Mayiladuthurai

UNDERSTANDING TAXI SERVICE STRATEGIES FROM TAXI GPS TRACES Mining taxi GPS traces has received increasing attention from the data mining, intelligent transportation, database, and ubiquitous computing communities [2], [34]. A variety of re- search issues have been addressed leveraging large-scale GPS traces, such as urban human mobility understanding [1], [4], [8], [11], [17], [19], [26], urban planning [16], [29], [30], [35], traffic prediction [9], [23], [24], anomalous trajectory detection [6], [33], city region function identification [20], [22], [28], and taxi/passenger search strategies [4], [13], [17], [21], [26]. In this section, we briefly present the related work on improving taxi drivers’ performance, which can be roughly classified into the following three categories. The first category of research focuses on identifying and recommending popular pickup areas [4], [13], [17], [21], [26]. Since the change of taxi status from vacant to occupie...

Strategies From Taxi GPS Traces Passenger-searching strategies . These strategies con- sider the practical factors affecting a taxi driver’s decision making when searching for new passengers. The taxi driver may prefer to wait at a popular location nearby (e.g., grand hotels and railway stations) or go to a familiar area far away to search for new passengers. • Passenger-delivery strategies . These strategies character- ize the taxi drivers’ intelligence in delivering passengers. A driver may choose one route among several possible ones to deliver passengers, considering factors such as the traffic conditions, and trip fares. • Service area preference . In a particular time period of the day, taxi drivers may prefer to serve in certain regions in a city by considering the traffic, passenger demands, and their familiarity with the regions. In order to link the service strategies to the resulted rev- enue of taxi drivers, we need to address the f...

Taxi-RS: Taxi GPS Data TRAJECTORY can be regarded as the trace of mobile objects in space as times change. The convergence of trajectory data allows the easy acquisition of information about the trajectories of users using mobile devices [1]. Global Posi- tioning System (GPS), one of the growing technologies of ge- olocation, is widely used on taxis and makes the GPS-equipped taxi be regarded as the detector of urban transport system. It becomes possible to access the location information of taxis of a whole city at any time. Since the amount of trajectory data [3] and the complexity of data structure grow, it has become a more interesting and challenging topic to find an efficient way to process, model, and analyze the mass data of movement. A taxi is an important tool, for people, to travel in the city, but sometimes it is common, for people, to face an awkward position [2]. Most people had such experience: you had to wait for more than 10 min to get a taxi ride only because you w...

FTG in driving system This part introduces how to design the FTG model and how to construct the storage and related data structure of the frequent trajectory model. We obtain frequent pattern substrings of taxi trajectories according to the offline preprocessing results. Since the key of the paper is to solve the probability that can get a taxi ride at a query point in m minutes, we need to analyze and construct the factors that can affect the probability of the final solution. If an available taxi arrived at a designated location in m minutes, we must ensure the following conditions. 1) To ensure that an available taxi can reach a location in m minutes, we need to analyze the speed and the distance from historical data. 2) To ensure that other people would not get the car ride when it comes on its way, we need to analyze the trigger conditions in historical data, and calculate the PGTR by others on its way in each direction. 3) To ensure that query location is on the preferred traj...

  The Influences of Social Software Characteristics Knowledge sharing among students is one of the essential activities in learning. Recently, it is found that besides face-to-face interactions, students also share their knowledge through online channels such as social software tools. This study aims to examine the relationship between social software characteristics and knowledge sharing behavior of students in Malaysian university context. The findings of the questionnaire survey with 307 students in five different universities revealed that informal setting, communication, network and community, and user-generated content characteristics of social software tools could positively influence students’ knowledge sharing behavior. The web-based technology has led to many changes in the higher education. This technology increased the growth in complexity of informa...