In order for travelers to arrive at their destination comfortably, it is important that they plan their trip carefully. Our motivation in this paper is to free the traveler from tedious planning tasks. Several studies and applications have been proposed to help travelers plan their trip according to explicit preferences, but most ignore implicit preferences. For the first time, to the best of our knowledge, we propose a Personalized and Optimized Door-to-door TRavel Planning two-stage approach named PD-TRP, which combines different kinds of transportation services to provide high quality and cost-effective travel plan recommendations, taking into consideration individual explicit and implicit preferences in real time. In order to recommend a set of suitable plans with explicit attribute values, we propose an efficient heuristic search algorithm to generate the top-K candidate plans, and rank them according to the information entropy of implicit preferences discovered from the travelers' previous travel behaviour. We simulate information on transportation services, crawled from the Internet, on 2000 travelers and their past travel behaviour over the last three years, collected from an online travel company, and extensive experimental results show the two-stage approach PD-TRP can effectively improve the degree of satisfaction of the travelers.

Crowdsourcing to mobile users has emerged as a compelling paradigm for collecting sensing data over a vast area for various monitoring applications. It is of paramount importance for such crowdsourcing paradigm to provide effective incentive mechanisms. State-of-the-art auction mechanisms for crowdsourcing to mobile users are typically deterministic in the sense that for a given sensing job from a crowdsourcer, only a small set of smartphones are selected to perform sensing tasks and the rest are not selected. One apparent disadvantage of such deterministic auction mechanisms is that the diversity with respect to the sensing job is reduced. As a consequence, the quality of the collected sensing data is also decreased. This is due to failure to exploit the intrinsic advantage of the large set of diverse mobile users in a mobile crowdsourcing network. In this paper, we propose a randomized combinatorial auction mechanism for the social cost minimization problem, which is proven to be NP-hard. We design an approximate task allocation algorithm that is near optimal with polynomial-time complexity and use it as a building block to construct the whole randomized auction mechanism. Compared with deterministic auction mechanisms, the proposed randomized auction mechanism increases the diversity in contributing users for a given sensing job. We carry out both solid theoretical analysis and extensive numerical studies and show that our randomized auction mechanism achieves approximate truthfulness, individual rationality, and high computational efficiency.

Mobile crowdsourcing to smartphones has emerged as a compelling paradigm for collecting sensing data over a vast area for various monitoring applications. It is of paramount importance for mobile crowdsourcing to provide incentive mechanisms. State-of-the-art auction mechanisms for mobile crowdsourcing are deterministic in the sense that given the real costs of the smartphone users, a fixed set of smartphone users are recruited for performing sensing tasks. This leads to serious issues including reduced diversity of sensing devices and starvation of some users. In this paper, we propose an approximate-truthful randomized combinatorial auction mechanism for the social cost minimization problem, which is NP-hard. We design an approximate task allocation algorithm that is near optimal with polynomial-time complexity and use it as a block to construct the whole randomized auction mechanism. We carry out numerical studies and show that our randomized auction mechanism achieves approximate truthfulness, individual rationality, and high computation efficiency. Moreover, the proposed mechanism increases diversity of devices and prevents starvation of some smartphones.