Freeway travel time estimation & prediction : a literature review

This report is the second deliverable of the Real Time and Predictive Traveller Information project and the first deliverable of the Freeway Travel Time Information sub-project in the Integrated Traveller Information research Domain of the Smart Transport Research Centre. The primary objective of the Freeway Travel Time Information sub-project is to develop algorithms for real-time travel time estimation and prediction models for Freeway traffic. The objective of this report is to review the literature pertaining to travel time estimation and prediction models for freeway traffic.

Arterial travel time estimation & prediction : a literature review

This report is the fourth deliverable of the Real Time and Predictive Traveller Information project and the first deliverable of the Arterial Travel Time Information sub-project in the Integrated Traveller Information research Domain of the Smart Transport Research Centre. The primary objective of the Arterial Travel Time Information sub-project is to develop algorithms for real-time travel time estimation and prediction models for arterial traffic. The objective of this report is to review the literature pertaining to travel time estimation and prediction models for arterial traffic.

Fundamental understanding on the use of Bluetooth MAC scanner for arterial travel time estimation

This report is the eight deliverable of the Real Time and Predictive Traveller Information project and the third deliverable of the Arterial Travel Time Information sub-project in the Integrated Traveller Information research Domain of the Smart Transport Research Centre. The primary objective of the Arterial Travel Time Information sub-project is to develop algorithms for real-time travel time estimation and prediction models for arterial traffic. Brisbane arterial network is highly equipped with Bluetooth MAC Scanners, which can provide travel time information. Literature is limited with the knowledge on the Bluetooth protocol based data acquisition process and accuracy and reliability of the analysis performed using the data. This report expands the body of knowledge surrounding the use of data from Bluetooth MAC Scanner (BMS) as a complementary traffic data source. A multi layer simulation model named Traffic and Communication Simulation (TCS) is developed. TCS is utilised to model the theoretical properties of the BMS data and analyse the accuracy and reliability of travel time estimation using the BMS data.

Algorithm for queue estimation with loop detector of time occupancy in off-ramps on signalized motorways

Long traffic queues on off-ramps significantly compromise the safety and throughput of motorways. Obtaining accurate queue information is crucial for countermeasure strategies. However, it is challenging to estimate traffic queues with locally installed inductive loop detectors. This paper deals with the problem of queue estimation with the interpretation of queuing dynamics and the corresponding time-occupancy distribution over motorway off-ramps. A novel algorithm for real-time queue estimation with two detectors is presented and discussed. Results derived from microscopic traffic simulation validated the effectiveness of the algorithm and revealed some of its useful features: (a) long and intermediate traffic queues could be accurately measured, (b) relatively simple detector input (i.e., time occupancy) was required, and (c) the estimation philosophy was independent with signal timing changes and provided the potential to cooperate with advanced strategies for signal control. Some issues concerning field implementation are also discussed.

Traffic queue estimation for metered motorway on-ramps through use of loop detector time occupancies

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is a vital input for dynamic queue management on metered on-ramps. Accurate and reliable queue information enables the management of on-ramp queue in an adaptive manner to the actual traffic queue size and thus minimises the adverse impacts of queue flush while increasing the benefit of ramp metering. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.

A Kalman filter based queue estimation algorithm using time occupancies for motorway on-ramps

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is the most vital input for a dynamic queue management that can treat long queues on metered on-ramps more sophistically. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in the congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.

Assessing the Impact of Sensor Accuracy and Sensor Choice on Air Traffic Conflict Risk Estimation using Risk-Ratio Concepts

One of the primary desired capabilities of any future air traffic separation management system is the ability to provide early conflict detection and resolution effectively and efficiently. In this paper, we consider the risk of conflict as a primary measurement to be used for early conflict detection. This paper focuses on developing a novel approach to assess the impact of different measurement uncertainty models on the estimated risk of conflict. The measurement uncertainty model can be used to represent different sensor accuracy and sensor choices. Our study demonstrates the value of modelling measurement uncertainty in the conflict risk estimation problem and presents techniques providing a means of assessing sensor requirements to achieve desired conflict detection performance.

Permeability estimation conditioned to geophysical downhole log data in sandstones of the northern Galilee Basin, Queensland : methods and application

This study uses borehole geophysical log data of sonic velocity and electrical resistivity to estimate permeability in sandstones in the northern Galilee Basin, Queensland. The prior estimates of permeability are calculated according to the deterministic log–log linear empirical correlations between electrical resistivity and measured permeability. Both negative and positive relationships are influenced by the clay content. The prior estimates of permeability are updated in a Bayesian framework for three boreholes using both the cokriging (CK) method and a normal linear regression (NLR) approach to infer the likelihood function. The results show that the mean permeability estimated from the CK-based Bayesian method is in better agreement with the measured permeability when a fairly apparent linear relationship exists between the logarithm of permeability and sonic velocity. In contrast, the NLR-based Bayesian approach gives better estimates of permeability for boreholes where no linear relationship exists between logarithm permeability and sonic velocity.

