Search

In construction, the accurate measurements are important in ensuring the quality of work delivered. Different measuring tools have been developed to help workers conduct accurate measuring. However, they may be subject to manipulation difficulties, such as the need for tap/gesture interaction. This paper proposes a novel eye gaze-aided virtual tape measure framework that provides a hands-free manner for conducting the measurements in construction. This framework consists of three components: data collection for point of interest, sensor calibration, and distance calculation. Its effectiveness is tested by measuring the dimensions of 15 common objects in laboratory and on-site environments and achieves the average absolute and relative errors of 2.4 cm and 4.8%. The absolute errors range from 0.3 to 7.3 cm. A comparison study is conducted to demonstrate its superior performance over iPhone's Measure application. The results illustrate the feasibility and potential of using the framework to enable measures for smart construction. [ABSTRACT FROM AUTHOR]

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements. [ABSTRACT FROM AUTHOR]

This paper presents a detailed methodological framework for collecting microscopic driver and vehicle behaviour data over a long road segment with an application to the entire stretch of a freeway ramp segment using single and multiple unmanned aerial vehicles (UAVs). The methodology allows users to collect reliable and complete trajectories of traffic movements in areas with challenging physical characteristics (long road segment, horizontal curvature, changing elevation, and presence of shadow), challenging traffic characteristics (high traffic volume, high speeds, and high-speed changes), and restrictive regulations (UAVs prohibited from hovering over the freeway or the right-of-way). Different UAV setups are recommended and can be used depending on the site conditions. Specific commercial software and procedures used to complete the data collection are explained. The methodology was applied at two ramps and verified with speed data acquired from differential GPS receivers using three different error metrics. The results showed good performance of the proposed methodology, including when aerial videos were taken from oblique angles. [ABSTRACT FROM AUTHOR]

Work zones which are usually referred to as traffic bottlenecks or critical points may lead to reductions in both operational performance and safety. Researchers have conducted extensive studies on speed characteristics in freeway work zones. However, most of these studies are based on simulation data or are travel direction-based rather than lane-based. By utilizing the video recording capability of drones, this paper aims to provide methodologies and the workflow in exploring the vehicular speed characteristics in freeway work zones with considerations for lane and location deviations. A four-to-three lane work zone on Huhang Expressway between Downtown Shanghai and Songjiang in China was selected for data collection. Then free flow speed variation pattern is analyzed, a speed prediction model is proposed, and the speed–flow relationship is investigated on each lane at nine virtual sections along the work zone. It is found that the merging area affects the speeds mainly in the segment from 200 m upstream of the taper to the start of the activity area, which is the most important risk area in the work zone, and the speed characteristics depend on vehicle types (trucks versus automobiles) as well as lane locations. It is also showed that drones are valuable and effective tools for data collection for vehicular speed characteristics analysis in freeway work zones and it is easy to extract the information needed. [ABSTRACT FROM AUTHOR]

Traditionally, forest management has focused on forestry-related practices whereas other industries have been managed separately. Forest management requires the integration of all natural resource development activities, along with other anthropogenic and natural forest disturbances (e.g., climate change, pollution, wildfire, pest disturbance) to understand how human activities can change forested ecosystems. The term cumulative effects has been used to describe these attempts to integrate all disturbances to develop an understanding of past, current, and future impacts on environmental, social, and economic components of the system. In this review, we focus on the science required to understand the past, current, and future impacts of the cumulative effects of anthropogenic and natural disturbances on forested ecosystems or their components. We have primarily focused on the terrestrial system with an emphasis on northern forests in Canada. Our paper is not intended to be a comprehensive review of all cumulative effects science but a synthesis of the challenges and approaches currently being used. Central repositories were identified as an approach to deal with issues of availability of remotely sensed data on anthropogenic and natural disturbances. Data integration projects, open data, and well-designed large-scale data collection efforts are needed to provide sufficient data on environmental responses to cumulative effects. As well, large-scale integrated, modularized ecosystem models are needed to bring stressor and environmental response data together to explore responses to, and interactions between, multiple stressors to project these effects into the future and to identify future data collection needs. [ABSTRACT FROM AUTHOR]

Limited by the low-frequency data acquisition, vehicle global positioning system (GPS) data are difficult to implement in the area of microtraffic simulation. Based on the functional design of mobile phone positioning technology, mobile phones can be used to acquire bus GPS data every second. In this paper, an analytical model is proposed to determine the parameters of signal coordination for bus priority along an arterial based on GPS data of mobile phones. First, bus priority evaluation indicators are established using bus GPS data, which are acquired by mobile phones. Second, the signal timing parameters of the arterial road are optimized, and a preliminary timing plan is developed by evaluating small changes in the plan. Finally, the corresponding final plan is developed using VISSIM micro simulation software. The feasibility of the analytical model is verified by simulating an actual arterial in Fuzhou city, China. [ABSTRACT FROM AUTHOR]

