Real Time Crash Prediction Of Urban Highways Using Machine Learning Algorithms

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Real-time Crash Prediction of Urban Highways Using Machine Learning Algorithms

Author : Mirza Ahammad Sharif
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)

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Book Synopsis Real-time Crash Prediction of Urban Highways Using Machine Learning Algorithms by : Mirza Ahammad Sharif

Download or read book Real-time Crash Prediction of Urban Highways Using Machine Learning Algorithms written by Mirza Ahammad Sharif and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motor 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.

A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials

Author : Pei Li
Publisher :
ISBN 13 :
Total Pages : 34 pages
Book Rating : 4.:/5 (116 download)

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Book Synopsis A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials by : Pei Li

Download or read book A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials written by Pei Li and published by . This book was released on 2020 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. Four urban arterials in Orlando, FL are selected to conduct a case study. Different types of data are utilized to predict the crash risk, including traffic data, signal timing data, and weather data. Various data preparation techniques are applied also. In addition, the synthetic minority over-sampling technique (SMOTE) is used for oversampling the crash cases to address the data imbalance issue. The LSTM-CNN is fine-tuned on the training data and validated on the test data via different metrics. In the end, five other benchmarks models are also developed for model comparison, including Bayesian Logistics Regression, XGBoost, LSTM, CNN, and Sequential LSTM-CNN. Experimental results suggest that the proposed LSTM-CNN outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this thesis indicate the promising performance of using LSTM-CNN to predict real-time crash risk at arterials.

Developing a Real-time Freeway Incident Detection Model Using Machine Learning Techniques

Author : Moggan Motamed
Publisher :
ISBN 13 :
Total Pages : 280 pages
Book Rating : 4.:/5 (957 download)

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Book Synopsis Developing a Real-time Freeway Incident Detection Model Using Machine Learning Techniques by : Moggan Motamed

Download or read book Developing a Real-time Freeway Incident Detection Model Using Machine Learning Techniques written by Moggan Motamed and published by . This book was released on 2016 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time incident detection on freeways plays an important part in any modern traffic management operation by maximizing road system performance. The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. An effective incident detection and management operation cannot prevent incidents, however, it can diminish the impacts of non-recurring congestion problems. The main purpose of real-time incident detection is to reduce delay and the number of secondary accidents, and to improve safety and travel information during unusual traffic conditions. The majority of automatic incident detection algorithms are focused on identifying traffic incident patterns but do not adequately investigate possible similarities in patterns observed under incident-free conditions. When traffic demand exceeds road capacity, density exceeds critical values and traffic speed decreases, the traffic flow process enters a highly unstable regime, often referred to as “stop-and-go” conditions. The most challenging part of real-time incident detection is the recognition of traffic pattern changes when incidents happen during stop-and-go conditions. Recently, short-term freeway congestion detection algorithms have been proposed as solutions to real-time incident detection, using procedures known as dynamic time warping (DTW) and the support vector machine (SVM). Some studies have shown these procedures to produce higher detection rates than Artificial Intelligence (AI) algorithms with lower false alarm rates. These proposed methods combine data mining and time series classification techniques. Such methods comprise interdisciplinary efforts, with the confluence of a set of disciplines, including statistics, machine learning, Artificial Intelligence, and information science. A literature review of the methodology and application of these two models will be presented in the following chapters. SVM, Naïve Bayes (NB), and Random Forest classifier models incorporating temporal data and an ensemble technique, when compared with the original SVM model, achieve improved detection rates by optimizing the parameter thresholds. The main purpose of this dissertation is to examine the most robust algorithms (DTW, SVM, Naïve Bayes, Decision Tree, SVM Ensemble) and to develop a generalized automatic incident detection algorithm characterized by high detection rates and low false alarm rates during peak hours. In this dissertation, the transferability of the developed incident detection model was tested using the Dallas and Miami field datasets. Even though the primary service of urban traffic control centers includes detecting incidents and facilitating incident clearance, estimating freeway incident durations remains a significant incident management challenge for traffic operations centers. As a next step this study examines the effect of V/C (volume/capacity) ratio, level of service (LOS), weather condition, detection mode, number of involved lanes, and incident type on the time duration of traffic incidents. Results of this effort can benefit traffic control centers improving the accuracy of estimated incident duration, thereby improving the authenticity of traveler guidance information.

Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

Author : Kevin Daimi
Publisher : Springer Nature
ISBN 13 : 3031353080
Total Pages : 460 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23) by : Kevin Daimi

Download or read book Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23) written by Kevin Daimi and published by Springer Nature. This book was released on 2023-06-16 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Second International Conference on Innovations in Computing Research (ICR’23) brings together a diverse group of researchers from all over the world with the intent of fostering collaboration and dissemination of the innovations in computing technologies. The conference is aptly segmented into six tracks: Data Science, Computer and Network Security, Health Informatics and Medical Imaging, Computer Science and Computer Engineering Education, Internet of Things, and Smart Cities/Smart Energy. These tracks aim to promote a birds-of-the-same-feather congregation and maximize participation. The Data Science track covers a wide range of topics including complexity score for missing data, deep learning and fake news, cyberbullying and hate speech, surface area estimation, analysis of gambling data, car accidents predication model, augmenting character designers’ creativity, deep learning for road safety, effect of sleep disturbances on the quality of sleep, deep learning-based path-planning, vehicle data collection and analysis, predicting future stocks prices, and trading robot for foreign exchange. Computer and Network Security track is dedicated to various areas of cybersecurity. Among these are decentralized solution for secure management of IoT access rights, multi-factor authentication as a service (MFAaaS) for federated cloud environments, user attitude toward personal data privacy and data privacy economy, host IP obfuscation and performance analysis, and vehicle OBD-II port countermeasures. The Computer Science and Engineering Education track enfolds various educational areas, such as data management in industry–academia joint research: a perspective of conflicts and coordination in Japan, security culture and security education, training and awareness (SETA), influencing information security management, engaging undergraduate students in developing graphical user interfaces for NSF funded research project, and emotional intelligence of computer science teachers in higher education. On the Internet of Things (IoT) track, the focus is on industrial air quality sensor visual analytics, social spider optimization meta-heuristic for node localization optimization in wireless sensor networks, and privacy aware IoT-based fall detection with infrared sensors and deep learning. The Smart Cities and Smart Energy track spans various areas, which include, among others, research topics on heterogeneous transfer learning in structural health monitoring for high-rise structures and energy routing in energy Internet using the firefly algorithm.

Applied Mathematics, Modeling and Computer Simulation

Author : C.-H. Chen
Publisher : IOS Press
ISBN 13 : 1643682555
Total Pages : 1154 pages
Book Rating : 4.6/5 (436 download)

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Book Synopsis Applied Mathematics, Modeling and Computer Simulation by : C.-H. Chen

Download or read book Applied Mathematics, Modeling and Computer Simulation written by C.-H. Chen and published by IOS Press. This book was released on 2022-02-25 with total page 1154 pages. Available in PDF, EPUB and Kindle. Book excerpt: The pervasiveness of computers in every field of science, industry and everyday life has meant that applied mathematics, particularly in relation to modeling and simulation, has become ever more important in recent years. This book presents the proceedings of the 2021 International Conference on Applied Mathematics, Modeling and Computer Simulation (AMMCS 2021), hosted in Wuhan, China, and held as a virtual event from 13 to 14 November 2021. The aim of the conference is to foster the knowledge and understanding of recent advances across the broad fields of applied mathematics, modeling and computer simulation, and it provides an annual platform for scholars and researchers to communicate important recent developments in their areas of specialization to colleagues and other scientists in related disciplines. This year more than 150 participants were able to exchange knowledge and discuss recent developments via the conference. The book contains 115 peer-reviewed papers, selected from more than 250 submissions and ranging from the theoretical and conceptual to the strongly pragmatic and all addressing industrial best practice. Topics covered include mathematical modeling and applications, engineering applications and scientific computations, and the simulation of intelligent systems. Providing an overview of recent development and with a mix of practical experiences and enlightening ideas, the book will be of interest to researchers and practitioners everywhere.

