TransitLabel

Abstract

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

Time–space analysis to evaluate cell-based quality of service in bus rapid transit station platforms through passenger-specific area

“…We define the activity of circulating to include walking and moving with aids including, but not limited to, wheelchairs and pushers. While performing these activities, passengers undertake other incidental actions, such as looking for the information provided on the displayed bus timetables and real-time bus arrival information screen , utilizing smart card and food vending machines (Elhamshary et al 2016), use of mobile phones, chatting with fellow passengers, and use of help phones for disability assistance. This study focuses on the major passenger activities that are performed on the platform (irrespective of the interaction with buses).…”

Finding a Parked Car Location in a Multi-Storey Building without GPS Service

“…Once we detect this level change, we will record its time as the end of this floor trajectory and the start of the next floor trajectory. We also determine the elevation's means; elevators, escalators, half-landing stairs, and straight stairs by employing Elevation Change Semantics Detection in [18] that applied a decision tree classifier on the sensor's readings based on the observations of semantics usage scenarios and their physical structures.…”

An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint

“…However, the implementation of advanced environmental monitoring applications can be costly and complex [1]. For example, smartphone users' equipped with portable devices are able to perform jobs likes measuring the noise level by microphones or quantifying the environment visibility by builtin cameras [2,3,4], map semantic labeling [5,6,7,8,9], perform road condition monitoring [10,11,12], estimate pollution [13,14] and tra c [15], or they can collect crowdsource images [16,17] performing, e.g., target identification [18,19,20]. A new wave of applications is expected from the adoption of augmented reality sensors, e.g., LIDAR available in new generation phones and tablets [21].…”