Flexible Assignment of Loading Bays for Efficient Vehicle Routing in Urban Last Mile Delivery

Letnik, Tomislav; Mencinger, Matej; Peruš, Iztok

doi:10.3390/su12187500

Cited by 15 publications

(6 citation statements)

References 45 publications

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“…This leads to an increase in the number of commercial tours and low load factor of freight vehicles. Several studies pointed out the high contribution to unsustainability given by goods movements and logistics (Letnik et al [38]; Russo and Comi [46]; and references quoted therein). Besides, the simulation showed that the current delivery bay locations is not optimal.…”

Section: Discussionmentioning

confidence: 99%

“…There is a relationship, researched by Wenneman et al [50], between delivery bay demand, delivery bay supply, and the number of freight vehicles that park illegally. One of the problems related to delivery bays in cities is that, in many cases, parking supply is lower than demand by carriers (CERTU [10]; Alho and e Silva [2]; De Oliveira and Guerra [24]; Letnik et al [38]). In consequence, freight vehicles double-park along the street or at intersections to load and unload (Ezquerro et al [30]).…”

Section: The Backgroundmentioning

confidence: 99%

“…Referring to the large literature on city logistics (ex-ante and ex-post) measure assessment (De Marco et al [23]; Croce et al [21]; Russo and Comi [47], and references quoted therein), one of the more promising measures both under the point of view of freight operators and city users is the management and control of delivery operations (Baudel et al [7]; Comi et al [15,16], Letnik et al [38]). In fact, aiming to contain the time for delivering, freight vehicles usually park as close as possible to their customers, neglecting if no dedicated areas are available.…”

Section: Introductionmentioning

confidence: 99%

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A methodology for assessing the urban supply of on-street delivery bays

Comi

Moura

Ezquerro

2022

Green Energy and Intelligent Transportation

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Section: Discussionmentioning

confidence: 99%

Section: The Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

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A methodology for assessing the urban supply of on-street delivery bays

Comi

Moura

Ezquerro

2022

Green Energy and Intelligent Transportation

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“…Loading bays are sometimes occupied, requiring rerouting delivery vehicles and searching for an alternative loading bay. The authors in [78] introduced a fuzzy clustering method to test different optimization approaches and make the system flexible enough to accommodate this problem. We believe that edge AI and computer vision can help address where and how many loading bays should be used to perform this transshipment and execute last-mile delivery most efficiently.…”

Section: Edge Ai For Traffic Monitoring and Managementmentioning

confidence: 99%

Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Badidi

2022

Sustainability

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Modern cities worldwide are undergoing radical changes to foster a clean, sustainable and secure environment, install smart infrastructures, deliver intelligent services to residents, and facilitate access for vulnerable groups. The adoption of new technologies is at the heart of implementing many initiatives to address critical concerns in urban mobility, healthcare, water management, clean energy production and consumption, energy saving, housing, safety, and accessibility. Given the advancements in sensing and communication technologies over the past few decades, exploring the adoption of recent and innovative technologies is critical to addressing these concerns and making cities more innovative, sustainable, and safer. This article provides a broad understanding of the current urban challenges faced by smart cities. It highlights two new technological advances, edge artificial intelligence (edge AI) and Blockchain, and analyzes their transformative potential to make our cities smarter. In addition, it explores the multiple uses of edge AI and Blockchain technologies in the fields of smart mobility and smart energy and reviews relevant research efforts in these two critical areas of modern smart cities. It highlights the various algorithms to handle vehicle detection, counting, speed identification to address the problem of traffic congestion and the different use-cases of Blockchain in terms of trustworthy communications and trading between vehicles and smart energy trading. This review paper is expected to serve as a guideline for future research on adopting edge AI and Blockchain in other smart city domains.

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“…The upsurge of new technologies eases the process of re-thinking curbs and their dynamic management ( 6 ). Some cities have already experienced progress in implementing Loading Zones (LZ) technology ( 7 ). For instance, city authorities installed parking meters and loop vehicle detection sensors on the LZ floor in Lisbon (Portugal).…”

mentioning

confidence: 99%

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Castrellon

Sánchez-Díaz

Kalahasthi

2022

Transportation Research Record: Journal of the Transportation R

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Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management.

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Flexible Assignment of Loading Bays for Efficient Vehicle Routing in Urban Last Mile Delivery

Cited by 15 publications

References 45 publications

A methodology for assessing the urban supply of on-street delivery bays

A methodology for assessing the urban supply of on-street delivery bays

Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

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