Download or read book Development of a Model for Predicting Travel Time on an Urban Freeway written by Carroll J. Messer and published by . This book was released on 1974 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Developoment of a model for predicting travel time on an urban freeway written by and published by . This book was released on 1974 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Real Time Prediction of Traffic Speed and Travel Time Characteristics on Freeways written by Reza Noroozisanani and published by . This book was released on 2017 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Travel time is one of the important transportation performance measures, and represents the quality of service for most of the facilities. In other words, one of the essential goals of any traffic treatment is to reduce the average travel time. Therefore, extensive work has been done to measure, estimate, and predict travel time. Using historical observations, traditional traffic analysis methods try to calibrate empirical models to estimate the average travel time of different transportation facilities. However, real-time traffic responsive management strategies require that estimates of travel time also be available in real-time. As a result, real time estimation and prediction of travel time has attracted increasing attention. Various factors influence the travel time of a road segment including: road geometry, traffic demand, traffic control devices, weather conditions, driving behaviors, and incidents. Consequently, the travel time of a road segment varies as a result of the variation of the influencing factors. Predicting near-future freeway traffic conditions is challenging, especially when traffic conditions are in transition from one state to another (e.g. changing from free flow conditions to congestion and vice versa). This research aims to develop a method to perform real-time prediction of near-future freeway traffic stream characteristics (namely speed) and that relies on spot speed, volume, and occupancy measurements commonly available from loop detectors or other similar traffic sensors. The framework of this study consists of a set of individual modules. The first module is called the Base Predictor. This module provides prediction for traffic variables while state of the traffic remains constant i.e free flow or congested. The Congestion Detection Module monitors the traffic state at each detector station of the study route to identify whether traffic conditions are congested or uncongested. When a congestion condition is detected, the Traffic Propagation Module is activated to update the prediction results of the Steady-State Module. The Traffic Propagation Module consists of two separate components: Congestion Spillback activates when traffic enters a congested state; Congestion Dissipation is activated when a congested state enters a recovery phase. The proposed framework of this study is calibrated and evaluated using data from an urban expressway in the City of Toronto, Canada. Data were obtained from the westbound direction of the Gardiner Expressway which has a fixed posted speed limit of 90 km/hr. This expressway is equipped with mainline dual loop detector stations. Traffic volume, occupancy and speed are collected every 20 seconds for each lane at all the stations. The data set used in this study was collected over the period from January 2009 to December 2011. For the Steady-State Module, a model based on Kalman filter was developed to predict the near future traffic conditions (speed, flow, and occupancy) at the location of fixed point detectors (i.e. loop detector in this study). Traffic propagation was proposed to be predicted based on either a static or dynamic traffic pattern. In the static pattern it was assumed that traffic conditions can be attributed based on the observed conditions in the same time of day; however, in the dynamic pattern, expected traffic conditions are estimated based on the current measurements of upstream and downstream detectors. The prediction results were compared to a naïve method, and it was shown that the average prediction error during the “change period” when traffic conditions are changing from free flow to congestion and vice versa is significantly lower when compared to the naïve method for the sample locations (approximately 25% improvement) For the Traffic Propagation Module, a model has been developed to predict the speed of backward forming and forward recovery shockwaves. Unlike classic shockwave theory which is deterministic, the proposed model expresses the spillback and recovery as a stochastic process. The transition probability matrix is defined as a function of traffic occupancy on upstream and downstream stations in a Markov framework. Then, the probability of spillback and recovery was computed given the traffic conditions. An evaluation using field data has shown that the proposed stochastic model performs better than a classical shockwave model in term of correctly predicting the occurrence of backward forming and forward recovery shockwaves on the field data from the urban expressway. A procedure has been proposed to improve the prediction error of a time series model (Steady-State Module) by using the results of the proposed Markov model. It has been shown that the combined procedure significantly reduces the prediction error of the time series model. For the real-time application of the proposed shockwave model, a module (Congestion Detection Module) is required to simultaneously work with the shockwave model, and identify the state of the traffic based on the available measurements. A model based on Support Vector Machine (SVM) was developed to estimate the current traffic state based on the available information from a fixed point detector. A binary model for the traffic state was considered i.e. free follow versus congested conditions. The model was shown to perform better compared to a Naïve model. The classification model was utilized to inform the Traffic Propagation Module. The combined model showed significant improvement in prediction error of traffic speed during the “Change Period” when traffic conditions are changing from free flow to congestion and vice versa. Variability of travel speed in the near future was also investigated in this research. A continuous-time Markov model has been developed to predict the state of the traffic for the near future. Four traffic states were considered to characterize the state of traffic: two free flow states, one transition state, and one congested state. Using the proposed model, we are able to predict the probability of the traffic being in each of the possible states in the near future based on the current traffic conditions. The predicted probabilities then were utilized to characterize the expected distribution of traffic speed. Based on historical observations, the distribution of traffic speed was characterized for each traffic state separately. Given these empirical distributions and the predicted probabilities, distribution of traffic speed was predicted for the near future. The distribution of traffic speed then was used to predict a confidence interval for the near future. The confidence interval can be used to identify the expected range of future speeds at a given confidence level.

