Commuting Origin-destination (OD) flows, capturing daily population mobility of citizens, are vital for sustainable development across cities around the world. However, it is challenging to obtain the data due to the high cost of travel surveys and privacy concerns. Surprisingly, we find that satellite imagery, publicly available across the globe, contains rich urban semantic signals to support high-quality OD flow generation, with over 98\% expressiveness of traditional multisource hard-to-collect urban sociodemographic, economics, land use, and point of interest data. This inspires us to design a novel data generator, GlODGen (Global-scale OriginDestination Flow Generator), which can generate OD flow data for any cities of interest around the world. Specifically, GlODGen first leverages Vision-Language Geo-Foundation Models to extract urban semantic signals related to human mobility from satellite imagery. These features are then combined with population data to form region-level representations, which are used to generate OD flows via graph diffusion models. Extensive experiments on 4 continents and 6 representative cities show that GlODGen has great generalizability across diverse urban environments on different continents and can generate OD flow data for global cities highly consistent with real-world mobility data. We implement GlODGen as an automated tool, seamlessly integrating data acquisition and curation, urban semantic feature extraction, and OD flow generation together. It has been released at https://github.com/tsinghua-fib-lab/generate-od-pubtools.

The fastest route search, which is to find a path with the shortest travel time when the user initiates a query, has become one of the most important services in many map applications. To enhance the user experience of travel, it is necessary to achieve accurate and real-time route search. However, traffic conditions are changing dynamically, and the frequent occurrence of traffic congestion may greatly increase travel time. Thus, it is challenging to achieve the above goal. To deal with it, we present a congestion-aware spatio-temporal graph convolutional network-based A* search algorithm for the task of fastest route search. We first identify a sequence of consecutive congested traffic conditions as a traffic congestion event. Then, we propose a spatio-temporal graph convolutional network that jointly models the congestion events and changing travel time to capture their complex spatio-temporal correlations, which can predict the future travel-time information of each road segment as the basis of route planning. Further, we design a path-aided neural network to achieve effective origin-destination (OD) shortest travel-time estimation by encoding the complex relationships between OD pairs and their corresponding fastest paths. Finally, the cost function in the A* algorithm is set by fusing the output results of the two components, which is used to guide the route search. Our experimental results on the two real-world datasets show the superior performance of the proposed method.

Improving logistics efficiency is a challenging task in logistic systems, since planning the vehicle routes highly relies on the changing traffic conditions and diverse demand scenarios. However, most existing approaches either neglect the dynamic traffic environment or adopt manually designed rules, which fails to efficiently find a high-quality routing strategy. In this paper, we present a novel artificial intelligence (AI) based framework for logistic systems. This framework can simulate the spatio-temporal traffic conditions to form a dynamic environment in a data-driven manner. Under such a simulated environment, it adopts deep reinforcement learning techniques to intelligently generate the optimized routing strategy. Meanwhile, we also design an interactive frontend to visualize the simulated environment and routing strategies, which help operators evaluate the task performance. We will showcase the results of AI-based simulation and optimization in our demonstration.

Traffic congestion has a negative impact on our daily life. Predicting the trend of traffic congestion can provide a valuable guideline to address such problems. Most existing approaches focus on the tasks of predicting traffic volume or traffic speed, which do not effectively address the challenges of traffic congestion prediction. First, traffic congestion exhibits daily and weekly temporal patterns, but these patterns are not strictly the same, which indicates complicated long-term periodicity. Second, traffic congestion sparsely distributes over different periods of time, which leads to complex short-term and mid-term temporal dependencies. Third, since traffic congestion will propagate to adjacent road segments over time, it exhibits complex spatio-temporal correlations. To address them, we propose a periodic shift and event-aware spatio-temporal graph convolutional network for traffic congestion prediction. Specifically, we propose to capture the differences and similarities of long-term periodic temporal patterns to handle the complicated long-term periodicity. To effectively capture short-term and mid-term temporal dependencies, we regard a continuous time sequence of the congested condition as a traffic congestion event, and then adopt the widely-used long short-term memory model to learn the sequential dependencies of traffic congestion events. Finally, we integrate the graph convolutional network into the modeling of temporal dependencies to capture the complex spatio-temporal correlations. Extensive experiments demonstrate the superiority of our model. In addition, we deploy our model in production at Amap, and it achieves great performance improvement in terms of the F1-score compared to the production baseline. This confirms that our model is a practical solution for real-world congestion prediction services.

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively usek-means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap.

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim to discover the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: 1) traffic congestion is affected by several factors with complex spatio-temporal relations; 2) the amount of congestion data with known causes is small due to the limitation of human label; 3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: 1) congestion feature extraction, which extracts the important features influencing congestion; and 2) congestion cause discovery, which utilize a deep semi-supervised learning based method to discover the causes of traffic congestion with limited labeled causes. Specifically, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, the k-means algorithm is performed to produce the clusters. Extensive experiments show that the performance of our proposed method is superior to the baselines. Additionally, our system is deployed and used in the practical production environment at Amap.