Urban mobility is one of the main challenges in large cities due to historical, geographical, social, and political factors such as rapid population growth, urban sprawl, and lack of adequate infrastructure to manage traffic [1]. The increasing concentration of people in urban areas increases the demand for transport, resulting in congestion, long waiting times, and growing environmental pollution [2]. The lack of efficient and accessible public transport systems in many cities forces residents to rely on private cars, exacerbating traffic problems, increasing carbon emissions, and affecting the citizens' quality of life [3].

In Latin America, cities such as Lima, Mexico City, and Bogotá face serious traffic problems due to rapid urbanization, lack of infrastructure, and increased use of private vehicles, leading to high congestion rates in the region, according to the TomTom traffic index [4]. Various strategies have been explored to improve urban mobility, such as vehicle restraint [5], road pricing [6], enhanced public transport [7], [8], [9], bicycle and pedestrian infrastructure [10], [11], low emission zones (LEZs) [12], car sharing [13], [14], [15], electric vehicle infrastructure [16], urban planning with a '15-minute city' approach [17], teleworking [18], and the use of smart technologies for real-time traffic management [19].

Technological tools such as mobile apps, traffic sensors, big data analytics, and artificial intelligence have become essential to improve the efficiency and sustainability of urban mobility, supporting decision-making and the implementation of intelligent mobility systems.

The Waze application has gained prominence by providing real-time traffic information, including accidents, road works, and other obstacles, to offer alternative routes and improve navigation efficiency [20]. This tool enables user participation, both passively through automatic data sharing and actively by sending information about traffic conditions [21]. Data collected in real-time from applications such as Waze, sensors, and other sources is essential for efficient traffic management. It can reduce travel times, monitor roads, identify congestion patterns, and respond to incidents. Its integration into intelligent transport systems (ITS) improves decision-making and optimizes mobility [22].

This paper describes the implementation of a computer system that uses Waze data to collect, process, store, monitor, and analyze road incidents to support decision-making in urban traffic management in large or densely populated cities.

Four of the most recognised and widely used software development methodologies were analysed for the implementation of the system for monitoring and analysing traffic incidents reported in Waze: Waterfall, RAD, Boehm's six-stage spiral model, and Scrum. The choice was made by considering the advantages of each approach in relation to certain key characteristics relevant to the project. Table 1 compares the characteristics of each method concerning the aspects analyzed.

Table 1

Characteristic	Waterfall	RAD	Scrum	Boehm's six-stage spiral model
Flexibility and Adaptability	Rigid and sequential, making it difficult to incorporate changes once the project has started [23].	Offers more flexibility by focusing on prototypes and rapid delivery. However, it depends on available resources and tools [24].	It is very adaptable because it sprints, which makes it possible to keep adapting according to customer needs [25].	Combines structured planning with continuous risk assessment and feedback. This allows constant adjustments throughout the project [26]
Risk management	It is limited because it only addresses risks at the beginning of the project, making it difficult to manage them at later stages [23].	Although it allows for rapid adjustments, its rapid approach means it struggles to identify long-term risks [24].	With its iterative approach, it manages the risks in each sprint, but it may not provide a complete view of the long-term risks [25].	It is characterised by its ability to manage risks on an ongoing basis, allowing them to be identified, analysed, and mitigated throughout the project through a cyclical planning and evaluation approach [27].
Continuous Feedback and Communication	Limits feedback to certain stages, which can cause a delay in the identification of problems [23].	Allows for more frequent feedback. However, it focuses on specific functionality and may miss important details [24].	Ensures issues are addressed quickly by encouraging continuous feedback through short sprints, daily meetings, and retrospectives [28].	It allows for ongoing evaluation, continuous feedback, and fluid communication between stakeholders, adapting quickly to change and keeping the focus on the project objectives [26].
Error Correction and Continuous Product Improvement	It is the least flexible for error correction because errors are not identified until the end of each phase, which can delay corrections and hinder continuous improvement [23].	Allows for rapid error correction through prototyping and continuous feedback, but its accelerated approach may not allow for deep, long-term correction [24].	Encourage continuous improvement in retrospectives by working in short sprints, making it easier to identify and correct errors quickly [28].	With its cyclical approach and continuous evaluation, it allows early and continuous identification and correction of errors, ensuring continuous improvement in line with the project's objectives, thanks to constant feedback from stakeholders [26].

In summary, the six-stage adaptation of Boehm's spiral model is ideal for dynamic, high-risk projects such as the Waze traffic incident monitoring system because of its flexibility, risk management, continuous feedback, and incremental product delivery. This approach outperforms more rigid methods, such as the waterfall model and agile approaches that prioritise rapid delivery without detailed planning and explicit risk assessment.

Applying the six-stage adaptation of Boehm's spiral model to the development of the proposed system, four iterations were undertaken, allowing incremental improvements and functionality to be incorporated. This approach facilitated risk management, adaptation to change, and ensured that the final product was robust and fit for purpose. Figure 1 illustrates the methodology used, highlighting each of the phases and iterations of the model.

Figure 1

The tasks carried out in each phase were:

Communication: Ongoing communication was established with potential users to identify needs, define goals, and gather relevant information.

Planning: In each iteration, goals, specifications, time, and resources were defined, and components were adjusted and added to the system.

Risk Analysis: Each iteration evaluated potential risks, including technical challenges, tools, resources, and their availability.

Engineering: Performed component analysis and design, recording tasks in Azure DevOps for detailed progress tracking.

Build and Deploy: Software components were built and customized, tested, implemented, and integrated into the system, and related documentation was created.

Evaluation: Obtained feedback from users and experts, reviewed the product globally, and implemented improvements in subsequent releases.

