TY - CHAP

T1 - Delivery Service in Congested Urban Areas

AU - Zakharov, Victor

AU - Krylatov, Alexander

AU - Mugayskikh, Alexander

N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - 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%.

AB - 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%.

KW - Congested road networks

KW - Delivery service

KW - Traffic assignment problem

KW - Vehicle routing problem

UR - http://www.scopus.com/inward/record.url?scp=85080951006&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/c9a433d2-9953-3686-bbcd-f4f8ff3be589/

U2 - 10.1007/978-3-030-37752-6_9

DO - 10.1007/978-3-030-37752-6_9

M3 - Chapter

AN - SCOPUS:85080951006

SN - 9783030377519

T3 - Computational Methods in Applied Sciences

SP - 155

EP - 165

BT - Computation and Big Data for Transport

A2 - Diez, Pedro

A2 - Neittaanmäki, Pekka

A2 - Periaux, Jacques

A2 - at al.,

PB - Springer Nature

CY - Cham

ER -