# Advanced Smartphone-Based Identification of Transport Modes: Resilience under GNSS-Based Attacks
## 1 Introduction  
Over the last decade, cities have been transitioning from traditional mobility to Mobility as a Service (MaaS). MaaS is the integration of and access to different transport services, such as public transport, ride/car/bike/scooter sharing, taxis, car rental and ride-hailing, among others, in a single digital mobility service based on active mobility and an efficient public transport system [1]. While the goal of MaaS is to offer passengers a seamless, end-to-end and easily accessible journey, one of its key enablers is the ticketing [2]. The passenger experience of ticket purchase should be flexible, seamless and based on real-time information. The ticketing service in MaaS should integrate the public and individual transport modes together with the billing platforms into a unified ticketing service. If the ticketing experience is realized on everyone's personal smartphone, passengers could receive a consolidated bill for their monthly/weekly use of the unified transport network without even having to buy a ticket. To realize this concept, it is necessary to develop a seamless identification of the transport modes used, based on commercially available smartphones. In the literature, there are approaches to realize the seamless identification of the transport modes used based on smartphones. For example, reading a QR code that identifies the transport mode when hopping in and out of the vehicle is a possible implementation of smartphone-based ticketing [3]. However, this solution relies either on the good will of the passengers when reading the QR code with the smartphone or the existence of a controller in the transport mode. Other implementations of ticketing with smartphones involve the use of Near-Field Communication (NFC) devices [4]. This implementation implies the use of check-in andcheck-out terminals that are able to determine the duration of the journey. The NFC terminals are normally placed outside the transport mode, beside turnstiles or gates. Unlike the above mentioned solutions, a seamless identification of the used transport mode for ticketing is possible based on the use of Global Navigation Satellite Systems (GNSSs) [5]. This implementation uses the position and velocity information from satellite systems to identify the transport mode that is being used and the journey that has been traveled. Nevertheless, the GNSS position and velocity can be forged with spoofing attacks, for instance, to avoid or modify the billing. Additionally, there are plenty of urban scenarios with no satellite reception, such as stations. As the identification of transport modes should be used for billing, resilience is of high importance. Resilience is the ability of the ticketing system to recover from a malicious attack and provide correct identification of transport modes throughout the journey. Depending on the particular technology used to identify the transport mode with the smartphone, the system might be to a greater or lesser extent vulnerable to attacks. We will focus on the smartphone-based identification of transport modes without the use of external infrastructure, such as QR codes or NFC devices. As described above, one of the most extended methods to identify the transport mode with the smartphone is to use the position and the velocity provided by GNSSs. The identification of transport modes purely or mainly based on GNSSs is vulnerable to jamming and spoofing attacks. The jamming attack consists on an intentional interference with enough power to force the receiver to lose the legitimate GNSS signal. The spoofing attack consists of making the receiver believe it is positioned at a different place than it is actually located and eventually that it is moving with a wrong velocity. This attack is a common practice in the military context. However, the civilian context is not left out, e.g., for criminal purposes, such as defeating automated payment systems, violating a forbidden fishing zone or cheating on the driving hours of truck drivers.