Abstract

Traffic flow is not a strange phenomenon anymore. Currently, traffic density is a big problem faced in big cities. Increasing the vehicle amount which is not balanced with the capacity of road impact on the transportation system, especially the density of vehicles on the road. Thus, traffic monitoring is needed to minimize congestion and traffic accidents and also develope ITS (Intelligent Transport System). This paper discusses realistic vehicle mobility simulation based on traffic surveys of a city using a microscopic model simulation by using Simulation of Urban Mobility (SUMO). This paper discusses realistic vehicle mobility simulation based on traffic surveys of a city using a microscopic model simulation by using Simulation of Urban Mobility (SUMO). SUMO is an open source road traffic simulator program that allows users to build simulations of vehicle movement on network topology. In particular, the SUMO network has some information like each edge as a collection of paths including the position, shape and speed limit of each path, the logic of traffic lights sourced from the crossroads, the correct junction rules, the relationship between the lanes at the intersection. The input format which is used is demand data of survey results from the vehicles number at a certain time in each way. It can be known the pattern the traffic distribution. This traffic distribution pattern will be modeled on SUMO applications so that this application can simulate the mobility of the original vehicle in the big city. The data traffic will be managed and computed with Kalman Filter to enhance location accuration. Then, it will be displayed on Website. Based on analyst of vehicle traffic volume in Central Surabaya, it has normal distibution traffic which is the rush hour in 4 pm. - 5 pm Based on SUMO result, Surabaya map from OSM could be integrated with SUMO GUI. adjust the speed of the road limit at Central Surabaya is the maximum speed limit on the motor, car and taxi is 33.33 m / s and from the test results obtained parameter value Q = 1E-04 and R = 1E-04 on Kalman filter seen that in estimating vehicle speed has the biggest percentage of error up to 49,17%.

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