Download or read book Reliable Travel Time Prediction for Freeways written by J. W. C. van Lint and published by . This book was released on 2004 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Special Report Highway Research Board written by National Research Council (U.S.). Highway Research Board and published by . This book was released on 1971 with total page 1034 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Urban Travel written by David E. Boyce and published by Edward Elgar Publishing. This book was released on 2015-02-27 with total page 661 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting Urban Travel presents in a non-mathematical way the evolution of methods, models and theories underpinning travel forecasts and policy analysis, from the early urban transportation studies of the 1950s to current applications throughout the

Download or read book The Evolution of Travel Time Information Systems written by Margarita Martínez-Díaz and published by Springer Nature. This book was released on 2022-01-21 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with the estimation of travel time in a very comprehensive and exhaustive way. Travel time information is and will continue to be one key indicator of the quality of service of a road network and a highly valued knowledge for drivers. Moreover, travel times are key inputs for comprehensive traffic management systems. All the above-mentioned aspects are covered in this book. The first chapters expound on the different types of travel time information that traffic management centers work with, their estimation, their utility and their dissemination. They also remark those aspects in which this information should be improved, especially considering future cooperative driving environments.Next, the book introduces and validates two new methodologies designed to improve current travel time information systems, which additionally have a high degree of applicability: since they use data from widely disseminated sources, they could be immediately implemented by many administrations without the need for large investments. Finally, travel times are addressed in the context of dynamic traffic management systems. The evolution of these systems in parallel with technological and communication advancements is thoroughly discussed. Special attention is paid to data analytics and models, including data-driven approaches, aimed at understanding and predicting travel patterns in urban scenarios. Additionally, the role of dynamic origin-to-destination matrices in these schemes is analyzed in detail.

Download or read book Travel Time Prediction Using Machine Learning written by Vignaan Vardhan Nampalli and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid growth of urban populations and increasing vehicular traffic, congestion has become a major challenge for transportation systems worldwide. Accurate estimation of travel time plays a crucial role in mitigating congestion and enhancing traffic management. This research focuses on developing a novel methodology that utilizes machine learning models to estimate travel time using real-time traffic data collected through Bluetooth sensors deployed at traffic intersections. The research compares five different prediction systems for replicating travel time estimation, evaluating their performance and accuracy. The results highlight the effectiveness of the machine learning models in accurately predicting travel time. Lastly, the research explores the creation of a model specifically designed to predict the travel time during peak hours, considering the impact of traffic lights on travel time between intersections. The findings of this study contribute to the development of efficient and reliable travel time prediction systems, enabling commuters to make informed decisions and improving traffic management strategies.

Download or read book Public Roads written by and published by . This book was released on 1974 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Travel Time Prediction for Urban Networks written by Hao Liu and published by . This book was released on 2008 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Short Term Travel Time Prediction on Freeways written by WENFU. WANG and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology's advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models.

