Estimating Lateral Placement and Lane Indiscipline of Urban Mixed Traffic in Developing Country: An ANN Assisted Approach

Information of lateral placement and lane indiscipline are useful in simulation of a mixed traffic stream and identifying the distressed portion of a pavement. In spite of these utilities, inadequate investigation was made to estimate the lateral placement of vehicles under prevailing traffic conditions. In a typical mixed traffic situation, vehicles having different static and dynamic characteristics take any lateral gap across the carriageway left empty by other surrounding vehicles and move in an untidy manner. It leads to variation in lateral placement of vehicles governed by the subject vehicle type. This paper explores the potential factors that influence lateral placement of vehicles and presents an Artificial Neural Network based approach to quantify lateral placement and lane indiscipline in context of undivided urban roads. Further, sensitivity analysis revealed how different traffic parameters like traffic volume, traffic composition and directional split influence lateral placement and lane indiscipline of a vehicle category.

Single-Point Adaptive Control Method for Urban Mixed Traffic Flow

In urban traffic, motor vehicles, nonmotor vehicles, pedestrians, and other traffic are mixed, which seriously affects the urban traffic efficiency. At the intersections of urban roads especially, the priority setting of various travel modes is crucial to improve the urban traffic efficiency. In the urban hybrid traffic system, the architecture and algorithm flowchart of the single-point adaptive control system for urban hybrid traffic flow are presented. The control effect of this method is verified by VISSIM software. The simulation results show that the traffic efficiency of the priority objects at the intersection is increased by 6.03%, and the overall traffic efficiency is also significantly improved. This method has a certain practical value.