Navigation App Research Articles

When apps rule the road: The proliferation of navigation apps is causing traffic chaos. It's time to restore order

Miguel Street is a winding, narrow route through the Glen Park neighborhood of San Francisco. Until a few years ago, only those living along the road traveled it, and they understood its challenges well. Now it's packed with cars that use it as a shortcut from congested Mission Street to heavily traveled Market Street. Residents must struggle to get to their homes, and accidents are a daily occurrence. The problem began when smartphone apps like Waze, Apple Maps, and Google Maps came into widespread use, offering drivers real-time routing around traffic tie-ups. An estimated 1 billion drivers use such apps in the United States alone. Today, traffic jams are popping up unexpectedly in previously quiet neighborhoods around the country and the world. Along Adams Street, in the Boston neighborhood of Dorchester, residents complain of speeding vehicles at rush hour, many with drivers who stare down at their phones to determine their next maneuver. London shortcuts, once a secret of black-cab drivers, are now overrun with app users. Israel was one of the first to feel the pain because Waze was founded there; it quickly caused such havoc that a resident of the Herzliya Bet neighborhood sued the company.

As open as possible and as closed as needed

As open as possible and as closed as needed

Coordinated Freight Routing With Individual Incentives for Participation

The sharp increase in e-commerce over the last few years has led to an increase in the volume of trucks both in ports and in commercial areas. Truck traffic has a negative impact on traffic flow in general due to the size of trucks and their slower dynamics. The continuously increasing use of navigation apps has led drivers to make their routing decisions in an independent manner in an effort to minimize their own individual travel time, with possible significant deviation from a socially optimum solution. In this paper, we consider the use of coordinated routing in order to achieve load balancing. Users send their OD matrices as well as their preferred departure time to the coordinator who gives them routing instructions based on a socially optimum cost. This design enables us to derive sufficient conditions under which we prove the existence of mechanisms which are truthful in equilibrium, budget balanced on average and create individual incentives for voluntary participation of the truck drivers. Subsequently, we design our mechanism in a way that only uses a minimal set of sufficient conditions in order to guarantee the existence of a solution and maximize its efficiency. Finally, the extensive simulation results of our approach in the Braess and the Sioux Falls networks demonstrate that the proposed mechanism can approach the system optimum solution.

Wayfinding with my iPhone: An autoethnographic account of technological companionship and its affects

Location-aware technologies such as GPS devices and smartphones are integral to everyday, mundane navigational practices or ‘wayfinding’. The personalisation, portability and popularity of these devices means that wayfinding can be accomplished with near-instant access to place-based information. But how do people connect to these devices in more intimate, emotional, and haptic ways? To address this question, this paper draws on autoethnographic fieldwork involving wayfinding devices, using a series of iPhone navigation apps. The paper presents a series of short narratives taken from my field diaries exploring the ways in which I perceived and performed my iPhone device as a companion during my everyday mobilities. The paper focuses on three mechanisms that facilitated relations of companionship: product design, sensory engagement and emotional/affective encounters. Building on these insights, the paper argues that relations of companionship with technological devices come into focus in particular moments when the life cycles of the users and their devices collide. The various emotions and affects, which circulate in these moments, are critical to how we make sense of space, place, and our mobilities, as well as ongoing engagements with human/technology relations.

Joint Representation Learning for Multi-Modal Transportation Recommendation

Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to satisfy the diversified needs of travelers and improve the efficiency of transport networks. However, existing transport recommender systems mainly focus on unimodal transport planning. To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph. Specifically, we first extract a multi-modal transportation graph from large-scale map query data to describe the concurrency of users, Origin-Destination (OD) pairs, and transport modes. Then, we provide effective solutions for the optimization problem and develop an anchor embedding for transport modes to initialize the embeddings of transport modes. Moreover, we infer user relevance and OD pair relevance, and incorporate them to regularize the representation learning. Finally, we exploit the learned representations for online multimodal transportation recommendations. Indeed, our method has been deployed into one of the largest navigation Apps to serve hundreds of millions of users, and extensive experimental results with real-world map query data demonstrate the enhanced performance of the proposed method for multimodal transportation recommendations.

Privacy Implication of Location-Based Service: Multi-Class Stochastic User Equilibrium and Incentive Mechanism

As a tool to assist traffic guidance and improve service quality, location-based service (LBS) platforms such as route navigation apps rely heavily on the collection and analysis of users’ location/trajectory information, which may evoke privacy concerns. Because of such privacy concerns, users may choose not to provide their information. In certain cases, this may lead to the problem of insufficient data for LBS applications (e.g., travel time estimation). To address this issue, the paper develops a modeling framework to quantify the levels of privacy for mixed user groups and proposes an incentive mechanism to encourage users to provide their location/trajectory information. It is assumed that LBS users have smaller travel time perception error but experience some extra privacy costs compared with the non-LBS users. A bi-level optimal incentive model with stochastic user equilibrium and elastic demand is developed to capture the mixed behavior of multi-class network users. The problem is solved using a meta-heuristic approach combined of genetic algorithm, successive average algorithm, and multiple behavior equilibrium assignment algorithm. The results reveal that the modeling framework can capture the mixed behavior of groups with different privacy levels. The proposed incentive mechanism is able to ensure sufficient data, and simultaneously minimize the required incentive.

Expressway bottleneck pattern identification using traffic big data—The case of ring roads in Beijing, China

How to efficiently identify traffic bottlenecks in the expressway network and implement targeted measures is an important issue to mitigate traffic congestion. In recent years, with the proliferation of smartphones, smartphone-based navigation applications are receiving the worldwide popularity, which are being treated as an extremely rich source of traffic data. In this paper, we are devoted to proposing a novel approach to investigate traffic state and identify recurrent bottlenecks quickly and accurately from macroscopic network perspective, through fusing the data sources of fixed detectors and mobile navigation apps. First of all, this paper plots flow-speed fundamental diagram to derive critical speed, which is treated as the criterion to determine traffic state. Once the criterion has been established, typical bottlenecks in the entire expressway network can be efficiently identified through only utilizing smartphone-based probe speed data. Secondly, three pioneering indicators are put forward to quantify oversaturated traffic state and classify bottleneck patterns on urban expressway network. Applying this methodology, we take Beijing expressway network as a case to identify different patterns of bottlenecks, which are validated to comply with the reality. Moreover, the relationship between critical speed and the associated road segment features is explored and shows the possibility of predicting the critical speed even without flow data. As an end, some recommendations are proposed for improving different types of traffic bottlenecks.

How Far Are We from Having Air Quality in Navigation Apps?

Checking traffic using navigation apps has become the norm when we plan routes for car trips. A real-time air quality model with high spatial resolution built into route selecting navigation apps could greatly benefit people doing outdoor exercise or frequent commuting, in addition to the EPA’s Air Quality Index (AQI) forecast (https://www.airnow.gov/) that uses data from federal reference or equivalent monitoring techniques typically at rather coarse spatial resolution, e.g. 10 sites in NYC. This real-time high-resolution air quality model is the long-term goal of the Map My Air project (https://project.wnyc.org/map-my-air/). In this pilot study, output of seasonal spatial surface of fine particulate matter (PM2.5) from a land use regression model based on bi-weekly data from the New York City Community Air Survey (originally 150 monitoring sites, now ~70) adjusted by hourly temporal variation provided from AirNow PM2.5 levels is compared to 3-years of data collected by personal air monitors (RTI microPEM and Aethlabs microAeth AE51) from biking commuters in NYC at 1-minute and approximately 300 m resolution. Model outputs show a median PM2.5 concentration of 11.6 µg/m3, a good agreement with personal monitor observation data of 11.7 µg/m3, but show a much smaller range of predicted PM2.5 (6.6 - 21.5 µg/m3, IQR 10.1 - 13.4 µg/m3) in comparison to that of observation (0.5 - 710 µg/m3, IQR 7.7 - 18.1 µg/m3) and a very weak correlation with observation data due to the inherent incapability of this simple model in predicting extreme values. Real-time traffic data and meteorology data will be added together with the personal air monitor observation data to refine the model’s spatial resolution to street level and improve its temporal sensitivity to sub-hour level.

Design of Wearable Guide Blind Auxiliary System

Aiming at the safety problem of blind people, this design is studied from obstacle detection, fall help, travel navigation and missing alarm. This design uses Gould map SDK to develop a navigation APP suitable for blind people. An obstacle detection system is designed by ultrasonic testing, and obstacles in 5m can be detected. A fall detection system is designed using a gyroscope to quickly detect the state of the blind. After testing, all functions of the system are running well.

Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth

As users of mobile devices make phone calls, browse the web, or use an app, large volumes of data are routinely generated that are a potentially useful source for investigating human behavior in space. However, as such data are usually collected only as a by-product, they often lack stringent experimental design and ground truth, which makes interpretation and derivation of valid behavioral conclusions challenging. Here, we propose an unsupervised, data-driven approach to identify different user types based on high-resolution human movement data collected from a smartphone navigation app, in the absence of ground truth. We capture spatio-temporal footprints of users, characterized by meaningful summary statistics, which are then used in an unsupervised step to identify user types. Based on an extensive dataset of users of the mobile navigation app Sygic in Australia, we show how the proposed methodology allows to identify two distinct groups of users: ‘travelers’, visiting different areas with distinct, salient characteristics, and ‘locals’, covering shorter distances and revisiting many of their locations. We verify our approach by relating user types to space use: we find that travelers and locals prefer to visit distinct, different locations in the Australian cities Sydney and Melbourne, as suggested independently by other studies. Although we use high-resolution GPS data, the proposed methodology is potentially transferable to low-resolution movement data (e.g. Call Detail Records), since we rely only on summary statistics.

Deep learning over IoT big data-based ubiquitous parking guidance robot for parking near destination especially hospital

This paper offered a ubiquitous parking guidance robot system used for assisting drivers in selection of parking lots near their destination locations and recommending the parking lot with optimum conditions based on the forecast results by deep learning the big data of parking lots obtained through Internet of Things, which not only decreased the cost and the required time of parking, but also reduced the parking failure by a relatively accurate guidance way, and is important for drivers to save time on parking when drivers hurry to their destination locations such as hospital. The ubiquitous parking guidance robot system can be implemented as parking guidance apps or parking guidance plugins of navigation apps installed in drivers' mobile phones. Drivers can independently set their filtering criteria of guidance, and then the parking lot with the optimum conditions such as shortest distance, largest number of parking spaces and best environment will be recommended.

Estimation of the Impact of Traveler Information Apps on Urban Air Quality Improvement

Abstract With the rapid growth of vehicle population and vehicle miles traveled, automobile emission has become a severe issue in the metropolitan cities of China. There are policies that concentrate on the management of emission sources. However, improving the operation of the transportation system through apps on mobile devices, especially navigation apps, may have a unique role in promoting urban air quality. Real-time traveler information can not only help travelers avoid traffic congestion, but also advise them to adjust their departure time, mode, or route, or even to cancel trips. Will such changes in personal travel patterns have a significant impact in decreasing emissions? If so, to what extent will they impact urban air quality? The aim of this study is to determine how urban traffic emission is affected by the use of navigation apps. With this work, we attempt to answer the question of whether the real-time traffic information provided by navigation apps can help to improve urban air quality. Some of these findings may provide references for the formulation of urban traffic and environmental policies.

Development of a digital patient navigation application: Preliminary results from patient interviews.

161 Background: People living with cancer face challenges too numerous to attend to during brief clinic visits, and thus may benefit from additional help with supportive care needs. While patient navigators offer promise, they are a limited resource. Digital navigation offers a scalable solution, to extend navigators’ reach and connect patients with existing resources, thereby enhancing our ability to address unmet needs. Methods: Utilizing a “design thinking” approach, we developed and tested a digital navigation platform to enhance utilization of support resources in the Duke Cancer Institute (DCI). The alpha version of the application (app) was based upon insights gleaned from interactions with patients, caregivers, advisory board members, and staff. It was then tested among adult patients with advanced cancer at the DCI. Subjects participated in qualitative interviews with design/content experts, to elicit supportive care needs and gauge interest in utilizing the app to improve their care experiences. Detailed notes were taken, and emerging themes compiled. Results: We enrolled 10 patients and caregivers. Subjects reported feeling overwhelmed by information, feeling that “they don’t know where to start when looking for support.” They also expressed uncertainty about what questions to ask, and said they are not always comfortable asking for help. All struggled to define “supportive care;” only one knew it refers to a set of services. Subjects felt a digital navigation app would enable them to explore and access services as needed, to help them “feel less alone,” and to know “someone or something was there for them.” They expressed dislike for being asked lots of questions, wanting to explore options on their own instead of being led somewhere. Digital navigation has the promise to increase their sense of control, with one subject noting: “I like to know what my options are and decide for myself…people try to assume they know what you need all the time.” Conclusions: Digital supportive care navigation may be an effective, scalable, and cost-efficient intervention to support patients and caregivers, and ultimately improve outcomes. Further refinement and testing of this approach is needed.

