Automated Driver’s Logbook To smartphone or to paper

LAHTI UNIVERSITY OF APPLIED SCIENCES Degree programme in Business Infromation Technology Thesis Autumn 2015 Leevi Vuorinen

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Lahti University of Applied Sciences Degree Programme in Business Information Technology VUORINEN, LEEVI: Automated Driver’s Logbook To smartphone or to paper Bachelor’s Thesis in Business Information Technology 21 pages Autumn 2015 ABSTRACT This study aims to find out if the technology could replace the old-and-faithful pen-and-paper method as a driver’s logbook. The basic framework is the Design Science paradigm, and the study aims to evaluate if the artefact is viable or not. To obtain this evaluation the study focuses on examining the effectiveness of a system per process -time spent - per event in a process of logging the trips made. The reason for why the logging process should be made is out of scope of this study. The systems can be made in many different ways but as the smart phones are currently popular, the smartphone-based solutions are preferred in this paper. Key words: smartphone, GPS, driver’s log, app

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CONTENTS 1 INTRODUCTION 4

1.1 Background 4 1.2 Basic framework of the study 5 1.2.1 Environment 6 1.2.2 People 6 1.2.3 Organizations 6 1.2.4 Technology 7 1.3 Knowledge Base 7 1.4 Design science in this study 7

2 MAIN CONCEPTS, FRAMEWORK AND THEORY 8 2.1 Application domain 8 2.2 The logging process 9 2.3 Terminology 10 2.3.1 GPS 10 2.3.2 Driver’s log 10 2.3.3 Smartphone 10 2.3.4 App 10

3 DATA COLLECTION AND ANALYSIS METHODS 11 3.1 The applications 11

4 LITERATURE REVIEW 12 5 EMPIRICAL DATA 13

5.1 Data collection process 13 5.2 The data analysis 14 5.3 The problems during data collection 15

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6 CONCLUSIONS 16 6.1 Discussion 17 6.1.1 Integrated solution 17 6.1.2 Define the business need 17 6.1.3 Other external technologies 18 6.2 Security and privacy concerns 18 6.2.1 Security concerns 18 6.2.2 Privacy concerns 19

REFERENCES 20

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1 INTRODUCTION

1.1 Background Being efficient and eco-friendly is one of the main global megatrends (Megatrends 2015, 9) and digitalization is the number one megatrend (Megatrends 2015, 7). In order to follow the global trends, it would be great to update the traditional driver’s logbook and to update the process outcome to more precise data. More precise data could be used to monitor the driver’s habits, the possible gasoline consumption and also route optimization in routine trips. The most widespread smartphone in Finland in 2015 is iPhone 4S (Vesselkov, A. 2015) – which can use GPS/GLONASS data to acquire the precise location of the phone. By tracking the movements of the phone and by ensuring that it is based on the movement of a car the log can be made automatic. Hypothetically, this could be a faster and more efficient method than using paper and pen. In addition, this could be a more accurate method as it allows to obtain the location information per every moment of the trip. Location tracking can be made in several ways. It can be based on tracking the location change from satellites. Accelerometers can be used as an inertial compass or it can be based on triangulating the location from cellular network base stations using latencies. The newer versions of the mobile operating systems include a location sensing system, which integrates all these aforementioned methods into a single method. This aggregate system allows a more passive sensing of the current location. Car trips are traditionally logged with the paper and pen –method. In private use the information recorded is usually the starting- and ending time and the starting and stopping kilometre readings.

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1.2 Basic framework of the study The aim for this study is to examine if the automated driver’s logbook systems are more effective in logging car trips than the paper and pen -logbooks. The limitation of this study is that the systems will be designed for personal use, and not the thesis will not study systems focused on fleet management. The basic framework for this study is the Design Science –method from Hevner et al. (Hevner et al, 2004). The framework is visualized in Figure 1. The design science -method’s goal is to prove the utility of the artefact. -The methodology divides the research framework into environment and knowledge base.

Figure 1 - Figure taken from the design science study by Hevner et al in MIS Quarterly 2004

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1.2.1 Environment The environment is the problem space where the artefact studied is functioning. Studying the environment ensures that the artefact is relevant to the original problem. The environment is then divided into three distinct sub parts: People, Organizations and Technology.