Cross-calbration of push-broom 2D LIDARs and cameras in natural scenes

This paper addresses the problem of automatically estimating the relative pose between a push-broom LIDAR and a camera without the need for artificial calibration targets or other human intervention. Further we do not require the sensors to have an overlapping field of view, it is enough that they observe the same scene but at different times from a moving platform. Matching between sensor modalities is achieved without feature extraction. We present results from field trials which suggest that this new approach achieves an extrinsic calibration accuracy of millimeters in translation and deci-degrees in rotation.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Measurement error of rearfoot kinematics during running between a 100Hz and 30Hz camera

Background: Measurement accuracy is critical for biomechanical gait assessment. Very few studies have determined the accuracy of common clinical rearfoot variables between cameras with different collection frequencies. Research question: What is the measurement error for common rearfoot gait parameters when using a standard 30Hz digital camera compared to 100Hz camera? Type of study: Descriptive. Methods: 100 footfalls were recorded from 10 subjects ( 10 footfalls per subject) running on a treadmill at 2.68m/s. A high-speed digital timer, accurate within 1ms served as an external reference. Markers were placed along the vertical axis of the heel counter and the long axis of the shank. 2D coordinates for the four markers were determined from heel strike to heel lift. Variables of interest included time of heel strike (THS), time of heel lift (THL), time to maximum eversion (TMax), and maximum rearfoot eversion angle (EvMax). Results: THS difference was 29.77ms (+/- 8.77), THL difference was 35.64ms (+/- 6.85), and TMax difference was 16.50ms (+/- 2.54). These temporal values represent a difference equal to 11.9%, 14.3%, and 6.6% of the stance phase of running gait, respectively. EvMax difference was 1.02 degrees (+/- 0.46). Conclusions: A 30Hz camera is accurate, compared to a high-frequency camera, in determining TMax and EvMax during a clinical gait analysis. However, relatively large differences, in excess of 12% of the stance phase of gait, for THS and THL variables were measured.

Estimation Of concrete resistance To chloride-ion penetration based On water accessible porosity and unit weight Of concrete

To ensure better concrete quality and long-term durability, there has been an increasing focus in recent years on the development of test methods for quality control of concrete. This paper presents a study to evaluate the effect of water accessible porosity and oven-dry unit weight on the resistance of concrete to chloride-ion penetration. Based on the experimental results and regression analyses, empirical relationships of the charge passed (ASTM C 1202) and chloride migration coefficient (NT Build 492) versus the water accessible porosity and oven dry unit weight of the concrete are established. Using basic physical properties of water accessible porosity and oven dry unit weight which can be easily determined, total charge passed and migration coefficient of the concrete can be estimated for quality control and for estimating durability of concrete.

A UKF-based estimation strategy for actuator fault detection of UASs

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating correspond- ing velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Online calibration of stereo rigs for long-term autonomy

Stereo-based visual odometry algorithms are heavily dependent on an accurate calibration of the rigidly fixed stereo pair. Even small shifts in the rigid transform between the cameras can impact on feature matching and 3D scene triangulation, adversely affecting pose estimates and applications dependent on long-term autonomy. In many field-based scenarios where vibration, knocks and pressure change affect a robotic vehicle, maintaining an accurate stereo calibration cannot be guaranteed over long periods. This paper presents a novel method of recalibrating overlapping stereo camera rigs from online visual data while simultaneously providing an up-to-date and up-to-scale pose estimate. The proposed technique implements a novel form of partitioned bundle adjustment that explicitly includes the homogeneous transform between a stereo camera pair to generate an optimal calibration. Pose estimates are computed in parallel to the calibration, providing online recalibration which seamlessly integrates into a stereo visual odometry framework. We present results demonstrating accurate performance of the algorithm on both simulated scenarios and real data gathered from a wide-baseline stereo pair on a ground vehicle traversing urban roads.

Spatiotemporal modelling in estimation of nitrous oxide emissions from soil

Nitrous oxide is a major greenhouse gas emission. The aim of this research was to develop and apply statistical models to characterize the complex spatial and temporal variation in nitrous oxide emissions from soils under different land use conditions. This is critical when developing site-specific management plans to reduce nitrous oxide emissions. These studies can improve predictions and increase our understanding of environmental factors that influence nitrous oxide emissions. They also help to identify areas for future research, which can further improve the prediction of nitrous oxide in practice.