Download or read book Use of Neural Network dynamic Algorithms to Predict Bus Travel Times Under Congested Conditions written by I-Jy Steven Chien and published by . This book was released on 2003 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, a dynamic model for predicting bus arrival times is developed using data collected by a real-world Automatic Passenger Counter (APC) system. The model consists of two major elements. The first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-of-week, and weather condition. The second one is a Kalman filter based dynamic algorithm to adjust the arrival time prediction using up-to-the-minute bus location (operational) information. Test runs show that the developed model is quite powerful in dealing with variations in bus arrival times along the service route.

Download or read book Use of Neural Network dynamic Algorithms to Predict Bus Travel Times Under Congested Conditions written by and published by . This book was released on 2003 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Dynamically Predicting Corridor Travel Time Under Incident Conditions Using a Neural Network Approach written by Xiaosi Zeng and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The artificial neural network (ANN) approach has been recognized as a capable technique to model the highly complex and nonlinear problem of travel time prediction. In addition to the nonlinearity, a traffic system is also temporally and spatially dynamic. Addressing the temporal-spatial relationships of a traffic system in the context of neural networks, however, has not received much attention. Furthermore, many of the past studies have not fully explored the inclusion of incident information into the ANN model development, despite that incident might be a major source of prediction degradations. Additionally, directly deriving corridor travel times in a one-step manner raises some intractable problems, such as pairing input-target data, which have not yet been adequately discussed. In this study, the corridor travel time prediction problem has been divided into two stages with the first stage on prediction of the segment travel time and the second stage on corridor travel time aggregation methodologies of the predicted segmental results. To address the dynamic nature of traffic system that are often under the influence of incidents, time delay neural network (TDNN), state-space neural network (SSNN), and an extended state-space neural network (ExtSSNN) that incorporates incident inputs are evaluated for travel time prediction along with a traditional back propagation neural network (BP) and compared with baseline methods based on historical data. In the first stage, the empirical results show that the SSNN and ExtSSNN, which are both trained with Bayesian regulated Levenberg Marquardt algorithm, outperform other models. It is also concluded that the incident information is redundant to the travel time prediction problem with speed and volume data as inputs. In the second stage, the evaluations on the applications of the SSNN model to predict snapshot travel times and experienced travel times are made. The outcomes of these evaluations are satisfactory and the method is found to be practically significant in that it (1) explicitly reconstructs the temporalspatial traffic dynamics in the model, (2) is extendable to arbitrary O-D pairs without complete retraining of the model, and (3) can be used to predict both traveler experiences and system overall conditions.

Download or read book Travel Time Prediction under Egypt Heterogeneous Traffic Conditions using Neural Network and Data Fusion written by Mohamed Zaki and published by Infinite Study. This book was released on with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cairo is experiencing traffic congestion that places it among the worst in the world. Obviously, it is difficult if not impossible to solve the transportation problem because it is multi-dimensional problem but it's good to reduce this waste of money and the associated waste of time resulting from congestion.

Download or read book Travel Time Predictions Under Dynamic Route Guidance with a Recursive Adaptive Algorithm written by Chronis Stamatiadis and published by . This book was released on 1992 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving the Prediction of Bus Arrival Using Real time Network State written by Tom Elliott and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The real-time prediction of bus arrival time has been a central focus of real-time transit information research over the past few decades. Much of this research has shown that the most important predictors of bus arrival time are travel time between and dwell time at bus stops. Despite this, estimated times of arrival available in Auckland, New Zealand, make no account of real-time traffic state information. As road networks are dynamic and congestion can change quickly, we present a generalised prediction procedure that uses buses to estimate traffic conditions, which are in turn used in the prediction of arrival times for all other buses travelling along the same roads, irrespective of the route they are servicing. We construct a road network from data in the General Transit Feed Specification format, allowing us to estimate real-time traffic conditions along physical roads. We use a particle filter to estimate vehicle states and road speeds, and a Kalman filter to update the road network state, together allowing us to predict bus arrival times that account for real-time traffic conditions. We use a simplified, discrete arrival time cumulative density function to make point and interval estimates, as well as estimate the probabilities of events pertinent to journey planning. Throughout, we assess the real-time feasibility of the application and show that our method, despite being computationally complex, can provide arrival time estimates for all active vehicles in 6 - 10 seconds.

Download or read book Travel Time Prediction in the Prescence of Traffic Incidents written by Yang Tao and published by . This book was released on 2005 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predicting Experienced Travel Time for Freeway and Arterial Systems written by Charles D. Mark and published by . This book was released on 2005 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development and Application of Dynamic Models for Predicting Transit Arrival Times written by Yuqing Ding and published by . This book was released on 2000 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic variations in traffic conditions and ridership often have a negative impact in transit operations resulting in the deterioration of schedule/headway adherence and lengthening of passenger wait times. Providing accurate information on transit vehicle arrival times is critical to reduce the negative impacts on transit users. In this study, models for dynamically predicting transit arrival times in urban settings are developed, including a basic model, a Kalman filtering model, link-based and stop-based artificial neural networks (ANNs) and Neural/Dynamic (ND) models. The reliability of these models is assessed by enhancing the microscopic simulation program CORSIM which can calculate bus dwell and passenger wait times based on time-dependent passenger demands and vehicle inter-departure times (headways) at stops. The proposed prediction models are integrated with the enhanced CORSIM individually to predict bus arrival times while simulating the operations of a bus transit route in New Jersey. The reliability analysis of prediction results demonstrates that ANNs are superior to the basic and Kalman filtering models. The stop-based ANN generally predicts more accurately than the link-based ANN. By integrating an ANN (either link-based or stop-based) with the Kalman filtering algorithm, two ND models (NDL and NDS) are developed to decrease prediction error. The results show that the performance of the ND models is fairly close. The NDS model performs better than the NDL model when stop-spacing is relatively long and the number of intersections between a pair of stops is relatively large. In the study, an application of the proposed prediction models to a real-time headway control model is also explored and experimented through simulating a high frequency light rail transit route. The results show that with the accurate prediction of vehicle arrival information from the proposed models, the regularity of headways between any pair of consecutive operating vehicles is improved, while the average passenger wait times at stops are reduced significantly.

Download or read book Service Science Management and Engineering written by Gang Xiong and published by Academic Press. This book was released on 2012-04-17 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Intelligent Systems Series comprises titles that present state of the art knowledge and the latest advances in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. Service Science, Management, and Engineering presents the latest issues and development in service science. Both theory and applications issues are covered in this book, which integrates a variety of disciplines, including engineering, management, and information systems. These topics are each related to service science from various perspectives, and the book is supported throughout by applications and case studies that showcase best practice and provide insight and guidelines to assist in building successful service systems. Presents the latest research on service science, management and engineering, from both theory and applications perspectives Includes coverage of applications in high-growth sectors, along with real-world frameworks and design techniques Applications and case studies showcase best practices and provide insights and guidelines to those building and managing service systems

Download or read book A Kalman Filter based Dynamic Model for Bus Travel Time Prediction written by Abdulaziz Aldokhayel and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Urban areas are currently facing challenges in terms of traffic congestion due to city expansion and population increase. In some cases, physical solutions are limited. For example, in certain areas it is not possible to expand roads or build a new bridge. Therefore, making public transpiration (PT) affordable, more attractive and intelligent could be a potential solution for these challenges. Accuracy in bus running time and bus arrival time is a key component of making PT attractive to ridership. In this thesis, a dynamic model based on Kalman filter (KF) has been developed to predict bus running time and dwell time while taking into account real-time road incidents. The model uses historical data collected by Automatic Vehicle Location system (AVL) and Automatic Passenger Counters (APC) system. To predict the bus travel time, the model has two components of running time prediction (long and short distance prediction) and dwell time prediction. When the bus closes its doors before leaving a bus stop, the model predicts the travel time to all downstream bus stops. This is long distance prediction. The model will then update the prediction between the bus's current position and the upcoming bus stop based on real-time data from AVL. This is short distance prediction. Also, the model predicts the dwell time at each coming bus stop. As a result, the model reduces the difference between the predicted arrival time and the actual arrival time and provides a better understanding for the transit network which allows lead to have a good traffic management.

Download or read book Passenger Counting Systems written by Daniel K. Boyle and published by Transportation Research Board. This book was released on 2008 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report documents the state of analytical tools and technologies for measuring transit ridership via automatic passenger counter systems and other subsidiary data.

Download or read book Graph Theory Heuristic Methods written by N.B. Singh and published by N.B. Singh. This book was released on with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Graph Theory: Heuristic Methods" explores the intersection of graph theory and heuristic algorithms, offering a comprehensive exploration of how these methodologies contribute to solving diverse real-world challenges in network design and optimization. Covering fundamental concepts, advanced applications, and emerging trends, this book serves as a vital resource for researchers, practitioners, and students seeking to leverage heuristic approaches for tackling complex problems across various domains."

Download or read book Recent Advances in Traffic Engineering written by Ashish Dhamaniya and published by Springer Nature. This book was released on 2023-10-28 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises select peer-reviewed proceedings of the National Conference on Recent Advances in Traffic Engineering (RATE 2022). The contents includes in-depth insights into the domain of traffic engineering and planning and presents the latest advancements by focusing on traffic engineering, traffic flow, road safety, advanced techniques for transportation surveys, and data collection. It covers topics including travel demand modeling and transportation planning issues. The contents of this book offer up-to-date and practical knowledge on different aspects of traffic engineering. It will be useful for researchers as well as practitioners.

Download or read book Pervasive Computing A Networking Perspective and Future Directions written by Deepshikha Bhargava and published by Springer. This book was released on 2019-01-29 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an accessible guide to ubiquitous computing, with an emphasis on pervasive networking. It addresses various technical obstacles, such as connectivity, levels of service, performance, reliability and fairness. The focus is on describing currently available off-the-shelf technologies, novel algorithms and techniques in areas such as: underwater sensor networks, ant colony based routing, heterogeneous networks, agent based distributed networks, cognitive radio networks, real-time WSN applications, machine translation, intelligent computing and ontology based bit masking. By introducing the core topics and exploring assistive pervasive systems that draw on pervasive networking, the book provides readers with a robust foundation of knowledge on this growing field of research. Written in a straightforward style, the book is also accessible to a broad audience of researchers and designers who are interested in exploring pervasive computing further.

Download or read book The Prediction of Bus Arrival Time Using Automatic Vehicle Location Systems Data written by Ran Hee Jeong and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.