Research On The Forecasting Method Of Traffic Meteorology In Laoning

Bad weather often coused trsffic accidents, caused casualties and economic losses. Therefore, it was significant to study the bad weather conditongs and the influence of traffic accidents. The precipitation, air tempreature,wind speed and fog data in 59 weather stations in Liaoning Province from 1980 to 2009 were analyzed by statisticals and weather variables methods,the spatial and temporal distribution characteristics of high impact weather were summarized. By used of Shenyang to Shanhaiguan expressway along the 8 stations, meteorological data includong precipitation, air temperature, land surface temperature, relative humidity,wind and visibility, through methods including least square techique, MIP neural network andstepwise regression, The forecasting methods of visibility,precipitation, wind and air temperature in Liaoning Province were established. By use of the mathematical conversion model for land surface temperature and ice-snow road traffic meteorological environment were identified, by random sampling method, the models were tested. The results indicated :(1) Annual precipitation increased gradually from northwest to southeast in Liaoning Province. The southeast area could reach 800 to 1050 mm, the northwest area was 400 to 500 mm, and Dandong area had big probability of the rain-storm and the extraordinary rainstorn. The yearly average fog occurrence in Liaoning Province was between3.5days to 57.5days. There were less blizzards in the in eastern and western hill or mountainous area than in plain and coastal area. The maximum snow depth appeared in eastern of Liaoning. The number of high temperature days in the south coastal areas was less than that in the central plains, the number of low temperature days in the northeast areas was more than that in the coastal areas.(2) The forecasting model suitable for northern traffic environment, especially the forecasting equation established by using the stepwise regression method, the heavy fog’s average precision rate was about 73% in 12 hours; The icy road surface recognition model, identification of the sunny road condition rate reacheed 87.6%, snow pavement condition,s recognition rate reacheed 83.4%.(3)The forecast and early warning of traffic safety model was built up, in different kinds of pavement and different speed vehicle’s absolutely safe distance was obtained, the vehicle safety driving speed of different visibility and road conditions was calculated; traffic sensitivity analysis under different weather conditions was carried out, high impact weather’s grade division was built up, the results of this pafer could provide reasonable suggestions for the transportation department to ensure traffic safety.