Vision-and-Language Navigation (VLN) is a key task in embodied AI, yet most agents remain reactive, task specific, and cognitively limited. As these systems extend to real-world areas like assistive guidance, disaster response, and multi agent teaming, the lack of ability to reason, reflect, and adapt presents critical flaws. This paper introduces Cognitive Foundation Agent (CFA), a conceptual model that reconceives VLN as a problem of spatial cognition and collaborative intelligence. CFA integrates perception, language, memory, and planning in a cognitive process that supports real time adaptation in complex environments. The model comprises five asynchronous modules: multimodal perception, meta-cognition, self-evolving world model, spatiotemporal planning, and multi agent collaboration, linked by a real-time Cognitive Feedback Loop (CFL) that enables agents to perceive, coordinate, reason, and adapt across tasks and environments. To drive progress in this space, this paper outlines the need for CFA-Bench, a dedicated evaluation suite for cognitively grounded navigation. CFA represents a shift toward embodied agents that move, reason, and collaborate with human-aligned spatial intelligence.

Vision-and-Language Navigation (VLN) is a challenging multimodal task in which an autonomous agent must navigate unknown environments by following natural language instructions. Recent zero-shot VLN approaches leverage Large Language Models (LLMs), such as GPT, to interpret instructions and visual inputs for navigation inference without environment-specific training. However, these methods rely solely on the inherent spatial reasoning abilities of LLMs, which often fail to align panoramic observations with language instructions in zero-shot settings. To address this limitation, we propose SpatialGPT, a novel GPT-based VLN agent that incorporates spatial domain knowledge and the Chain-of-Thought (CoT) paradigm to enhance spatial reasoning. SpatialGPT integrates a Directional Connected Landmark List and a Spatial Knowledge Graph to jointly model local and global visual context as structured spatial memory. Built on this memory, we introduce a Synchronize-Align-Backtrack reasoning chain that synchronizes with instruction progress, aligns panoramic views to determine the next action, retrieves alternative paths or infers new frontiers during backtracking. Extensive experiments on the Room-to-Room (R2R) benchmark demonstrate that SpatialGPT achieves state-of-the-art zero-shot performance across all evaluation metrics, showcasing its enhanced spatial reasoning capabilities and strong generalization as an LLM-based VLN agent. The source code is available at SpatialGPT (GitHub)1.

Deep Reinforcement Learning (DRL)-based frameworks, featuring Transformer-style policy networks, have demonstrated their efficacy across various Vehicle Routing Problem (VRP) variants. However, the application of these methods to the Multi-Trip Time-Dependent Vehicle Routing Problem (MTTDVRP) with maximum working hours constraints—a pivotal element of urban logistics—remains largely unexplored. This article introduces a DRL-based method called the Simultaneous Encoder and Dual Decoder Attention Model (SED2AM), tailored for the MTTDVRP with maximum working hours constraints. The proposed method introduces a temporal locality inductive bias to the encoding module of the policy networks, enabling it to effectively account for the time dependency in travel distance/time. The decoding module of SED2AM includes a vehicle selection decoder that selects a vehicle from the fleet, effectively associating trips with vehicles for functional multi-trip routing. Additionally, this decoding module is equipped with a trip construction decoder leveraged for constructing trips for the vehicles. This policy model is equipped with two classes of state representations, fleet state, and routing state, providing the information needed for effective route construction in the presence of maximum working hours constraints. Experimental results using real-world datasets from two major Canadian cities not only show that SED2AM outperforms the current state-of-the-art DRL-based and metaheuristic-based baselines but also demonstrate its generalizability to solve larger scale problems.

Federated Learning (FL) presents a paradigm shift towards distributed model training across isolated data repositories or edge devices without explicit data sharing. Despite of its advantages, FL is inherently less efficient than centralized training models, leading to increased energy consumption and, consequently, higher carbon emissions. In this paper, we propose CAMA, a carbon-aware FL framework, promoting the operation on renewable excess energy and spare computing capacity, aiming to minimize operational carbon emissions. CAMA introduces a dynamic model adaptation strategy which adapts the model sizes based on the availability of energy and computing resources. Ordered dropout is integratged to enable the aggregation with varying model sizes. Empirical evaluations on real-world energy and load traces demonstrate that our method achieves faster convergence and ensures equitable client participation, while scaling efficiently to handle large numbers of clients. The source code of CAMA is available at https://github.com/denoslab/CAMA.

