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Enregistrement W2766712912

Observatory of Strategic Developments Impacting
\nUrban Logistics (2017 version)

2017· preprint· en· W2766712912 sur OpenAlexaff
Lætitia Dablanc, Zeting Liu, Martin Koning, Jens Klauenberg, Leise Kelli de Oliveira, Corinne Blanquart, François Combes, Nicolas Coulombel, Adeline Heitz, Saskia Seidel

Notice bibliographique

RevueHAL (Le Centre pour la Communication Scientifique Directe) · 2017
Typepreprint
Langueen
DomaineEngineering
ThématiqueUrban and Freight Transport Logistics
Établissements canadiensMinistère des Transports
Organismes subventionnairesnon disponible
Mots-clésObservatoryBusinessGeographyAstronomyPhysics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Urban freight living labs need to operate in full recognition of the challenges that will shape the mobility of goods in urban areas in the future. These challenges are several: macroeconomic, micro-economic, demographic, technological, societal, and legal. To help CITYLAB cities implement their urban freight initiatives, a better understanding of These challenges is necessary. This is what this Observatory of strategic developments impacting urban logistics intends to do, by providing data and analysis on some of the most important, or less well known, trends that will shape the urban mobility of goods in the future.
\nThis second version (2017) of the Observatory provides data and analyses on 1) Logistics
\nSprawl; 2) E-commerce; and 3) Service trips. Our findings about the main impacts of these three trends for cities involved in urban freight living labs are the following:
\n- The number of logistics facilities (in their diversity: warehouses, fulfilment centres, distribution centres, cross-dock terminals) is increasing in cities, especially cities of some logistics importance as large consumer markets and/or logistics hubs processing the flow of goods generated by the global economy. These facilities are generally located in suburban areas, but a new niche market of urban warehouses
\nis emerging.
\n- Both e-commerce and logistics sprawl generate a rise in freight vehicles in urban areas, dominated by small vehicles, while medium to large lorries are relatively less important. These vehicles performing delivery operations are visible in neighbourhoods and at times of day when they were not identified before: residential neighbourhoods, residential building blocks, side streets, in the early evening and on week-ends. Emerging new types of vehicles (clean delivery vehicles, two and three wheelers) are now visible in urban centres.
\n- Innovations in the urban supply chains include diverse forms of pick-up points and click-and-collect solutions, while the recent but extremely rapid rise in Technologies and algorithms supporting instant deliveries (on-demand deliveries within less than
\ntwo hours) brings with it a flourish of new companies connecting customers, suppliers and independent couriers, often using bicycles.
\n- The overall impact of these new trends on energy and carbon emission related to urban freight is difficult to assess. Urban freight in general, for the Paris region, brings the following environmental impact: the share of traffic-related CO2, NOx and PM10 due to urban freight is 2.5 times larger than the share of vans and trucks in the regional traffic. The contribution of urban freight to air pollution is larger in the city of Paris. Social costs of air pollution caused by road traffic in general amount to 0.9% of the regional GDP in 2012. Some of the new trends bring more CO2 emissions, such as the relocation of logistics facilities far away in the suburbs, as deconsolidated shipments are delivered to urban consumers and businesses in smaller and more numerous vans. Some trends bring less CO2 emissions, with a rise in cleaner vehicles and innovative solutions such as drop-off/pickup points or bikesupported instant deliveries. Substitution patterns between personal mobility and professional freight mobility can be a good, or a bad, thing for CO2 emissions, depending on the initial circumstances and the way personal shopping was done before online orders.
\n- What is certain is that these changes bring diversity in the urban traffic flow.
\nInstant couriers are using all sorts of transport modes, including foot, bicycles, electrically assisted cargocycles, motorbikes, and various types of vans and lorries.
\nThis can negatively impact traffic management, road safety and conflicts in road uses, congestion, air pollution. Also, the trends we have looked at bring new types of urban jobs, with many unresolved legal issues and poor working conditions in many instances. New types of logistics buildings bring architectural diversity and innovation in cities, but also complaints about noise, aesthetics, as well as congestion and pollution at entrance and exit points.
\n- These environmental and social impacts have been so far poorly documented and researched. Consumers are the main drivers of the changes we have observed, but they are also the residents or visitors of urban areas, and for that they carry an important share of the burdens, as well as the benefits, of the new landscape of urban logistics.
\n- Service trips are trips in commercial traffic induced by service oriented activities.
\nAccording to the German study KiD 2010 service traffic accounts for 11.8% of traffic
\nin terms of trips and for 19.9% in terms of kilometres travelled. There are differences in terms of vehicle types and economic sectors but few variations in Terms of spatial types.
\n- An observatory on service traffic must take into account light duty vehicles and passenger cars in general. Establishment based analysis as well as vehicle based
\nanalysis can give detailed insight in traffic generation and traffic behaviour in Service traffic.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,670
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,234
Écart entre enseignants0,167 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2017
Routes d'admission1
Résumé présentoui

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