  With the emerging connected-vehicle technologies and smart roads, the need
for intelligent adaptive traffic signal controls is more than ever before. This
paper proposes a novel Economic-driven Adaptive Traffic Signal Control (eATSC)
model with a hyper control variable - interest rate defined in economics for
traffic signal control at signalized intersections. The eATSC uses a continuous
compounding function that captures both the total number of vehicles and the
accumulated waiting time of each vehicle to compute penalties for different
directions. The computed penalties grow with waiting time and is used for
signal control decisions. Each intersection is assigned two intelligent agents
adjusting interest rate and signal length for different directions according to
the traffic patterns, respectively. The problem is formulated as a Markov
Decision Process (MDP) problem to reduce congestions, and a two-agent Double
Dueling Deep Q Network (DDDQN) is utilized to solve the problem. Under the
optimal policy, the agents can select the optimal interest rates and signal
time to minimize the likelihood of traffic congestion. To evaluate the
superiority of our method, a VISSIM simulation model with classic four-leg
signalized intersections is developed. The results indicate that the proposed
model is adequately able to maintain healthy traffic flow at the intersection.