Machine Learning Algorithms for Forecasting the Impacts of Connected and Automated Vehicles on Highway Construction Costs

Author : Amirsaman Mahdavian
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Machine Learning Algorithms for Forecasting the Impacts of Connected and Automated Vehicles on Highway Construction Costs by : Amirsaman Mahdavian

Download or read book Machine Learning Algorithms for Forecasting the Impacts of Connected and Automated Vehicles on Highway Construction Costs written by Amirsaman Mahdavian and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A multitude of externalities affects transport efficiency and numbers of trips. Population expansion, urban development, political issues, fiscal trends, and growth in the field of connected, automated, shared, and electric (CASE) vehicles have all played prominent roles. While the market is keenly aware of the upcoming shift to the CASE vehicles, the transformation itself is reliant upon the development of technologies, customer outlook, and guidelines. The purpose of this research is to establish an overview of the possible network design problems, as well as potential consequences to vehicle automation systems by employing machine learning and system dynamics analysis. Finally, the cost of the required highway expansion for the critical links in the traffic network will be predicted. First, model was created for calculating traffic flow activity and necessitated highways to consider the impact of CASE vehicles between 2021 and 2050. Second, an economic evaluation outline was created to calculate optimum time and roadway improvement scenarios by a cost-prediction model using machine learning. Florida's interstate highways were employed as the subjects for the case study. The research showed that non-linear models had a better ability in the estimation of traffic flow, while linear models were better predictors of highway construction cost. These results also showed new technologies would add to traffic flow and capacity, with the increase in flow outpacing the increase in capacity. The consequences of this would be the level of service (LOS) of the current infrastructure decreasing. This study's results can assist discussion at the national and local level between government, networkers, automotive companies, tech-providers, logistics companies, and stakeholders for whom the practicality provided by the transportation infrastructure is crucial. This allows executives to create effective guidelines for subsequent transportation networks, ultimately accelerating the CASE vehicle network rollout to increase our current road network's level of service.

Proceedings of the 5th International Conference on Big Data and Internet of Things

Author : Mohamed Lazaar
Publisher : Springer Nature
ISBN 13 : 3031079698
Total Pages : 600 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Proceedings of the 5th International Conference on Big Data and Internet of Things by : Mohamed Lazaar

Download or read book Proceedings of the 5th International Conference on Big Data and Internet of Things written by Mohamed Lazaar and published by Springer Nature. This book was released on 2022-07-03 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers in the research area of big data, cloud computing, cybersecurity, machine learning, deep learning, e-learning, Internet of Things, reinforcement learning, information system, social media and natural language processing. This book includes papers presented at the 5th International Conference on Big Data Cloud and Internet of Things, BDIoT 2021 during March 17–18, 2021, at ENSIAS, Mohammed V University in Rabat, Morocco.

Artificial Intelligence in Highway Safety

Author : Subasish Das
Publisher : CRC Press
ISBN 13 : 1000726266
Total Pages : 354 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Artificial Intelligence in Highway Safety by : Subasish Das

Download or read book Artificial Intelligence in Highway Safety written by Subasish Das and published by CRC Press. This book was released on 2022-09-29 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.

Real-time Freeway Crash Prediction Using Conditional Logistic Regression Models

Author : 呂悦慈
Publisher :
ISBN 13 :
Total Pages : 43 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Real-time Freeway Crash Prediction Using Conditional Logistic Regression Models by : 呂悦慈

Download or read book Real-time Freeway Crash Prediction Using Conditional Logistic Regression Models written by 呂悦慈 and published by . This book was released on 2018 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Recent Advances in Transportation Systems Engineering and Management

Author : M. V. L. R. Anjaneyulu
Publisher : Springer Nature
ISBN 13 : 981192273X
Total Pages : 903 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Recent Advances in Transportation Systems Engineering and Management by : M. V. L. R. Anjaneyulu

Download or read book Recent Advances in Transportation Systems Engineering and Management written by M. V. L. R. Anjaneyulu and published by Springer Nature. This book was released on 2022-11-10 with total page 903 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the select proceedings of the 8th International Conference on Transportation Systems Engineering and Management (CTSEM 2021). The book covers topics pertaining to three broad areas of transportation engineering, namely Transportation Planning, Traffic Engineering and Pavement Technology. The topics covered include transportation and land use, urban and regional transportation planning, travel behavior modeling, travel demand analysis, forecasting and management, transportation and ICT, public transport planning and management, freight transport, traffic flow modeling and management, highway design and maintenance, capacity and level of service, traffic crashes and safety, ITS and applications, non-motorized transportation, transportation economics and policy, road and parking pricing, pedestrian facilities and safety, road asset management, pavement materials and characterization, pavement design and construction, pavement evaluation and management, transportation infrastructure financing, innovative trends in transportation systems, sustainable transportation, smart cities, resilience of transportation systems and environmental and ecological aspects. This book will be useful for the students, researchers and the professionals in the area of civil engineering, especially transportation and traffic engineering.