1.2.2 People The human interaction is studied in this part of study. Who will be using the artefact, how it will be used and what are the characteristics of the use cases. The people part in this study is limited to individual users. The user installs the app to a personal mobile phone and can track his or her own trips. Furthermore, the artefact could be made to upload the trip data into a server, which could input the data into an ERP system or equivalent infrastructure. If the person is entitled to have compensation per driven kilometre the payment could be automated. However, this follow-up question is beyond this study’s limits.

1.2.3 Organizations Here the emphasis to study the organizations that are connected with the artefact. What are the main goals of the organizations? What are the internal structures of the organizations and where is the artefact embedded into organizational processes? The organization focus in this study is on the individual user without covering the surrounding organizational structures. In this way the researcher assumes the findings can be applied to a wider range of use cases.

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1.2.4 Technology In technology part of the study the aim is to study the artefact’s surrounding technologies and also study the technologies needed to support the artefact.

1.3 Knowledge Base Knowledge base is the theoretical foundation of references and set of theories and methodologies from where the research is evolving.

Foundations: Foundations are the theories, abstractions and models found from earlier studies or research.

Methodologies: Methodologies are the valid forms, which ensure the study will be carried in a scientific way.

The knowledge base in this study is based on previous studies. Namely, the Design Science method as a framework and other materials found on similar topics or somehow related to the problem space.

1.4 Design science in this study The design science was selected for this study as a framework as the goal of this study is to evaluate the artefact/artefacts and whether they are relevant in the selected business context.

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2 MAIN CONCEPTS, FRAMEWORK AND THEORY The artefact itself is a solution to a certain problem but the focus and approach differs. The solution itself is tied to a certain need, and to fulfil the needs the environment should be investigated thoroughly. This focus on environment ensures the artefacts relevance so that even in the design phase it is possible to evaluate if the solution is relevant to the problem at hand. To ensure the artefact’s relevant scientific foundation the researcher should investigate the existing knowledge base thoroughly. The knowledge in previous studies may help to avoid the largest pitfalls in development.

2.1 Application domain This study focuses on an app’s ability to provide a simple user interface and efficiency of user actions compared to the ease-of-use of traditional pen-and-paper system. The problem space in this paper is the personal use or tracking of trips in a company car. The artefact is not intended to be uses in fleet tracking situations, and it does not send real-time information to servers. Smartphones have become a standard in Finland (Vesselkov, 2015), so the researcher assumes that the possible reader most probably has a smartphone. Also, Vesselkov’s study indicates that the most popular handset in September 2014 in Finland is still iPhone 4S, so the device used in in this study will be iPhone 4S. One app (custom-made) is made for Windows Phone 8.1 operating system, so it will be tested with a device from that ecosystem.

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2.2 The logging process The logging process in the application can be divided for example in the following way:

Figure 2 - The logging process This example process is from the Logger app. The logging process is automated to the point where the trip’s type is queried from the user. The basic trip types are private or work trip. These can be stored differently or into the same format, but compensation could be calculated differently according to the trip type. The trip in this study means a one-way journey from a starting point to an ending point.

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2.3 Terminology

2.3.1 GPS The GPS acronym stands for Global Positioning System. GPS is a space-based 24-satellite network maintained by the US Air Forces. The satellites emit the navigation signals to earth, and by comparing time differences in these signals, the listening device can pinpoint its three-dimensional location information precisely (US Air Force 2010)

2.3.2 Driver’s log The driver’s log in its most simplistic form is just a record of trips made in a car. The information recorded depends on the use case and what information is required.

2.3.3 Smartphone The Oxford dictionary defines the term smartphone very clearly:

A mobile phone that is able to perform many of the functions of a computer, typically having a relatively large screen and an operating system capable of running general-purpose applications. (Oxford Dictionary of English 2013)

2.3.4 App App is a short word for application and in the context of this study the app is used as a term for a smartphone application.

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3 DATA COLLECTION AND ANALYSIS METHODS The data is collected by a field test of selected artefacts. The processes of starting the trip and ending the trip are timed and then compared. The ease of exporting the data forward is also considered a vital point. The method for collecting data is collecting the time used per process of start logging and stop logging. These results are then inputted to a worksheet, for example MS Excel. The timing tool used in this study is stopwatch. Multiple performances are recorded in order to have more legitimate results. The apps selected for this thesis were: Vezma Tracker (iOS), Magical Miles (iOS) and Logger (WP8.1 - custom made). In same event the same events were logged by paper and pen to ensure similarity in recording the events. The retrieved data is analysed and compared to the results of a traditional paper-and-pen method.

