Smart Transportation Systems: Architecture, Enabling Technologies, and Open Issues

Xu, Hansong; Lin, Jie; Yu, Wei

doi:10.1007/978-981-10-3892-1_2

Cited by 20 publications

(3 citation statements)

References 93 publications

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“…Uber also plans to select users who want easy transportation and want to move from one place to another. It can be concluded that Uber is aimed at mass market users [16,[34][35].…”

Section: A) Customer Sectionmentioning

confidence: 99%

The Role Of Smart Transport In Urban Planning And The Transition From Traditional To Smart Cities In Developing Countries With Sustainability Requirements

Shojarazavi,

Mohammadzadeh,

Soleymani

et al. 2023

Preprint

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Background: Smart transport plays a crucial role in urban planning and the transition from traditional to smart cities in developing countries with sustainability requirements.Traditional cities in developing countries often suffer from traffic congestion,air pollution, and inadequate public transportation systems,which can impede economic growth and negatively impact the quality of life for residents. Smart transport solutions can address these challenges by providing more efficient, sustainable.Here is a possible business model for an ITS system in a high-potential city in Iran,Based on IoT analysis, Mashhad city in Iran selected as a high-potential city to be changed to a smart city in the transportation sector.In the localization business model,the Uber smart transportation business model as a successful world experience is considered the base model. Methods: Based on the literature review,various criteria and sub-criteria were identified and determined in designing a new localized transportation business model.A questionnaire was prepared and filled with the help of 11 experts,method IAHP technique; Results:The present study showed the possibility of monthly payments for customers,software development, and the addition of luxury cars are among the most important sub-criteria with high weights;Conclusions: Working with transport companies,schools and universities as key partners solves reaching a strong and sustainable smart transportation system.

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“…Uber also plans to select users who want easy transportation and want to move from one place to another. It can be concluded that Uber is aimed at mass market users [16,[34][35].…”

Section: A) Customer Sectionmentioning

confidence: 99%

The Role Of Smart Transport In Urban Planning And The Transition From Traditional To Smart Cities In Developing Countries With Sustainability Requirements

Shojarazavi,

Mohammadzadeh,

Soleymani

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…These data streams concern inter alia mil-lions of shipments and the movements of fleet that are tracked every day [2]. This data is then considered as a source for contextaware applications and intelligent services, aiming to improve traffic efficiency, safety, and security [3] as well as last-mile delivery optimization, route optimization, fleet management, and detection of anomalous behavior [2] These services are applied in various transport modes, such as road, maritime or public transport as well as in various forms of shared transport (e.g., carsharing, bike-sharing). For example, data extracted from computers embedded in vehicles, that concern an object in motion, i.e., its origin, destination, content, and location, can be used to better understand and predict the flow of goods and people in real time.…”

Section: Introductionmentioning

confidence: 99%

Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data

2021

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Recently we observe a significant increase in the amount of easily accessible data on transport and mobility. This data is mostly massive streams of high velocity, magnitude, and heterogeneity, which represent a flow of goods, shipments and the movements of fleet. It is therefore necessary to develop a scalable framework and apply tools capable of handling these streams. In the paper we propose an approach for the selection of software for stream processing solutions that may be used in the transportation domain. We provide an overview of potential stream processing technologies, followed by the method for choosing the selected software for real-time analysis of data streams coming from objects in motion. We have selected two solutions: Apache Spark Streaming and Apache Flink, and benchmarked them on a real-world task. We identified the caveats and challenges when it comes to implementation of the solution in practice.

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“…Applications in this domain must adjust to dynamic environments. Data producers must communicate with each other and exchange information, and be robust to intermittent connectivity [6]. Furthermore, these applications produce large amounts of data and are highly time-sensitive, so any network delays affect the data integrity.…”

mentioning

confidence: 99%

Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT

2020

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Existing research recognizes the critical role of quality data in the current big-data and Internet of Things (IoT) era. Quality data has a direct impact on model results and hence business decisions. The growth in the number of IoT-connected devices makes it hard to access data quality using traditional assessments methods. This is exacerbated by the need to share data across different IoT domains as it increases the heterogeneity of the data. Data-shared IoT defines a new perspective of IoT applications which benefit from sharing data among different domains of IoT to create new use-case applications. For example, sharing data between smart transport and smart industry can lead to other use-case applications such as intelligent logistics management and warehouse management. The benefits of such applications, however, can only be achieved if the shared data is of acceptable quality. There are three main practices in data quality (DQ) determination approaches that are restricting their effective use in data-shared platforms: (1) most DQ techniques validate test data against a known quantity considered to be a reference; a gold reference. (2) narrow sets of static metrics are used to describe the quality. Each consumer uses these metrics in similar ways. (3) data quality is evaluated in isolated stages throughout the processing pipeline. Data-shared IoT presents unique challenges; (1) each application and use-case in shared IoT has a unique description of data quality and requires a different set of metrics. This leads to an extensive list of DQ dimensions which are difficult to implement in real-world applications. (2) most data in IoT scenarios does not have a gold reference. (3) factors endangering DQ in shared IoT exist throughout the entire big-data model from data collection to data visualization, and data use. This paper aims to describe data-shared IoT and shared data pools while highlighting the importance of sharing quality data across various domains. The article examines how we can use trust as a measure of quality in data-shared IoT. We conclude that researchers can combine such trust-based techniques with blockchain for secure end-to-end data quality assessment.

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Smart Transportation Systems: Architecture, Enabling Technologies, and Open Issues

Cited by 20 publications

References 93 publications

The Role Of Smart Transport In Urban Planning And The Transition From Traditional To Smart Cities In Developing Countries With Sustainability Requirements

The Role Of Smart Transport In Urban Planning And The Transition From Traditional To Smart Cities In Developing Countries With Sustainability Requirements

Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data

Data Quality and Trust: Review of Challenges and Opportunities for Data Sharing in IoT

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