C-ITS based prediction of driver red light running and turning behaviours

Red light running is a major traffic violation. Drivers often aggressively or unintentionally violate red signal and cause traffic collisions. Moreover, Vision impairment of turning vehicles by large vehicles and road side static structures near intersections often lead to VRU crashes during their crossing at the intersection. In this research, we have developed models to predict drivers’ red light running and turning behaviour at intersections using Long Short Term Memory and Gated Recurrent Unit algorithms. We have used vehicle kinematic dataset of the C-ITS project: Ipswich Connected Vehicle Pilot, Queensland, taken from the Department of Transport and Main Road, Queensland

Design and construction of product separating conveyor based on color

This research paper is a work on control engineering or production technology. This paper aims at the problem we are attempting to solve to create an automation system of product passing &amp; separating through color difference. The products will be placed on moving conveyor belt through different colored packaging system. A color detecting device will be situated in a position of conveyor belt that will detect two different color of the packaged product and a divider will separate different colored package product to different destination. This product passing and separating to the intended destination is done by a color difference in an automatic way. In many packaging industries color object sorting and separating is a major task that needs to be done at final dispatch system. Manual sorting is a traditional approach that is preferred by industries. This approach is performed by human operators which is tedious, time-consuming, slow and non-consistent. Therefore, the efforts are made to design and implement an automatic technique of product passing &amp; separating through color difference.</p

Diagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning

Addiction such as tobacco smoking affects the human brain and thus causes significant changes in the brainwaves. The changes in brain wave due to smoking can be identified by focusing on changes in electroencephalogram pattern, extracting different time-frequency domain features. In this aspect, a laboratory-based study has been presented in this paper, for assessing the brain signal changes due to the tobacco addiction. Four classifier models, namely, Logistic Regression (LR), K- Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest Classifier (RFC) were trained and tested for assessing the performance of the time domain, frequency domain and fusion of time-frequency domain features, with a five-fold cross-validation. Four different performance measures (sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve) were used to measure the overall performance, and the results suggested that the classifiers based on time-frequency domain features perform the best while using combinedly. Using the utilized fusion of the time-frequency domain features, the classification models can identify the smoker group with an accuracy ranged from (86.5-91.3%), where the RFC shows the best accuracy of 91.3%, which is higher than the three other classifiers models

Design of Automated Bascule Bridge and Collision Avoidance with Water Traffic

The development of movable bridges is a modern technological advancement that requires the integration of multi-disciplinary concepts such as control, automation, and design. In order to demonstrate how a collision avoidance system works, this research offers the design of an automated double-leaf bascule bridge with a pulley-rope moving mechanism and Pratt truss. The bridge is created where a settled railway or roadway intersection of a navigable stream cannot attain a steep profile. The design took into account the length, height, and width of the bridge as well as automatic ship identification and scaffold leaf movement activity for ship crossing. For the safe passage of cars on the bridge and for stopping vehicles during bridge movement, the system includes a collision avoidance control system and an automated road barrier system. After sensor adjustment, the model bridge’s performance under test produced results that were adequate, with a success rate of 100%

Crash severity analysis of vulnerable road users using machine learning

Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users - pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups – for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists

Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

This paper presents an early prediction framework to classify drivers&amp;#x2019; intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers&amp;#x2019; intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users&amp;#x2019; safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers&amp;#x2019; intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.</p

A Review on Drivers' Red Light Running Behavior Predictions and Technology Based Countermeasures

Red light running at signalised intersections is a growing road safety issue worldwide, leading to the rapid development of advanced intelligent transportation technologies and countermeasures. However, existing studies have yet to summarise and present the effect of these technology-based innovations in improving safety. This paper represents a comprehensive review of red-light running behaviour prediction methodologies and technology-based countermeasures. Specifically, the major focus of this study is to provide a comprehensive review on two streams of literature targeting red-light running and stop-and-go behaviour at signalised intersection - (1) studies focusing on modelling and predicting the red-light running and stop-and-go related driver behaviour and (2) studies focusing on the effectiveness of different technology-based countermeasures which combat such unsafe behaviour. The study provides a systematic guide to assist researchers and stakeholders in understanding how to best identify red-light running and stop-and-go associated driving behaviour and subsequently implement countermeasures to combat such risky behaviour and improve the associated safety.</p

