Saiedeh Razavi

Multisensor data fusion: A review of the state-of-the-art

There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.

Data-Fusion Approaches and Applications for Construction Engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.

Locating sensor nodes on construction projects

Localization of randomly distributed wireless sensor nodes is a significant and fundamental problem in a broad range of emerging civil engineering applications. Densely deployed in physical environments, they are envisioned to form ad hoc communication networks and provide sensed data without relying on a fixed communications infrastructure. To establish ad hoc communication networks among wireless sensor nodes, it is useful and sometimes necessary to determine sensors’ positions in static and dynamic sensor arrays. As well, the location of sensor nodes becomes of immediate use if construction resources, such as materials and components, are to be tracked. Tracking the location of construction resources enables effortless progress monitoring and supports real-time construction state sensing. This paper compares several models for localizing RFID nodes on construction job sites. They range from those based on triangulation with reference to transmission space maps, to roving RFID reader and tag systems using multiple proximity constraints, to approaches for processing uncertainty and imprecision in proximity measurements. They are compared qualitatively on the basis of cost, flexibility, scalability, computational complexity, ability to manage uncertainty and imprecision, and ability to handle dynamic sensor arrays. Results of field experiments and simulations are also presented where applicable.

Reliability-Based Hybrid Data Fusion Method for Adaptive Location Estimation in Construction

Materials tracking and locating, which can be accomplished through various technologies and data sources, are key elements affecting construction productivity. The need for developing fundamental methods to take advantage of the relative strengths of each technology and data source while dealing with their limitations motivates the development in this paper of data fusion methods for improving materials location estimation. Particular attention is paid to situations in a construction environment in which radio-frequency identification (RFID) tags are attached to each piece of material, and the materials may be repeatedly moved around the site. The construction dynamics, the high noise ratio, and the limitations of the utilized sensing systems result in imperfect data that is imprecise and uncertain. A key challenge is using this imperfect data to improve accuracy and precision while maintaining cost-effectiveness and scalability. To address this issue, a hybrid data-fusion method was developed to increase...

An implementation model for automated construction materials tracking and locating

Good materials management on large construction projects is critical for maximizing productivity and project performance. When key materials are temporarily lost, whole crews may be left idle and the project may be delayed. When key materials are completely lost, the impact can be enormous. In fact, one of the major problems in managing construction materials and equipment is tracking them in the supply chain and knowing their location on large job sites. Fortunately, location can now be automatically estimated within metres using emerging technologies. This paper proposes a general implementation model for automated construction materials tracking and locating on large industrial projects, such as refineries and power plants. It includes a methodology for determining what type of technology should be used for different types of projects and construction materials. It is based on an analysis of the capabilities of emerging technologies and on experience gained from implementing automated materials trackin...

Impact of Connected Vehicle on Work Zone Network Safety through Dynamic Route Guidance

AbstractDespite enhanced safety strategies, in-vehicles technologies, and improvements in infrastructure, urban transportation networks are still accident-prone. Connected vehicle offers the possibility to exchange data with vehicles and infrastructure in an effort to improve safety. The main objective of the research reported in this paper is to evaluate the potential safety benefits of deploying a connected vehicle system on a traffic network in the presence of a work zone. The modeled connected vehicle system in the research reported in this paper uses vehicle-to-vehicle (VTV) communication to share information about work zone links and link travel times. Vehicles which receive work zone information will also modify their driving behavior by increasing awareness and decreasing aggressiveness. This paper also proposes a decaying average travel time dynamic route guidance algorithm which exhibits weighted information decay. Traffic microsimulation software is used to model the network and a C plugin is d...

RFID deployment protocols for indoor construction

Purpose – Location awareness is essential to decisions pertinent to tracking and progress reporting, as well as to safety in construction projects. However, these applications have been mostly limited to the outdoor environment, where satellites for positioning information are in view. Recent studies on indoor location sensing systems are now overcoming this limitation and offering significant potential on construction practices, and radio frequency identification (RFID) is the most widely utilised technology for such application. The purpose of this paper is to address a wide range of protocols that are vital for RFID deployment for indoor construction. The paper identifies deployment settings to provide data acquisition with higher accuracy for indoor location sensing in construction.Design/methodology/approach – A computational platform was designed to assess and evaluate the most suitable condition related to deployment of reference tags in construction. In this platform, a number of protocols and par...

Multisensor data fusion: Antecedents and directions

The notion of combining redundant and complementary sensory data to achieve higher quality information has been around for long time. For example, biological systems usually rely on fusion of multimodality sensory data to perceive the surrounding environment. There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology driven by its versatility and diverse areas of application. This paper presents an overview of data fusion state of the art discussing advances in fusion methodologies and architectures. In addition, future directions of research in data fusion community including large-scale, hybrid, secure fusion, and importance of standard evaluation framework are highlighted and described.

Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment

ABSTRACT This article introduces a methodology for determining the optimal number and locations of roadside equipment (RSE) units for travel time estimation in vehicle-to-infrastructure and vehicle-to-vehicle communication environments. The developed approach is a novel technique for modeling RSE placement to optimize the number and positions of RSE units while minimizing the travel time estimation error rate. A non-dominated sorting genetic algorithm (NSGA-II) was used to optimize this multi-objective problem. A microsimulation model of the highway 401 network in Toronto, Canada, was used as a testbed to evaluate the proposed approach. The NSGA-II approach produces an optimal Pareto front that minimizes the number, and hence cost, of RSE units while maximizing travel time estimation accuracy. Points on the Pareto front are equally optimal, dominate over all other points in the cost-accuracy search space, and offer the option to optimize the trade-off between infrastructure cost and estimation accuracy. This empirical study illustrates the impact of RSE placement on travel time accuracy in a connected vehicle environment. The optimization results indicate that the actual locations of the RSE units have a greater influence on the quality of the estimates than the number of RSE units. Thus, the accuracy of travel time estimates depends primarily on the locations of the RSE units and less on the total RSE density. Expanding RSE deployment might improve the accuracy of estimation; however, the associated costs will simultaneously increase.

Qualitative traffic analysis using image processing and time-delayed neural network

We present an online, feature-based approach to estimate traffic qualitative parameters from a sequence of traffic images. Considering the factor of time and attempting to simulate human behavior, a time-delay neural network is used to determine the traffic status through traffic lanes. The acquired frames are divided into a number of blocks based on number of lanes and road boundary coordinates, which are obtained automatically by a part of the system called the road boundary detection system. Two extracted principal features from each block of a lane which are vehicle detector and movement detector will form the input vector of the neural network. The neural network classifies each lane into a level of traffic congestion. The neural network was previously trained with various traffic and different lighting conditions. Finally a description of traffic scene is obtained using descriptions of all lanes.