3.1 The applications The Magical Miles is the only fully automatic application for logging. The Logger starts and stops the logging process autonomously. After the log process the app asks for user interaction to ensure whether the trip was business or private trip. So the start of the log is not timed but ending is recorded. The Vezma Tracker is manual application for mobile devices where a user starts and stops the process manually. The paper-and-pen system is a fully analogue logging system with the simplest UI of all the contenders.

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4 LITERATURE REVIEW There has been a lot of research in the location-tracking field. These studies have been concentrating on how to extract the trip type from the GPS and other external data. Researchers have also concentrated efforts in finding out how to recognise errors in data. As with all systems there are cases when the result is not 100% valid. In these cases the location tracking system should use some kind of an error correction or error filtering method. The GPS signal coverage is not available in all situations. For example, in buildings and in tunnels the listening device is not able to receive the signal from the satellite array. In these cases it could be possible to use a smartphones’ integarated sensor array to aid in location tracking. (Lei G, et al. 2014). In the emerging markets, the growing trend is entrepreneurship (Megatrends 2015, 16). If these entrepreneurs want to log their trips, the automated logging system could increase the effectiveness of trip logging.

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5 EMPIRICAL DATA The data for this study was collected in a real life situation where a person was driving a car and logging the trips.

5.1 Data collection process The data collection was conducted in a real life situation where person was driving a car around the Lahti region and collected the logs using different technologies. Actual location data was not recorded or spared, as the final precision of the trips was not the main focus of this study. The times were collected with a stopwatch and were written down to laptop. The test phones were running with other programs in the background to simulate real life situations where users tend to have many applications running simultaneously. The paper and the pen were on the passenger’s seat. The recorded times include the time passed on taking the device to hand and to stop or start the process.

Figure 3 – Recorded times per process (in seconds)

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5.2 The data analysis The data in Figure 3 on the previous page shows the actual recorded times per logging event. The average is plain average without any weighting as the sample size is relatively small. The recorded results also show some deviation in the recorded times, as there was some variables in the recording process. At some point, the pen was not in the front seat where it should have been and when using a phone other open apps hindered the state-change process. The paper-and-pen method, on average, took about 18 seconds to start or stop the logging process. Therefore cumulative average time spent per trip would be 36 seconds. The Vezma tracker took about 12 seconds to start the process and 10 seconds to stop so the cumulative average per trip would be 22 seconds. The Logger was automated to start and stop the logging but the app asked from the user the reason of the trip. To answer the query about the trip type took about 9 second and this value would also be the cumulative for the whole trip. The Logger operated in Windows Phone 8.1. and operating systems overlay and user interface logic completely differs from iOS. The process of opening the app is different than in iOS. These times were not compensated, as both systems were familiar to the tester. The Magical Miles was fully automatic and it did not need any user interaction with the logging process. However, the user could still change the trip type in the app. This time was not recorded, as it was not necessary for this study. As per these results the Magical Miles (the fully automatic app) would be the most efficient in usage of time scale. The Logger version used in this study did not have any manual controls to start or stop logging process. Therefore it can not be used manually, the user has to trust that the automation can sense the travelling states from motion sensors or GPS.

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5.3 The problems during data collection As with all automated systems there are glitches and quirks when automation cannot predict what is happening in the actual world. The Magical miles app is fully automatic and user interaction is not needed in the logging process. During the recording process, it became clear that the software is not working on iPhone 4S as intended. The data was distorted, and it did not reflect the real life trips. The logging started very randomly so the application is not reliable - at least not in this context. According to the author this could be a problem with iPhone 4S and its memory limitations. The operating system shuts or suspends the logging process if the memory is needed elsewhere. (Magical Miles user voice) The next in line was the Logger - which at some point stopped logging before the trip was finished. Otherwise the start and stop were somewhat reliable. The idea to search for certain Bluetooth device and tie the logging events to the reachability of this device, would be fantastic and easy-to-implement idea. According to author’s cumulative empiricial experience from the Logger development process, the Bluetooth devices are tricky and not always dependable. The devices have different implementations, limitations, power savings schemes etc. All these variables lead to situations where connections that were working yesterday are not working today, and again tomorrow, they may work. The Vezma Tracker and paper & pen did not show any weaknesses in data reliability.

