Pipeline for Modeling Urban Traffic Density as the Data Basis for Micrologistics and Autonomous Delivery Robots: Project MoVMi

  • This paper presents a framework for modeling urban traffic density to support micrologistics using automated guided vehicles (AGV), more specifically autonomous delivery robots (ADR). The project MoVMi integrates survey data, pedestrian simulations using SUMO, and AGV interaction modeling in CARLA to optimize last-mile delivery in urban environments. Our approach addresses challenges such as congestion and access restrictions, and balances routing efficiency with delivery time windows. The framework’s adaptability allows application in various cities globally, enhancing urban freight transportation and sustainability. Future work will refine models with real-time data and extend applicability to diverse urban settings.

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Author:Philipp Münst, Lara Damer, Selina Meier, Marcel Graef, Rick Voßwinkel
DOI:https://doi.org/10.3233/ATDE241210
ISBN:9781643685601 Available in library?
ISSN:2352-751X Available in library?
Parent Title (English):Emerging Cutting-Edge Applied Research and Development in Intelligent Traffic and Transportation Systems : Proceedings of the 8th International Conference on Intelligent Traffic and Transportation (ICITT), Incorporating the 8th International Conference on Communication and Network Technology (ICCNT), Florence, Italy, 16-18 September 2024
Series (Serial Number):Advances in Transdisciplinary Engineering (63)
Publisher:IOS Press
Place of publication:Amsterdam
Editor:Mahmoud Shafik
Document Type:Part of a Book
Language:English
Year of first Publication:2024
First Page:360
Last Page:369
Faculty:Westsächsische Hochschule Zwickau / Kraftfahrzeugtechnik
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
3 Sozialwissenschaften / 38 Handel, Kommunikation, Verkehr / 388 Verkehr; Landverkehr
Open Access:open_access
Release Date:2025/01/03
Licence (German):License LogoCreative Commons 4.0 International - Namensnennung - Nicht kommerziell

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