Ridesharing and Crowdsourcing for Smart Cities: Technologies, Paradigms and Use Cases
- Resource Type
- Periodical
- Authors
- Seng, K.P.; Ang, L.; Ngharamike, E.; Peter, E.
- Source
- IEEE Access Access, IEEE. 11:18038-18081 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Crowdsourcing
Smart cities
Computer architecture
Intelligent vehicles
Public transportation
Network topology
Urban areas
Artificial intelligence
Deep learning
Shared transport
crowdsourcing
deep learning
machine learning
ridesharing
transportation
smart cities
- Language
- ISSN
- 2169-3536
Recent technology developments and the numerous availabilities of mobile users, devices and Internet technologies together with the growing focus on reducing traffic congestion and emissions in urban areas have led to the emergence of new paradigms for ridesharing and crowdsourcing for smart cities. Compared to carpooling approaches where the driver and participant passengers or riders are usually prearranged and the journey details known beforehand, the paradigm for ridesharing requires the participants to be selected at short notice and the rider trips are often dynamically formed. Crowdsourcing techniques and approaches are well suited to match drivers and riders for these dynamic scenarios, although there are many challenges to be addressed. This paper aims to survey this new paradigm of ridesharing and crowdsourcing for smart city transportation environments from several technological and social perspectives including: 1) ridesharing and architecture in transportation; 2) techniques for ridesharing; 3) artificial intelligence for ridesharing; 4) autonomous vehicles and systems ridesharing; and 5) security, policy and pricing strategies. The paper concludes with some use cases and lessons learned for the practical deployment of ridesharing and crowdsourcing platforms for smart cities.