The use of real-time data for urban traffic management is a growing trend worldwide. The works and information systems that have been developed to support this task vary widely in complexity, coverage, and operational approach. Systems such as that of the City of Singapore described in its ITS (Intelligent Transport Systems) report [35], the SCATS system developed by the New South Wales (Australia) Government's Main Roads Department [36], and the ATSAC system in Los Angeles [37] focus on integrating data from multiple sources, such as real-time sensors and cameras, to optimise traffic flows through adaptive traffic signal control. These systems are highly centralised and operate within a robust public infrastructure, intending to maximise urban traffic efficiency on a large scale.

Similarly, proposals such as that of Yisheng, Lv et al. [38] and Mohamed Abdel et al. [39] use adaptive control and predictive algorithms to manage traffic more accurately by making automatic adjustments based on the analysis of large amounts of data. These approaches are designed for rigorous and centralised control, allowing long-term planning and continuous optimisation of the transport infrastructure.

Real-time traffic management systems in Brazil and Barcelona are characterised by the integration of different technologies and data sources, both infrastructure and user data, to monitor and optimise urban traffic. Barcelona [10] uses real-time information on vehicle flows and public transport to improve mobility, while Brazil [40] focuses on planning and operational efficiency by analysing large amounts of data. These approaches prioritise local coordination, technological interoperability, and strategic planning, but require large investments in infrastructure and efficient data integration to be effective.

In contrast, platforms such as Waze, which allow users to report traffic incidents in real time, offer a more decentralised and collaborative approach. While not relying on the centralised infrastructure of the aforementioned systems, Waze relies on the active collaboration of users to share information about accidents, road closures, and congestion. This community feedback enables a more agile response to unexpected traffic events, although its effectiveness depends largely on the volume of active users and the accuracy of the reports. In contrast to centralised traffic management systems, Waze relies more on the democratisation of information, allowing flexibility for rapid response, but with less control over data quality and accuracy.

In summary, traffic systems such as SCATS, ATSAC and those implemented in Singapore, Barcelona and Brazil, as well as those proposed by Yisheng, Lv et al. [38] and Mohamed Abdel et al. [39], are characterised by their ability to handle large amounts of data in a controlled manner and with global optimisation goals. However, real-time data sharing, as facilitated by the Waze for Cities programme, offers a more distributed and cost-effective alternative, allowing users to generate real-time traffic information, but relying more on community input. Both approaches have their advantages and limitations, depending on the specific needs of cities and the infrastructure available.

The Waze for Cities program demonstrates how public-private partnerships can improve the ability of local governments to address the challenges of limited infrastructure and high traffic density. Through the use of mobile technology and collaborative data analysis, traffic management can be more agile and effective.

This partnership has been used in a variety of ways in several Latin American cities. In Mexico, for example, the collaboration with Waze has enabled city authorities to receive real-time data on traffic congestion, helping to make traffic management decisions, especially in areas of high congestion [41]. In Buenos Aires, Waze has facilitated the collection of information on accidents and road closures, allowing drivers to choose alternative routes in real time, contributing to the decongestion of the city, particularly on the main road network [42]. In São Paulo, the system has worked closely with the government to improve mobility planning and reduce the environmental impact of transport, with a particular focus on promoting sustainable mobility and integration with other public transport platforms [43]. Meanwhile, Chile has used Waze to optimise traffic management in Santiago, integrating real-time data with public transport decisions to improve traffic flow during peak hours [44].

In Bogotá, the traffic incident monitoring system uses information from Waze for Cities to improve real-time traffic management. Continuous data analysis identifies patterns that allow for better urban planning and preventive measures. Since its implementation, traffic distribution and incident response have been optimised. In addition, the Mobility Secretariat shares advanced information on closures and events, helping to reduce congestion and improve the driver experience, as well as reducing congestion caused by unforeseen events [45]. Some of the applications and dashboards created with data from Waze are shared on the Mobility Observatory website (https://observatorio.movilidadbogota.gov.co/), in the Integrated Regional Urban Mobility Information System SIMUR (https://www.simur.gov.co/), and others are available at the Bogotá Transit Management Center (CGT), which ratifies their relevance for incident monitoring, decision making and knowledge building in the mobility sector. Similarly, with the large amount of information collected, studies are being carried out to generate congestion indices [46] and analyze the road accident situation [47].

The use of the Waze incident monitoring system for urban traffic management has important practical and theoretical implications. On the practical side, it allows for real-time data collection and improved decision-making to optimise vehicle flow and incident response. On the theoretical side, it raises challenges about data accuracy, as it relies on user participation, and opens up new lines of study on collective intelligence and its application in urban traffic. Questions also arise about the scalability of the system and its integration with emerging technologies such as autonomous vehicles and intelligent transport systems.

On the other hand, interpreting the results of the traffic incident monitoring system provides an interesting picture of real-time traffic dynamics. Data collected from Waze users, including reports of accidents, congestion, obstructions, and adverse weather conditions, provides a detailed and up-to-date view of road conditions. By analysing these incidents, problem areas can be quickly identified, allowing traffic authorities to take appropriate action. However, relying on user reports also presents challenges in terms of accuracy and coverage, as not all incidents are reported, and the information may be subject to bias or delays. Nevertheless, the results generated by this system are a valuable tool for improving urban traffic management, providing dynamic information that can be used to reduce congestion and improve road safety.

The prospects of the IT system for collecting, processing, storing, monitoring, and analysing data on road incidents reported on Waze open up a wide field for innovation and the development of new solutions in traffic management and intelligent mobility. Among the most promising lines of research are the incorporation of artificial intelligence to predict traffic patterns, the development of advanced interactive analysis tools, and the integration with public transport and micro-mobility platforms to promote a more sustainable and efficient ecosystem for cities and their inhabitants.