Surging e-commerce and global trade necessitate highly efficient cargo terminal operations. Modern automated terminals, crucial for supply chains, employ complex networks of static and movable equipment. This integration introduces a core challenge: movable equipment creates dynamic connectivity and state-dependent travel times, rendering classic shortest path algorithms based on static edge weights ineffective. Unlike typical time-dependent problems driven by external factors such as traffic congestion or fixed schedules, our dynamics stem from internal equipment state, presenting a unique optimization challenge. We address the problem of finding optimal cargo routes within these dynamic environments. We propose a novel approach by modeling the terminal as a cargo network, where virtual edges induced by movable equipment are explicitly materialised and edge costs reflect the status of the real-time equipment. We propose an efficient Dijkstra's-based algorithm to solve the cargo routing problem within this framework considering the system dynamics. The primary contributions of this paper are this novel modeling technique for dynamic terminals and the adapted algorithm for optimal routing, offering significant benefits for logistics optimization and automated warehouse design. Experimental results demonstrate that our approach significantly reduces cargo travel times compared to baseline methods, offering substantial improvements for logistics efficiency in automated terminals.

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.

Route planning is ubiquitous and has a profound impact on our daily life. However, the existing path algorithms tend to produce similar paths between similar OD (Origin-Destination) pairs because they optimize query results without considering their influence on the whole network, which further introduces congestions. Therefore, we investigate the problem of diversifying the top-k paths between an OD pair such that their similarities are under a threshold while their total length is minimal. However, the current solutions all depend on the expensive graph traversal which is too slow to apply in practice. Therefore, we first propose an edge deviation and concatenation-based method to avoid the expensive graph search in path enumeration. After that, we dive into the path relations and propose a path similarity computation method with constant complexity, and propose a pruning technique to improve efficiency. Finally, we provide the completeness and efficiency-oriented solutions to further accelerate the query answering. Evaluations on the real-life road networks demonstrate the effectiveness and efficiency of our algorithm over the state-of-the-art.

Route reconstruction targets at recovering the actual routes of objects moving on an underlying road network from their times-tamped position measurements. This fundamental pre-processing step to many location-based applications has been extensively studied for GPS data, which are object-centric and relatively densely sampled data. In this paper, we investigate the problem of route reconstruction using data collected from road-side Bluetooth scanners. In many cities, Bluetooth scanners are installed in road networks for monitoring the movement of Bluetooth-enabled devices. To address new challenges caused by such reader-centric Bluetooth data including spatial and temporal distortion, a new route reconstruction framework is proposed to transform Bluetooth readings through a family of distortion suppression strategies such that the transformed data can work well with the Hidden Markov model (HMM) map-matching approach. Extensive experiments are conducted to evaluate different transformation strategies with real-world datasets. The experimental results show that when the algorithm uses the baseline or the proposed transformation strategies, the map matching F1 score can be increased by up to 10% depending on the severity of distortion.