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)
Article
Computer Science, Artificial Intelligence
Feng Shao, Hu Shao, Dongle Wang, William H. K. Lam, Mei Lam Tam
Summary: This paper introduces the travel time reliability-generative adversarial network (TTRGAN) model for predicting network-wide travel time reliability using automatic vehicle identification data. The TTR-GAN model is capable of generating predicted travel time samples without assuming a specific travel time distribution. Experimental results demonstrate that the TTR-GAN model outperforms several benchmark models in terms of statistical, buffer time, and probability distribution measures.
KNOWLEDGE-BASED SYSTEMS
(2024)
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
Engineering, Industrial
Xiuzhen Yang, Yihai He, Ruoyu Liao, Yuqi Cai, Wei Dai
Summary: This paper proposes a mission reliability-centered opportunistic maintenance optimization model for multistate manufacturing systems to realize the optimal combination of maintenance activities. By predicting the actual operational state and analyzing the failure mechanism, the optimal maintenance strategy is obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Mathematics, Interdisciplinary Applications
Zheng Li, Jinlei Qin
Summary: Multistate systems have become a general trend in complex industrial products and systems, with fault-tolerant technology playing a key role in improving reliability. Imperfect coverage failure in a work-sharing group can reduce reliability, but a method using universal generating function and matrix-based algorithm can assess and enhance the reliability of multistate systems. Sensitivity analysis helps identify which work-sharing group should be prioritized for elimination under resource limitations.
Article
Engineering, Environmental
Xing Guo, Qiang Feng, Dongming Fan, Zili Wang, Yi Ren, Bo Sun, Dezhen Yang
Summary: An agent-based method is developed to evaluate the dynamic reliability of multistate systems (MSSs), with a reliability modeling framework proposed and Monte Carlo simulation used for evaluation. A visual failure paths analysis method is also proposed to inhibit potential risks, and the effectiveness of the method is verified using the LH4-1 horizontal Christmas tree. The study identifies key components that have a significant impact on system reliability.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
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
Transportation Science & Technology
Lea Ricard, Guy Desaulniers, Andrea Lodi, Louis-Martin Rousseau
Summary: This study compares two types of probabilistic models and finds that a similarity-based density estimation model and a smoothed logistic regression model for probabilistic classification perform best in predicting the conditional probability density function of travel time. These models can improve the accuracy of predicting the reliability of public transport services.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Civil
Emily K. M. Moylan, Michiel C. J. Bliemer, Taha Hossein Rashidi
Summary: This paper investigates travellers' understanding and value of travel time reliability, finding that standard deviation alone may not be sufficient to capture their preferences. Other aspects of variability such as frequency of exceeding a lateness threshold or likelihood of rare events should also be considered.
Article
Engineering, Civil
Lijuan Shi, Siqi Zhou, Yuntao Chang, Qirui Zhang
Summary: This study quantitatively investigates the effects of intersections and different periods on tram travel-time reliability. The results show that lognormal distribution describes the tram travel time better than normal and gamma distributions. scenarios with no intersections, one intersection, and off-peak hours have higher reliability, while scenarios with two intersections and three intersections have low reliability. An optimized signal-control strategy based on travel-time reliability is proposed and validated through VISSIM simulation.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Computer Science, Hardware & Architecture
Lechang Yang, Chenxing Wang, Chunyan Ling, Min Xie
Summary: This article proposes a survival signature-based reliability framework for an imprecise multistate system (IMSS) to address the challenges of reliability evaluation for complex systems with imprecise parameters. The framework defines the survival signature and calculates the multistate survival functions based on the combination of states of composing elements. A simulation method is developed for probability estimation when imprecision is involved. An approximate Bayesian computation method with a Jensen-Shannon divergence-based kernel is developed to perform stochastic model updating and calibrate imprecise parameters. The proposed framework is validated with a numerical case of a typical bridge system and a real application example.
IEEE TRANSACTIONS ON RELIABILITY
(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
Shin-Guang Chen, Yi-Kuei Lin
Summary: The article proposes an improved merge search method for evaluating the reliability of network systems, and demonstrates its efficiency through benchmark comparisons with well-known algorithms. The results show that the proposed algorithm can save as much as 10 times in computation time compared to existing methods in the literature.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Operations Research & Management Science
Ding-Hsiang Huang, Cheng-Fu Huang, Yi-Kuei Lin
Summary: This paper introduces a deep neural network (DNN) model for predicting the reliability of a multistate stochastic cloud/edge-based network (MCEN). By transforming MCEN information into a suitable format and using Bayesian optimization to determine relevant functions and hyperparameters, the model can learn the capability of MCEN with different data promptly. Illustrative and practical cases of Amazon Web Service demonstrate the availability and efficiency of the prediction model for MCEN reliability.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Transportation
Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Mohammed Almannaa, Leanna House
Summary: This paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System using machine learning at two levels: network and station. While univariate models had slightly lower prediction errors, multivariate models were found to be promising for network-level prediction, particularly in systems with a large number of spatially correlated stations. The analysis at the station level suggested that demographic information and other environmental variables are significant factors in modeling bikes in bike share systems.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Energy & Fuels
Hao Chen, Hesham A. Rakha
Summary: A Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed and tested on diesel buses to validate its potential benefits. The system utilizes a fuel consumption model, vehicle dynamics model, traffic signal timings, and vehicle speed-distance relationship to optimize the vehicle trajectory through signalized intersections. Field tests demonstrated that the B-GLOSA system can significantly reduce fuel consumption and travel time for buses.
