## 1 Introduction and Related Works  
Assessments of level of service (LOS) measures for various modes of transportation are crucial in monitoring and managing the performance of a transportation system network that consists of multiple available modes of transportation via inter-modal connection points. Traditionally, departments of transportation (DOTs) have used either fixed-point sensors or probe vehicles for traffic monitoring purposes. Fixed-point sensors include loop detectors and video cameras. Loop detectors embedded under the road surfaces sense fluctuations in electric currents as vehicles pass over it and estimate their speeds based on the time it took for vehicles to travel the distance between their two axles. Video cameras are either monitored by dedicated personnel at DOTs or enhanced with motion detection algorithms in efforts to estimate their speeds on screen that are often challenged by weather conditions affecting the accuracy, such as rainfall, making the road surface darker, or snowfall, making the road too bright. Fixed-point sensors are expensive, at first, with high capital costs associated with their installations, yet provide near-permanent raw data at economically feasible costs continuously afterwards. However, they are only capable of monitoring traffic conditions at those fixed points instead of throughout the route which can often result in misleading local traffic condition estimations. In order to overcome the issue, DOTs have regularly dispatched dedicated probe vehicles [1] with dedicated drivers and data logging personnel who are later replaced by conventional GPS data loggers in efforts to reduce labor costs. The collected data have been typically post-processed to estimate the traffic conditions throughout the route after the probes have run. This after-the-fact approach provides insights on near future traffic conditions for planning purposes which is not suitable for real-time traffic monitoring. With an emergence and rapid market penetration rates of smartphones, there are new opportunities for collecting traffic data as smartphone users voluntarily travel throughout all sectors of transportation networks in all modes of transportation. The majority of smartphones are typically equipped with multiple sensors including assisted GPS (AGPS) chips, accelerometers, and magnetometers connected on an economically feasible data plan that continues to lower in costs. AGPS is an enhanced version of traditional GPS sensors that is typically assisted by cellular tower signals that improve the startup performance. If the mode of transportation a smartphone user is in can reliably be identified, technically speaking, the users can be considered as traffic probes themselves that are hovering over an entire transportation network and can provide enormous amounts of raw data that greatly benefit traffic monitoring and management applications in the field of intelligent transportation system (ITS). Privacy issues associated with such an approach in data collection is beyond the scope of this paper. However, the users may still give consent to reliable smartphone apps to collect such data with expectations of valuable real-time traffic information or tax-related benefits from the government in return, for example. In fact, many users of Internet service providers, such as GoogleTM, and various other social network services (SNS) have given such consents to them in exchange of different forms of benefit. Among all modes of transportation, an \"auto\" mode is especially useful for traffic monitoring purposes as it refers to standard private vehicles that have movement patterns that account for the majority of vehicles on the roads. The auto mode users are also the major audiences of real-time traffic information. A transit bus, for example, would not be a good candidate to be categorized as an auto mode. A transit bus on an uncongested road would still frequently stop and typically operates at a lower maximum speed than private vehicles.