The Practical Method For Small And Medium-sized City Traffic Demanding Forecasting With Mixed Traffic Conditions

Recently, non-motor-driven mixed traffic has been more and more popular in medium-small cities. The classical method of traffic forecasts, which based on western country’s traffic designed for small cars, is not appropriate to the mixed traffic. At present, the actual traffic forecast is often operated mainly to the motor vehicle, and on the basis of that conducts the conversion or other appropriate consideration of the non motor vehicle. Further, the relevant theoretical research can’t guide the front-line work. So front-line staff are short of a systematical and specific method in the actual operation. There is a big space to improve the operation method of practicability and accuracy. Meanwhile this paper analyses and summarized the methods upon the traffic data investigation of mixed traffic in medium-small cities; and carries on traffic demand forecast with four steps aiming at the traffic stream by the same network from double attributes and TransCAD software. Further, the paper analyses the sections of saturation and adjusts the cross-sectional constantly according to the traffic assignment result.This paper gives the traffic investigation methods of medium-small cities in system. On the basis of the travel traits of mixed traffic, the paper specifically analyses and concludes methods about the basic data, residents travel, motor vehicle travel, non-motorized travel and resistance parameters calibration data of medium-small city planing areas at mixed traffic condition. Then here gives a calibration for road impedance function of non motorized vehicle lane, with electric bicycles as a standard car in the specific circumstances of the electric bicycle in a large proportion of the non motor vehicle flow.A forecast about motor vehicle and non motor vehicle traffic flow occurs, traffic distribution, traffic mode split and traffic assignment, has been conducted by applying TransCAD software and editing Geographic file with network from double attributes.(1)Edit Geographic file. According to the specific circumstances of the electric bicycle in a large proportion of the non motor vehicle flow, the network from double attributes is used to edit Geographic file. Aiming at the motor vehicles and non motorized of the road cross-section, two attributes has been set on the road.(2) In the traffic generation forecast, the planning years of motor vehicles and non motor vehicles occurred attraction is forecast by regression analysis method using different impact factors.(3) In the traffic distribution forecast, the gravity model parameter is calibrated for the motor vehicle and non motor vehicle flow, in the light of Traffic impedance matrix and the distribution matrix status years; and using that, the paper carry out traffic distribution prediction respectively.(4) In traffic mode split forecast, on the basis of the present situation of vehicle’s and non motor vehicle’s time, cost matrix and traffic mode discrete selection table, this paper estimates the Logit model parameter of motor vehicle and non motor vehicle, and judges whether “t” meets the requirements; then the paper conducts a forecast for the division of motor vehicles and non motor vehicle traffic prediction mode by applying the motor vehicles’ s and non motor vehicle’s time, cost matrix and the Logit model.(5) In the traffic assignment, SUE model is adopted to calibrate the motor vehicle and non motor vehicle traffic impedance function; then the paper uses that to assign the traffic of the planning years of motor vehicles and non motor vehicles travel data.The method to adjust the network planning based on the road cross-section is put forward, after the prediction of transportation planning between the motor vehicle an d non-motor vehicles. Meanwhile the features for these two methods are analyzed and compared. By combining the practical methods for the traffic demand forecasting wit h the condition of section saturation obtained from the traffic assignment, the dynami c adjustment model for cross-section is established and analyzed for it’s setting princi ple, which qualitatively clarified the network structure should be adjusted in case of t he adjustment for cross-section failed to meet the needs. The classical method and the characteristics of traffic demand forecast methods are analyzed, and then a comparati ve study for the characteristics of the two methods is conducted.This method is verified on the mixed traffic of our country under the condition of a small city. It show that the method is proper to the specific traffic conditions of motor vehicles and non motor vehicles occupying larger proportion.This paper puts forward the prediction method of network traffic planning of dual attribute at mixed traffic. Though the mixed traffic takes over a large quantity in China, the non motor vehicle is payed little attention for the traffic planning prediction method in actual operation common to the vehicle based. Because the mainly related to method inheritance in the western developed countries, for the first-line staff, a specific method of practical is badly in need. This paper proposes methods with the specific situation in our country, concisely and practically, which can provides a help for specific traffic planning front-line staff.