Waiting Time of Public Transport Passengers in Jordan: Magnitude and Cost

Shtayat, Amir; Abu-Alfoul, Mohammed N.; Moridpour, Sara; Al-Hurr, Njoud; Magableh, Khalid; Harahsheh, Ishraq

doi:10.2174/1874447801913010227

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Several studies have divided the database of pavement degradation models into two categories, including an observation database and an online database. In the observation category, the data collection is conducted using visual inspections by equipped modes of transport, e.g., automobile, bicycle [11][12][13][14][15][16], and intelligent monitoring techniques [17,18]. In addition, many pavement prediction models used the long-term pavement performance (LTPP) or short-term pavement performance (STPP) dataset to predict the future pavement performance [19][20][21].…”

Section: Data Sourcementioning

confidence: 99%

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An Overview of Pavement Degradation Prediction Models

Shtayat

Moridpour

Best

et al. 2022

Journal of Advanced Transportation

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Pavement management systems (PMSs) have a primary role in determining pavement condition monitoring and maintenance strategies. Moreover, many researchers have focused on pavement condition evaluation tools, starting with data collection, followed by processing, analyzing, and ultimately reaching practical conclusions regarding pavement condition. The analysis step is considered an essential part of the pavement condition evaluation process, as it focuses on the tools used to find the most accurate results. On the other hand, prediction models are important tools used in pavement condition evaluation to determine the current and future performance of the road pavement. Therefore, pavement condition prediction has an effective and significant role in identifying the appropriate maintenance techniques and treatment processes. Moreover, pavement performance indices are commonly used as key indicators to describe the condition of pavement surfaces and the level of pavement degradation. This paper systematically summarizes the existing performance prediction models conducted to predict the condition of asphalt pavement degradation using pavement condition indexes (PCI) and the international roughness index (IRI). These performance indices are commonly used in pavement monitoring to accurately evaluate the health status of pavement. The paper also identifies and summarizes the most influencing parameters in road pavement condition prediction models and presents the strength and weaknesses of each prediction model. The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.

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Section: Data Sourcementioning

confidence: 99%

“…ree nonlinear kernel equations were applied to describe the prediction model equations ( 12)- (14). ese equations represent the polynomial, radial basis function, and Pearson VII universal, respectively [54].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

An Overview of Pavement Degradation Prediction Models

Shtayat

Moridpour

Best

et al. 2022

Journal of Advanced Transportation

Self Cite

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“…In addition, due to various reasons (compliance with the bus schedule, the level of passenger awareness, etc. ), the nature of the arrival of passengers at a stop may be different for each route [14,15].…”

Section: Bus Stop Operation Analysismentioning

confidence: 99%

Modeling of buses operation at stops with intensive use

Dashdamirov

2022

InterConf+

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City bus routes often use joint stops on certain streets. To reduce the delay time of buses at stops where several bus routes pass, it is important to coordinate the schedules of the routes. The article proposes the use of agent-based simulation to describe the operation of bus routes at the same stop. After determining the location of frequently used bus stops, a logical diagram of the movement of buses at one of these stops in the city of Baku is displayed using the Anylogic 8.7.7 software. Options for entering bus movement parameters in different ways are considered.

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“…In the early 19th century, there was a group of English textile workers named Luddites that protested against the changes brought about by the Industrial Revolution. They occasionally demolished the innovative machines because they saw them as a threat to their livelihoods [18]. Nowadays, there are people who think that their jobs can be threatened by autonomous vehicles; however, as the Luddites had only short term and limited success, the protest against the autonomous car is expected to have the same short term and limited success.…”

Section: Fig (1) Platoon Of Autonomous Vehiclesmentioning

confidence: 99%

Intelligent Transportation Systems along with the COVID-19 Pandemic will Significantly Change the Transportation Market

Wiseman¹

2021

TOTJ

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Background: Reducing human presence in vehicles and transportation infrastructure is a common concept of Intelligent Transportation Systems and COVID-19 guidelines. However, the motivation for the reduced human presence is different. Intelligent Transportation Systems aim to improve transportation services with innovative technologies that can replace human handling and supervision. In contrast, COVID-19 guidelines aim to make the transportation means less busy, because a busy bus or a busy train means more opportunities for potential infection of passengers. Objective: The study aims to explain how the transportation market will be changed and why these changes are going to take place. Methods: The paper analyzes the current situation with its pros and cons and evaluates the ways in which the Intelligent Transportation Systems and the COVID-19 pandemic will affect this situation. A case study is also considered. Results: Intelligent Transportation Systems and the COVID-19 pandemic are two major factors in the transportation market that will significantly change the transportation means we know. Conclusion: It seems that the influence of the intense Intelligent Transportation Systems development and the impact of the COVID-19 outbreak is still underestimated by many governments; hence they continue to invest in traditional transportation instead of moving forward for a new generation of transportation.

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Waiting Time of Public Transport Passengers in Jordan: Magnitude and Cost

Cited by 10 publications

References 8 publications

An Overview of Pavement Degradation Prediction Models

An Overview of Pavement Degradation Prediction Models

Modeling of buses operation at stops with intensive use

Intelligent Transportation Systems along with the COVID-19 Pandemic will Significantly Change the Transportation Market

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