Planning Suitable Transport Networks for E-Scooters to Foster Micromobility Spreading

Fazio, Martina; Giuffrida, Nadia; Pira, Michela Le; Inturri, Giuseppe; Ignaccolo, Matteo

doi:10.3390/su132011422

Cited by 28 publications

(10 citation statements)

References 36 publications

(62 reference statements)

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“…Fourth, in other Italian cities where both e-scooter and bicycle trip data are available, there is a strong correlation (see Section S8 in the Supplementary Information). Lastly, the approaches to non-cycling micromobility network planning are structurally identical or very similar to bicycle network planning which we used here (Ignaccolo et al, 2022; Fazio et al, 2021; Comi et al, 2022). Many modes of micromobility are relatively understudied due to their novelty; therefore, research on the topic is highly fluid and expected to provide new insights in the near future (Cook et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Data-driven micromobility network planning for demand and safety

Folco

Gauvin

Tizzoni

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Developing safe infrastructure for micromobility like bicycles or e-scooters is an efficient pathway towards climate-friendly, sustainable, and livable cities. However, urban micromobility infrastructure is typically planned ad-hoc and at best informed by survey data. Here, we study how data of micromobility trips and crashes can shape and automatize such network planning processes. We introduce a parameter that tunes the focus between demand-based and safety-based development, and investigate systematically this tradeoff for the city of Turin. We find that a full focus on demand or safety generates different network extensions in the short term, with an optimal tradeoff in-between. In the long term, our framework improves overall network quality independent of short-term focus. Thus, we show how a data-driven process can provide urban planners with automated assistance for variable short-term scenario planning while maintaining the long-term goal of a sustainable, city-spanning micromobility network.

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Section: Discussionmentioning

confidence: 99%

Data-driven micromobility network planning for demand and safety

Folco

Gauvin

Tizzoni

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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“…The above-mentioned articles dealt with decision-making problems related to conventional bikes, while the following papers mainly related to e-micromobility. Fazio et al [26] used the SMCA method to study the adjustment of the road network to the needs of e-scooters. Kalakoni et al [27] developed an environment matching index for different types of micromobility based on the AHP method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Reducing the dimensionality of the space R n (R K ) to the plane R 2 causes that some information about alternatives and criteria or the views of decision makers is lost. The amount of information transferred to the 2-dimensional space is represented by the δ value calculated using Formula (25) or Formula (26), for criteria or decision makers, respectively:…”

Section: Gaia Analysismentioning

confidence: 99%

Compromise Multi-Criteria Selection of E-Scooters for the Vehicle Sharing System in Poland

Ziemba

Gago

2022

Energies

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In Poland, there is a high ratio of private transport and unfavorable patterns of daily commuting. These patterns can be changed by introducing comfortable and eco-friendly vehicles, such as e-scooters and e-bikes. At the same time, the development of the e-micromobility-based vehicle sharing services market is developing. The aim of the article is to analyze selected e-scooters available on the Polish market and to identify the most useful vehicles from two opposing perspectives, i.e., the potential customer and owner of the vehicle sharing system. The PROSA GDSS (PROMETHEE for Sustainability Assessment—Group Decision Support System) method and the graphical representation of GAIA (Geometrical Analysis for Interactive Assistance) were used to search for a compromise and balance between the needs of the indicated stakeholders. The results of the methods used were compared with the results of the PROMETHEE GDSS method, which does not take into account the balance between the stakeholders and allows for a strong compensation of the assessments of decision makers. The conducted research allowed indicating the optimal e-scooter to meet the needs of both decision makers, and it is the JEEP 2xe Urban Camou. Both the sensitivity analysis and the solution obtained with the use of the PROMETHEE GDSS method confirmed that it is the optimal alternative, the least sensitive to changes in criteria weights and changes in the decision makers’ compensation coefficients.

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“…All of these factors contribute to a poorer quality of life for urban inhabitants [5]. While transport is an integral part of the economic activity and social life of city dwellers [6], it is also one of the largest sources of air pollution in urban areas [7,8], which in turn has adverse effects on human health, the climate, and ecosystems [9]. Internal combustion vehicles are responsible for the emission of particulate matter (PM2.5 and PM10), soot (BC), particles (UFP), nitrogen oxide (NO2), and carbon monoxide (CO), which are harmful to the body [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Sustainable Transport System—The MMQUAL Model of Shared Micromobility Service Quality Assessment

2022

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Shared micromobility is a new phenomenon being observed in urban transport. It is a response to the problems associated with congestion and environmental pollution. Small electric vehicles such as e-scooters are highly suitable for crowded city centres, often providing an alternative to private motor vehicles or public transport, and serve as a good first- and last-mile transport option. While they have become a feature of sustainable transport systems in cities, their impact on the environment often depends on the services offered by operators of this mode of personal transport. There are many tools available to measure the quality of transport, e-services and shared mobility services. However, no specific mechanism has been designed for vehicles in the field of shared e-scooters (research gap). The aim of the article is to verify whether the three dimensions identified by the authors: mobile application functions, device features, and customer service are valid for examining the quality of shared e-micromobility factors on the example of e-scooters. Based on the obtained results, the authors created the MMQUAL (MicroMobility QUALity) model, which accurately describes the quality of the studied phenomenon. The results of the study can serve as a platform for researchers interested in further exploring the issue and improving the proposed model. They may also be of commercial value to operators, who could use this tool to boost the competitiveness of their services by enhancing those features that have the greatest impact on their quality.

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Planning Suitable Transport Networks for E-Scooters to Foster Micromobility Spreading

Cited by 28 publications

References 36 publications

Data-driven micromobility network planning for demand and safety

Data-driven micromobility network planning for demand and safety

Compromise Multi-Criteria Selection of E-Scooters for the Vehicle Sharing System in Poland

A Sustainable Transport System—The MMQUAL Model of Shared Micromobility Service Quality Assessment

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