A Statistics-based Framework for Bus Travel Time Prediction PDF Download

Author: Weiping Si
Publisher:
ISBN:
Category :
Languages : en
Pages : 58

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Author: Ryan Williams
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Surface transit systems face many challenges in their operation, which can result in poor schedule adherence. Passenger information systems that predict arrival times using real-time data from location tracking devices on transit vehicles can reduce the inconvenience caused by schedule deviations. However, real-time location data streams are often noisy, incomplete or inaccurate, requiring special consideration in real-time prediction applications. Additionally, transit may operate in conditions of high or low service reliability, which may require different treatments. This study proposes and tests a real-time prediction framework that uses measures of both service reliability and data reliability/quality to characterize conditions while making predictions sequentially and leveraging a variety of Machine Learning methods. The tests performed show that the proposed framework offers distinct advantages, while additional investigations carried out as part of the study highlight the framework's limitations and potential for future research.

Author: Sonia Leva
Publisher: MDPI
ISBN: 3036510303
Category : Science
Languages : en
Pages : 188

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Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented.

Author: Ran Hee Jeong
Publisher:
ISBN:
Category :
Languages : en
Pages :

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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.

Author: Jeng-Shyang Pan
Publisher: Springer
ISBN: 3319707302
Category : Technology & Engineering
Languages : en
Pages : 408

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This book presents papers from the First International Conference on Smart Vehicular Technology, Transportation, Communication and Applications (VTCA 2017). Held from 6 to 8 November 2017 in Kaohsiung, Taiwan, the conference was co-sponsored by Springer, Fujian University of Technology in China, Fujian Provincial Key Laboratory of Digital Equipment, Fujian Provincial Key Lab of Big Data Mining and Applications, and National Kaohsiung University of Applied Sciences in Taiwan. The book is a valuable resource for researchers and professionals engaged in all areas of smart vehicular technology, vehicular transportation, vehicular communication, and applications.

Author: Fang Chen
Publisher: Springer Nature
ISBN: 3030721884
Category : Computers
Languages : en
Pages : 330

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Book Description
Artificial Intelligence (AI) is changing the world around us, and it is changing the way people are living, working, and entertaining. As a result, demands for understanding how AI functions to achieve and enhance human goals from basic needs to high level well-being (whilst maintaining human health) are increasing. This edited book systematically investigates how AI facilitates enhancing human needs in the digital age, and reports on the state-of-the-art advances in theories, techniques, and applications of humanity driven AI. Consisting of five parts, it covers the fundamentals of AI and humanity, AI for productivity, AI for well-being, AI for sustainability, and human-AI partnership. Humanity Driven AI creates an important opportunity to not only promote AI techniques from a humanity perspective, but also to invent novel AI applications to benefit humanity. It aims to serve as the dedicated source for the theories, methodologies, and applications on humanity driven AI, establishing state-of-the-art research, and providing a ground-breaking book for graduate students, research professionals, and AI practitioners.

Author: Dinesh Goyal
Publisher: Springer Nature
ISBN: 9811920656
Category : Technology & Engineering
Languages : en
Pages : 640

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This book features selected papers presented at Third International Conference on International Conference on Information Management and Machine Intelligence (ICIMMI 2021) held at Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, India during 23 – 24 December 2021. It covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation; applications of artificial intelligence in soft computing and pattern recognition; cloud-based applications for machine learning; application of IoT in power distribution systems; as well as wireless sensor networks and adaptive wireless communication.

Author: Faisal Saeed
Publisher: Springer Nature
ISBN: 3030335828
Category : Technology & Engineering
Languages : en
Pages : 1188

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This book presents the proceedings of the 4th International Conference of Reliable Information and Communication Technology 2019 (IRICT 2019), which was held in Pulai Springs Resort, Johor, Malaysia, on September 22–23, 2019. Featuring 109 papers, the book covers hot topics such as artificial intelligence and soft computing, data science and big data analytics, internet of things (IoT), intelligent communication systems, advances in information security, advances in information systems and software engineering.

Author: Ana Lucia Martins
Publisher: Springer Nature
ISBN: 3031308557
Category : Computers
Languages : en
Pages : 239

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This book constitutes the proceedings of the 6th International Conference on Intelligent Transport Systems, INTSYS 2022, which was held in Lisbon, Portugal, in December 15-16, 2022. With the globalization of trade and transportation and the consequent multi-modal solutions used, additional challenges are faced by organizations and countries. Intelligent Transport Systems make transport safer, more efficient, and more sustainable by applying information and communication technologies to all transportation modes. The 15 revised full papers in this book were selected from 45 submissions and are organized in three thematic sessions on smart city; transportation modes and AI; intelligent transportation and electric vehicles.

Author: Zhisheng Huang
Publisher: Springer Nature
ISBN: 3030620050
Category : Computers
Languages : en
Pages : 585

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This book constitutes the proceedings of the 21st International Conference on Web Information Systems Engineering, WISE 2020, held in Amsterdam, The Netherlands, in October 2020. The 81 full papers presented were carefully reviewed and selected from 190 submissions. The papers are organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph and entity linkage; spatial temporal data analysis; and service computing and cloud computing Part II: information extraction; text mining; security and privacy; recommender system; database system and workflow; and data mining and applications