Taxi-RS: Taxi GPS Data

Taxi-RS: Taxi GPS Data

TRAJECTORY can be regarded as the trace of mobileobjects in space as times change. The convergence oftrajectory data allows the easy acquisition of information aboutthe 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-equippedtaxi be regarded as the detector of urban transport system. Itbecomes possible to access the location information of taxis ofa whole city at any time. Since the amount of trajectory data[3] and the complexity of data structure grow, it has become amore interesting and challenging topic to find an efficient wayto 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 waitfor more than 10 min to get a taxi ride only because you were2 min late in the specific place. Another example: you had beenstanding by the street waiting for a cab for over 15 min, butyour neighbor just came out of his home, crossed the street,walked a few meters, and got a taxi ride immediately. It seemslike people have to collect more information to get a taxi rideinstead of longer hopeless waiting. Along with the increase inthe amount of taxis, it becomes urgent to solve recommendedlocation information and calculate waiting time by means oflarge-scale taxi GPS data.
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Our contributions are the following. According to the taxiGPS historical data, our paper proposes a taxi-hunting systemof data processing, i.e., Taxi-RS, in the environment of big data.Taxi-RS includes two phases: offline processing and fast onlineprocessing. The first phase includes offline data preprocessingand an offline graph model constructor. First, we calculatedeigenvalues between the image data from the GPS data. Wedivided the big data file into seven parts, according to thedifference degree between the eigenvalues. Then, we scan hotpoints on compressed data to identify the preference trajecto-ries. According to the preference trajectories, we can constructthe offline graph trajectory model. When inquiring online, wecan search quickly in the trajectory graph to calculate queryresults by using the probability model based on the query inputtime and location. Finally, we output query results.There are several aspects in extending this current workfurther. In real life, a city’s taxi system is a dynamic systemchanging gradually with time passing. In addition, the systemis not closed and dramatically affected by external factors (suchas road construction, traffic control, etc.). Therefore, one-monthGPS data are not enough to support the continuous outputs.After a long period of time, the old GPS data will not match thereal-time taxi system; hence, it is necessary to timely update themodel based on the latest GPS data. To deal with this problem,we have the following suggestions.1) We could check the time validity of the data set and dis-card invalid data that are past their expiry date. Therefore,we need to cover the offline model with the latest data.This approach is relatively simple, but it is possible toabandon some of the old data, which are still valuableand may result in some error.2) On this basis, we could replace the old data moregradually. For example, we can iteratively update olddata bydata in the model=the latest data×0.8+the old data×0.2. The advantage is that we could retainthe reference of the old data during discard.However, if we use the two methods mentioned, we will takea long time when updating the offline model every time. Wecould optimize the updating time by using the dynamic graphor dynamic programming algorithm, so that we can constructthe offline model to update new data. click here to view full website

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