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 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 Performing Short Term Travel Time Prediction on Arterials written by Soroush Salek Moghaddam and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As urban centers become larger and more densely developed, their roadway networks tend to experience more severe congestion for longer periods of the day and increasingly unreliable travel times. Proactive traffic management (PTM) strategies such as proactive traffic signal control systems and advanced traveler information systems provide the potential to cost effectively improve road network operations. However, these proactive management strategies require an ability to accurately predict near-future traffic conditions. Traffic conditions can be described using a variety of measures of performance and travel time is one of the most valued by both travelers and transportation system managers. Consequently, there exists a large body of literature dedicated to methods for performing travel time prediction. The majority of the existing body of research on travel time prediction has focused on freeway travel time prediction using fixed point sensor data. Predicting travel times on signalized arterials is more challenging than on freeways mainly as a result of the higher variation of travel times in these environments. For both freeways and arterial environments, making predictions in real-time is more challenging than performing off-line predictions, mainly because of data availability issues that arise for real-time applications. Recently, Bluetooth detectors have been utilized for collecting both spatial (i.e. travel time) and fixed point (e.g. number of detections) data. Bluetooth detectors have surpassed most of the conventional travel time measuring techniques in three main capacities: (i) direct measurement of travel time, (ii) continuous collection of travel times provides large samples, and (iii) anonymous detection. Beside these advantages, there are also caveats when using these detectors: (i) the Bluetooth obtained data include different sources of outliers and measurement errors that should be filtered out before the data are used in any travel time analysis and (ii) there is an inherent time lag in acquiring Bluetooth travel times (due to the matching of the detections at the upstream and downstream sensors) that should be carefully handled in real-time applications. In this thesis, (1) the magnitude of Bluetooth travel time measurement error has been examined through a simulation framework; (2) a real-time proactive outlier detection algorithm, which is suitable for filtering out data anomalies in Bluetooth obtained travel times, has been proposed; (3) the performance of the existing real-time outlier detection algorithms has been evaluated using both field data and simulation data; and (4) two different data-driven methodologies, that are appropriate for real-time applications, have been developed to predict near future travel times on arterials using data obtained from Bluetooth detectors. The results of this research demonstrate that (1) although the mean Bluetooth travel time measurement error is sufficiently close to zero across all the examined traffic conditions, for some situations the 95% confidence interval of the mentioned error approaches 35% of the true mean travel time; (2) the proposed proactive filtering algorithm appropriately detects the Bluetooth travel time outliers in real time and outperforms the existing data-driven filtering techniques; (3) the performance of different outlier detection algorithms can be objectively quantified under different conditions using the developed simulation framework; (4) the proposed prediction approaches significantly improved the accuracy of travel time predictions for 5-minutre prediction horizon. The daily mean absolute relative errors are improved by 18% to 24% for the proposed k-NN model and 8% to 14% for the proposed Markov model; (5) prevailing arterial traffic state and its transition through the course of the day can be adequately modeled using data obtained from Bluetooth technology.

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 Highway Travel Time Estimation With Data Fusion written by Francesc Soriguera Martí and published by Springer. This book was released on 2015-11-30 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a simple, innovative approach for the measurement and short-term prediction of highway travel times based on the fusion of inductive loop detector and toll ticket data. The methodology is generic and not technologically captive, allowing it to be easily generalized for other equivalent types of data. The book shows how Bayesian analysis can be used to obtain fused estimates that are more reliable than the original inputs, overcoming some of the drawbacks of travel-time estimations based on unique data sources. The developed methodology adds value and obtains the maximum (in terms of travel time estimation) from the available data, without recurrent and costly requirements for additional data. The application of the algorithms to empirical testing in the AP-7 toll highway in Barcelona proves that it is possible to develop an accurate real-time, travel-time information system on closed-toll highways with the existing surveillance equipment, suggesting that highway operators might provide their customers with such an added value with little additional investment in technology.

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 Short Term Travel Time Prediction on Freeways in Conjunction with Detector Coverage Analysis written by and published by . This book was released on 2007 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Freeway Travel Time Prediction Using Data from Mobile Probes written by Pedram Izadpanah and published by . This book was released on 2010 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections.

Download or read book International Encyclopedia of Transportation written by and published by Elsevier. This book was released on 2021-05-13 with total page 4418 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an increasingly globalised world, despite reductions in costs and time, transportation has become even more important as a facilitator of economic and human interaction; this is reflected in technical advances in transportation systems, increasing interest in how transportation interacts with society and the need to provide novel approaches to understanding its impacts. This has become particularly acute with the impact that Covid-19 has had on transportation across the world, at local, national and international levels. Encyclopedia of Transportation, Seven Volume Set - containing almost 600 articles - brings a cross-cutting and integrated approach to all aspects of transportation from a variety of interdisciplinary fields including engineering, operations research, economics, geography and sociology in order to understand the changes taking place. Emphasising the interaction between these different aspects of research, it offers new solutions to modern-day problems related to transportation. Each of its nine sections is based around familiar themes, but brings together the views of experts from different disciplinary perspectives. Each section is edited by a subject expert who has commissioned articles from a range of authors representing different disciplines, different parts of the world and different social perspectives. The nine sections are structured around the following themes: Transport Modes; Freight Transport and Logistics; Transport Safety and Security; Transport Economics; Traffic Management; Transport Modelling and Data Management; Transport Policy and Planning; Transport Psychology; Sustainability and Health Issues in Transportation. Some articles provide a technical introduction to a topic whilst others provide a bridge between topics or a more future-oriented view of new research areas or challenges. The end result is a reference work that offers researchers and practitioners new approaches, new ways of thinking and novel solutions to problems. All-encompassing and expertly authored, this outstanding reference work will be essential reading for all students and researchers interested in transportation and its global impact in what is a very uncertain world. Provides a forward looking and integrated approach to transportation Updated with future technological impacts, such as self-driving vehicles, cyber-physical systems and big data analytics Includes comprehensive coverage Presents a worldwide approach, including sets of comparative studies and applications

