Navigation App Research Articles

Machine-Learning Applications in Thrombosis and Hemostasis.

The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applications that we can no longer do without, such as navigation apps or translation software. However, many people are still unsure if ML algorithms should be used in medicine in their current form. Doctors are doubtful to what extent they can trust the predictions of algorithms. Shortcomings in development and unclear regulatory oversight can lead to bias, inequality, applicability concerns, and nontransparent assessments. Past mistakes, however, have led to a better understanding of what is needed to develop effective models for clinical use. Physicians and clinical researchers must participate in all development phases and understand their pitfalls. In this review, we explain the basic concepts of ML, present examples in the field of thrombosis and hemostasis, discuss common pitfalls, and present a methodological framework that can be used to develop effective algorithms.

Blind indoor navigation app for enhancing accessible tourism in smart cities

Purpose People with visual impairments or blindness (PwVIB) are mostly excluded from tourism activities. Despite the rise of assistive technology (AT) solutions in Tourism, acceptance remains low because of the difficulty of providing the right functionality, effectiveness and usability. Arguably, it can be said that disability-oriented training can affect the latter two and, therefore, an AT solution’s acceptance. This paper aims to contribute to the theory development and conceptualization of technology acceptance of AT solutions in Tourism by studying, in the context of the Unified Theory of Acceptance and Use of Technology (UTAUT), the effects of training PwVIB on using AT solutions. This study presents the effects of training on the tourism behavior of PwVIB and provides valuable information to the stakeholders. Design/methodology/approach Questionnaire data collected from 128 PwVIB after evaluating an AT were subjected to exploratory and confirmatory factor analysis and structural equation modeling followed by post-evaluation interviews. The used application, called BlindMuseumTourer, enables high-precision autonomous indoor navigation for PwVIB in tourist places like museums and places of health care. Findings The results of this study indicate the partial satisfaction of the extended model validating the importance of performance expectancy and training (the new factor) in predicting the behavioral intention of PwVIB tourists toward using ATs during their tourist activities. This suggests that practitioners have to provide performant technological solutions accompanied by special training sessions for improved engagement and satisfaction. Originality/value This study contributes to the UTAUT theory in the context of Tourism for PwVIB by adding a new factor and replacing two moderator variables. To the best of the authors’ knowledge, no similar work is studying AT acceptance by PwVIB in the tourism literature. Furthermore, the validation process used a novel indoor navigation application, demonstrating its effectiveness in the Tactual Museum of Greece.

Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 min on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 min. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 h, approximately 113 times faster than the same simulation scenario running on an Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz, which takes 4.49 h to complete. This performance not only demonstrates its speed and scalability advantages over traditional simulation techniques but also highlights LPSim’s unique position as the first traffic simulation framework that is scalable for both single- and multiple-GPU configurations. Consequently, LPSim provides an invaluable tool for individuals and extensive research teams alike, enabling the acquisition of large-scale traffic simulation results in a time-efficient manner. LPSim code is available at: https://github.com/Xuan-1998/LPSim

A specialized inclusive road dataset with elevation profiles for realistic pedestrian navigation using open geospatial data and deep learning

Built environment characteristics can greatly influence pedestrians' route choices with factors beyond distance, such as accessibility, convenience, safety, and aesthetics, playing crucial roles. Although current navigation apps, such as Google Maps and Waze, have successfully provided driving directions, their navigation services are insufficient and sometimes unrealistic for addressing pedestrians' needs, largely due to the lack of dedicated pedestrian networks and the associated navigation algorithms. To address the research gaps, this paper proposes a novel approach that integrates freely available geospatial data and computer vision technology to create a specialized inclusive network dataset for outdoor pedestrian navigation. Moreover, a pedestrian navigation algorithm is developed to generate more practical “shortest” and “alternative” paths by incorporating various sidewalk attributes. We applied the method to create a pedestrian navigation network in Las Vegas. SpaceNet's open imagery dataset was used to extract Las Vegas's road networks. A virtual audit process assessed the visual and operational properties of the sidewalk networks using Google street-level images, evaluating factors including sidewalk presence, widths, surface types and conditions, missing curb ramps, greenery, protection from weather conditions, and lighting. Google Earth's open elevation data were used to analyze road elevation profiles as meaningful 3D indicators of sidewalk accessibility for wheelchair users. Further, additional geometric properties of the network, including road curviness, proximity to road intersections, and directional changes, were detected and analyzed. A navigation experiment conducted with individuals having varying mobility abilities, including regular pedestrians, older adults, and wheelchair users demonstrated the effectiveness of the newly developed network and algorithm in meeting the diverse needs of pedestrians.