Deep Transfer Learning Based Intersection Trajectory Movement Classification for Big Connected Vehicle Data

Trajectory movement labelling is an important pre-stage for predicting connected vehicle (CV) movement at intersections. Drivers' movement prediction and warning at intersections ensure advanced transportation safety and researchers use machine learning-based data-driven approaches to implement these technologies. However, prediction of drivers' movements at intersections requires labelling the train and test dataset accurately with different vehicle movements at intersections to evaluate the performance of the prediction model by comparing the actual and predicted intersection movements. Moreover, due to GPS detection error or missing co-operative awareness messages (CAM), the data resides with many abnormal trajectories which are unable to be matched with regular straight or any turning movements. Especially for big data with million trajectories, it is tedious to label the movements manually. To solve this problem, we have created an automated trajectory movement classification technique using a dual approach of map matching technique and deep transfer learning modelling. Data of connected vehicle trajectory information is taken from the Ipswich Connected Vehicle Pilot (ICVP) Project, which is one of the largest connected vehicle pilots within a naturalistic driving environment in Australia. Map matching approach is performed as initial labelling by analysing the origin and destination of the vehicle CAM messages at intersections and then was converted as image datasets of 19202 samples. The map matching error and abnormal trajectories are identified by visual inspection. With properly labelled 9496 training images, 10 transfer learning models are built and tested through the remaining 9706 testing images. The maximum testing accuracy (99.73%) is achieved from the Densenet169 model, and the result shows satisfactory accuracy for individual classes: straight (99.85%), turn left (99.59), turn right (99.25), u-turn (100%), abnormal (98.63%). This model becomes a routine tool that is used daily to automatically classify thousands of trajectory movements of the C-ITS data in the ICVP project.</p

A new algorithm for solving uncapacitated transportation problem with interval-defined demands and suppliers capacities

The uncapacitated transportation problem (UTP) deals with minimizing the transportation costs related to the delivery of a homogeneous product from multi- suppliers to multi-consumers. The application of the UTP can be extended to other areas of operations research, including inventory control, personnel assignment, signature matching, product distribution with uncertainty, multi-period production and inventory planning, employment scheduling, and cash management. Such a UTP with interval- defined demands and suppliers capacities (UTPIDS) is investigated in this paper. In UTPIDS, the demands and suppliers capacities may not be known exactly but vary within an interval due to variation in the economic conditions of the global economy. Following the variation, the minimal total cost of the transportation can also be varied within an interval and thus, the cost bounds can be obtained. Here, although the lower bound solution can be attained methodologically, the correct estimation of the worst case realization (the exact upper bound) on the minimal total transportation cost of the UTPIDS is an NP-hard problem. So, the decision-makers seek for minimizing the transportation costs and they are interested in the estimation of the worst case realization on these minimal costs for better decision making especially, for proper investment and return. In literature very few approaches are available to find this estimation of the worst case realization with some shortcomings. First, we demonstrate that the available heuristic methods fail to obtain the correct estimation of the worst case realization always. In this situation, development of a better heuristic method to find the better near optimal estimation of the worst case realization on the minimal total costs of the UTPIDS is desirable. Then this paper provides a new polynomial time algorithm that runs in O (N2) time (N, higher of the numbers of source and destination nodes) for better estimation. A comparative assessment on solutions of available benchmark instances, some randomly generated numerical example problems and a real-world application shows promising performance of the current technique. So, our new finding would definitely be benefited to practitioners, academics and decision makers who deal with such type of decision making instance