Traffic congestion is a typical phenomenon when motorways meet urban road networks.
At this special location, the weaving area is a recurrent traffic bottleneck. Numerous
research activities have been conducted to improve traffic efficiency and sustainability
at bottleneck areas. Variable speed limit control (VSL) is one of the effective control
strategies. The primary objective of this paper is twofold. On the one hand, turbulent
traffic flow is to be smoothed on the special weaving area of motorways and urban
roads using VSL control. On the other hand, another control method is provided to
tackle the carbon dioxide emission problem over the network. For both control methods,
a multi-agent reinforcement learning algorithm is used (MAPPO: multi-agent proximal
policy optimization). The VSL control framework utilizes the real-time traffic state
and the speed limit value in the last control step as the input of the optimization
algorithm. Two reward functions are constructed to guide the algorithm to output the
value of the dynamic speed limit enforced within the VSL control area. The effectiveness
of the proposed control framework is verified via microscopic traffic simulation using
simulation of urban mobility (SUMO). The results show that the proposed control method
could shape a more homogeneous traffic flow, and reduces the total waiting time over
the network by 15.8%. In the case of the carbon dioxide minimization strategy, the
carbon dioxide emission can be reduced by 10.79% in the recurrent bottleneck area
caused by the transition from motorways to urban roads.