A more sophisticated and technical method to protect users against efficient Wi‐Fi tracking is provided by Apple’s current mobile operating System (iOS 9). Since iOS 8, a mechanism for automatic MAC‐address randomization is integrated in the OS which fakes the real hardware identifier of the device. Hence, continuous tracing or recognizing an iOS device by distributed Wi‐Fi sniffers becomes more difficult. When Apple introduced this feature in 2014, it was hardly criticized as impractical, due to the fact that the new randomization process only worked for devices which entered into full sleep mode which was only in case of both disabled cellular data connection and disabled location services [17]. Hence, most users didn’t really had MAC‐randomization activated in their daily life. Thus, Apple recently improved and extended the mechanism to location and auto‐join scans, meaning that MAC randomization is now available also for active devices and during IEEE 802.11 active scans [18]. Note, that the randomization process is not activated for associated devices.
So for the first time, Apple as one of the leading providers for mobile devices, has established an automatic method for protecting users’ privacy against Wi‐Fi sniffing which is directly integrated in the operating system. However, MAC‐randomization makes Wi‐Fi based analyzations more complicate, but it doesn’t fulfill a complete privacy protection, as it is stated in [19]. Furthermore, Pang et al. [20] have already demonstrated in 2007 that so‐called implicit identifiers and certain characteristics of 802.11 traffic can be used to identify many users with high accuracy and without knowing the device specific MAC‐address. Hence, Apple’s mechanism is just a first but also important step to react on the needs for complete privacy protection in our digital society. Overall, it has to be observed in the near future, how companies and people deal with this topic and how retail analytics can be performed when Wi‐Fi tracking becomes inaccurate due to more sophisticated privacy‐preserving mechanisms. Probably, the fairest and best way would always be to ask people for compliance before tracking them.
42.6 Conclusion and Future Impacts
In this book chapter, we have demonstrated the possibilities, risks and limitations of tracking mobile customer devices in our more and more digitalized world. Technical backgrounds to standard IEEE 802.11 Wi‐Fi tracking have been described. Furthermore, both scientific and commercial works have been presented focusing on the type of crowd information which can be extracted from Wi‐Fi tracking data. It was seen that this technique has gathered a high interest also for innovative business models and shows great potentials for the near future, due to an increasing amount of Wi‐Fi capable mobile devices. On the other hand, the technique includes serious risks for users’ privacy and people should be aware of the fact, that their phone is sending data without their awareness.
We have also demonstrated, that new privacy‐preserving mechanisms are developed and partially integrated in current mobile operating systems. However, they still do not guarantee a complete protection against being tracked in public spaces, due to implicit identifiers. Hence, if users want to be sure they just have to switch off the Wi‐Fi interface of their device.
For the near future, we assume that Wi‐Fi tracking will even spread in public spaces, due to low cost, more mobile devices, and more sophisticated data mining algorithms. Especially retailers who require similar analytic tools as in online‐shops will install such a technique in their business. The highest uncertainty for our prediction will be the prospective user acceptance and the development of more advanced privacy‐preserving mechanism. Overall, voluntary tracking of mobile users will always be possible, which is the fairest way in our opinion.
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Footnotes
1 https://42reports.com.
2 https://sensalytics.net/de.
3 https://www.infsoft.de.
4 http://crosscan.com/de.
5 https://www.retailreports.de.
6 http://www.walkbase.com.
7 http://euclidanalytics.com.
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_43
43. Improving Urban Transportation: an Open Plat-Form for Digital Mobility Services
Maximilian Schreieck1 , Christoph Pflügler1 , David Soto Setzke1 , Manuel Wiesche1 and Helmut Krcmar1
(1)Technical University of Munich, Munich, Germany
Maximilian Schreieck (Corresponding author)
Email: [email protected]
Christoph Pflügler
Email: [email protected]
David Soto Setzke
Email: [email protected]
Manuel Wiesche
Email: [email protected]
Helmut Krcmar
Email: [email protected]
43.1 Introduction1 2
Today, the traffic situation in many cities is challenging. Due to an increasing degree of urbanization and traffic complexity people lose more and more time in traffic jams and during the search for parking spots. It is estimated that European cities lose between 2.69 and 4.63% of their Gross Domestic Product (GDP) because of traffic congestions [1]. Additionally, the increase in congestion leads to an aggravation of air pollution and greenhouse gas emissions in cities all around the globe [2].
