Taxi-Hunting Recommendation System
Recommender systems are constructed to search thecontent of interest from overloaded information by acquiring use-full knowledge from massive and complex data. Since the amountof information and the complexity of the data structure grow,it has become a more interesting and challenging topic to findan efficient way to process, model, and analyze the information.Due to the Global Positioning System (GPS) data recording thetaxi’s driving time and location, the GPS-equipped taxi can beregarded as the detector of an urban transport system. This pa-per proposes a Taxi-hunting Recommendation System (Taxi-RS)processing the large-scale taxi trajectory data, in order to providepassengers with a waiting time to get a taxi ride in a particularlocation. We formulated the data offline processing system basedon HotSpotScan and Preference Trajectory Scan algorithms. Wealso proposed a new data structure for frequent trajectory graph.Finally, we provided an optimized online querying subsystemto calculate the probability and the waiting time of getting ataxi. Taxi-RS is built based on the real-world trajectory data setgenerated by 12 000 taxis in one month. Under the condition ofguaranteeing the accuracy, the experimental results show that oursystem can provide more accurate waiting time in a given locationcompared with a naïve algorithm
RAJECTORY can be regarded as the trace of mobileobjects in space as times change.
Call Taxi in Kumbakonam
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.In the information age, it is very easy to collect in-formation. For the mentioned taxi problem, it becomes averyinterestingquestiontorecommendlocationinformationandcalculate waiting time by means of large-scale taxi GPS data.In this paper, we propose a processing method based on bigdata environment for the taxi GPS historical data, includingoffline and online processing. We implement a recommendationsystem called Taxi-hunting Recommendation System (Taxi-RS), and our system can calculate the probability and time ofgetting a taxi ride when giving information of time and point.The following are the major contents of this paper.1) We design data offline processing. First, we design analgorithm called HotSpotScan (HSS) algorithm to scanhotspots in a city. Then, the taxi preference trajectory isrecognized by a new algorithm called Preference Trajec-tory Scan (PTScan) algorithm.2) We propose an offline graph model by taxi preferencetrajectory and store offline compressed data by using amultiple adjacent table.3) Weconstructaprobabilitymodelbyusingtheofflinemodelto compute the probability and the waiting time of getting ataxi ride and analyze the complexity of online processing.The rest of this paper is organized as follows. Section IIgives a brief review on related literature. In Section III, someconception is formally introduced. The overview of the Taxi-RS is given in Section IV. In Section V, we introduce thekey algorithms of offline processing. In Section VI, we presentthe frequent trajectory graph (FTG) model. In Section VII, wepropose a calculation model to get a taxi ride. The results of theexperimental evaluation are given in Section VIII. Remarks arestated under the conclusion https://srisivasakthitravels.com/
https://www.facebook.com/Sri-Siva-sakthi-Travels-102008227873939/
https://goo.gl/maps/1rNVoifRFQ9snSor6
Recommender systems are constructed to search thecontent of interest from overloaded information by acquiring use-full knowledge from massive and complex data. Since the amountof information and the complexity of the data structure grow,it has become a more interesting and challenging topic to findan efficient way to process, model, and analyze the information.Due to the Global Positioning System (GPS) data recording thetaxi’s driving time and location, the GPS-equipped taxi can beregarded as the detector of an urban transport system. This pa-per proposes a Taxi-hunting Recommendation System (Taxi-RS)processing the large-scale taxi trajectory data, in order to providepassengers with a waiting time to get a taxi ride in a particularlocation. We formulated the data offline processing system basedon HotSpotScan and Preference Trajectory Scan algorithms. Wealso proposed a new data structure for frequent trajectory graph.Finally, we provided an optimized online querying subsystemto calculate the probability and the waiting time of getting ataxi. Taxi-RS is built based on the real-world trajectory data setgenerated by 12 000 taxis in one month. Under the condition ofguaranteeing the accuracy, the experimental results show that oursystem can provide more accurate waiting time in a given locationcompared with a naïve algorithm
RAJECTORY can be regarded as the trace of mobileobjects in space as times change.
Call Taxi in Kumbakonam
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.In the information age, it is very easy to collect in-formation. For the mentioned taxi problem, it becomes averyinterestingquestiontorecommendlocationinformationandcalculate waiting time by means of large-scale taxi GPS data.In this paper, we propose a processing method based on bigdata environment for the taxi GPS historical data, includingoffline and online processing. We implement a recommendationsystem called Taxi-hunting Recommendation System (Taxi-RS), and our system can calculate the probability and time ofgetting a taxi ride when giving information of time and point.The following are the major contents of this paper.1) We design data offline processing. First, we design analgorithm called HotSpotScan (HSS) algorithm to scanhotspots in a city. Then, the taxi preference trajectory isrecognized by a new algorithm called Preference Trajec-tory Scan (PTScan) algorithm.2) We propose an offline graph model by taxi preferencetrajectory and store offline compressed data by using amultiple adjacent table.3) Weconstructaprobabilitymodelbyusingtheofflinemodelto compute the probability and the waiting time of getting ataxi ride and analyze the complexity of online processing.The rest of this paper is organized as follows. Section IIgives a brief review on related literature. In Section III, someconception is formally introduced. The overview of the Taxi-RS is given in Section IV. In Section V, we introduce thekey algorithms of offline processing. In Section VI, we presentthe frequent trajectory graph (FTG) model. In Section VII, wepropose a calculation model to get a taxi ride. The results of theexperimental evaluation are given in Section VIII. Remarks arestated under the conclusion https://srisivasakthitravels.com/
https://www.facebook.com/Sri-Siva-sakthi-Travels-102008227873939/
https://goo.gl/maps/1rNVoifRFQ9snSor6
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