1,539 research outputs found

    A simulation study of predicting real-time conflict-prone traffic conditions

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    Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative to pre-collision traffic dynamics. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e. Support Vector Machines, k-Nearest Neighbours and Random Forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of Random Forests with 5-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective prediction of conflicts

    DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE

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    Traffic crashes have resulted in significant cost to society in terms of life and economic losses, and comprehensive examination of crash injury outcome patterns is of practical importance. By inferring the parameters of interest from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically examine the applicability of Bayesian methods in traffic crash driver injury severity prediction in traffic crashes. In this study, three types of Bayesian models are defined: hierarchical Bayesian regression model, Bayesian non-regression model and knowledge-based Bayesian non-parametric model, and a conceptual framework is developed for selecting the appropriate Bayesian model based on discrete research purposes. Five Bayesian models are applied accordingly to test their effectiveness in traffic crash driver injury severity prediction and variable impact estimation: hierarchical Bayesian binary logit model, hierarchical Bayesian ordered logit model, hierarchical Bayesian random intercept model with cross-level interactions, multinomial logit (MNL)-Bayesian Network (BN) model, and decision table/na\xefve Bayes (DTNB) model. A complete dataset containing all crashes occurring on New Mexico roadways in 2010 and 2011 is used for model analyses. The studied dataset is composed of three major sub-datasets: crash dataset, vehicle dataset and driver dataset, and all included variables are therefore divided into two hierarchical levels accordingly: crash-level variables and vehicle/driver variables. From all these five models, the model performance and analysis results have shown promising performance on injury severity prediction and variable influence analysis, and these results underscore the heterogeneous impacts of these significant variables on driver injury severity outcomes. The performances of these models are also compared among these methods or with traditional traffic safety models. With the analyzed results, tentative suggestions regarding countermeasures and further research efforts to reduce crash injury severity are proposed. The research results enhance the understandings of the applicability of Bayesian methods in traffic safety analysis and the mechanisms of crash injury severity outcomes, and provide beneficial inference to improve safety performance of the transportation system

    Real-time classification of aggregated traffic conditions using relevance vector machines

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    This paper examines the theory and application of a recently developed machine learning technique namely Relevance Vector Machines (RVMs) in the task of traffic conditions classification. Traffic conditions are labelled as dangerous (i.e. probably leading to a collision) and safe (i.e. a normal driving) based on 15-minute measurements of average speed and volume. Two different RVM algorithms are trained with two real-world datasets and validated with one real-world dataset describing traffic conditions of a motorway and two A-class roads in the UK. The performance of these classifiers is compared to the popular and successfully applied technique of Support vector machines (SVMs). The main findings indicate that RVMs could successfully be employed in real-time classification of traffic conditions. They rely on a fewer number of decision vectors, their training time could be reduced to the level of seconds and their classification rates are similar to those of SVMs. However, RVM algorithms with a larger training dataset consisting of highly disaggregated traffic data, as well as the incorporation of other traffic or network variables so as to better describe traffic dynamics, may lead to higher classification accuracy than the one presented in this paper

    A simulation study of predicting conflict-prone traffic conditions in real-time

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    Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing the traffic conditions just prior to collisions with the traffic conditions during normal operations. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the aggregated traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. This may subsequently lead to an incorrect calibration of the model used to predict the probability of a collision. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data (i.e. vehicle trajectories) from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts (i.e. dangerous concurrences between vehicles) data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety, and data on traffic collisions are therefore not needed. Two classifiers are then employed to examine the proposed idea: (i) Support Vector Machines (SVMs) – a sophisticated classifier and (ii) k-Nearest Neighbors (kNN) – a relatively simple classifier. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of 3-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective conflicts prediction

    Real-time crash prediction of urban highways using machine learning algorithms

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    Doctor of PhilosophyDepartment of Civil EngineeringEric J. FitzsimmonsMotor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools. The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study’s selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring. Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution. Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately. The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well. This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase

    Machine Learning Approaches for Traffic Flow Forecasting

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    Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time
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