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Taxi is a major transportation in the urban area, offering great benefits and convenience to our daily life. However, one of the major business fraud in taxis is the charging fraud, specifically overcharging for the actual distance. In practice, it is hard for us to always monitor taxis and detect such fraud. Due to the Global Positioning System (GPS) embedded in taxis, we can collect the GPS reports from the taxis' locations, and thus, it is possible for us to retrieve their traces. Intuitively, we can utilize such information to construct taxis' trajectories, compute the actual service distance on the city map, and detect fraudulent behaviors. However, in practice, due to the extremely limited reports, notable location errors, complex city map, and road networks, our task to detect taxi fraud faces significant challenges, and the previous methods cannot work well. In this paper, we have a critical and interesting observation that fraudulent taxis always play a secret trick, i.e., modifying the taximeter to a smaller scale. As a result, it not only makes the service distance larger but also makes the reported taxi speed larger. Fortunately, the speed information collected from the GPS reports is accurate. Hence, we utilize the speed information to design a system, which is called the Speed-based Fraud Detection System (SFDS), to model taxi behaviors and detect taxi fraud. Our method is robust to the location errors and independent of the map information and road networks. At the same time, the experiments on real-life data sets confirm that our method has better accuracy, scalability, and more efficient computation, compared with the previous related methods. Finally, interesting findings of our work and discussions on potential issues are provided in this paper for future city transportation and human behavior research. [ABSTRACT FROM PUBLISHER]

Crowdedness spot is a crowded area with an abnormal number of objects. Detecting the crowdedness spots of moving vehicles in an urban area is essential to many applications. An intuitive method is to cluster the objects in areas to get the density information. Unfortunately, the data capturing vehicle mobility possesses some new features, such as highly mobile environments, supremely limited size of sample objects, and nonuniform biased samples, and all these features have raised new challenges that make traditional density-based clustering algorithms fail to retrieve the real clustering property of objects, making the results less meaningful. In this paper, we propose a novel nondensity-based approach called mobility-based clustering. The key idea is that sample objects are employed as “sensors” to perceive the vehicle crowdedness in nearby areas using their instant mobility rather than the “object representatives.” As such, the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified, and techniques to compensate these effects are accordingly proposed. Furthermore, taking the detected crowdedness spots as a label of the taxi, we can identify one particular taxi to be a crowdedness taxi that crosses a number of different crowdedness spots. We evaluate the performance of our methods and baseline approaches based on real traffic situations (to retrieve the real traffic crowdedness) and real-life data sets. Finally, the interesting findings are provided for further discussions. [ABSTRACT FROM PUBLISHER]

Deploying wireless sensor networks in the ocean poses many challenges due to the harsh conditions of the ocean and the nonnegligible node mobility. In this paper, we propose hybrid ocean sensor networks called drifting restricted floating ocean sensor networks (DR-OSNs) for long-term maritime surveillance monitoring tasks, which combines both the advantages of wireless sensor networks and underwater wireless acoustic sensor networks. We present a localization scheme termed localization for double-head maritime sensor networks (LDSN) for DR-OSNs, which leverages the unique characteristics of DR-OSNs to establish the whole localization system after the network is deployed from a plane or a ship, and it does not need the presence of designated anchor nodes deployed underwater. The whole localization process consists of three steps with algorithms self-moored node localization (SML), underwater sensor localization (USD), and floating-node localization algorithm (FLA). The first step is for the super group nodes to localize their underwater moored nodes via an SML algorithm by leveraging the free-drifting movement of their surface nodes. Once the moored nodes in the super group nodes have localized themselves, they turn into anchor nodes underwater. Thus, in the second step, with the help of these new anchor nodes, the unlocalized underwater moored nodes use the USD algorithm to localize their positions. In the last step, when the free-drifting floating nodes without a Global Positioning System (GPS) module need to know their instant position, they apply the FLA to figure out their position. We conduct extensive simulations to evaluate the scheme, with the results indicating that LDSN achieves high localization accuracy and is an effective localization scheme for DR-OSNs. [ABSTRACT FROM PUBLISHER]

Intelligent transportation systems (ITSs) have become increasingly important for public transportation in Shanghai, China. In response, ShanghaiGrid (SG) aims to provide abundant intelligent transportation services to improve traffic conditions. A fundamental service in SG is to locate the nearest desirable vehicles for users. In this paper, we propose an innovative protocol called ANTS to locate a desirable vehicle close to the querying user. The protocol finely mimics the efficient searching strategy adopted by a lost ant searching for its nest. Taking query locality into account, ANTS can retrieve the closest vehicles satisfying the query with high probability but incurs small query latency and modest network traffic. ANTS is a fully distributed and robust protocol and, therefore, has good scalability. Extensive simulations based on the real road network and the trace data of vehicle movements in Shanghai demonstrate the efficacy of ANTS. [ABSTRACT FROM AUTHOR]