Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

Optimizing the Routing of Urban Logistics by Context-Based Social Network and Multi-Criteria Decision Analysis

The proper vehicle-route selection is a key challenge affecting the quality of urban logistics since any delay may cause disasters. This study proposes a novel approach of using symmetry/asymmetry traffic context data and multi-criteria decision analysis to optimize vehicle-route selection as part of urban-logistical planning. The traffic context data are collected from official urban transportation databases and metadata of Google Maps route planning to construct a context-based social network. The traffic features and routing criteria have symmetry/asymmetry properties to influence the decision of path selection. Multi-criteria decision analysis can generate a ranking of candidate paths based on an evaluation of traffic data in context-based social networks to recommend to the deliveryman. The deliveryman can select a reasonable path for delivering products according to the ranking of candidate paths. A case study demonstrates the steps of the proposed approach. Experimental results show that the precision is 79.65%, recall is 80.70%, and F1-score is 80.17%, thus proving the vehicle-route recommendation effectiveness. The contribution of this work is to optimize traffic-routing solutions for improved urban logistics in smart cities. It helps deliverymen send products as soon as possible to customers to retain quality, especially in cold-chain logistics.