Research On Urban Traffic Flow Perception Method Based On Floating Car Data

With the rapid urbanization process, countries around the world increasingly aware the great influence of traffic to national economy and take the transportation construction as the key content of nation development. On the other hand, along with the rapid development of the national economy, the quality of people’s life have get a steady improvement which means the reducing of social living costs. At the same time, the rapid development of economy have also brought many serious social problems, including road congestion, air pollution, water pollution and so on. Among them, the increasing serious road traffic congestion has become an giant obstacle to sustained economic development lifeline.With the traffic problem coming, countries around the world have realized that relying solely on traditional methods of traffic control and traffic guidance has been difficult to deal with increasingly complex and diverse traffic problem, they more and more realized the importance of research on intelligent transportation systems.First, based on analysis the weakness of traditional traffic data collection technology, the paper introduces the advantages of floating car data outperform other technology. Then,introduces the urban road network data acquisition by the program method. After the data preprocessing, obtain the scale-free characteristics of urban citizen travel behavior as a result.Then, based on the analysis of the existing floating car map-matching algorithms lead to high error rate when GPS data sampling rate is low, a global-voting map matching algorithm is proposed. Based on floating car GPS track data, the algorithm does not only consider the influence to matching process caused by the topological information of the road network but also the different spatial distance of GPS track data.Last, based on the analysis of the relationship among the three parameters in traffic flow system, consider focusing on a weighted average speed estimation model, the model avoids the volatility of floating car data and the small number of abnormal data error on the average speedof the impact, obtain more accuracy road average speed.