RPR: recommendation for passengers by roads based on cloud computing and taxis traces data

Abstract

Traffic jam has grown to be a more and more difficult problem to be solved in big cities around the world, and people are getting less chance to take taxis. In order to remit this issue, we propose a recommendation strategy based on taxi traces data for passenger by the roads to make it easier. Considering the scale of taxi traces data, Hadoop is employed to handle the traces data, whose tasks include filtering and cleaning of the data, mapping taxi traces, and computing the average passage time and empty taxi arrival rate on the roads. When a user uploads his position and the time, assisted by weather condition gain from the Internet, we get the very model that corresponds to the date and the weather; the time interval is thought to be the expected waiting time between the moment when user requests the service and the moment when the cumulative number of empty taxi is greater than or equal to 1 after adding the time the taxi spends on the road, and is pushed to the user. The experiment is conducted on the base of a real-world dataset generated by over 12,000 taxis over a period of 3 months in Beijing. Experimental results demonstrate that the processing speed of Hadoop is nine times faster than serial's, which displays the feasibility of Hadoop in the application of massive traces data. In addition, the accuracy of the recommendation strategy reaches up to 88.75 %, and it meets the demand of real-time service.