Article
Engineering, Civil
Huthaifa Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Leanna House
Summary: Bike sharing systems are an important part of urban mobility, and the traditional measure of service quality lacks spatial correlation and discrimination between stations. Therefore, this study proposes a new measure called Optimal Occupancy, which takes into account the temporal variations and spatial dependencies of individual stations and provides better prediction of service quality at nearby locations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Ahmed A. Hussein, Hesham A. Rakha
Summary: This paper develops models to investigate the impact of vehicle position and distance gap on the drag coefficient. The results demonstrate that different vehicle gaps can significantly reduce fuel consumption.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Software Engineering
Hao Chen, Qi Han, Qiong Li, Xiaojun Tong
Summary: This paper proposes a blind detection model for image forensics based on weak feature extraction, which can detect unnatural features in images with a wide detection range and good accuracy. The model utilizes artificial neural networks and feature extraction methods to extract weak features from images, and uses convolution and deep residual networks for feature extraction. The final judgment is made through feature classification networks and a target regression network.
Article
Engineering, Civil
Kyoungho Ahn, Jianhe Du, Mohamed Farag, Hesham A. Rakha
Summary: The paper evaluates the effectiveness of an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network in reducing fuel and energy consumption, travel time, and delays. Results show that the system is effective in heavily congested conditions, but the outcomes vary depending on different vehicle compositions and traffic conditions.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Economics
Javier Bas, Jose L. Zofio, Cinzia Cirillo, Hao Chen, Hesham A. Rakha
Summary: The study explores the potential market share of the Eco-Cooperative Adaptive Cruise Control (Eco-CACC) using a stated choice experiment and models of discrete choice. The results show that potential purchasers consider the trade-off between system cost and fuel savings, with electric vehicle purchasers being less favorable due to the lower cost of electricity. However, there is a significant market share for alternatives that include the Eco-CACC, suggesting a positive attitude towards environmentally friendly technological innovations.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Green & Sustainable Science & Technology
Mubarak Alrumaidhi, Hesham A. Rakha
Summary: This study modeled the crash severity of elderly drivers using data from Virginia, and found that crashes on two-way roads, involvement of older, distracted, and/or drowsy drivers, unbelted drivers, and high-speed violations are associated with more severe crashes. Crashes involving animals tend to result in property damage only.
Review
Engineering, Industrial
Ammar Sohail, Muhammad Aamir Cheema, Mohammed Eunus Ali, Adel N. Toosi, Hesham A. Rakha
Summary: Road crashes cause significant loss of lives, prompting researchers and transport engineers to focus on data-driven approaches to improve road safety. This study reviews 70 relevant articles, categorizing data sources, equipment, sensors, and analysis methodologies used in data-driven road safety research, highlighting future directions and challenges, such as data collection, poor data quality, and lack of ground truth data.
Article
Energy & Fuels
Kyoungho Ahn, Ahmed Aredah, Hesham A. Rakha, Tongchuan Wei, H. Christopher Frey
Summary: This paper introduces a simple diesel train energy consumption model based on vehicle operational input variables, which can be easily obtained from GPS loggers. The model was tested against real-world data and showed good accuracy, with a total error of -1.33% for all data and varying errors for different train datasets. The study also validated the model with separate data, yielding a relative error of -1.55% for total energy consumption. The proposed model can be useful for evaluating energy consumption effects and conducting train simulations in transportation planning.
Article
Chemistry, Analytical
Mohamed M. G. Farag, Hesham A. Rakha
Summary: Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various applications in safety, mobility, and environment, characterized by higher reliability compared to other technologies. The performance of C-V2X-enabled intelligent transportation system (ITS) applications is influenced by the C-V2X communication technology (mainly packet loss), while the communication performance is affected by the vehicular traffic density, which is determined by traffic mobility patterns and vehicle routing strategies.
Article
Engineering, Civil
Amr K. Shafik, Seifeldeen Eteifa, Hesham A. Rakha
Summary: This paper introduces a robust green light optimal speed advisory (GLOSA) system that considers a probability distribution. The system finds the least-cost vehicle trajectory using a computationally efficient algorithm and dynamic programming to minimize fuel consumption while ensuring safety and passenger comfort. Simulation results show significant fuel savings compared to uninformed driver behavior, and a sensitivity analysis demonstrates the required levels of confidence in predictive timing accuracy to achieve optimal fuel consumption.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Mujahid I. Ashqer, Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Marwan Bikdash
Summary: This study introduces a novel approach using probe vehicle data for traffic density estimation, and validates it using datasets from intersections in Greece and Germany. The results show that even with low market penetration rate, relying solely on probe vehicle data can effectively predict traffic density, and having signal phase and timing information is not necessarily important for accuracy.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Proceedings Paper
Hao Chen, Hesham A. Rakha
Summary: This paper integrates an Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I) controller with a multi-objective dynamic router and tests it on a large-scale metropolitan network. The test results demonstrate that the integrated controller improves system-level performance by reducing energy consumption and delays.
2022 IEEE INTERNATIONAL CONFERENCE ON SMART MOBILITY (ICSM 2022)
(2022)
Article
Energy & Fuels
Kyoungho Ahn, Hesham A. Rakha
Summary: This paper presents a simple energy consumption model for hydrogen fuel cell vehicles. The model accurately estimates energy consumption using input variables such as vehicle speed, acceleration, and roadway grade. It can be used by transportation engineers, policy makers, automakers, and environmental engineers to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.
