and maintenance schedules.

Context

Network monitoring and analysis of consumption behavior represents an important aspect for network operators allowing to obtain vital information about consumption trends in order to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over-the-top (OTT) media and communications services and applications are shifting the Internet consumption by increasing the traffic generation over the different available networks. OTT refers to applications that deliver audio, video, and other media over the Internet by leveraging the infrastructure deployed by network operators but without their involvement in the control or distribution of the content and are known by their large consumption of network resources.

Content

This dataset contains 1581 instances and 131 attributes on a single file. Each instance represents a user’s consumption profile which holds summarized information about the consumption behavior of the user related to the 29 OTT applications identified in the different IP flows captured in order to create the dataset

The OTT applications that the users interacted with during the capture experiment and were stored on the dataset are: Amazon, Apple store, Apple Icloud, Apple Itunes, Deezer, Dropbox, EasyTaxi, Ebay, Facebook, Gmail, Google suite, Google Maps, Browsing (HTTP, HTTP_Connect, HTTP_Download, HTTP_Proxy), Instagram, LastFM, Microsoft One Drive (MS_One_Drive), Facebook Messenger (MSN), Netflix, Skype, Spotify, Teamspeak, Teamviewer, Twitch, Twitter, Waze, Whatsapp, Wikipedia, Yahoo and Youtube.

Each application has 4 different types of attributes (quantity of generated flows, mean duration of the flows, average size of the packets exchanged on the flows and the mean bytes per second on the flows). These attributes summarizes the interaction that the user had with the respective OTT application in terms of consumption. Furthermore, the dataset contains the user’s IP address in network and decimal format which are used as user identifiers. Finally the User Group attribute represents the objective class (high consumption, medium consumption and low consumption) in which a user is classified considering his/her OTT consumption behavior. All of this information gives a total of 131 attributes.

For further information you can read and please cite the following papers:

Research Gate: https://www.researchgate.net/publication/326150046_Personalized_Service_Degradation_Policies_on_OTT_Applications_Based_on_the_Consumption_Behavior_of_Users

Springer: https://link.springer.com/chapter/10.1007/978-3-319-95168-3_37

Research Gate: https://www.researchgate.net/publication/335954240_Consumption_Behavior_Analysis_of_Over_The_Top_Services_Incremental_Learning_or_Traditional_Methods

IEEExplore: https://ieeexplore.ieee.org/document/8845576

Attribute Description

The structure of the attributes and its definition is presented below:

• Source.Decimal: This attribute holds the user’s IP address in decimal format and it is mainly used as a user identifier.

• Source.IP: This attribute holds the user’s IP address in network format (e.g., 192.168.14.35) and as in the previous case its main function is to work as a user identifier.

• Application-Name.Flows: This type of attributes hold the information about the quantity of IP flows that a user generated toward an OTT application. As was mentioned before each application has a group of 4 attributes that describe the interaction of the user with a specific OTT application (an example for this case would be Netflix.Flows or Facebook.Flows).

• Application-Name.Flow.Duration.Mean: This type of attributes hold the information related to the mean duration (time) of the flows generated by the user towards a specific OTT application, measured in microseconds. Examples of how this attributes are stored in the dataset are: Amazon.Flow.Duration.Mean or Instagram.Flow.Duration.Mean.

• Application-Name.AVG.Packet.Size: This type of attributes hold the average size of the IP packets that were exchanged in all the flows generated by the user towards a specific OTT application, measured in bytes. It is important to notice that this size is focused on the packet’s header only. Examples of how this attribute are presented on the dataset are: Google_Maps.AVG.Packet.Size or Spotify.AVG.Packet.Size.

• Application-Name.Flow.Bytes.Per.Sec: This type of attributes hold the mean number of bytes per second that were exchanged in the flows generated by the user towards a specific OTT application. Examples of this kind of attributes in the dataset are: Deezer.Flow.Bytes.Per.Sec or Skype.Flow.Bytes.Per.Sec.

• User.Group: This type of attribute represents the objective class of the dataset i.e., the different groups that the users are classified in according to their OTT consumption behavior...