  With the emerging technologies in Intelligent Transportation System (ITS),
the adaptive operation of road space is likely to be realised within decades.
An intelligent street can learn and improve its decision-making on the
right-of-way (ROW) for road users, liberating more active pedestrian space
while maintaining traffic safety and efficiency. However, there is a lack of
effective controlling techniques for these adaptive street infrastructures. To
fill this gap in existing studies, we formulate this control problem as a
Markov Game and develop a solution based on the multi-agent Deep Deterministic
Policy Gradient (MADDPG) algorithm. The proposed model can dynamically assign
ROW for sidewalks, autonomous vehicles (AVs) driving lanes and on-street
parking areas in real-time. Integrated with the SUMO traffic simulator, this
model was evaluated using the road network of the South Kensington District
against three cases of divergent traffic conditions: pedestrian flow rates, AVs
traffic flow rates and parking demands. Results reveal that our model can
achieve an average reduction of 3.87% and 6.26% in street space assigned for
on-street parking and vehicular operations. Combined with space gained by
limiting the number of driving lanes, the average proportion of sidewalks to
total widths of streets can significantly increase by 10.13%.
