Where to : crowd-aided path selection by Selective Bayesian Network

Abstract

With the wide usage of geo-positioning services (GPS), GPS-based navigation systems have become more and more of an integral part of people’s daily lives. GPS-based navigation systems usually suggest multiple paths for a pair of given source and target. Therefore, users become perplexed when trying to select the best one among them, namely the problem of best path selection. Too many suggested paths may jeopardize the usability of the recommendation data, and decrease user satisfaction. Although the existing studies have already partially relieved this problem through integrating historical traffic logs or updating traffic conditions periodically, their solutions neglect the potential contribution of human experiences. In this paper, we resort to crowdsourcing to ease the pain of best path selection. However, the first step of using the crowd is to ask the right questions. For best path selection problem, the simple questions (e.g. binary voting) on crowdsourcing platforms cannot be directly applied to road networks. Thus, in this paper, we have made the first contribution by designing two right types of questions, namely Routing Query (RQ) to ask the crowd to decide the direction at each road intersection. Secondly, we propose a series of efficient algorithms to dynamically manage the questions in order to reduce the selection hardness within a limited budget. In particular, we show that there are two factors affecting the informativeness of a question: the randomness (entropy) of the question and the structural position of the road intersection. Furthermore, we extend the framework to enable multiple RQs per round. To ease the pain of the sample sensitiveness, we propose a new approach to reduce the selection hardness by reasoning on a so-called Selective Bayesian network. We compare our approach against several baselines, and the effectiveness and efficiency of our proposal are verified by the results in simulations and experiments on real-world datasets. The experimental results show that, even the Selective Bayesian Network provides only partial information of causality, the performance on the reduction of the selection hardness are dramatically improved, especially when the size of samples are relatively small.