Due to the rising of traffic volumes, city expressways are experiencing significant congestion, leading cities to introduce parallel surface roads as alternative routes to help drivers bypass traffic bottlenecks. In such scenarios, road pricing on expressways and traffic signal controls on surface roads have shown effectiveness in alleviating citywide congestion. However, existing research on these strategies often neglects the optimization of the entire parallel road network, failing to simultaneously address congestion on both expressways and surface roads. In this paper, we propose a Collaborative Optimization mechanism of Price and Traffic Signal Control (CO-PTSC) based on deep reinforcement learning, a novel approach that integrates road pricing and traffic signal control, using deep reinforcement learning to optimize traffic flow and minimize travel times across the parallel road network. Our experimental results demonstrate significant improvements in network efficiency and reduced travel times.

Road pricing is an urban traffic management mechanism to reduce traffic congestion. Currently, most of the road pricing systems based on predefined charging tolls fail to consider the dynamics of urban traffic flows and travelers’ demands on the arrival time. In this paper, we propose a method to dynamically adjust online road toll based on traffic conditions and travelers’ demands to resolve the above-mentioned problems. The method, based on deep reinforcement learning, automatically allocates the optimal toll for each road during peak hours and guides vehicles to roads with lower toll charges. Moreover, it further considers travelers’ demands to ensure that more vehicles arrive at their destinations before their estimated arrival time. Our method can increase the traffic volume effectively, as compared to the existing static mechanisms.

Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status, debugging, and error records every single day. To guarantee the safety and sustainability of electric power systems, massive electric power data need to be processed and analyzed quickly to make real-time decisions. Traditional solutions typically use relational databases to manage electric power data. However, relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly. In this paper, we show how electric power data can be managed by using HBase, a distributed database maintained by Apache. Our system consists of clients, HBase database, status monitors, data migration modules, and data fragmentation modules. We evaluate the performance of our system through a series of experiments. We also show how HBase’s parameters can be tuned to improve the efficiency of our system.