DeCoR co-optimizes crosswalk placement and signal control via reinforcement learning on a real 750 m urban corridor, reporting 23% faster pedestrian access to crossings and 79%/65% reductions in pedestrian/vehicle wait times versus fixed-time baselines.
CCF Transactions on Pervasive Computing and Interaction5(1), 31–44 (2023)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learning
DeCoR co-optimizes crosswalk placement and signal control via reinforcement learning on a real 750 m urban corridor, reporting 23% faster pedestrian access to crossings and 79%/65% reductions in pedestrian/vehicle wait times versus fixed-time baselines.