Detecting and quantifying the contribution made by aircraft emissions to ambient concentrations of nitrogen oxides in the vicinity of a large international airport

Plans to build a third runway at London Heathrow Airport (LHR) have been held back because of concerns that the development would lead to annual mean concentrations of nitrogen dioxide (NO2) in excess of EU Directives, which must be met by 2010. The dominant effect of other sources of NOX close to the airport, primarily from road traffic, makes it difficult to detect and quantify the contribution made by the airport to local NOX and NO2 concentrations. This work presents approaches that aim to detect and quantify the airport contribution to NOX at a network of seven measurement sites close to the airport. Two principal approaches are used. First, a graphical technique using bivariate polar plots that develops the idea of a pollution rose is used to help discriminate between different source types. The sampling uncertainties associated with the technique have been calculated through a randomised re-sampling approach. Second, the unique pattern of aircraft activity at LHR enables data filtering techniques to be used to statistically verify the presence of aircraft sources. It is shown that aircraft NOX sources can be detected to at least 2.7 km from the airport, despite that the airport contribution is very small at that distance. Using these approaches, estimates have been made of the airport contribution to long-term mean concentrations of NOX and NO2. At the airport boundary we estimate that approximately 28 % (34 μg m-3) of the annual mean NOX is due to airport operations. At background locations 2-3 km downwind of the airport we estimate that the upper limit of the airport contribution to be less than 15 % (< 10 μg m-3). This work also provides approaches that would help validate and refine dispersion modelling studies used for airport assessments

Evaluation of Parametric and Nonparametric Statistical Models in Wrong-way Driving Crash Severity Prediction

Wrong-way driving (WWD) crashes result in more fatalities per crash, involve more vehicles, and cause extended road closures compared to other types of crashes. Although crashes involving wrong-way drivers are relatively few, they often lead to fatalities and serious injuries. Researchers have been using parametric statistical models to identify factors that affect WWD crash severity. However, these parametric models are generally based on several assumptions, and the results could generate numerous errors and become questionable when these assumptions are violated. On the other hand, nonparametric methods such as data mining or machine learning techniques do not use a predetermined functional form, can address the correlation problem among independent variables, display results graphically, and simplify the potential complex relationship between the variables. The main objective of this research was to demonstrate the applicability of nonparametric statistical models in successfully identifying factors affecting traffic crash severity. To achieve this goal, the performance of parametric and nonparametric statistical models in WWD crash severity prediction was evaluated. The following parametric methods were evaluated: Logistic Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), and Gaussian Naïve Bayes (GNB). The following nonparametric methods were evaluated: Random Forests (RF), Decision Trees (DT), and Support Vector Machine (SVM). The evaluation was based on sensitivity, specificity, and prediction accuracy. The research also demonstrated the applicability of nonparametric supervised learning algorithms on crash severity analysis by combining tree-based data mining techniques and marginal effect analysis to show the correlation between the response and the predictor variables. The analysis was based on 1,475 WWD crashes that occurred on arterial road networks from 2012-2016 in Florida. The results showed that nonparametric models provided better prediction accuracy on predicting serious injury compared to parametric models. By conducting prediction accuracy comparison, contributor variables’ marginal effect analysis, variable importance evaluation, and crash severity pattern recognition analysis, the nonparametric models have been demonstrated to be valid and proved to serve as an alternative tool in transportation safety studies. The results showed that head-on collisions, weekends, high-speed facilities, crashes involving vehicles entering from a driveway, dark-not lighted roadways, older drivers, and driver impairment are important factors that play a crucial role in WWD crash severity on non-limited access facilities. This information may assist researchers and safety engineers in identifying specific strategies to reduce the severity of WWD crashes on arterial streets. Besides unveiling the factors contributing to WWD crash severity and their relationship with each other, this research has demonstrated the potential of using data mining techniques in yielding results that are easily understandable and interpretable

Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments

Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data

This monograph is an exposition of several practice-ready methodologies for automatic vehicle identification (AVI) data collection systems. This includes considerations in the physical setup of the collection system as well as the interpretation of the data. An extended discussion is provided, with examples, demonstrating data techniques for converting the raw data into more concise metrics and views. Examples of statistical before-after tests are also provided. A series of case studies were presented that focus on various real-world applications, including the impact of winter weather on freeway operations, the economic benefit of traffic signal retiming, and the estimation of origin-destination matrices from travel time data. The technology used in this report is Bluetooth MAC address matching, but the concepts are extendible to other AVI data sources

An Evaluation of Touch and Pressure-Based Scrolling and Haptic Feedback for In-car Touchscreens

An in-car study was conducted to examine different input techniques for list-based scrolling tasks and the effectiveness of haptic feedback for in-car touchscreens. The use of physical switchgear on centre consoles is decreasing which allows designers to develop new ways to interact with in-car applications. However, these new methods need to be evaluated to ensure they are usable. Therefore, three input techniques were tested: direct scrolling, pressure-based scrolling and scrolling using onscreen buttons on a touchscreen. The results showed that direct scrolling was less accurate than using onscreen buttons and pressure input, but took almost half the time when compared to the onscreen buttons and was almost three times quicker than pressure input. Vibrotactile feedback did not improve input performance but was preferred by the users. Understanding the speed vs. accuracy trade-off between these input techniques will allow better decisions when designing safer in-car interfaces for scrolling applications

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Homing orientation in salamanders: A mechanism involving chemical cues

A detailed description is given of experiments made to determine the senses and chemical cues used by salamanders for homing orientation. Sensory impairment and cue manipulative techniques were used in the investigation. All experiments were carried out at night. Results show that sense impaired animals did not home as readily as those who were blind but retained their sensory mechanism. This fact suggests that the olfactory mechanism is necessary for homing in the salamander. It was determined that after the impaired salamander regenerated its sensory mechanism it too returned home. It was concluded that homing ability in salamanders is direction independent, distant dependent, and vision independent

Managed information gathering and fusion for transient transport problems

This paper deals with vehicular traffic management by communication technologies from Traffic Control Center point of view in road networks. The global goal is to manage the urban traffic by road traffic operations, controlling and interventional possibilities in order to minimize the traffic delays and stops and to improve traffic safety on the roads. This paper focuses on transient transport, when the controlling management is crucial. The aim was to detect the beginning time of the transient traffic on the roads, to gather the most appropriate data and to get reliable information for interventional suggestions. More reliable information can be created by information fusion, several fusion techniques are expounded in this paper. A half-automatic solution with Decision Support System has been developed to help with engineers in suggestions of interventions based on real time traffic data. The information fusion has benefits for Decision Support System: the complementary sensors may fill the gaps of one another, the system is able to detect the changing of the percentage of different vehicle types in traffic. An example of detection and interventional suggestion about transient traffic on transport networks of a little town is presented at the end of the paper. The novelty of this paper is the gathering of information - triggered by the state changing from stationer to transient - from ad hoc channels and combining them with information from developed regular channels. --information gathering,information fusion,Kalman filter,transient traffic,Decision Support System

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized