230.548 People
Haddad, Hedi
in Cooperation with on an Cooperation-Score of 37%
Topics
- public transit
- vehicle
- data
- case study
- passenger
- urban area
- market
- bottleneck
- constraint
- student
- ridesharing
- female
- ponding
- market survey
- market research
- traffic congestion
- city
- monitoring
- simulation
- freeway
- intelligent transportation system
- engineering
- data file
- highway
- transient
- traffic incident
- image processing
- traffic estimation
- aggregate
- incident detection
- speed data
- speed measurement
- commodity
- highway traffic
- highway transportation
- fuel
- control system
- forecasting
- fuel consumption
- pollution
- road network
- dispatching
- metropolitan area
- economic growth
- hour of labor
- traffic control system
- show 16 more
Publications
- 2022Socially Structured Vanpooling: A Case Study in Salalah, Oman
- 2021Toward a cost-effective motorway traffic state estimation from sparse speed and GPS datacitations
- 2020A two-stage road traffic congestion prediction and resource dispatching toward a self-organizing traffic control systemcitations
- 2020Socially-Structured Vanpooling: A Case Study in Salalah, Oman
Places of action
article
Socially Structured Vanpooling: A Case Study in Salalah, Oman
Abstract
The shared transportation systems of many countries in the Middle East and Arab world are commonly challenged by sociocultural constraints that need to be satisfied to attract people to ridesharing. As an example, in this article, we address the chartered vanpooling problem, which relates to a ridesharing form widely used in many of these countries, and propose the concept of socially structured vanpooling as an accommodation of the sociocultural preferences of riders. The proposed framework aims at improving the social satisfaction of riders by grouping them into socially compatible pools of co-riders who are comfortable using chartered van services. We present the proposed framework through a case study of female students in Salalah, Oman, and implement it as a three-step clustering algorithm consisting of 1) crisp spatiotemporal pooling, 2) fuzzy agglomerative clustering based on riders' social connections, and 3) fuzzy clustering according to riders' preferences. Based on data collected from 500 students, our experimental results show that the proposed framework leads to a better tradeoff between riders' satisfaction and van operators' benefits.
Topics
Search in FID move catalog