DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting

Traffic speed forecasting is one of the core problems in Intelligent Transportation Systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focus on modeling the spatial dependencies only with the distance. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the smallest building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into prediction network for traffic forecasting. We evaluate the proposed model with two large-scale real-world datasets, and find 7.40% average improvement for 1-hour forecasting in highly complex urban networks

Dynamic Body VSLAM with Semantic Constraints

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modeling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by a significant amount for moving object trajectory reconstruction relative to state-of-the-art methods like VISO 2, as well as standard bundle adjustment algorithms

Urban road network crisis response management: time-sensitive decision optimization

With the increasing global stock of vehicles, traffic congestion is becoming more severe and costly in many urban road networks. Road network modeling and optimization are essential tools in predicting traffic flow and reducing network congestion. Markov chains are remarkably capable in modeling complex, dynamic, and large-scale networks; Google’s PageRank algorithm is a living proof. In this article, we leverage Markov chains theory and its powerful statistical analysis tools to model urban road networks and infer road network performance and traffic congestion patterns, and propose an optimization approach that is based on Genetic Algorithm to model network-wide optimization decisions. Such decisions target relief from traffic congestion arising from sudden network changes (e.g. rapid increase in vehicles flow, or lanes and roads closures). The proposed network optimization approach can be used in time-sensitive decision making situations such as crisis response management, where decision time requirements for finding optimal network design to handle such abrupt changes typically don’t allow for the traditional agent-based simulation and iterative network design approaches. We detail the mathematical modeling and algorithmic optimization approach and present preliminary results from a sample application

Görsel-ataletsel duyaç tümleştirme kullanılarak şehirlerde 3b modelleme.

In this dissertation, a real-time, autonomous and geo-registered approach is presented to tackle the large scale 3D urban modeling problem using a camera and inertial sensors. The proposed approach exploits the special structures of urban areas and visual-inertial sensor fusion. The buildings in urban areas are assumed to have planar facades that are perpendicular to the local level. A sparse 3D point cloud of the imaged scene is obtained from visual feature matches using camera poses estimates, and planar patches are obtained by an iterative Hough Transform on the 2D projection of the sparse 3D point cloud in the direction of gravity. The result is a compact and dense depth map of the building facades in terms of planar patches. The plane extraction is performed on sequential frames and a complete model is obtained by plane fusion. Inertial sensor integration helps to improve camera pose estimation, 3D reconstruction and planar modeling stages. For camera pose estimation, the visual measurements are integrated with the inertial sensors by means of an indirect feedback Kalman filter. This integration helps to get reliable and geo-referenced camera pose estimates in the absence of GPS. The inertial sensors are also used to filter out spurious visual feature matches in the 3D reconstruction stage, find the direction of gravity in plane search stage, and eliminate out of scope objects from the model using elevation data. The visual-inertial sensor fusion and urban heuristics utilization are shown to outperform the classical approaches for large scale urban modeling in terms of consistency and real-time applicability.Ph.D. - Doctoral Progra

Modeling the Internet of Things: a simulation perspective

This paper deals with the problem of properly simulating the Internet of Things (IoT). Simulating an IoT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the provision of scalable simulation scenarios, that enable the real-time execution of massively populated IoT environments. Attention is given to novel hybrid and multi-level simulation techniques that, when combined with agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches, can provide means to perform highly detailed simulations on demand. To support this claim, we detail a use case concerned with the simulation of vehicular transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High Performance Computing and Simulation (HPCS 2017

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update