Investigating the Role of Artificial Intelligence in Developing Eco-Friendly Assistive Technologies for People with Disabilities

Current research indicates that artificial intelligence has immense scope to further the cause of assistive technology in improving the quality of life for persons with disabilities by rendering customized support to mobility aids, visual aids, hearing aids, and smart homes. AI-driven devices make communication, adaptive learning, and independence easier for all, with innovations in prosthetics, wheel chairs, and satellite navigation apps such as Google Maps and Moovit. Voice- activated AI-powered smart devices, like Amazon Echo and Google Home, facilitate independent living with voice activation of light and appliances. AI is also in OrCam to further autonomous living. The study examines that artificial intelligence has immense scope to further the cause of assistive technology in enhancing the quality of life for people with disabilities by providing customized support to mobility aids, visual aids, hearing aids, and smart homes. AI makes environmental sustainability a part of the life cycle of assistive technologies—from design to the use of materials, energy efficiency, and e-waste recycling. Efficient waste management is made possible through AI-based sorting systems and smart recycling bins. Blockchain brings transparency into these processes. It is in social integration and economic efficiency that the following devices and services related to sustainable assistive technology can create environmental sustainability, empowering persons with disabilities, reducing healthcare expenditure, and infusing green practices toward an all-inclusive and sustainable world. UN News, Assistive Ware.

Accessible routes: Evaluation and implementation of an inclusive indoor navigation app for people with disabilities

Accessible routes: Evaluation and implementation of an inclusive indoor navigation app for people with disabilities

Impact of navigation apps on congestion and spread dynamics on a transportation network

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to known fastest paths. Key traffic metrics, including flow, density, speed, travel time, delay time, and queue lengths, are assessed to evaluate the outcomes. Additionally, we explore congestion propagation at various levels of navigation app adoption. To understand congestion dynamics, we apply a susceptible–infected–recovered (SIR) model, commonly used in disease spread studies. Our findings reveal that traffic system performance improves when 30–60% of users follow dynamic routing. The SIR model supports these findings, highlighting the most efficient congestion propagation-to-dissipation ratio when 40% of users adopt dynamic routing, as indicated by the lowest basic reproductive number. This research provides valuable insights into the intricate relationship between navigation apps and traffic congestion, with implications for transportation planning and management.

Understanding the Utilization of Real-Time Traffic Information during Hurricane Evacuations in Texas

Understanding how evacuees use real-time traffic information is crucial for developing effective emergency evacuation response plans for hurricane-prone areas. This paper investigates how such data were used during past hurricane evacuations and post-evacuation returns in Texas with a survey dataset collected between August 2022 and February 2023. We examined the usage patterns of various platforms, including navigation apps, social media, TV, radio, and information provided by public agencies. We found that a larger household size, longer distance to evacuation destinations, and past experience with hurricane evacuations are associated with greater use of real-time information platforms. Experienced evacuees tend to rely on navigation apps and social media, and those with experience before 2010 are more inclined to use the TV and radio as their primary sources of information. Motivation for using these platforms varies among users of different platforms. Although both navigation app users and social media users value their familiarity with the platform, the former also prioritize the convenience of using it. It was also found that TV users prioritize service accessibility, radio users emphasize service availability, and users of official agency information sources place a high value on data accuracy. These findings have implications for policymakers, emergency planners, and traffic engineers involved with disaster response operations to improve the resilience of transportation systems.

Driving in Cars with Noise

Driving in Cars with Noise

Controlling Traffic Congestion in a Residential Area via GLOSA Development

The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society’s constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what causes congestion. The consequences are well-known: delays, air pollution, reduced speed, and dissatisfaction (which may lead to risky maneuvers, reducing pedestrian and other driver safety). Our objective is to simulate the change in traffic patterns brought about by app users in residential areas (using navigational tools like Google Maps and Apple Maps), where the majority of navigational tools provide shortcuts that go through residential areas. In addition to discouraging navigation apps from directing drivers through residential areas during peak hours to mitigate pollution levels, by developing an algorithm based on the technology of Green Light Optimized Speed Advisory (GLOSA) and implementing it in a simulated environment (VISSIM), we can see the effect of changing the duration of red lights while keeping green lights constant. Overall, this solution can be implemented to change the times of traffic lights without the need for supplies, additional equipment, or warning signs because most cities’ traffic lights are already remotely controlled. In addition, this procedure is temporary to provide some freedom and does not adhere to the speed specified for drivers who wish to pass through residential areas outside of rush hour.