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6 CONCLUSIONS According to the findings of this study, automated systems could be more efficient if the reliability of the logging processes were a little better. Still the more automated systems lead to more efficient workflow in logging process. If the user travels by car in 250 days a year and makes 6 trips per day the time spent on writing the trips down in paper-and-pen format takes 15 hours and 32 minutes. With Vezma tracker the time used for loggin the trips takes 9 hours and 28 minutes. Furthermore, with Logger the time used for logging the trips takes 3 hours and 37 seconds. The time spent on logging the trips could be cut to almost fifth from the original pen and paper method, and if this time is included in work time the employer could save from salary expenses. The idea of automated location tracking is not new – it has been used in truck fleet management for some time already. For individual users this system could offer benefits in time saving. In future, the applications could automatically upload the business trips’ data into an employer’s ERP, and in that way it could streamline the mileage compensation process. This possibility should be researched more or develop a demonstration for such a system. For serious business needs the apps should be as automated as possible and also more reliable than the apps presented in this study.

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6.1 Discussion Further development ideas:

6.1.1 Integrated solution The potential of logging apps for smartphones is promising, but still the most reliable way could be to integrate logging software to an on-board computer in a car. New research or a case study would be needed to verify this.

6.1.2 Define the business need This study does not comment on the possible business needs in depth. Some research about real business needs could be done to target the app’s functionalities more precisely.

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6.1.3 Other external technologies The logging process, if not fully automated, could be started and stopped with NFC technology. Using an NFC tag in a car to start and to stop the trip would be easy and cheap to implement. Using manual technology would still resolve into more efficient time usage and could remedy certain errors of a fully automated system.

6.2 Security and privacy concerns When dealing with personal data, it is imperative to have the data linking to individuals as securely as possible. Private data, even data thought to be harmless, can be valuable to some entity.

6.2.1 Security concerns Precise location data stored on a server could possibly be harmful in if data security is not fully up to date. Still, there is also the possibility that some malicious entity could gain access to the database. In masses location data is meaningless, but if the entity would like to pinpoint an individual’s current or latest location, it is possible with precise location data. One solution to circumvent such a situation would be to store only the travel times and kilometre readings that link to the individual user. If the other location data would be saved without user data, it could be used in traffic monitoring. In this way the sensitive data linking to individual user is never stored in the database. There is still the possibility of cross-referencing the user-sensitive data with the non-user-sensitive data and linking it to individual user, but the risk is smaller. To still lower the risk of detecting individual user from the data the non-user-sensitive data could lower the precision of start and end-times, or shifting randomly whole trip’s timestamps.

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6.2.2 Privacy concerns The service provider’s data handling should be in a level appropriate to data content. Storing the user sensitive data in different database than the non-user-sensitive the probability of violating the private data would lessen. If used with an ERP (Enteriprise Resource Planning) system or other efficiency monitoring system by employer the limits of employee privacy and employer rights should be noted in employer policies (Smith D.V, Burg J. 2012). Both sides of the job contract should approve these policies. In this way all entities knows their responsibilities and rights.

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REFERENCES Ernst & Young. 2015. “Megatrends 2015 Making sense of a world in motion,” [Referenced 14. October 2015], Available in: http://www.ey.com/Publication/vwLUAssets/ey-megatrends-report-2015/$FILE/ey-megatrends-report-2015.pdf Hevner, A., March, S., Park, J. & Ram, S. 2004. “Design Science in Information Systems Research,” MIS Quarterly (28:1), pp. 75-105 Vesselkov, A. 2015. “Mobile Handset Population in Finland 2005-2014,” [Referenced 1. September 2015], Available in: https://research.comnet.aalto.fi/public/Mobile_Handset_Population_2005-2013.pdf US Air Force. 2010. Fact Sheets. [Referenced in 3. November 2015], Available in: http://www.af.mil/AboutUs/FactSheets/Display/tabid/224/Article/104610/global-positioning-system.aspx Lei G., Takayuki M., Toshiyuki Y., Hitomi S. 2014 “Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies,” Procedia - Social and Behavioral Sciences 138 (2014) 557 – 565 Magical Miles user voice. Troubleshoot FAQ. [Referenced in 8. November 2015], Available in: http://magicalmiles.uservoice.com/knowledgebase/articles/333784-magical-miles-isn-t-automatically-creating-logs Smith, D.V. & Burg, J. 2012, "What Are the Limits of Employee Privacy?" GPSolo, vol. 29, no. 6, pp. 9-11.