Mobility Tracking System for CO 2 Footprint Determination 1st Author 1st author's affiliation 1st line of address 2nd line of address Telephone number, incl. country code 1st author's E-mail address 2nd Author 2nd author's affiliation 1st line of address 2nd line of address Telephone number, incl. country code 2nd E-mail 3rd Author 3rd author's affiliation 1st line of address 2nd line of address Telephone number, incl. country code 3rd E-mail ABSTRACT Tracking the mobility behavior of participants with smartphones to determine the CO2 emissions is an overcharging task for researchers. In a fleet test with 52 participants, 9968 datasets were generated, making the manual analysis a long-lasting endeavor. With our work, we are attempting to reduce the analysis time of the generated data and provide in the same way immediate feedback to the participants. We propose an automated mobility tracking system that makes use of a track analyzer that identifies the mode of mobility to calculate CO2 emissions. We will describe the system functions, how the datasets are collected, processed and led back to the users. Based on the setup, calculation accuracy and the feedback from the participants, benefits for user studies in the automotive context are identified. This system will influence the setup of future large data user studies with smartphones. Author Keywords Mobility Tacking, CO2 Emissions, Mode of Mobility, Smartphones, User Study, Fleet Test ACM Classification Keywords H.5.2. User Interfaces: Evaluation/methodology INTRODUCTION Tracking the mobility behavior of individuals is a challenging task facing the global community today and increasing effort is expended on understanding how to make use of sensor data generated by smartphones. The majority of work on smartphones within the car context has focused on which data can be tracked and how the data can be gathered. However, like with many sensor-based devices large data sets can be generated quickly: it is the challenge to make use and sense of the data and, especially in the case of smartphones, it is necessary to post-process the data due to the low recording quality. Smartphone sensor data can be greatly affected by the hardware, the environment and by the individual user itself. This comes in hand with the recording frequency that might not be sufficient for certain applications due to hardware limitations. As this needs to be considered while using smartphone-generated data sets in a scientific context, several advantages compensate these shortcomings to a certain extend. The vast amount of recordable data bears the potential that there is still enough usable data left after sorting out data sets that cannot be used and further it is possible to deploy computational methods to smoothen data inaccuracies. Smartphones these days contain a variety of sensors. Depending on the hardware configuration, it is possible to get data from an image sensor, humidity sensor, gyroscope, digital compass, atmosphere pressure sensor, accelerometer sensor, temperature sensor, capacitive touch sensor, ambient light senor, magnetic field sensor, optical proximity sensor and a GPS/GLONASS module. The availability of the sensors depends on the manufacturer and further sensors like a pollution sensor might get integrated in the near future. Smartphones provide several ways to access the data from these sensors e.g. via Wi-Fi, Bluetooth, near field communication, and mobile internet service. The connectivity is thus an integral part of how a setup within a scientific study needs to be planned. Recent studies made use of smartphones as a low-cost multi sensor platform to plainly store data e.g. in a field operational test with electric vehicles [1], while more specialized applications show a higher degree of connectivity and e.g. are able to automatically detect traffic accidents by using accelerometer and acoustic data and connecting to an emergency call system via 3G [2]. Other approaches made use of the peer to peer connectivity between smartphones for mobile crowd sensing, where individual smartphones recorded, computed and shared data collectively and extracted information to measure and map phenomena of common interest [3]. Combining and connecting internet services, data from the vehicle’s controller area network together with sensor data of a smart device allow to realize driving-related functions e.g. as part of an open HMI platform [4]. The accumulation and combination of data sources is thus a valuable approach, which is why we combined data from smartphone sensors Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. AutomotiveUI '14, September 17 - 19 2014, Seattle, WA, USA Copyright 2014 ACM 978-1-4503-3212-5/14/09…$15.00 http://dx.doi.org/10.1145/2667317.2667334 Maria Kugler, Sebastian Osswald, Christopher Frank, Markus Lienkamp Technische Universität München 85748 Boltzmannstraße 15 Garching b. München [Kugler, Osswald, Frank, Lienkamp]@ftm.mw.tum.de