Recently, IT has emerged as one of the key influencing factors on traffic [3]. City administrators operate intelligent transportation systems (ITS) that use IT to improve the safety, efficiency, and convenience of surface transportation [4] and it has been shown that ITS have a greater impact on energy and environmental benefits than construction‐phase measures [5]. In the last few years, mobility and location‐based services emerged on smartphones as well as within cars. As a result, mobile services have become an important influencing factor on individual mobility in addition to existing ITS [6]. The variety of services that is used by end users include journey planning, ride‐sharing matching, maps, navigation etc. and use a variety of data sources.
Many of these solutions are dependent on accurate data, e. g. the location of the users, time‐schedule of public transportation or information on the current traffic or parking situation. However, it is difficult for developers of mobility solutions to gather this data, because there are only a few platforms, such as Google Maps or Bing Maps, that offer mobility data and services through standardized interfaces. Service providers only offer isolated services with a specific focus; access to their data and services is often limited and restricted. Furthermore, existing services are not yet integrated and the landscape of digital solutions is vast and unstructured. On the other hand, smart cities generate extensive mobility data such as floating car data of individual vehicles but this data is not offered to external providers nor is it standardized. Making this data accessible and offering standardized modular services that aggregate and analyze the available mobility data would ease the effort for solution providers and foster further development of innovative mobility services. Especially in the case of small and middle‐sized enterprises, this could lead to an increased use of mobility data since in these companies there often is a lack of highly specialized knowledge which would be required to analyze and make use of unstructured mobility data produced by smart cities [7]. However, until now it remains unclear what the concept for the architecture of an open and modular digital mobility services platform should look like.
In order to develop the requirements for a solution, we have conducted an analysis of already existing services [8]. We have identified distinct service modules and data sources that are used across these existing services in order to show that an integration of these can increase the value of digital mobility services. Based on our findings from the analysis, which will be presented in the next chapter, we developed a proposition of an architectural concept for an open digital mobility services platform, which we will present and discuss in the subsequent chapters. Our concept contributes to theory by giving guidance for future research on service platforms, especially in the context of mobility services. It also contributes to practice by showing how currently available mobility‐related data can be made accessible for developers of digital mobility solutions.
43.2 Analysis of Existing Digital Mobility Services
For our analysis, we applied a methodology framework by Dörbecker and Böhmann [9] for the design of modular service systems. Based on service systems engineering theory, it helps with analyzing, designing, implementing and monitoring service modules as parts of a modular service system architecture. While previous research on modularity has focused mainly on products, recent studies have applied these concepts in the design of services [10]. Dörbecker and Böhmann [11] identified and analyzed 12 methods for designing modular service systems and, as a result, proposed their own iterative design framework. It addresses several limitations and weaknesses of the analyzed methods such as missing generalizability and introduces new aspects such as an iterative design approach across several distinct phases. Böhmann et al. [12] call for future research on service systems engineering in information systems and mention sustainable mobility as an area where services can generate significant benefits. With our analysis, we apply the second step of modularization within the mentioned framework, which comprises the identification and analysis of the service system’s modules.
In order to provide an overview of existing mobility services for urban transportation, we conducted a broad search within app stores of mobile devices and tech blogs. All together, we identified 59 mobility services that we analyzed in more detail.
These services were first grouped into six categories, following a taxonomy development process described by Nickerson et al. [13]: Trip planners, ride and car sharing services, navigation services, smart logistics services, location‐based services and parking services. A summary along with a description and example services is presented in Table 43.1. In a second step, we analyzed modules and data sources of all services. We will now further describe the results of our analysis. Table 43.1Categories of Digital Mobility Services
Category
Description
Example service
Trip planners
Provide information for planning trips
moovel
Ride/Car sharing
Share cars and rides
flinc/drivy
Navigation
Follow a route by giving directions
Google Maps
Smart logistics
Facilitate the movement of goods
foodora
Location‐based information
Provide location‐relevant information
Services for radar controls
Parking
Provide information on parking lots
Parknav
/>
Table 43.2 presents an overview of the modules that we identified. They can be structured according to the origin of their value proposition. One group of modules provides information, while a second group contributes analytics to enhance existing information. The modules map view, POIs, location sharing, traffic information and parking information provide the user with information he or she needs in a specific context. The modules routing and matching are based on analytic capabilities and combine existing information to derive new information. Table 43.2Modules of Digital Mobility Services
Module
Description
Example service
Map view
Show current location and surroundings, relevant information and directions
DriveNow
Routing
Provide suggestions on how to travel to a destination
Google Maps
POIs (Points of interest)
Provide information about relevant points of interest
Digital Marketplaces Unleashed Page 66