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Deep reinforcement learning algorithm for dynamic pricing of express lanes with multiple access locations
الگوریتم یادگیری تقویتی عمیق برای قیمت گذاری پویا خطوط اکسپرس با مکان های دسترسی متعدد-2020 This article develops a deep reinforcement learning (Deep-RL) framework for dynamic pricing on
managed lanes with multiple access locations and heterogeneity in travelers’ value of time,
origin, and destination. This framework relaxes assumptions in the literature by considering
multiple origins and destinations, multiple access locations to the managed lane, en route diversion
of travelers, partial observability of the sensor readings, and stochastic demand and
observations. The problem is formulated as a partially observable Markov decision process
(POMDP) and policy gradient methods are used to determine tolls as a function of real-time
observations. Tolls are modeled as continuous and stochastic variables and are determined using
a feedforward neural network. The method is compared against a feedback control method used
for dynamic pricing. We show that Deep-RL is effective in learning toll policies for maximizing
revenue, minimizing total system travel time, and other joint weighted objectives, when tested on
real-world transportation networks. The Deep-RL toll policies outperform the feedback control
heuristic for the revenue maximization objective by generating revenues up to 8.5% higher than
the heuristic and for the objective minimizing total system travel time (TSTT) by generating TSTT
up to 8.4% lower than the heuristic. We also propose reward shaping methods for the POMDP to
overcome the undesired behavior of toll policies, like the jam-and-harvest behavior of revenuemaximizing
policies. Additionally, we test transferability of the algorithm trained on one set of
inputs for new input distributions and offer recommendations on real-time implementations of
Deep-RL algorithms. Keywords: Managed lanes | Express lanes | High occupancy/toll (HOT) lanes | Dynamic pricing | Deep reinforcement learning | Traffic control | Feedback control heuristic |
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