City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particularly challenging. Given the prohibitive computational cost of learning individual policies for each intersection, some researchers explore learning a universal policy to control each intersection in a decentralized manner, where the key challenge is to construct a universal representation method for heterogeneous intersections. However, existing methods are limited to universally representing information of heterogeneous ego intersections, neglecting the essential representation of influence from their heterogeneous neighbors. Universally incorporating neighborhood information is nontrivial due to the intrinsic complexity of traffic flow interactions, as well as the challenge of modeling collective influences from neighbor intersections. To address these challenges, we propose CityLight, which learns a universal policy based on representations obtained with two major modules: a Neighbor Influence Encoder to explicitly model neighbor's influence with specified traffic flow relation and connectivity to the ego intersection; a Neighbor Influence Aggregator to attentively aggregate the influence of neighbors based on their mutual competitive relations. Extensive experiments on five city-scale datasets, ranging from 97 to 13,952 intersections, confirm the efficacy of CityLight, with an average throughput improvement of 11.68% and a lift of 22.59% for generalization. Our codes and datasets are released: https://github.com/tsinghua-fib-lab/CityLight.

The two-dimensional bin packing problem (2DBP) is a critical optimization problem in the furniture production and glass cutting industries, where the objective is to cut smaller-sized items from a minimum number of large standard-sized raw materials. In practice, factories manufacture hundreds of customer orders (sets of items) every day, and to relieve pressure in management, a common practice is to group the orders into batches for production, ensuring that items from one order are in the same batch instead of scattered across the production line. In this work, we formulate this problem as the grouped 2D bin packing problem, a bi-level problem where the upper level partitions orders into groups and the lower level solves 2DBP for items in each group. The main challenges are (1) the coupled optimization of upper and lower levels and (2) the high computational efficiency required for practical application. To tackle these challenges, we propose an iteration-based hierarchical reinforcement learning framework, which can learn to solve the optimization problem in a data-driven way and provide fast online performance after offline training. Extensive experiments demonstrate that our method not only achieves the best performance compared to all baselines but is also robust to changes in dataset distribution and problem constraints. Finally, we deployed our method in the ARROW Home factory in China, resulting in a 4.1% reduction in raw material costs. We have released the source code and datasets to facilitate future research.

Large-scale vehicle trajectories bring great benefits in understanding urban mobility, and can be used to promote a wide range of applications in building intelligent transportation systems. Traditional approaches cannot recover the trajectories of all the vehicles on the roads since they are based on partial trajectory data. To address it, we study the all-vehicle trajectory recovery based on traffic camera video data. However, there are two challenges in this study. First, the quality of the images captured by traffic cameras is unbalanced, so it is hard to identify the same vehicles. Second, the traffic camera observation data are sparse due to the incompleteness of the traffic cameras and possible vehicle miss from the traffic cameras. To deal with these challenges, we design a novel system to recover the vehicle trajectory with the granularity of the road intersection. In this system, we propose an iterative framework to jointly optimize the vehicle re-identification and trajectory recovery tasks. In the vehicle re-identification task, we propose an effective strategy to guide the vehicle clustering based on visual features and the spatio-temporal constraint features updated by the trajectory discovery task. In the trajectory recovery task, we model the spatial and temporal relations as well as the vehicle miss problem by a probabilistic approach to recover the trajectories. Extensive experiments demonstrate that our framework outperforms the existing state-of-art solutions. Finally, our system is deployed in practical applications of SenseTime, China, including traffic congestion analysis and traffic signal control.