Abstract
This paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories. Moreover, it is also a dynamic process with abilities of automaticity, memory and performance feedback. Numerical cases are given in this study to demonstrate RLRM such as calculating outflow rate, density, average speed, and travel time compared to no control and fixed-time control. Results indicate that the greater is the inflow, the more is the effect. In addition, the stability of RLRM is better than fixed-time control.
Highlights
Increasing dependence on car-based travel has led to the daily occurrence of recurrent and nonrecurrent freeway congestions in China and around the world
reinforcement learning ramp metering (RLRM) consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories
Numerical cases are given in this study to demonstrate RLRM such as calculating outflow rate, density, average speed, and travel time compared to no control and fixed-time control
Summary
Increasing dependence on car-based travel has led to the daily occurrence of recurrent and nonrecurrent freeway congestions in China and around the world. Congestion on highways forms when the demand exceeds capacity. DC (demand-capacity), OCC (occupancy), and ALNEA [2] are among the well-known local response type ramp metering [3]. OCC uses a predetermined relationship between occupancy rate and lane volume, developed from data previously collected at the highway adjacent to the ramp being considered. ALNEA is the ramp metering which sets up the private-use rate of an onramp based on the measured value of main line traffic. Tsubota, and Kawashima have proposed the ramp metering technique using the predicted value by a traffic simulator [4]. Reinforcement learning ramp metering based on traffic simulation model with desired speed was proposed by Wang et al [5]. The aim of this study is to propose reinforcement learning ramp metering without complete information
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