The effect of flow experience on reuse intention of mobile navigation apps: The mediating role of location-based mobile service quality

This study aims to determine the effect of consumers' flow experience on their intention to reuse mobile navigation apps and reveal the mediating role of location-based mobile service quality in this effect. Data were collected through an online survey of 513 respondents who were actively using mobile navigation apps and analyzed using structural equation modeling and mediation analysis. The results reveal that flow experience affects the intention to reuse mobile navigation apps and that location-based mobile service quality mediates this effect. When the mediating role of location-based service quality was tested, the direct effect of flow experience on reuse intention became insignificant. Information, reliability, and design quality are the most significant dimensions of location-based service quality. Considering the limited number of studies on mobile navigation services, particularly user behavior, this study contributes substantially to services marketing literature. In addition, this study provides insights for practitioners on how to design service proposals and the realization processes of mobile navigation apps.

Achieving social routing via navigation apps: User acceptance of travel time sacrifice

Trip information and navigation systems are expected to become key components of future traffic management strategies, which, if properly exploited, may contribute to the mitigation of car usage externalities. In this study, we investigate social routing recommendations, which could be associated with nudges, and delivered via a navigation app, aiming at promoting sustainable routing behavior, where some drivers are asked to take longer routes and make travel time sacrifices (TTS). In particular, we propose a framework including data collection and behavioral modeling to identify the impacts of various types of information delivered to drivers, goals of the detour, and personal characteristics on drivers’ TTS behavior. The methodology includes stated choice and revealed choice experiments in two European cities, Amsterdam and Helsinki, and a mixed ordered-response logit model to provide insights into TTS behavior. Our analyses show that delivering different information and nudges results in different levels of TTS. However, regardless of the goal of the detour, offering incentives to drivers enables achieving a higher level of TTS. Comparing the stated and revealed data, regarding TTS and compliance rate, also clarifies significant differences between these two types of data.

Field Evaluation of a Mobile App for Assisting Blind and Visually Impaired Travelers to Find Bus Stops.

GPS location-based navigation apps are insufficient to aid blind and visually impaired (BVI) travelers for micro-navigation tasks, such as finding the exact location of bus stops. The resulting large gaps could lead to BVI travelers missing their bus. We evaluated the ability of a signage detection mobile app, All_Aboard, to guide BVI travelers precisely to the bus stops compared to Google Maps alone. The All_Aboard app detected bus stop signs in real-time via smartphone camera using a deep neural network model, and provided distance coded audio feedback to help localize the detected sign. BVI individuals used the All_Aboard and Google Maps app to localize 10 bus stops each in downtown and suburban Boston, Massachusetts. For each bus stop, the subjects used both apps to navigate as close as possible to the physical bus stop sign, starting from 30 to 50meters away. The outcome measures were success rate and gap distance between the app-indicated location and the actual physical location of the bus stop. The study involved 24 legally blind participants (mean age [SD] = 51 [14] years; 11 [46%] women). The success rate of the All_Aboard app (91%) was significantly higher than the Google Maps (52%, P < 0.001). The gap distance when using the All_Aboard app was significantly lower (mean = 1.8, 95% confidence interval [CI] = 1.2-2.3meters) compared to the Google Maps alone (mean = 7, 95% CI = 6.5-7.5meters, P < 0.001). All_Aboard micro-navigation app guided BVI travelers to bus stops more accurately and reliably than a location-based macro-navigation app alone. The All_Aboard app together with a macro-navigation app can potentially help BVI individuals independently access public transportation.

