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Nianbo Liu, Yong Feng, Feng Wang, Bang Liu,and Jinchuan Tang (( The authors work in the School of Computer Science and Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu 611731,  China or in Yunnan Key Laboratory of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500,  China)) (2013), Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone, International Journal of Distributed Sensor Networks, 9 pages, http://dx.doi.org/10.1155/2013/615282 ((This paper is supported in part by the China NSF Grants (61103226, 60903158, 61170256, 61173172, and 61272526), and the Fundamental Research Funds for the Central Universities Grants (ZYGX2010J074, ZYGX2011J102, and ZYGX2012J083). ))In current carpooling systems, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpool website by smartphone, for finding a possible match of the journey. While efforts have been made to achieve fast matching for known trips, the need for accurate mobile tracking for individual users still remains a bottleneck. For example, drivers feel impatient to input their routes before driving, or centralized systems haves difficulties to track a large number of vehicles in real time. In this paper, we present the idea of Mobility Crowdsourcing (MobiCrowd), which leverages private smartphone to collect individual trips for carpooling, without any explicit effort on the part of users. Our scheme generates daily trips and mobility models for each user, and then makes carpooling zero-effort by enabling travel data to be crowdsourced instead of tracking vehicles or asking users to input their trips. With prior mobility knowledge, one user's travel routes and positions for carpooling can be predicted according to the location of the time and other mobility context. Based on a realistic travel survey and simulation, we prove that our scheme can provide efficient and accurate position estimation for individual carpools. Introduction Nowadays, quick and easy transportation has been an essential part of modern society. Vehicles offer flexibility and mobility when it comes to our work-related and personal lives, enable rapid and timely delivery of goods, but can also cause traffic jams, carbon emissions, pollution, accidents, energy crisis issues, and other problems, inevitably. On the one hand, vehicle transportation plays a vital role in global economy; therefore, any efficiency improvement will yield great profits. On the other hand, the efficiency of vehicle transportation points out how we use vehicles and, also, the degree of expensive social costs. Although lots of institutions, resources, and research are dedicated to improving transportation efficiency, the waste of transportation capacities is still ubiquitous in current vehicle transportation.According to NHTS  from the US Department of Transportation, the average occupancy rate of personal vehicle trips is 1.6 persons per vehicle mile. Since a regular vehicle carries 5 persons in full occupancy, 68% of transportation capacities are wasted during personal trips. In US alone, this involves 204 million personal vehicles and causes a great deal of loss. Similarly, such inefficiency has also been observed in business transportation. Taxies, vans, trucks, and other vehicles are often running in low occupancy or utilization or are sometimes even unoccupied. The oversupply does not just come from wasted transportation capacities, but also comes from information opacity between supply and demand.Carpooling seems an effective method to achieve green and efficient transportation. Traditional carpools are neighbors or coworkers with similar routes, who can easily contact with each other for possible carpooling. Casual carpools, as impromptu carpools formed among strangers, can team up in public areas near HOV lanes, but it is severely limited in deployed roads [2]. Later carpool associations allow people to match their respective trips through the Internet, even if they are strangers. In the “dynamic carpooling” concept, casual carpooling is proposed, and many researchers try to achieve this aim through special designed systems. In such carpooling, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpooling website by smartphone, for finding a possible match of the journey. However, these systems failed to provide convenient and flexible carpool services for common users. While efforts have been made to achieve fast matching for known trips, the need for accurate mobile tracking for individual users still remains a bottleneck in current carpooling systems. For example, drivers feel impatient to input their routes before driving, or centralized systems have difficulties to track a large number of vehicles and to acquire their real-time positions.Crowdsourcing has been evolving as a distributed problem solving and business production model in recent years, which was proposed to reduce production costs and make more efficient use of labor and resources with the aids of participators. An example of crowdsourcing tasks is seen in indoor localization, the system Zee makes the calibration zero-effort, by enabling WiFi fingerprint training data to be crowdsourced without any explicit effort on the part of users. In this paper, we present the idea of MobiCrowd, which leverages private smartphone to collect individual trips for carpooling, without any explicit effort on the part of users. Our scheme acquires location information from smartphone, generates daily trips and mobility models for each user, and then makes carpooling zero-effort by enabling travel data to be crowdsourced instead of tracking vehicles or asking users to input their trips. With prior mobility knowledge, a user’s travel routes and positions can be predicted according to the location of the time, and then possible carpooling can be arranged to fit the mobility context. Based on a realistic travel survey and simulation, we prove that our scheme can provide efficient and accurate position estimation for individual carpools.The remainder of this paper is structured as follows: Section 2 presents a brief overview of related work. In Section 3, we explain the design of MobiCrowd step by step, including human mobility patterns, system overview, driving sensing, trajectory prediction, and position estimation. Section 4 evaluates our scheme via a survey and simulation, while Section 5 finally summarizes the paper.Read the full text article