Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas

Nigro, Marialisa; Ferrara, Marina; Vincentis, Rosita De; Liberto, Carlo; Valenti, Gaetano

doi:10.3390/en14133949

Cited by 16 publications

(14 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the requirements of low latency, high bandwidth, and high reliability of Internet of Vehicles, deploying servers near the edge of users has become a better choice than the cloud [8]. In MEC architecture, because the vehicle is closer to the edge server both logically and practically, the unloading delay is greatly reduced compared with cloud computing, and the pressure on the network transmission bandwidth is also greatly reduced for the short transmission distance [9]. Because of the superiority of datadriven edge computing, the research on it is also hot in recent years.…”

Section: Related Workmentioning

confidence: 99%

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

Wang¹

2022

Security and Communication Networks

View full text Add to dashboard Cite

With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles. Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced; second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced; then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is increased by 0.32%, which improves the practicability of the system.

show abstract

Section: Related Workmentioning

confidence: 99%

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

Wang¹

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Moreover, local communities can encourage planning of soft mobility modes (pedestrians and cycling), by adopting measures such as the creation of green districts and mixed land use. This will help to improve sustainable urban policies targeted to advance the use of public spaces and to increase road safety measures in local communities (Nigro et al , 2021). In addition, other initiatives can be implemented, such as the creation of dedicated routes for soft mobility and lower speed limit zones (30 km/h) , the provision of safe cycle tracks beside the road, the provision of protected corridors for public buses and the provision of interconnected street road networks .…”

Section: Resultsmentioning

confidence: 99%

“…However, for soft mobilities such as walking and cycling to contend with motorized vehicles for instance cars, bikes, public transportation must be developed with the readiness of physical infrastructures that supports the use of electric mobility assets for the easiness of travel and to promote first-to-last mile mobility services to commuters in local communities (Singh and Singh, 2018). The application of green urban mobility practices within the planning and management of local communities involves addressing different expectations and needs of all stakeholders and must consider the measures that may influence the sustainability of the public transportation of the city (Ribeiro and Mendes, 2013;Nikolaeva et al, 2019). The quality of available public transportation is another factor that assesses the consistency of the input and output of the mobility process in relation to compliance, travelers' satisfaction and availability of space, e.g.…”

Section: Green Urban Mobility Policiesmentioning

confidence: 99%

Developing green urban mobility policies for sustainable public transportation in local communities: a Norwegian perspective

Anthony Jnr.

2023

JPMD

View full text Add to dashboard Cite

Purpose The concept of green urban mobility has emerged as one of the best approaches for promoting environmental-friendly transportation in local communities. Green urban mobility aims to reshape public transportation system and enhance mobility, with emphasis on deploying digital technologies to promote sustainable public transportation. Therefore, this study aims to analyze existing public transportation policies by exploring how local communities can facilitate green urban mobility by developing a sociotechnical urban-based mobility model highlighting key factors that impact regions transitioning toward sustainable transportation. Design/methodology/approach This study investigates “the role of data for green urban mobility policies toward sustainable public transportation in local communities” in the form of a systematic literature review and insights from Norway. Secondary data from the literature and qualitative analysis of the national transport plan document was descriptively analyzed to provide inference. Findings Findings from this study provides specific measures and recommendations as actions for achieving a national green mobility practice. More important, findings from this study offers evidence from the Norwegian context to support decision-makers and stakeholders on how sustainable public transportation can be achieved in local communities. In addition, findings present data-driven initiatives being put in place to promote green urban mobility to decrease the footprint from public transportation in local municipalities. Practical implications This study provides green mobility policies as mechanisms to be used to achieve a sustainable public transportation in local communities. Practically, this study advocates for the use of data to support green urban mobility for transport providers, businesses and municipalities administration by analyzing and forecasting mobility demand and supply in terms of route, cost, time, network connection and mode choice. Social implications This study provides factors that would promote public and nonmotorized transportation and also aid toward achieving a national green urban mobility strategy. Socially, findings from this study provides evidence on specific green urban mobility measures to be adopted by stakeholders in local communities. Originality/value This study presents a sociotechnical urban-based mobility model that is positioned between the intersection of “human behavior” and “infrastructural design” grounded on the factors that influence green urban mobility policies for local communities transiting to a sustainable public transportation. Also, this study explores key factors that may influence green urban mobility policies for local communities toward achieving a more sustainable public transportation leading to a more inclusive, equitable and accessible urban environment.

show abstract

“…Examples of airports with EV charging facilities to various extents are Stuttgart [41], London Luton [42], and Toronto Pearson [43] airports. Studies exist on approaches for the deployment of electrical mobility in urban areas [44].…”

Section: Field Of Action Advantage Disadvantagementioning

confidence: 99%

Forecast of the Demand for Electric Mobility for Rome–Fiumicino International Airport

et al. 2021

View full text Add to dashboard Cite

Following electrification of automotive transport, studies on the penetration of electric vehicles (EVs) are widespread, especially in defined contexts. As major transport hubs, airports fall within contexts worthy of interest. In this work, a forecast of the demand for electric mobility in an Italian international airport (Rome–Fiumicino) is presented. The main goal of the research is to build up a methodology that allows evaluating the penetration index of EVs that will access the airport parks in 2025 and 2030, to be able to have a preliminary assessment of the number of charging points necessary for serving them. In the paper, first, a wide review of proposed scenarios on the penetration of EVs at international and national level and available data on local automotive transport are presented, as a preliminary study for the definition of reference scenarios for the local context. Then, the proposed methodology is presented and applied to the specific case study. Finally, a preliminary sizing of the required charging infrastructure is reported. The results show that a significant impact on the airport electricity network can be foreseen, and it requires proper planning of adaptation/upgrading actions. The proposed approach can be considered as a reference for similar studies on electrical mobility in other airport areas around the world.

show abstract

Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas

Cited by 16 publications

References 40 publications

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

Developing green urban mobility policies for sustainable public transportation in local communities: a Norwegian perspective

Forecast of the Demand for Electric Mobility for Rome–Fiumicino International Airport

Contact Info

Product

Resources

About