Article
Engineering, Industrial
Abdullah Alkaff
Summary: This study introduces modeling techniques for dynamic reliability analysis of systems with components' lifetimes following independent and nonidentical DPH distributions. By modeling system lifetime as DPH distribution, the analysis is simplified for systems with multistate components, as demonstrated by results from complex structure systems.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Industrial
Cheng-Ta Yeh, Yi-Kuei Lin, Louis Cheng-Lu Yeng, Pei-Tzu Huang
Summary: Railway trains are the preferred option for travelers, and the capacity of seats or cabins in the railway transportation system should consider stochasticity. System reliability is an important decision indicator for travel agents and can be evaluated using a minimal paths algorithm.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Review
Transportation Science & Technology
Zhaoqi Zang, Xiangdong Xu, Kai Qu, Ruiya Chen, Anthony Chen
Summary: This paper introduces the importance of modeling travel time reliability (TTR) in transportation networks and provides an integrated framework for summarizing the methodological developments and applications of TTR. By adopting a network perspective, a better understanding of TTR characterization, evaluation and valuation, and traffic assignment can be achieved. The paper also discusses some common challenges in TTR modeling and potential directions for future research.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Multidisciplinary Sciences
Niaz Mahmud Zafri, Sadia Afroj, Mohammad Ashraf Ali, Md. Musleh Uddin Hasan, Md. Hamidur Rahman
Summary: The study in Dhaka, Bangladesh aimed to determine the effectiveness of containment strategies and local cognition in controlling traffic volume during the COVID-19 pandemic. It found that declaring a general holiday and closing educational institutions significantly increased the average daily journey speed, indicating reduced traffic movement. Local cognition did not have a significant impact on traffic condition, while the month of Ramadan was found to increase the average daily journey speed significantly.
Article
Computer Science, Interdisciplinary Applications
Spela Verovsek, Matevz Juvancic, Simon Petrovcic, Tadeja Zupancic, Matija Svetina, Miha Janez, Nina Velikajne, Miha Moskon
Summary: This research introduces a data collection and analysis framework to evaluate the network performance in modern cities for sustainable development. The framework selects and evaluates indicators based on publicly available traffic data and uses regression models to analyze the rhythmic behavior of travel time trends. The results show that travel times and their variations are influenced by factors such as route, day of the week, and weather conditions. The research also presents reliability metrics for integration with neighborhood sustainability assessment (NSA) standards, making it a valuable tool for monitoring and comparing travel time trends.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2022)
Article
Urban Studies
Xavier Delclos-Alio, Daniel A. Rodriguez, Nancy Lopez Olmedo, Carolina Perez Ferrer, Kari Moore, Dalia Stern, Mariana Carvalho de Menezes, Leticia de Oliveira Cardoso, Xize Wang, Joanna M. N. Guimaraes, J. Jaime Miranda, Olga L. Sarmiento
Summary: There is growing evidence that longer travel time by private car poses physical and mental risks. However, in Latin American cities, the relationship between peak hour travel time by car and obesity and diabetes is more complex than in other settings.
Article
Computer Science, Artificial Intelligence
Qin Li, Huachun Tan, Zhuxi Jiang, Yuankai Wu, Linhui Ye
Summary: A novel analytical training-free framework based on the coupled scalable Bayesian robust tensor factorization (Coupled SBRTF) is proposed to effectively couple multivariable traffic variables and capture high-dimensional temporal-spatial patterns of the traffic data. The framework outperforms existing NRTC detection models and provides more precise estimations of NRTC for daily commuters, especially when only traffic data in weekdays are utilized.
Article
Engineering, Civil
Mo Zhao, Xiaoxiao Zhang, Justice Appiah, Michael D. Fontaine
Summary: This study developed machine learning models to predict travel time reliability, using random forest algorithms and multiple data sources. The results showed that both models accurately predicted travel time reliability, with the GRF model performing better for predicting the 50th percentile travel time and the QRF model achieving slightly better predictions for the 90th percentile. A case study demonstrated the use of these models for estimating the impact of improvement projects on travel time reliability. Both models captured the trend in reliability change, with the GRF model preferred for estimating the level of travel time reliability.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Computer Science, Artificial Intelligence
Thi-Phuong Nguyen, Yi-Kuei Lin, Yi-Hao Chiu
Summary: This paper investigates the reliability of online food delivery services and focuses on their ability to meet customer needs within given time and space constraints. By constructing a multistate online food delivery network, the study evaluates the system's performance through the computation of reliability. The research helps managers understand whether their network is capable of meeting specific customer demands and enables them to make appropriate adjustments.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Yi Ding, Yu Lin, Ming J. Zuo
Summary: Multistate systems (MSSs) are a more flexible extension of the binary system model for modeling reliabilities of real-life engineering systems. In complex engineering systems with different forms of performances, traditional MSS theory struggles to accurately characterize the system, leading to the proposal of a general multiperformance measure MSS model with ordering methods and system property concepts introduced.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Transportation
Chaoru Lu, Jing Dong, Andrew Houchin, Chenhui Liu
Summary: This study found that standstill distances and time headways follow dispersed distributions, suggesting that microsimulation models should include options for these parameters to follow distributions and be specified separately for different vehicle classes. By incorporating stochastic standstill distance and time headway parameters in car-following models, travel-time-reliability measures can be estimated more precisely and efficiently compared to traditional models like VISSIM.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Transportation
Shahriar Afandizadeh Zargari, Navid Amoei Khorshidi, Hamid Mirzahossein, Hanif Heidari
Summary: Travel time and its predictability level play a crucial role in travel behavior, and traffic congestion is an important factor contributing to the unreliability of travel time. This study focuses on the impact of recurring congestion on the reliability of travel time and uses grey models (GM) and random forest regression (RFR) to analyze and predict the planning time index (PTI) on a specific freeway segment. The results show that RFR outperforms GM in predicting PTI values during congestion changes, and bagging and bootstrapping techniques further improve the accuracy of the model. The analysis of scatter plots demonstrates the reliability of PTI within certain congestion ranges and the increasing rate of PTI change as congestion decreases.
INTERNATIONAL JOURNAL OF TRANSPORTATION SCIENCE AND TECHNOLOGY
(2023)
Article
Transportation Science & Technology
Nicolas Chiabaut, Remi Faitout
Summary: This paper introduces a new method for real-time estimation of traffic conditions and travel times on highways by utilizing principal component analysis and clustering of historical dataset. The clustering results show similarity in traffic conditions and dynamics of days in the same group, and a consensual day is identified as the most representative day of each cluster. This method uses past observations to predict future traffic conditions and travel times based on the closest consensual day to a new day, showing promising results on a French freeway dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Economics
Ninad Gore, Shriniwas Arkatkar, Gaurang Joshi, Srinivas S. Pulugurtha
Summary: To mitigate congestion, strategies need to be developed by quantifying and analyzing the spatiotemporal variations of congestion using travel time as a measure, rather than extending free-flow travel time. An accelerated failure time model is proposed to accurately measure congestion, resulting in the development ofa congestion index.
Article
Economics
Zheng Zhu, Atabak Mardan, Shanjiang Zhu, Hai Yang
Summary: Travel time reliability is crucial in travelers' route choice behaviors. This study proposes various types of perceived knowledge in a generalized Bayesian traffic model to analyze travelers' daily route choices regarding travel time reliability. The results show that different types of perceived knowledge may lead to distinct route choice dynamics, but all ultimately converge to fixed points.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)