Abstract

Nowadays logistical costs are significant in many developing countries, for instance, basing upon the last researches, in Russian Federation they make up 20 %. No doubts that heavy traffic congestions in modern urban areas impact directly on vehicle routing costs in road networks. Moreover, logistics companies are faced with lost profits since actually they serve less number of customers then they could planned, because of traffic congestions. Thus, contemporary approaches for planning delivery routes should necessarily take into account traffic information. Herewith, accuracy of such information is crucial since all systems for traffic congestions prediction are highly sensitive to input data. Wide spread of traffic counters, plate-scanning sensors, in-vehicle guide systems can certainly provide accurate data collection. However, emphasize that data collection only is fruitless without intellectual data processing. The present paper is devoted to development of optimization approach which incorporates modern data collection systems and contemporary mathematical tools to cope with comprehensive delivery planning under traffic congestions in road networks. Implementation of the approach to Saint Petersburg city demonstrates reduction of actual travel time of delivery vehicles in the congested road network by 8–16%.

Original languageEnglish
Title of host publicationComputation and Big Data for Transport
Subtitle of host publicationDigital Innovations in Surface and Air Transport Systems
EditorsPedro Diez, Pekka Neittaanmäki, Jacques Periaux, at al.
Place of PublicationCham
PublisherSpringer Nature
Pages155-165
ISBN (Electronic)9783030377526
ISBN (Print)9783030377519
DOIs
Publication statusE-pub ahead of print - 29 Feb 2020

Publication series

NameComputational Methods in Applied Sciences
Volume54
ISSN (Print)1871-3033

Scopus subject areas

  • Civil and Structural Engineering
  • Modelling and Simulation
  • Biomedical Engineering
  • Computer Science Applications
  • Fluid Flow and Transfer Processes
  • Computational Mathematics
  • Electrical and Electronic Engineering

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