Modeling of Transport Demand

Author : V.A Profillidis
Publisher : Elsevier
ISBN 13 : 0128115149
Total Pages : 500 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Modeling of Transport Demand by : V.A Profillidis

Download or read book Modeling of Transport Demand written by V.A Profillidis and published by Elsevier. This book was released on 2018-10-23 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand Provides a theoretical analysis and formulations that are clearly presented for ease of understanding Covers analysis for all modes of transportation Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results

Freeway Crash Prediction Models for Long-range Urban Transportation Planning

Author : Vasin Kiattikomol
Publisher :
ISBN 13 :
Total Pages : 125 pages
Book Rating : 4.:/5 (625 download)

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Book Synopsis Freeway Crash Prediction Models for Long-range Urban Transportation Planning by : Vasin Kiattikomol

Download or read book Freeway Crash Prediction Models for Long-range Urban Transportation Planning written by Vasin Kiattikomol and published by . This book was released on 2005 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advanced Machine Learning Models for Online Travel-time Prediction on Freeways

Author : Adeel Yusuf
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (88 download)

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Book Synopsis Advanced Machine Learning Models for Online Travel-time Prediction on Freeways by : Adeel Yusuf

Download or read book Advanced Machine Learning Models for Online Travel-time Prediction on Freeways written by Adeel Yusuf and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of the research described in this dissertation is to improve the travel-time prediction process using machine learning methods for the Advanced Traffic In-formation Systems (ATIS). Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. The increased demand of the traffic flow has motivated the need for development of improved applications and frameworks, which could alleviate the problems arising due to traffic flow, without the need of addition to the roadway infrastructure. In this thesis, the basic building blocks of the travel-time prediction models are discussed, with a review of the significant prior art. The problem of travel-time prediction was addressed by different perspectives in the past. Mainly the data-driven approach and the traffic flow modeling approach are the two main paths adopted viz. a viz. travel-time prediction from the methodology perspective. This dissertation, works towards the im-provement of the data-driven method. The data-driven model, presented in this dissertation, for the travel-time predic-tion on freeways was based on wavelet packet decomposition and support vector regres-sion (WPSVR), which uses the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indi-cate that the wavelet reconstructed coefficients when used as an input to the support vec-tor machine for regression (WPSVR) give better performance (with selected wavelets on-ly), when compared against the support vector regression (without wavelet decomposi-tion). The data used in the model is downloaded from California Department of Trans-portation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5 minute intervals over a distance of 9.13 miles. The results indicate an improvement in accuracy when compared against the classical SVR method. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is present-ed with interchangeable prediction methods along with the details of the Matlab applica-tion used to implement the WPSVR algorithm. The initial results are computed over the set of 42 wavelets. To reduce the compu-tational cost involved in transforming the travel-time data into the set of wavelet packets using all possible mother wavelets available, a methodology of filtering the wavelets is devised, which measures the cross-correlation and redundancy properties of consecutive wavelet transformed values of same frequency band. An alternate configuration of travel-time prediction on freeways using the con-cepts of cloud computation is also presented, which has the ability to interchange the pre-diction modules with an alternate method using the same time-series data. Finally, a graphical user interface is described to connect the Matlab environment with the Caltrans data server for online travel-time prediction using both SVR and WPSVR modules and display the errors and plots of predicted values for both methods. The GUI also has the ability to compute forecast of custom travel-time data in the offline mode.

Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information

Author : Nancy Dutta
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information by : Nancy Dutta

Download or read book Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information written by Nancy Dutta and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crash analysis methods typically use annual average daily traffic as an exposure measure, which can be too aggregate to capture the safety effects of variations in traffic flow and operations that occur throughout the day. Flow characteristics such as variation in speed and level of congestion play a significant role in crash occurrence and are not currently accounted for in the American Association of State Highway and Transportation Officials' Highway Safety Manual. This study developed a methodology for creating crash prediction models using traffic, geometric, and control information that is provided at sub-daily aggregation intervals. Data from 110 rural four-lane segments and 80 urban six-lane segments were used. The volume data used in this study came from detectors that collect data ranging from continuous counts throughout the year to counts from only a couple of weeks every other year (short counts). Speed data were collected from both point sensors and probe data provided by INRIX. The results showed that models that used data aggregated to an average hourly level reflected the variation in volume and speed throughout the day without compromising model quality. Crash predictions for urban segments underwent a 20% improvement in mean absolute deviation for total crashes and a 9% improvement for injury crashes when models using average hourly volume, geometry, and flow variables were compared to the model based on annual average daily traffic. Corresponding improvements over annual average daily traffic models for rural segments were 11% and 9%. Average hourly speed, standard deviation of hourly speed, and differences between speed limit and average speed had statistically significant relationships with crash frequency. For all models, prediction accuracy was improved across all validation measures of effectiveness when the speed components were added. The positive effect of flow variables was true irrespective of the speed data source. Further investigation revealed that the improvement achieved in model prediction by using a more inclusive and bigger dataset was larger than the effect of accounting for spatial/temporal data correlation. For rural hourly models, mean absolute deviation improved by 52% when short counts were added in comparison to the continuous count station only models. The respective value for urban segments was 58%. This means that using short count stations as a data source does not diminish the quality of the developed models. Thus, a combination of different volume data sources with good quality speed data can lessen the dependency on volume data quality without compromising performance. Although accounting for spatial and temporal correlation improved model performance, it provided smaller benefits than inclusion of the short count data in the models. This study showed that it is possible to develop a broadly transferable crash prediction methodology using hourly level volume and flow data that are currently widely available to transportation agencies. These models have a broad spectrum of potential applications that involve assessing safety effects of events and countermeasures that create recurring and non-recurring short-term fluctuations in traffic characteristics.

Smart Infrastructure and Applications

Author : Rashid Mehmood
Publisher : Springer
ISBN 13 : 3030137058
Total Pages : 665 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Smart Infrastructure and Applications by : Rashid Mehmood

Download or read book Smart Infrastructure and Applications written by Rashid Mehmood and published by Springer. This book was released on 2019-06-20 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a multidisciplinary view of smart infrastructure through a range of diverse introductory and advanced topics. The book features an array of subjects that include: smart cities and infrastructure, e-healthcare, emergency and disaster management, Internet of Vehicles, supply chain management, eGovernance, and high performance computing. The book is divided into five parts: Smart Transportation, Smart Healthcare, Miscellaneous Applications, Big Data and High Performance Computing, and Internet of Things (IoT). Contributions are from academics, researchers, and industry professionals around the world. Features a broad mix of topics related to smart infrastructure and smart applications, particularly high performance computing, big data, and artificial intelligence; Includes a strong emphasis on methodological aspects of infrastructure, technology and application development; Presents a substantial overview of research and development on key economic sectors including healthcare and transportation.

Freeway Crash Predictions Based on Real-time Pattern Changes in Traffic Flow Characteristics

Author : Lili Luo
Publisher :
ISBN 13 :
Total Pages : 99 pages
Book Rating : 4.:/5 (665 download)

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Book Synopsis Freeway Crash Predictions Based on Real-time Pattern Changes in Traffic Flow Characteristics by : Lili Luo

Download or read book Freeway Crash Predictions Based on Real-time Pattern Changes in Traffic Flow Characteristics written by Lili Luo and published by . This book was released on 2006 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Crash Risk Analysis of Two Way Stop Control Intersections on Rural Two-lane Highways Using Machine Learning Classification Algorithms

Download Crash Risk Analysis of Two Way Stop Control Intersections on Rural Two-lane Highways Using Machine Learning Classification Algorithms PDF Online Free

Author : Mousumy Akter
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Crash Risk Analysis of Two Way Stop Control Intersections on Rural Two-lane Highways Using Machine Learning Classification Algorithms by : Mousumy Akter

Download or read book Crash Risk Analysis of Two Way Stop Control Intersections on Rural Two-lane Highways Using Machine Learning Classification Algorithms written by Mousumy Akter and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: