Study On The Methods Of Travel Time Collection And Estimation For Urban Roadway Based On FC

Lately, for industrialization quickening and the number of cars increasing rapidly, and road mileage and the vehicle's rate is in a lack of balance, a series of transportation problems appear. Presently, ITS is a best way to solve transportation problems in cities, and it can solve transportation congestion,reform traveling safety,enhance vehicles'speed. It also can make people,cars and road harmonious. Travel time data's collection and estimation, is an important part of ITS, can reflect transportation state'movement more truly. One way, it can provide transportation guidance service for users, make them save traveling time; On another way, provide data for transportation management department, they can identify traffic abnormal state and traffic incident automatically, to reduce transportation accidents and enhance transportation industry's efficiency.GPS floating cars are used to collect urban road datum, during datum's transmit, for there is a series of reasons in existence, misdata and missing data would appear. So, to improve data's quality and precision, guarantee travel time datum's stability and reliability, the paper did data processing of GPS datum. The paper adopts floating cars based on GPS to collect travel time, send place, velocity and time to TIC by GPRS, match with GIS, to receive travel time and section average velocity, then use multi-kind model and arithmetic, to estimate travel time in the future. Finally, by GPRS, feed information back to users, provide selective drive route to them.Travel time collection and estimation for urban roadway based on GPS Floating Cars can gain dynamic traffic information, provide traffic service information at present for users, and query travel manners, travel time, travel route, so they can arrive destination efficiently. It can also provide reliable datum for traffic management department, so they can differentiate traffic abnormal state and detect traffic incident, to save travel time, alleviate traffic congestion, reduce traffic accidents, and heighten traffic velocities.GPS Floating Cars early is used in transportation outside, but the study inside drops behind. Urban highway travel time estimation also lags behind outside, whose studies focuses on freeway and arterial roadways, many scholars inside also process the travel time study. The paper discusses study status quo of Travel time collection and estimation for urban roadway based on GPS Floating Cars, review and judge them.For all kinds of affective factors exist, the datum's quality, precision and reliability for GPS floating cars can't guarantee, so to improve precision and stability of the datum, the paper analyses the affective factors, such as GPS and GIS's precision, GPS signal losing, GPS sampling interval, transmitting interval, analyzing interval, section length, the number of floating cars and dependability lack of sample, confirm the studying work of the paper.Pretty sampling interval can enhance datum's precision and reliability, and can provide datum for traffic management department. So the paper divides GPS sampling interval into sampling cycle,transmitting cycle and analysis cycle, and analyze via datum survey practically, at last make the best GPS's sampling interval certain.GPS datum collected must combine with link, travel time would be confirmed. Link partition directly influence travel time datum's stability and reliability. So to improve travel time datum's quality, the paper studies link partition, and analyze via datum survey practically.To obtain road network's true state, there must be enough floating cans to satisfy basic requirement of transportation information collection. But, the amount of floating cars achieves to a certainty, we increase the number of floating cars continually, it can't improve datum's precision evidently, and travel time won't have stability. So the paper is to find the best floating car's number, and analyze via datum survey practically.As a result of SA policy, and there is error in GPS satellite,transmit pathway and user equipment, and all make GPS datum and GIS map matching's precision fall. So to improve vehicle's orientation precision,travel time's stability and reliability, the paper studies how to improve map matching precision.Based on GPS datum collection, only datum collected is of high quality, it can go to travel time estimation. The paper analyzes travel time's predictability so as to improve datum's precision and reduce economic cost.For ARMA model, Exponential Smoothing Technique, RBF Neural Network and MMFA model have commonness, in the same way, they can be used to estimate travel time estimation of highways. The paper sets up travel time estimation model, and analyses steps of them. The paper adopts SPSS software, matlab network toolbox based on history datum to estimate travel time. Based on precision, reliability and stability of datum collected, the paper adopts ARMA model, Exponential Smoothing Technique, RBF Neural Network and MMFA to estimate travel time, link average travel speed is input variable, output certain link's travel time. Judging estimation results via estimation guideline, the conclusion is that MMFA's estimation precision precedes to other models.