Dispatching large-scale fleets has been one of the fundamental aspects of managing heterogeneous truckload logistics. This operation involves optimization, visualization, and reporting of the resource plans meticulously crafted by expert planners and dispatchers on a daily basis. However, the limitations of human dispatchers, including errors in communication, routing, compliance, load planning, maintenance oversight, and neglect of driver preferences, can lead to lower customer satisfaction. We present FleetWiz, a Large Language Model-based (LLM) platform that enables logistics industry dispatchers to receive optimal recommendations based on real-time spatial data. FleetWiz seamlessly connects dispatchers, drivers, and resources, centralizing information within a unified resource-request network. This leads to enhanced transit times, reduced delays, and adaptive responses to dynamic conditions. It can execute tasks in different domains including filtering, optimizing, and answering questions based on the network of resources and requests. Specifically, a local Llama3 model is equipped with access to a geo-database for filtering, five different optimization methods for generating plans, and knowledge about the entire network and operations inside the company. Lastly, the tool's reliability, a generic interface for applying LLM agents alongside spatio-temporal optimization models, is demonstrated. The optimization models handle complex dispatching tasks requiring sequential reasoning, allowing the LLM to provide well-informed feedback based on the results.

The heterogeneous fleet and demand vehicle routing problem with time-window constraints (HFDVRPTW) is a crucial optimization problem of significant importance in real-world logistics operations. In this paper, we propose a deep reinforcement learning (DRL)-based method, termed spatial Edge-Feature EnhanCed mulTIgraph fusion encoder With spectral-based embedding and hieRarchical decOder with learnable TEmpoRal positional embedding (EFECTIW-ROTER, pronounced "Effective Router"), to tackle this complex and practical optimization problem. EFECTIW-ROTER utilizes two sparse graphs to represent node connectivity, where nodes correspond to customers and the depot. This sparsity results from the time-window constraints and customers' demand relative to the list of acceptable vehicle attributes specified for service within a heterogeneous fleet, determined by the reachability of the nodes based on these two factors. Leveraging two graph Transformer models, EFECTIW-ROTER's encoding module captures the interactions between the nodes based on these factors. One model encodes customers' heterogeneous demand with spatial edge features based on travel time between the nodes, while the second employs temporal positional embeddings to capture temporal relationships based on time-window ordering. A fusion model is introduced to integrate node interactions based on these graphs. Additionally, a spectral-attention-based pooling ensures effective state representation for the DRL-based method. EFECTIW-ROTER features a hierarchical attention decoder operating in two stages: heterogeneous vehicle selection and node selection. Enhanced with positional embeddings, the decoder is empowered to make effective routing decisions based on time-window constraints' ordering. Experimental results using real-world traffic data from two major Canadian cities confirm EFECTIW-ROTER's better performance over current state-of-the-art DRL-based and heuristic methods. EFECTIW-ROTER reduces travel times while also achieving faster computational times when compared to conventional heuristics. Additional experiments demonstrate its generalizability across larger instances.

Analyzing freight vehicle movements using GPS trajectory data presents challenges due to environmental conditions and hardware limitations impacting data accuracy. Map matching, the process of aligning GPS signals with road networks, facilitates accurate route reconstruction. However, existing methods have limitations, particularly with low and ultra-low sampling rates. They often assume the shortest path between points, overlook historical data insights and neglect diverse driving behaviors, which may not align with real-world scenarios where shortest paths are not always optimal and different drivers exhibit varied behaviors. These limitations affect existing methods’ reliability, especially when we face low sampling rate trajectories. In this study, we propose multi-intention deep inverse reinforcement learning for map matching (MIDIRL) to address these challenges. MIDIRL integrates deep neural networks and multi-intention capturing mechanisms with inverse reinforcement learning to model complex driving preferences from historical trajectories, improving map matching accuracy, especially in ultra-low-frequency trajectories. Our experiments on real-world datasets demonstrate MIDIRL’s improved accuracy and efficiency of map matching compared to previous methods, even with limited training data.

Efficient goods movement is a vital aspect of logistics and urban planning, impacting the flow of goods and the quality of life for residents. To aid in this, we present an interactive map-based system for visualizing and analyzing goods’ movements using GPS traces. The system takes raw GPS signal data and road network data as input, then performs preprocessing and spatial analysis on the data using Flask framework and Python scripts. The system offers a user-friendly interface to explore the patterns of goods movement dynamically. It displays the temporal and spatial movement trips in the city, providing an intuitive way to analyze and optimize goods movement. Our system’s ability to explore and visualize goods movement patterns makes it an essential addition to the existing literature on urban transportation analysis and a valuable tool for logistics companies and urban planners.

