Abstract
We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from connected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various penetration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetration rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous prediction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.
Highlights
Traffic congestion takes a massive toll on cities’ economies
In addition to adaptive traffic signals, traffic prediction is used in the advanced traveller information system (ATIS), emergency response system planning, variable message signs (VMSs) and real-time route guidance to assist drivers to select the best route among the existing alternatives [3, 4]
This paper presents a Kalman filter technique to predict traffic flows approaching an intersection based on the data of connected vehicles
Summary
Traffic congestion takes a massive toll on cities’ economies. The congestion cost for Sydney and Melbourne is around $6.1 billion and $4.6 billion a year, respectively, and it is projected to increase twofold by 2030 [1]. To tackle the problem of traffic congestion, intelligent transportation systems (ITSs) are considered as an appropriate choice to provide a reliable transport network [2]. To this end, adaptive traffic control is perceived as a useful tool in the ITS toolbox designed to allocate a fair amount of green times to vehicles in signalized intersections. It is essential to enhance the efficiency of traffic prediction algorithms by providing more exact and real-time data. In addition to adaptive traffic signals, traffic prediction is used in the advanced traveller information system (ATIS), emergency response system planning, variable message signs (VMSs) and real-time route guidance to assist drivers to select the best route among the existing alternatives [3, 4]. The data from various sources such as fixed sensors or floating sensors can be used as input for prediction algorithms
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