UTMOST: Unregistered Transport Mobility, Safety and new Technologies Midterm

Transcription

1 UTMOST: Unregistered Transport Mobility, Safety and new Technologies Midterm Martin Kolbeinsvik, Kjetil Sletten and Jon-Robert Skaarberg Wednesday 8 th October, 2014 i

2 Contents 1 Introduction Motivation Background Question/Problem/Hypothesis Theory GPS and location data Other solutions Technical review Logging Visualisation Relevant course literature Cityflocks Aspects of personal navigation with collborative user feedback Negotiating Privacy Boundaries in Social Applications for Accessibility Mapping Location-Based Crowdsourcing of Hyperlocal News Case - and method User and Stakeholders Methods to address the questions Conclusion and further work 9 ii

3 1 Introduction 1.1 Motivation The UTMOST project wants to improve the data on accident exposure in previously unregistered means of transportation, such as walking, cycling, leisure boating and snowmobiling. In this project there are three major issues, two of which we are addressing: collecting the data and visualizing it. This would be useful for several reasons; better knowledge of what activities that are done, where it happens and how prone the people doing them are to accidents. In this project, the main goal is to visualise the data from a pilot project by Oslo municipality. The secondary goals are to learn new mobile technologies and to explore the different approaches of digital logging. Specifically, secondary transport types. 1.2 Background main goal of the UTMOST project is to gather data on accident exposure, and we want to contribute to this. Little is known about how and where people move during leisure activities in Norway. Figure 1: Travel to School A study made by TØI in cooperation with Oslo municipality collected data from two schools. This was done by creating an agent app, called Skoleveiagenten, allowing the school kids to be secret agents and reporting to TØI. The data gathered is time and GPS data that registers the route travelled to school, as well as traffic issues. These issues are safety aspected and the agents can select issues such as low visibility and lots of traffic. 1

4 This app has been used by school children in a few selected Oslo schools, and some pilot data has been collected. The data collected by this app will be used by us for our representation purposes, but the app itself is not exactly what we are after. Cycle routes are somewhat of a hot topic in Oslo this year, and a number of news articles are found on osloby.no newspaper 1. In one of the articles a map with more than 2000 reader contributions as of October 2014 shows where in Oslo the readers feel unsafe when using their bicycle, but it only shows these single dangerous locations and not their route 2. These dangerous locations are of course interesting, but it is not sufficient for mapping dangerous routes. In Oslo there are city bikes, and early on this was discussed by the group as a way of obtaining travel data. However, the data is only about departure and arrival locations, and we would be left to guess what had been done in between. This data is not accurate enough for representing real-world usage and we abandoned this idea. 1.3 Question/Problem/Hypothesis As little is currently known about the unregistered types of travel mentioned earlier, one of our main problems is to obtain travel data. We explore several different possibilities for doing this. Neither Transportøkonomisk institutt (TØI) or Oslo municipality currently has a good way of visualizing this kind of big data, and we thus want to find a good option of doing this in a way that allows for large expansions of data in the future. Our two main questions are as follows: 1. Is there a good way of collecting travel data of previously unregistered travel? 2. What is the best way of representing this data?

5 2 Theory 2.1 GPS and location data Satellite based navigation systems, such as GPS and Galileo, help to pinpoint location based data based on the time and geological readings (latitude, longitude, elevation/altitude). The accuracy of such readings by the (GPS) receiver and satellite vary based on weather conditions, inside or outside position, and by the availability of land or satellite based enhancement/augmentation systems. GPS can be accurate to within 3 meters without enhancement systems and down to centimeters and millimeters in conjunction with some such systems 3. The GPS features in smartphones are manufactured for power consumption above location accuracy. On a clear sky such receivers can generally identify which road a user is travelling, as well as the direction and speed (via geospatial calculations). It can not, however, detect with certainty which side of the road the user is on or if the user is using the sidewalk or street. If the GPS device is used within the context of a car as a route planner the device can utilize computer algorithms to map the car to the correct side of the road based on direction and the legal restrictions that applies to cars. This solution is not as viable for bikes as cyclists are allowed to ride their bikes on sidewalks, in bike lanes and on the road. 2.2 Other solutions Mobile training apps such as Strava 4 and Endomondo 5 are very popular and has a big user base, the latter has more than one billion logged miles, more than half of which are logged by bike 6. These apps are, however, mostly used for logging workouts, and does not provide much data on how people move in their daily life. For this project we need data on how people get to work, how they travel to school, and generally patterns of travel all over the City. 2.3 Technical review Logging In terms of logging there are several applications and different gadgets to log travelling. However, by relying on user-feedback the data can be error-prone and lacking. It also drains power from the user s

