Making Sense of Trajectory Data in Indoor Spaces

Abstract

The increasing prevalence of positioning and tracking systems has helped simplify tracking large amounts of, e.g., people moving through buildings or cars traveling on roads, over long periods of time. However, technical limitations of positioning algorithms and traditional sensing infrastructures are likely, especially indoors, to induce errors and biases in the resulting data. In particular, the resulting motion trajectories often do not conform perfectly to the underlying route network. As a consequence, analyses of trajectory sets are impeded by these phenomena, as it becomes hard to identify which route was taken in a particular travel instance or whether two travel instances followed the same route. In this paper, we present a bootstrapping approach and several algorithms to mitigate error biases and related phenomena, focusing on indoor scenarios. In particular, we are able to estimate and iteratively refine an underlying route network from a set of motion trajectories. Secondly, we represent subtrajectories, i.e., movements on individual elements of the route network, by their median subtrajectory. The resulting aggregated and cleaned-up data set facilitates using further, domain-specific analysis tools. Additionally, it allows to predict the locally occurring expected positioning error biases. This in turn allows improved positioning, e.g., for real-time navigation assistance scenarios. We evaluate the proposed methods using trajectory data from employees at a large hospital complex. In particular, we show that we can reconstruct the hospital's route network accurately, and that we can furthermore extract median subtrajectories for almost all individual corridors. Finally, we illustrate that median trajectories deliver useful deviation maps to learn, and correct for, the expected local biases in positioning.

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

“…We are aware of only a few works [7][8][9][10] which directly involve this issue that generates user's trajectory in indoor space. Specifically, Prentow et al [7] proposed a bootstrapping approach to construct indoor trajectory for mitigating indoor positioning error biases.…”

“…Specifically, Prentow et al [7] proposed a bootstrapping approach to construct indoor trajectory for mitigating indoor positioning error biases. Werner et al [8] utilized WiFi RSSI as an information source to infer indoor trajectory from a given trajectories database.…”

“…(2) We only use the indoor trajectories to learn user's preferences for making recommendations, unlike literatures [17,18] that need additional user profiles for recommendation. (3) Existing indoor POI recommendation algorithms, such as [7][8][9][10], merely use user-based or item-based collaborative filtering for making recommendation and will suffer data sparsity problem since numerous users only have few checkin information. To address this challenge, we first utilize a two-layer relation graph to capture multirelation among users and POIs from user's historical trajectories.…”

“…The results suggest that our recommendation algorithm can learn user's preference even with few POI check-ins by using a graph-based model to capture the latent relation between users and POIs. To investigate the recommendation effectiveness for "cold-start" users, we further calculate the NDCG@ (i.e., ∈ [3,5,7]) for each user of E group, as shown in Figure 11. From this figure, we can observe that, for all cold-start users, the NDCG@k of our proposed model is much better than the compared models (user-based CF and RWR), showing again that the advantage of our method derives user's preference by using a graph-based model to capture the latent relation between users and POIs.…”

Towards Indoor Transportation Mode Detection Using Mobile Sensing

“…For other types of buildings, the floor plans may only be available in simple digital formats, such as pictures, which are ill suited for extracting e.g. the route network of hallways in a building [11].…”