The co-development of a mobile navigation app in an integrated care network in Ontario, Canada

Introduction: The complexity of navigating health and social care services is a common challenge to both providers and patients and their families. In the nineties, navigation programs arouse to assist cancer patients accessing services. Since then, those programs expanded to additional patient populations. Navigation programs can take various forms including virtual navigation. Virtual navigation in healthcare is a proactive process by which patients obtain information and support via internet resources to manage their illness demands.&#x0D; Approach: With a commitment to facilitate 24/7 access to its attributed population, Burlington Ontario Health Team (BOHT)-an integrated care network in Ontario- developed a mobile navigation app. Developing this app utilized experienced-based co-design approach. This approach took place via organizing a working group that included all stakeholders within the BOHT network of organizations and patient/caregiver partners. Over several months, the working group built potential personas of users, mapped their system navigation needs, created an inventory of navigation services within the network and designed the navigation app. Creation of the app was an iterative process including several testing and feedback loops to improve content and usability.&#x0D; Results: The first iteration of Burlington Navigation app was launched in Fall of 2021 accompanied by a comprehensive communication plan. The plan included digital signals at the hospital and primary care offices, social media posts and presentations to local providers including primary care physicians, community navigators. The first iteration focused on linking to health, social and community services offered by BOHT’s members and collaborators, and also services that were widely used in the community. Based on the feedback we received afterwards, the second iteration included links to provincial services. The third iteration includes capability to create a trusted account verified in the background by the same technology Interac uses. This will allow secure and seamless access to integrated services without a need for a separate username and password as well as access to the provincial patient portal&#x0D; Implications/highlights: Burlington Navigation app was the first community-based navigator app co-designed with patient partners, providers and community volunteers with lived experiences within the health and social care systems in Ontario. It resonated well with the users (providers and patients/caregivers) as local information became available at their fingertips. The success of the app allowed it to guide other OHTs on a similar digital navigator design journey. It also became the blueprint for a provincial solution for a multi-tenant navigator, which would allow multiple OHTs to showcase the local navigation information (based on geolocation) as well as provincial offerings.&#x0D; Conclusion: Sharing our journey co-developing the Burlington navigation app demonstrates that when local health integrated care networks utilize experienced-based co-design approaches to planning healthcare interventions, this has the potential to a) develop solutions that are well perceived by patients and providers and b) scale and spread these local solutions to larger-scale solutions that can improve the experiences of a wider spectrum of providers and patients.&#x0D;

The Effect of Crowdsourced Police Enforcement Data on Traffic Speed: A Case Study of The Netherlands

The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.

Real-World Navigation With Application: Characteristics of Gaze Behavior and Associated Factors in Older Adults.

Advancing age might impair real-world navigation ability. The use of mobile devices by older adults has grown rapidly in recent years. Navigation applications (apps) in mobile devices may facilitate the freedom of outings for older adults. Our aim is to investigate age-related differences in real-world app-based navigation walking in terms of accuracy, efficiency, and gaze behavior; and to explore clinical factors associated with navigation walking in older adults. A total of 20 community-dwelling older adults and 16 young adults completed a route navigation task using a navigation app while recording their gaze behavior. Outcomes were compared in both groups and a general linear regression was used to explore clinical factors associated with app-based navigation walk in older adults. Compared with young participants, older participants had more stops and root errors and less fixation time, smaller amplitude of saccades. Additionally, older adults were more likely to glance at their smartphones while app-based navigation walking. Furthermore, gait speed and the following assessment scores were significantly associated with navigation walking in older adults: Mini-Mental State Examination, Life-Space Assessment, and the short version of the Mobile Device Proficiency Questionnaire. For app-based navigation walks, differences in accuracy and gaze behavior were found to exist with age. Additionally, efficient real-world navigation walks in older adults require the extent of life space and proficiency with mobile devices, along with walking speed and cognitive function. It is possible that age-related functional decline, such as the visual field and shifting attention between mobile devices and the real world, may have influenced the results. The study also suggests the need to understand the level of proficiency with mobile devices so that older adults can continue to go out freely. These findings give the basis for providing older adults with appropriate navigation assistance.

Uses of Wayfinding Tools by People Who Are Blind and Low Vision.

This paper presents the results of a research study of people who are blind or low vision about their experiences using wayfinding tools. The results present the accessibility issues when using wayfinding tools and assistive technology to learn about new locations. An online survey followed by a series of interviews was conducted with ten people who identify as blind and six with low vision to learn their opinions and concerns about accessibility of three types of wayfinding tools, digital maps, navigation apps and camera apps.

Impact of Navigation Aid and Spatial Ability Skills on Wayfinding Performance and Workload in Indoor-Outdoor Campus Navigation: Challenges and Design

Wayfinding is important for everyone on a university campus to understand where they are and get to where they want to go to attend a meeting or a class. This study explores the dynamics of mobile navigation apps and the spatial ability skills of individuals on a wayfinding performance and perceived workload on a university campus wayfinding, including indoor-outdoor navigation, by focusing on three research objectives. (1) Compare the effectiveness of Google Maps (outdoor navigation app) and MazeMap (indoor-outdoor navigation app) on wayfinding performance and perceived workload in university campus wayfinding. (2) Investigate the impact of participants’ spatial ability skills on their wayfinding performance and perceived workload regardless of the used navigation app. (3) Highlight the challenges in indoor-outdoor university campus wayfinding using mobile navigation apps. To achieve this, a controlled experiment was conducted with 22 participants divided into a control (using Google Maps) and an experiment group (using MazeMap). Participants were required to complete a time-bound wayfinding task of navigating to meeting rooms in different buildings within the Gløshaugen campus of the Norwegian University of Science and Technology in Trondheim, Norway. Participants were assessed on spatial ability tests, mental workload, and wayfinding performance using a questionnaire, observation notes and a short follow-up interview about the challenges they faced in the task. The findings reveal a negative correlation between overall spatial ability score (spatial reasoning, spatial orientation, and sense of direction) and perceived workload (NASA TLX score and Subjective Workload Rating) and a negative correlation between sense of direction score and total hesitation during wayfinding task. However, no significant difference was found between the Google Maps and the MazeMap group for wayfinding performance and perceived workload. The qualitative analysis resulted in five key challenge categories in university campus wayfinding, providing implications for designing navigation systems that better facilitate indoor-outdoor campus navigation.

Examining data imbalance in crowdsourced reports for improving flash flood situational awareness

The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data (such as user-generated flood reports) for flash flood mapping and situational awareness, little attention has been paid to data imbalance issues that could introduce biases in data and assessment. To address this gap, in this study, we examine biases present in crowdsourced reports to identify data imbalance with a goal of improving disaster situational awareness. Three biases are examined: sample bias, spatial bias, and demographic bias. To examine these biases, we analyzed reported flooding from 3-1-1 reports (which is a citizen hotline allowing the community to report problems such as flooding) and Waze reports (which is a GPS navigation app that allows drivers to report flooded roads) with respect to FEMA damage data collected in the aftermaths of Tropical Storm Imelda in Harris County, Texas, in 2019 and Hurricane Ida in New York City in 2021. First, sample bias is assessed by expanding the flood-related categories in 3-1-1 reports. Integrating other flooding related topics into the Global Moran's I and Local Indicator of Spatial Association (LISA) revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the data sets—FEMA damage, 3-1-1 reports, and Waze reports—at the census tract level and census block group level. By looking at two geographical aggregations, we found that the larger spatial aggregations, census tracts, show less data imbalance in the results. Through a regression analysis, we found that 3-1-1 reports and Waze reports have data imbalance limitations in areas where minority populations and single parent households reside. The findings of this study advance understanding of data imbalance and biases in crowdsourced datasets that are growingly used for disaster situational awareness. Through addressing data imbalance issues, researchers and practitioners can proactively mitigate biases in crowdsourced data and prevent biased and inequitable decisions and actions.

Antecedents of Waze Mobile Application Usage as a Solution for Sustainable Traffic Management among Gen Z

The study aimed to investigate Gen Z’s intentions to utilize the Waze mobile application as a solution for sustainable traffic management in Metro Manila. The extended technology acceptance model (TAM) was used to define user behavior and technology usage to determine the factors influencing Filipino drivers’ acceptance of the Waze application. The study utilized a survey given to 300 Filipino drivers travelling within the National Capital Region to accurately measure various factors and their connections to usage intention and actual use. The analytical approach used for this investigation was partial least squares structural equation modelling (PLS-SEM). The study’s data results revealed that system quality, perceived location accuracy, perceived usefulness, and perceived ease of use affect Gen Z’s intentions to use traffic navigation applications, particularly Waze. The study’s results can be applied as a theoretical framework for future researchers of Waze mobile applications, Waze route accuracy, and Waze overall application performance, as well as the features of the Waze application, to evaluate how the use of the Waze mobile application could help in the effective management of traffic situations in Metro Manila. The study assessed the effectiveness of the Waze navigation app as a platform for efficiently managing traffic situations in Metro Manila and highlighted the application performance of Waze and its acceptance by Gen Z users in Metro Manila. The study also highlighted the need a critical review of Waze’s application performance. The study recommends that similar future studies must consider samples from a more diverse geographic backgrounds to obtain a more accurate representation of Filipino users. Future researchers are also advised to adopt a qualitative research design to gain more detailed insights into Filipinos regarding their experience with the Waze application.