6 Figure 2: The technical review of the proposed solution cellphone quite quickly. Another problem with user-feedback applications is that people tend to focus on their selfish self, and do not contribute with proper feedback [4]. As for the Skoleveiagenten, which works as a crowdsourcing application, it is important to understand readers willingness to participate and how they understand their position as collaborators in news making with media organisations [5]. The same applies for the data collection for Skoleveiagenten or similar logging applications. In order to obtain data from users (crowdsourcing), the user s affects has to be addressed and privacy issues has to be maintained. We think therefore, in terms of logging, a more passive approach is more suitable. By doing the data collection in a passive way, it ensures that the user does not forget to turn on/off the GPS and it is not dependent on the battery of the user s cellphone. A passive approach also includes problems regarding privacy issues. Not everyone wants to log their entire life digitally and exposing one whereabouts, preferences, needs and opinions in a more public sphere raises issues not only related to trust, but also about aspects of privacy [3]. A solution for an passive approach is by developing a prototype for bicycle with its underlying technology being Arduino. Arduino is an open-source physical computing platform, which makes it possible to interact with different sensors, motors, physical outputs etc. The hardware is cheap for prototyping and allows for cross-platform development. Our initial thought was to attach the Arduino-prototype to a bicycle, most preferably on some of the City bikes in Oslo. This will again rise some problems with regards to privacy, but more important : how will the data from the bike be collected? There are several approaches: 4

7 1. Attach an WiFi-shield to the Arduino-device Attach Bluetooth-shield to the Arduino-device. 3. Remove SD-card once a week/month. (1) The device will consume more power and there is not free WiFi everywhere. (2) More power consumption. The thought was to make an app that made the user of the bike share the travels via bluetooth. This will result in a tedious approach (many steps, no immediate gratification) and probably not gain many users. (3) A very cumbersome approach, this will take a lot of resources Visualisation The data that is obtained from the Skoleveiagenten project registered by the agents at the school. The data is stored in a GPX-format which is served as the de-facto XML-standard for lightweight interchange of GPS data 8. Our plan is then to use these data in an single-page web application. The underlying technology for the visualisation of the data will be; Polymer, D3.js, Bootstrap. Polymer Polymer is a library for building custom web components 9. With help of syntactic sugar and polyfills 10 it makes it possible for older browsers to implement such web components. We wanted to make an prototype with new technology and using web components falls into that category. Bootstrap Bootstrap is a collection of different tools to make it easier to build a website. It provides different sets of buttons, typography, forms. D3.js Javascript framework which makes it easier to visualise data in the web browser. D3.js is making it possible to bind data to the Data Object Model (DOM) and then apply transformations to those data. 2.4 Relevant course literature Cityflocks CityFlocks is a mobile service prototype for tourists and other newcomers, that allows them to benefit from local knowledge. The app uses both direct and indirect communication, and explores the difference

8 Figure 3: GUI mockup between these. The types of direct communication are SMS, phone calls and direct voice link and text messages, while the types of indirect communication are comments, reviews and browsing location based user comments. As people can be an indication of popularity, this can prove useful. GPS and geo-tagging is used to share knowledge [1] Aspects of personal navigation with collborative user feedback OurWay is a prototype for collaborative route planning with focus on wheelchairs. Their main objective is to provide better routes for the whole community - their findings resulted in a more selfish approach from the user. The user were only interested with finding the best route and rarely thought about the so-called community in OurWay. This is interesting in terms of the Skoleveiagenten project, where there were no particular problems in terms of logging and collaboration. But in OurWay it lacks 6

