Overturning Stability of Three Wheeled Motorized Vehicles

Three wheeled motorized vehicles are a major mode of public transport in many countries. These vehicles are prone to overturning even during normal turning and obstacle avoidance maneuvers. This paper presents a parametric analysis of a mathematical model of the vehicle and evolves guidelines for improving the overturning stability in terms of vehicle geometry and suspension properties.

Differential equations governing the dynamic behavior of the vehicle are derived on the basis of a six degree of freedom model. The vehicle response to variations in steering, engine power and braking inputs is then numerically simulated. The effects of vehicle geometry and elasto-damping suspension coefficients on the vehicle stability are presented. The results indicate an optimum position of the center of gravity where the vehicle is most stable. While stiffer suspensions favour stability, there exists an optimum value of suspension damping for which the minimum wheel load is a maximum.     

A GIS-Based traffic accident information system

This paper describes the process of developing a GIS-based traffic accident information system. A roadway network in Kent County, Delaware, was selected to demonstrate the graphic database. The development of the graphic and attribute databases on a workstation environment is described. Software was developed to graphically represent accident information pertaining to any point on the roadway network, and outputs for typical case scenarios are presented. The software also has the capability of displaying historical accident information pertaining to the site. This aspect would be helpful to the planner or designer in studying the impact of a particular roadway design with respect to safety. Some thoughts for future extensions of this study are also presented. Finally, it is concluded that a carefully designed GIS system can efficiently help transportation professionals with traffic safety studies and that its capabilities can be extended to include other areas such as pavement and inventory management, transit applications, and executive information systems.     

A method to estimate trip O‐D patterns using a neural network approach

This paper proposes a method which identifies the trip origin‐destination (O‐D) matrix when many pairs of values for the right hand side column (B) and the bottom row (A) of the matrix are given. The method considers B and A as the cause (input) and effect (output) of a system, respectively, and that the O‐D matrix represents the relationship between the cause and the effect. The relationship which satisfies all pairs of the cause and the effect data exactly may not be identified, but, should a general pattern of the relationship exist, it should emerge when many data sets of B and A are given. Two steps are involved in the method: the first step examines if a consistent O‐D pattern exists; if a pattern is found to exist, the second step identifies the values of the elements of the O‐D matrix. The first step is based on the shape of the possibility distributions of the values of the matrix elements. The second step uses a simple back‐propagation neural network. The method is useful to problems that require identification of the cause‐effect relationship when many sets of data for the cause and effect are available, for example, the station‐to‐station travel pattern on a rapid transit line when the total entering and exiting passengers are known at each station for many different days. The model can also be applied to other transportation problems which involve input and output relation.