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.

Download or read book Subject Catalog written by Library of Congress and published by . This book was released on with total page 932 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Short term Prediction of Freeway Travel Times Using Data from Bluetooth Detectors written by Yaxin Hu and published by . This book was released on 2013 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is increasing recognition among travelers, transportation professionals, and decision makers of the importance of the reliability of transportation facilities. An important step towards improving system reliability is developing methods that can be used in practice to predict freeway travel times for the near future (e.g. 5 -15 minutes). Reliable and accurate predictions of future travel times can be used by travelers to make better decisions and by system operators to engage in pre-active rather than reactive system management. Recent advances in wireless communications and the proliferation of personal devices that communicate wirelessly using the Bluetooth protocol have resulted in the development of a Bluetooth traffic monitoring system. This system is becoming increasingly popular for collecting vehicle travel time data in real-time, mainly because it has the following advantages over other technologies: (1) measuring travel time directly; (2) anonymous detection; (3) weatherproof; and (4) cost-effectiveness. The data collected from Bluetooth detectors are similar to data collected from Automatic Vehicle Identification (AVI) systems using dedicated transponders (e.g. such as electronic toll tags), and therefore using these data for travel time prediction faces some of the same challenges as using AVI measurements, namely: (1) determining the optimal spacing between detectors; (2) dynamic outlier detection and travel time estimation must be able to respond quickly to rapid travel time changes; and (3) a time lag exists between the time when vehicles enter the segment and the time that their travel time can be measured (i.e. when the vehicle exits the monitored segment). In this thesis, a generalized model was proposed to determine the optimal average spacing of Bluetooth detector deployments on urban freeways as a function of the length of the route for which travel times are to be estimated; a traffic flow filtering model was proposed to be applied as an enhancement to existing data-driven outlier detection algorithms as a mechanism to improve outlier detection performance; a short-term prediction model combining outlier filtering algorithm with Kalman filter was proposed for predicting near future freeway travel times using Bluetooth data with special attention to the time lag problem. The results of this thesis indicate that the optimal detector spacing ranges from 2km for routes of 4km in length to 5km for routes of 20km in length; the proposed filtering model is able to solve the problem of tracking sudden changes in travel times and enhance the performance of the data-driven outlier detection algorithms; the proposed short-term prediction model significantly improves the accuracy of travel time prediction for 5, 10 and 15 minutes prediction horizon under both free flow and non-free flow traffic states. The mean absolute relative errors (MARE) are improved by 8.8% to 30.6% under free flow traffic conditions, and 7.5% to 49.9% under non-free flow traffic conditions. The 90th percentile errors and standard deviation of the prediction errors are also improved.

Download or read book Feasibility of Using In Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion written by Hesham Rakha and published by Transportation Research Board. This book was released on 2011 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-L10-RR-1: Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion presents findings on the feasibility of using existing in-vehicle data sets, collected in naturalistic driving settings, to make inferences about the relationship between observed driver behavior and nonrecurring congestion.

Download or read book Explainable Artificial Intelligence for Intelligent Transportation Systems written by Amina Adadi and published by CRC Press. This book was released on 2023-10-20 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. FEATURES: Provides the necessary background for newcomers to the field (both academics and interested practitioners) Presents a timely snapshot of explainable and interpretable models in ITS applications Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS Identifies future research directions and open problems

Download or read book Handbook of Research on Pattern Engineering System Development for Big Data Analytics written by Tiwari, Vivek and published by IGI Global. This book was released on 2018-04-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. The Handbook of Research on Pattern Engineering System Development for Big Data Analytics is a critical scholarly resource that examines the incorporation of pattern management in business technologies as well as decision making and prediction process through the use of data management and analysis. Featuring coverage on a broad range of topics such as business intelligence, feature extraction, and data collection, this publication is geared towards professionals, academicians, practitioners, and researchers seeking current research on the development of pattern management systems for business applications.