9 collaboration with the users, because of the selfish approach [4] Negotiating Privacy Boundaries in Social Applications for Accessibility Mapping Discusses privacy issues that might arise with the OurWay application and its users. The way the OurWay users registers accessibility issues is similar to that of the Skoleveiagenten users. Whenever an issue arises - accessibility or traffic related - the users registers it on their respective apps. For both project the data gathered and registered by users can benefit other people and the effect is cumulative. The more users registration issues the richer the data set and the more valuable it is. As such there is a real privacy issue of how and how much of the data should be shared, as well as who to disclose this data to [3] Location-Based Crowdsourcing of Hyperlocal News Discusses mobile users preferences and concerns of using location-based assignments (LBA) and geotagging in crowdsourced news making. What are the attitudes toward geo-tagging and the anonymity of the user when location based data is published alongside the news. The users in the study found the benefits of geo-tagging to be greater than the perceived risks. This is relevant for the Skoleveiagenten project as it uses agents - school children - to crowdsource geo-tagging traffic issues with the intention to make traffic safer for them [5]. 7

10 3 Case - and method 3.1 User and Stakeholders The solution we are developing needs a solid foundation of data, as it will not give an accurate picture of travel patterns unless there are enough data entries to establish these patterns. The Skoleveiagenten project allows children to be agents and report their location and findings, and more of this data would be helpful in the project. It does, however, need different data in addition to this, and this is where we are using the cyclists. Several stakeholders would be interested in the result; TØI, Oslo municipality, the Norwegian government amongst others. The data allows for e.g. accident prevention and planning new cycle routes. 3.2 Methods to address the questions For quantitative data we will use different sources. For the earlier parts of the project, the Norwegian national travel survey is used [2]. This is first and foremost used for understanding how much the Norwegian people walks and use their bicycles, how often they do this, how long an average journey is, and in what context the travel is made. After much deliberation, we have decided against interviewing people in this project. We made this decision because we simply did not see the need for further elaboration of the data we can already get from the national travel survey. This means we are most interested in quantitative data. The data collection issues has been addressed in other parts of this report (see the technical review). 8

11 4 Conclusion and further work There are several approaches for the plan forward. We plan to look more at the technical aspect of the GPX format and how the data from the Skoleveiagenten project can be mapped to different visualizing formats, such as heat maps and perhaps tables. We will look at the national travel survey to find travel patterns and other useful information for deciding what kind of logging is preferable. Another approach involves mockups and paper prototypes. We want to make an webapp and the design must be simple yet flexible, so that users can select the different visualization format they feel is relevant to them. In this regard we will look at existing software and libraries to quickly prototype at least one solution. Bootstrap, Javascript libraries for map visualization and mapping services such as Google Maps or Openstreetmap is all highly relevant. We will also look at testing our application with real users. 9

12 References [1] Mark Bilandzic, Marcus Foth, and Alexander De Luca. CityFlocks: designing social navigation for urban mobile information systems. In: Proceedings of the 7th ACM conference on Designing interactive systems. ACM. 2008, pp [2] Inge Brechan, Randi Hjorthol, and Liva Vågane. Den nasjonale reisevaneundersøkelsen nøkkelrapport. Tech. rep. 1130/2011. Transportøkonomisk institutt, [3] Harald Holone and Jo Herstad. Negotiating privacy boundaries in social applications for accessibility mapping. In: Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries. ACM. 2010, pp [4] Harald Holone et al. Aspects of personal navigation with collaborative user feedback. In: Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges. ACM. 2008, pp [5] Heli Väätäjä, Teija Vainio, and Esa Sirkkunen. Location-based crowdsourcing of hyperlocal news: dimensions of participation preferences. In: Proceedings of the 17th ACM international conference on Supporting group work. ACM. 2012, pp