Analysing the impact of travel information for minimising the regret of route choice

In the route choice problem, self-interested drivers aim at choosing routes that minimise travel costs between their origins and destinations. We model this problem as a multiagent reinforcement learning scenario. Here, since agents must adapt to each others’ decisions, the minimisation goal is seen as a moving target. Regret is a well-known performance measure in such settings, and considers how much worse an agent performs compared to the best fixed action in hindsight. In general, regret cannot be computed (and used) by agents because its calculation requires observing the costs of all available routes (including non-taken ones). In contrast to previous works, here we show how agents can compute regret by building upon their experience and via information provided by a mobile (Waze-like) navigation app. Specifically, we compute the regret of each action as a linear combination of local (experience-based) and global (app-based) information. We refer to such a measure as the action regret, which can be used by the agents as reinforcement signal. Under these conditions, agents are able to minimise their external regret even when the cost of routes is not known in advance. Based on experimental evaluation in several abstract road networks, we show that the system converges to approximate User Equilibria.

Towards Improving User Interaction with Navigation Apps: an Information Quality Perspective

Traffic congestion is a major problem for large cities, and with the ubiquitous use of smartphones with GPS capabilities, drivers have increasingly come to rely on navigation applications for avoiding traffic congestion and routing to unfamiliar destinations. However, in certain situations the suggested route may not be what the user expects and could result in perceived delays over known routes, increased stress and frustration for the driver, or even back tracking. This has created a situation where drivers perceive that the information provided by navigation applications are not completely reliable and do not follow the suggested routes, thereby reducing the overall effectiveness of congestion avoidance. Additionally, drivers also make additional interaction with the navigation applications to verify the believability of the suggestions routes, creating more distraction and reducing on-road safety. As such, this preliminary work assesses mobility information quality provided by leading navigation applications (Google Maps and Waze) against four dimensions of the PSP/IQ information quality framework to identify areas for improving information quality in three common driving scenarios. The results indicate that both apps have similar levels of completeness, concise representation, and consistent representation. And while the relevancy of the information quality is also similar in both apps, Waze’s representation of the some information elements allowed for quicker comparison and decision making. The findings from this work can be used to enhance user interaction and information presentation in navigation applications in order to improve user perceptions of information quality.Keywords: smart mobility information, mobility information quality, congestion avoidance

Blind MuseumTourer: A System for Self-Guided Tours in Museums and Blind Indoor Navigation

Notably valuable efforts have focused on helping people with special needs. In this work, we build upon the experience from the BlindHelper smartphone outdoor pedestrian navigation app and present Blind MuseumTourer, a system for indoor interactive autonomous navigation for blind and visually impaired persons and groups (e.g., pupils), which has primarily addressed blind or visually impaired (BVI) accessibility and self-guided tours in museums. A pilot prototype has been developed and is currently under evaluation at the Tactual Museum with the collaboration of the Lighthouse for the Blind of Greece. This paper describes the functionality of the application and evaluates candidate indoor location determination technologies, such as wireless local area network (WLAN) and surface-mounted assistive tactile route indications combined with Bluetooth low energy (BLE) beacons and inertial dead-reckoning functionality, to come up with a reliable and highly accurate indoor positioning system adopting the latter solution. The developed concepts, including map matching, a key concept for indoor navigation, apply in a similar way to other indoor guidance use cases involving complex indoor places, such as in hospitals, shopping malls, airports, train stations, public and municipality buildings, office buildings, university buildings, hotel resorts, passenger ships, etc. The presented Android application is effectively a Blind IndoorGuide system for accurate and reliable blind indoor navigation.

What drives users’ intentions to purchase a GPS Navigation app

PurposeGrounded on the value-based adoption model and innovation diffusion theory, this study examined consumer purchase decisions of mobile Global Positioning System (GPS) navigation apps. In addition, this study also investigated the moderating role that perceived availability of free substitutes (PAFS) plays in the relationship between perceived value and purchase intention. The paper aims to discuss these issues.Design/methodology/approachData collected from 219 mobile users were analyzed against the research model using the partial least squares approach.FindingsThe results showed that compatibility, relative advantage, perceived enjoyment, perceived cost (positively), and complexity (negatively) influenced these users’ value perceptions and purchase decisions. Furthermore, PAFS significantly weakened the positive relationship between perceived value and purchase intentions.Practical implicationsBased on these findings, the authors provide practical suggestions for mobile app developers to increase mobile app sales. This study also helps advance knowledge of mobile internet marketing.Originality/valueThis study is a pioneering effort in explaining consumer purchase intentions in the context of mobile GPS navigation app.

Super-accurate GPS coming to smartphones in 2018 [News

We've all been there. You're driving down the highway, just as your navigation app instructed, when Siri tells you to "proceed east for one-half mile, then merge onto the highway." But you're already on the highway. After a moment of confusion and perhaps some rude words about Siri and her extended AI family, you realize the problem: Your GPS isn't accurate enough for your navigation app to tell if you're on the highway or on the road beside it. Those days are nearly at an end. At the Institute of Navigation GNSS+ conference in Portland, Ore., in September, Broadcom announced that it is providing customers samples of the first mass-market chip to take advantage of a new breed of global navigation satellite signals. This new chip will give the next generation of smartphones 30‑centimeter accuracy as opposed to today’s 5 meters. Even better, it works in a city's concrete canyons, and it consumes half the power of today's generation of chips. The chip, the BCM47755, has been included in the design of some smartphones slated for release in 2018, but Broadcom would not reveal which.

The Ethics of the Algorithm: Autonomous Systems and the Wrapper of Human Control

David Mindell notes in Our Robots, Ourselves, “For any apparently autonomous system, we can always find the wrapper of human control that makes it useful and returns meaningful data. In the words of a recent report by the Defense Science Board, ‘there are no fully autonomous systems just as there are no fully autonomous soldiers, sailors, airmen or Marines.’” Designing and using “the wrapper of human control” means making moral decisions — decisions about what ought to happen. The point is not new as the “soldiers, sailors, airmen or Marines” references shows. What is new is the rise of predictive analytics, the process of using large data sets in order to make predictions. Predictive analytics greatly exacerbates the long-standing problem about how to balance the benefits of data collection and analysis against the value of privacy, and its pervasive and its ever-increasing use of gives the tradeoff problems an urgency that can no longer be ignore. In tackling the tradeoff issues, it is not enough merely to address obviously invidious uses like a recent photo-editing app for photos of faces from a company called FaceApp. When users asked the app to increase the “hotness” of the photo, the app made skin tones lighter. We focus on widely accepted — or at least currently tolerated — uses of predictive analytics in credit rating, targeted advertising, navigation apps, search engine page ranking, and a variety of other areas. These uses yield considerable benefits, but they also impose significant costs through misclassification in the form of a large number of false positives and false negatives. Predictive analytics not only looks into your private life to construct its profiles of you, it often misrepresents who you are. How should society respond? Our working assumption is that predictive analytics has significant benefits and should not be eliminated, and moreover, that it is now utterly politically infeasible to eliminate it. Thus, we propose making tradeoffs between the benefits and the costs by constraining the use and distribution of information. The constraints would have to apply across a wide range of complex situations. Is there an existing system that makes relevant tradeoffs by constraining the distribution and use of information across a highly varied range of contexts? Indeed, there is: informational norms. Informational norms are social norms that constrain not only the collection, but also the use and distribution of information. We focus on the use and distribution constraints. Those constraints establish an appropriately selective flow of information in a wide range of cases. We contend they provide an essential “wrapper of human control” for predictive analytics. The obvious objection is that the relevant norms do not exist. Technological-driven economic, social, and political developments have far outpaced the slow evolution of norms. New norms will nonetheless evolve and existing norms will adapt to condone surveillance. Reasonable public policy requires controlling the evolution and adaption of norms to reach desirable outcomes.

Developing a typology of mobile apps in Higher Education: A national case-study

Mobile applications (apps) are used in higher education (HE) in a variety of ways, including as learning tools, study organisers, for marketing, and recruitment of new students. Purposed with easing student transition into university life, organiser apps have a capacity to assist students with various aspects of university experience, freeing up time and energy for study, while apps used as learning tools can help students solidify the content of lectures, self-test their knowledge of the subject, and collaborate with peers. Despite the proliferation of HE apps, there is still no systematic understanding of this field, with a number of important questions remaining unanswered, such as what types of apps are most commonly found in HE, what their complex uses are, and how their affordances and functionalities are deployed by universities and students. This study addresses this gap. After analysing 177 apps affiliated with Australian universities, a typology of HE apps is proposed. Study management and navigation apps emerge as the most common types of apps offered to students, with augmented and/or virtual reality apps forming another key category. New insights are offered pertaining to the complex terrain of HE mobile apps, and problematic areas arising from this research, such as safety, student support, privacy, and equity, are discussed.