The advancement of navigation systems has generated numerous future route data. However, neglecting such data could deteriorate the future traffic condition, as it reflects the actual state of future traffic flow. In this demonstration, we propose a route data management system (RouteSys), for the efficient and accurate prediction of future traffic conditions. First, a macroscopic traffic simulation algorithm integrated with multiple light weight machine learning models is developed to simulate the future traffic condition of routes. Then, the future temporal information is stored in a route record index structure. In real-world scenarios, it can efficiently update simulated traffic conditions in response to dynamic changed future route data, such as newly added routes. To demonstrate the functionality of RouteSys, we design a front-end platform within a real-world road network scenario with the generated route data. A video demonstration is available at https://youtu.be/3xuF2t_pV2M.

Traffic forecasting is essential for intelligent transportation systems, aiming to predict future traffic dynamics such as speed and travel time through the analysis of past observations. However, mainstream deep learning frameworks, which rely heavily on historical data, often struggle in realworld applications due to their inadaptability to dynamic future changes, neglect of future traffic flow as the root cause of traffic conditions, and the complexity of model structures for city-scale road networks. To solve these limitations, we propose a Route Data Management System (RouteSys) that integrates a macroscopic simulation module with lightweight traffic prediction models to estimate the future traffic conditions on individual road segments by accurately and efficiently simulating vehicle travel sequences and traffic states in advance. Additionally, we integrate the microscopic traffic simulation tool SUMO with the custom route planning logic to generate synthetic route data, supporting model training and application evaluation. RouteSys has been validated on real-world road networks in various scenarios, showing substantial improvements in prediction accuracy, efficiency, and scalability compared to the mainstream structures.

Route planning and navigation systems have played an increasingly important role in our society and have a growing impact on transportation systems. The current system takes the traffic prediction as input and optimizes the routes individually. However, such a paradigm could generate congestion and deteriorate traffic conditions because the routing algorithms are not aware of their results' influence on the traffic flow. Therefore, in this paper, we identify this flaw in the current paradigm and propose a route data management system to evaluate the influence of the routing results and help improve future downstream tasks. Specifically, we first formulate traffic evaluation as a clear traffic-aware network time calibration problem and propose a simulation-based method to evaluate hundreds of thousands of routes efficiently. To support route updates on the evaluation results, we propose an RR-Index to support high throughput of route insertion, deletion, and temporal update. After that, we propose several techniques like influence terminate condition, propagation merge and ordering, and parallel processing to make it efficient enough to work in real life. Evaluations on real-world road networks verify the necessity, effectiveness, and efficiency of our methods.

Travel planning plays an increasingly important role in our society. The travel plans, which consist of the paths each vehicle is suggested to follow and its corresponding departure time, influence the traffic conditions naturally. However, existing travel planning algorithms cannot consider the planning results and their influences simultaneously, so traffic congestion could be created when many vehicles are directed to adopt similar travel plans. In this paper, we propose theGlobal Optimal Travel Planning (GOTP)problem that aims to minimize traffic congestion by continuously evaluating traffic conditions for a set of planning tasks. Achieving this global optimization goal is non-trivial because travel planning and traffic evaluation are time-consuming and interdependent. To break this dependency, we first propose aGOTPparadigm that interleaves travel planning and traffic evaluation for queries, where the planning consists of departure time planning and travel path planning. To implement the paradigm, we propose theserial modelthat optimizes travel plans one by one, followed by thebatch modelthat improves processing efficiency, and theiterative modelthat further optimizes planning quality. Extensive experiments on large real-world networks with synthetic and real workloads validate the effectiveness and efficiency of our methods.

Route planning plays an increasingly important role in our society, and the routing results, which are the paths that vehicles actually travel in a road network, which influence the traffic condition naturally. However, the existing routing algorithms cannot consider the routing results and their influence simultaneously, so traffic congestion could be created when many vehicles are directed to follow similar routes. In this paper, we propose the Global Routing Optimization problem that aims to minimize traffic congestion by continuously evaluating traffic conditions for a set of routing tasks. It is non-trivial to achieve this global optimization goal, as routing and traffic condition evaluation is both time-consuming and interdependent. To break this dependency, we propose a global routing optimization paradigm that can evaluate the routing results’ influence on the traffic condition, and then plan the routes accordingly. To implement it, we first propose a serial model to optimize the next route, followed by a batch model to improve processing efficiency. After that, an iterative model is proposed to further optimize route qualities. Extensive experiments on large real-world networks with synthetic and real workloads validate the effectiveness and efficiency of our methods.