Truck speed prediction plays a key role in truck transportation management. However, it is a very challenging task since the truck traffic usually shows complex patterns. Most of the existing traffic prediction methods lack the ability to model the dynamic spatial-temporal correlations of truck traffic or ignore contributing contextual factors that impact traffic. Also, truck traffic data is typically sparse and noisy, which makes the truck speed prediction an even more challenging task. How to improve the truck speed prediction by taking advantage of other relevant truck traffic information (such as the truck flow) has not been investigated in depth. Additionally, traffic congestions and poor driving conditions caused by extreme weather conditions can make sudden changes in the general pattern of the truck speed. In this paper, we propose a novel Multi-Task Context Based Gated Recurrent Unit Graph Convolutional Network (MT-C2G) to predict the truck speed under extreme weather conditions. MT-C2G includes four major components: The spatial dependence learning component captures the spatial dependencies shaped by the topological structure of the road network. Truck traffic feature temporal dependence modeling component is built to acquire the temporal dependencies involved in the truck traffic features, and contextual feature temporal dependence modeling component employs a layer of GRU units to capture the temporal dependencies of contextual factors. The multi-task learning component then leverages the information between the truck speed and flow prediction tasks through attention mechanism for improving the performance. Moreover, a data augmentation method SMOTE is utilized to balance the data with the extreme weather conditions. Experiments on two real datasets demonstrate that the proposed MT-C2G fairly outperforms six state-of-the-art traffic prediction methods.

Trajectory segmentation is a fundamental issue in GPS trajectory analytics. The task of dividing a raw trajectory into reasonable sub-trajectories and annotating them based on moving subject’s intentions and application domains remains a challenge. This is due to the highly dynamic nature of individuals’ patterns of movement and the complex relationships between such patterns and surrounding points of interest. In this paper, we present a framework called SEMANTIC-SEG for automatic semantic segmentation of trajectories from GPS readings. For the decomposition component of SEMANTIC-SEG, a moving pattern change detection (MPCD) algorithm is proposed to divide the raw trajectory into segments that are homogeneous in their movement conditions. A generic ontology and a spatiotemporal probability model for segmentation are then introduced to implement a bottom-up ontology-based reasoning for semantic enrichment. The experimental results on three real-world datasets show that MPCD can more effectively identify the semantically significant change-points in a pattern of movement than four existing baseline methods. Moreover, experiments are conducted to demonstrate how the proposed SEMANTIC-SEG framework can be applied.

Location recommendation provides unvisited locations to the users for the rapidly growing location-based social networks. The service is based on the users' visiting histories and location related information such as location categories. In this paper, we propose a location recommendation algorithm called sPCLR that recommends locations to the users at a given time of the day by utilizing category information. The algorithm considers both temporal and spatial components. The temporal component utilizes the temporal influence of similar users' check-in behaviors by representing a user's periodic check-in behavior at different location categories as temporal curves. The similarity between users' periodic check-in behavior is calculated based on the difference between temporal curves. The spatial component utilizes the geographical influence of locations and filters out those locations that are not of interest to the user. The performance of sPCLR is compared with three existing location recommendation algorithms on a real-world dataset. Experimental results show that the sPCLR algorithm performs better than all other three algorithms.

Geospatial clustering is an important topic in knowledge discovery research and geospatial information systems. However, current clustering research emphasizes the development of more efficient and effective clustering methods without paying much attention to domain knowledge and users' goals during the clustering process. Making better use of geospatial and clustering knowledge to select proper methods and datasets will help achieve clustering results that better meet users' requirements. In this article, we present the GEO_CLUST framework for performing geospatial clustering. The framework consists of the GeoCO ontology for geospatial clustering and the ontology reasoner reasoning mechanism. The GeoCO ontology is used to represent geospatial and clustering domain knowledge. The ontology reasoner uses classification and decomposition techniques to specify users' tasks. Using the framework, users can identify the appropriate geospatial data and clustering method based on their specific goals. To demonstrate the framework, two case studies on finding population density clusters in Western Canada and locating five hospitals in South Carolina are discussed. The results show that the framework can select the proper datasets and clustering methods with respect to users' goals.