Download or read book Data Needs for Short Term Link Travel Time Prediction written by and published by . This book was released on 1992 with total page 22 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 Dynamic Freeway Travel Time Prediction Using Single Loop Detector and Incident Data written by Jingxin Xia and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook on Artificial Intelligence and Transport written by Hussein Dia and published by Edward Elgar Publishing. This book was released on 2023-10-06 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.

Download or read book Estimation and Prediction of Travel Time from Loop Detector Data for Intelligent Transportation Systems Applications written by Lelitha Devi Vanajakshi and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is on of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and predictions of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of the stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector dats is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.

Download or read book Advances in Multi Sensor Information Fusion Theory and Applications 2017 written by Xue-Bo Jin and published by MDPI. This book was released on 2018-06-26 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Advances in Multi-Sensor Information Fusion: Theory and Applications 2017" that was published in Sensors

Download or read book Freeway Travel Time Estimation Using Limited Loop Data written by Silin Ding and published by . This book was released on 2008 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing drivers with real-time, good-quality traveling information is becoming increasingly important as congestion increases in cities across the United States. Studies have shown as congestion increases, travel time reliability decreases. Travelers would like to have information about certain traffic conditions as particularly detours causing time delays, delays because of road constructions, and delays due to accidents. Since congestion is treated as a major factor influencing travel decisions, some metropolitan areas are providing travel time information to motorists through dynamic message signs (DMS), 511 programs, the Internet, highway advisory radio, and other sources. Traffic conditions are affected by current events and established travel patterns. Today, travel time data can be gathered from microwave radar, automatic vehicle tag matching, video detection, license plate matching, and most commonly, inductive loops. Loop detectors are placed in individual lanes to provide volume, occupancy and local speed information. Although closely spaced loop detectors are helpful to system operation, they are expensive to install and to maintain. With the proliferation of cell phone usage, loop detector data is no longer critical to incident detection. The effectiveness of using loop detector data to reliably estimate travel time has yet to be proved. In recent years, researchers discussed the pros and cons of detector spacing. This discussion is necessary and timely because of the widespread use of the loop detection system today. The focal point of the discussion is to determine the appropriate detector spacing needed for various applications while maintaining the same level of data quality for all users. This thesis studied different freeway travel time estimation methods and explored the impact of loop detector spacing on travel time estimation. The analysis was performed on a sixteen-mile stretch of I-75 in Cincinnati, Ohio and used both simulation and field tests to evaluate the results. First, the commonly used midpoint method for travel time estimation was examined under various traffic and roadway conditions. Starting with the existing 1/3 mile spacing, spacing was increased by using fewer detectors to obtain data for analysis. Then, enhancements were introduced over the midpoint method using different data processing methods reported by other researchers to improve its performance. Preliminary results showed that by using the midpoint method, different detector spacings result in different levels of accuracy and generally the estimation error increases with the detector spacing. Moreover, with increasing traffic congestion, the travel time errors from the existing methods increased significantly. After a congestion based error correction term is introduced, the improved midpoint method is able to make more accurate travel time estimates at larger spacings under work zone and incident conditions. The work was also tested against field data collected through probe vehicles. Based on field data, the estimated travel times from the improved method matches closely with those measured by the floating cars; the differences between the travel time are within 10%. Results from this study showed that a larger detector spacing than the commonly used 1/3 mile does not worsen the estimation results. Overall, the one-mile spacing scheme has outperformed the other tested alternatives in the testbed area. This thesis also studied the reliability of the probe vehicle technique. License Plate Matching Survey was conducted to carry out the analysis. The results showed that the accuracy of probe vehicle travel time is affected by the standard deviation of travel time and different analysis periods. Minimum sample size was examined as the last part of the thesis.

Download or read book Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition SoCPaR 2021 written by Ajith Abraham and published by Springer Nature. This book was released on 2022-02-21 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the recent research on soft computing, pattern recognition, nature-inspired computing and their various practical applications. It presents 53 selected papers from the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) and 11 papers from the 13th World Congress on Nature and Biologically Inspired Computing (NaBIC 2021), which was held online, from December 15 to 17, 2021. A premier conference in the field of soft computing, artificial intelligence and machine learning applications, SoCPaR-NaBIC 2021 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from over 20 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of computer science and engineering.