Point Me In the Right Direction: Understanding User Behaviour with As-The-Crow-Flies Navigation

Point Me In the Right Direction: Understanding User Behaviour with As-The-Crow-Flies Navigation

Table of Contents


Visual as-the-crow-flies (ATCF)  methods are an increasingly popular alternative to existing turn-by-turn (TBT) navigation for cyclists. To better understand how people use them in everyday navigation and how they cope with the novel navigation method in challenging situations, we studied two main issues posed by  ATCF: knowing whether one is on the right route to their destination and knowing whether a turn leads into a dead-end or detour. To investigate these two problems, we compared visual ATCF  against

(1) TBT navigation and (2) an improved  (ATCF)+ navigation system in two successive studies. We found that users encountered problems riding in the opposite direction to the destination and were often turning around, as a result, using the As-The-Crow-Flies Navigation (ATCF) method. Using color cues in the ATCF user interface we were able to reinforce correct route choices. Additionally, we found that unsuccessful route progression negatively correlates with user confidence.


• Human-centered computing → Human-computer interaction (HCI); User studies; Field studies; Empirical studies in ubiquitous and mobile computing.


As-The-Crow-Flies, Navigation, Bicycle, Wayfinding, Turn-By-Turn, Field-Study, Mobile


Turn-by-turn (TBT) guidance is the default navigation method for most kinds of transportation modes, including bicycles. Its ability to guide people from A to B along the shortest or fastest path, using clear instructions about where to turn at each decision point, has been proven to be the preferred routing choice for most people already back in 1995 [10]. And in spite of many research studies that investigate alternative routing options like safest, simplest, or most scenic [8, 17, 28, 35], the fastest route remains the default option in many car navigation systems more than twenty years later. In contrast to that, we now see novel navigation methods called “compass mode” or “as-the-crow-flies” being introduced into the commercial domain of bicycle navigation.

The as-the-crow-flies (ATCF)  method, while novel for most users, is not unknown to the research community. Instead of giving TBT instructions to guide the user, it indicates the general direction to the destination. It is a sub-domain of mobile navigation research and has its origins in the development of navigation methods for the blind [19]. When looking at bicycle navigation research many studies revolve around making navigation less intrusive, allowing cyclists to focus more on their surroundings as cycling in heavy traffic is not only demanding but also potentially dangerous [11, 18, 31]. This is often achieved by using auditory [1, 12, 16, 20, 36] or tactile [20, 25, 27, 33] cues to convey the navigation instructions. Other modalities include instructions conveyed through light signals inside the helmet [20, 34], on an external navigation device [7] or projections in front of the bicycle [5, 6]. Most of these navigation systems use TBT navigation, giving information on where to turn at each decision point. Some of them do use ATCF navigation, but only in combination with auditory or tactile cues. However, commercially available products [2, 4, 32] exclusively use visual user interfaces. The Beeline application shown in figure 1 is one example and was used in our studies. The interface features an arrow that indicates the direction of the destination or the next waypoint and information about the remaining distance.

Based on related literature by Hochmair [13] and Albrecht et al. [1], we identified two main challenges that confront users of ATCF navigation: (1) knowing whether they are currently on a route leading to their destination; (2) knowing whether a potential turn might result in a dead-end or detour. We studied these challenges of everyday use of visual ATCF navigation methods and by doing so contribute to the understanding of ATCF. We conducted two studies:

• Study 1 compares a commercial implementation of ATCF with a commercial implementation of TBT navigation.

• Study 2 takes the insights of study 1 to build an improved version of the implementation and compares two ATCF navigation interfaces.

Both studies use an A/B testing design allowing us to observe user behavior related to the above-mentioned challenges. We present quantitative data recorded from participants’ performances (route length, number of errors, task load, and orientation) and give insights about wayfinding and users’ navigation behavior with ATCF navigation based on observations and interviews. Our results show that whenever the destination lies in the opposite direction to the one in which the user is currently riding, users respond by either turning around or trying to find a different route, even if that means carrying their bike up a staircase. In order to travel strictly in the direction provided by the navigation system some participants even rode through private property. We derive and evaluate design implications to counter these reactions and improve users’ confidence while using ATCF method.

In line with previous literature [1], we find that a basic implementation of ATCF, as it is used in most commercial products, can have major disadvantages compared to TBT navigation when looking at navigation efficiency (reaching a destination as fast as possible without errors), at least with a route design like the one we used in our study. To explain these disadvantages we report on several situations that proved a challenge for ATCF  and test how much if at all, these same situations affect TBT navigation. We present indicators that ATCF navigation can lead to improved orientation abilities, as participants had better knowledge of the direction of their initial starting position compared to TBT users. Furthermore, we confirm findings from Pielot et al. [25] that participants liked the ATCF  method for leisure biking.

Additionally, we identify that communicating progress to the destination during the navigation task is a very crucial aspect of building confidence. In our implementation, this was communicated through the remaining straight line distance to the destination on the top half of the screen (see figure 4a). However, this distance increased whenever participants were getting further away from the destination, which (apart from errors) naturally happened when they had to find a way around some obstacle (see figure 6a and 6b). Being used to TBT, where the distance to the destination continuously decreases, many participants associated the rising distance with negative feedback. This clearly impacted users’ confidence about whether they were still on the right track.


Our results show that the route design effectively introduced a challenging situation for participants using the standard ATCF navigation method. Participants had problems reaching their destination without errors, detours, or creative wayfinding behavior (e.g. carry their bike up a staircase, riding through private property). Our ATCF+ navigation interface, using colored cues, successfully assisted participants in some of these situations, alongside highlighting further unanticipated effects such as over-reliance on the distance information. This increases our insights on ATCF. In agreement with related literature [1, 25], we find that ATCF  is less efficient than TBT navigation (in terms of route length and navigation errors) and users prefer TBT navigation in situations where they need error-free, fast, reliable navigation. Nevertheless, many users would prefer ATCF over TBT navigation for leisure biking. While ATCF is at the moment exclusive to bicycle navigation, it might only be a matter of time until it finds its way into other more mainstream navigation systems. With more research on this topic, HCI researchers will be able to successfully guide decisions in this domain and increasingly understand the advantages and disadvantages of ATCF.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:



Gian-Luca Savino University of Bremen Bremen, Germany gsavino@uni-bremen.de
Laura Meyer University of Bremen Bremen, Germany lameyer@uni-bremen.de
Eve Emily Sophie Schade University of Bremen Bremen, Germany eve.s@uni-bremen.de
Thora Tenbrink Bangor University Bangor, UK t.tenbrink@bangor.ac.uk
Johannes Schöning University of Bremen Bremen, Germany schoening@uni-bremen.de




Point Me In the Right Direction: Understanding User Behaviour with As-The-Crow-Flies Navigation

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International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI ’20)



PDF reference and original file: Click here

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.