DeepAI AI Chat
Log In Sign Up

Personalization of Itineraries search using Ontology and Rules to Avoid Congestion in Urban Areas

08/30/2014
by   Amir Zidi, et al.
0

There is a relatively small amount of research covering urban freight movements. Most research dealing with the subject of urban mobility focuses on passenger vehicles, not commercial vehicles hauling freight. However, in many ways, urban freight transport contributes to congestion, air pollution, noise, accident and more fuel consumption which raises logistic costs, and hence the price of products. The main focus of this paper is to propose a new solution for congestion in order to improve the distribution process of goods in urban areas and optimize transportation cost, time of delivery, fuel consumption, and environmental impact, while guaranteeing the safety of goods and passengers. A novel technique for personalization in itinerary search based on city logistics ontology and rules is proposed to overcome this problem. The integration of personalization plays a key role in capturing or inferring the needs of each stakeholder (user), and then satisfying these needs in a given context. The proposed approach is implemented to an itinerary search problem for freight transportation in urban areas to demonstrate its ability in facilitating intelligent decision support by retrieving the best itinerary that satisfies the most users preferences (stakeholders).

READ FULL TEXT
01/25/2023

Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey

Urban air mobility (UAM) has the potential to revolutionize transportati...
07/04/2019

Modeling and analysis of alternative distribution and Physical Internet schemes in urban area

Urban logistics is becoming more complicated and costlier due to new cha...
01/29/2019

A Wireless Sensor Network based approach to monitor and control air Pollution in large urban areas

Air pollution is a major concern in large urban areas. Various studies h...
06/04/2019

Classifying logistic vehicles in cities using Deep learning

Rapid growth in delivery and freight transportation is increasing in urb...
01/31/2020

Coupled Charging-and-Driving Incentives Design for Electric Vehicles in Urban Networks

Electric Vehicles (EV) impact urban networks both when driving (e.g., no...
05/26/2020

Multiple abrupt phase transitions in urban transport congestion

During the last decades, our view of cities has been deeply transformed ...
01/08/2019

New approach for a stable multi-criteria ridesharing system

The witnessed boom in mobility results in many problems such as urbaniza...