Trajectory data collected using video image processing techniques are prone to noise. Trajectory data extracted using commercially available video image processing software (TRAZER) contains the noise associated with the false detection in addition to the white noise. This paper proposes a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to smooth such trajectory data. In this approach, trajectory data are decomposed into a finite number of intrinsic modes and a unique residue is computed to obtain each mode. This monotonic residue gives the smoothed trajectory. The instantaneous speeds of the vehicles are then estimated using the method of continuous wavelet transforms, discrete wavelet transforms, and numerical differentiation. Internal consistency analyses show that the wavelet transforms methods are effective in reducing the noise amplification of the speed profile. It was also observed that the corrections applied on trajectory data have a significant effect on macroscopic traffic relations. [ABSTRACT FROM AUTHOR]

School safety is a high priority for road safety agencies worldwide due to the growing concerns about student safety and emphasis on community livability. However, the lack of consistent data available at schools makes it challenging for engineers to understand the safety issues faced accurately. Traffic conflicts have been advocated in the literature as a surrogate safety measure due to the advantages it offers for road safety evaluations. This paper demonstrates the capability of automated traffic safety diagnosis at a school using computer vision techniques. The selected school is located in a residential area in the City of Edmonton, Alberta, and is on a main roadway in the neighbourhood. The age group of the school is from 5 to 16 years old (corresponding to Grade 1 to 9 respectively). Data are collected during the fall and winter terms. The severity and frequency of conflicts and traffic violations were analyzed to quantify the safety concerns. These concerns included driving violations, jaywalking violations as well as conflicts between pedestrians and vehicles as well as between vehicles and each other. Hourly and seasonal trends were observed and analyzed to assist in the selection of treatments and recommendations to improve the safety around schools. The results show that pedestrian safety has improved in winter due to lower vehicle speeds. [ABSTRACT FROM AUTHOR]

In light of the newly released AASHTO cracking protocol PP67-10 and high quality cracking data produced from a novel 3D 1 mm pavement data collection and automated analysis system, this paper develops an index system for overall cracking evaluation. The Analytical Hierarchy Process (AHP) is employed to establish a general framework and fuzzy set theory is adopted to convert actual severity and intensity measures into normalized scores. Multiple data combination techniques are applied for data aggregation. The ultimate product of this system, a single number cracking index representing the overall cracking condition, can be used to rank pavements and prioritize crack-oriented maintenance and rehabilitation projects. Furthermore, cracking indices for different pavement zones can also be derived from the system, which would be significant to examine the pavement failure mechanism. A case study containing 10 pavement sections is performed to demonstrate the applicability of this proposed evaluation system. [ABSTRACT FROM AUTHOR]

Analysis and literature review have indicated that transportation agencies often report significant percentage of missing values of traffic volumes collected at permanent data collection stations (PDCS). These missing values can be as high as 60% in some cases. Although invested significantly in legacy systems that rely primarily on inductive loop detectors to collect traffic volumes, transportation agencies are faced with a challenge of relying only on a small set of the traffic data. Despite the fact that transportation agencies are often required to report on various annual traffic statistics such as average annual daily traffic (AADT) and vehicles miles travelled, little research is available on how transportation practitioners handled missing values in their traffic data collection efforts. In this paper, a simple iterative approach is introduced - based on the integration of an imputation algorithm and a time series model - to impute missing PDCS data. The approach is deigned such that the imputation algorithm is implemented first to impute relatively small to medium gaps forming a library of complete datasets; then a time series model is fitted to these stations to form the base for imputing large gaps. The data imputation algorithm is applied on a case study on Ministry of Transportation of Ontario PDCS stations, Canada. The average errors for imputing as high as 90% of missing data - with maximum continuous gaps of 76 h - were approximately 16% with an average median error of 11 veh/h. After all traffic data are imputed, a time series model is estimated to capture the multiple seasonalities and trends in the traffic data across multiple years. The estimated 'model' is then used to calculate and produce seasonal variation factors and graphs that are typically required for planning, design, control, operation, and management of traffic and highway facilities. By estimating the model parameters using existing raw data, the model was then tested to assess its accuracy to forecast future years. [ABSTRACT FROM AUTHOR]