Publikationen

WEtransport: A Context-based Ride Sharing Platform (Poster)

Kurzfassung

In densely populated urban areas high amounts of traffic
pose a major problem, which affects the environment,
economy, and our lives. From a user’s perspective, the
main issues include delays due to traffic jams, lack of parking
space and high costs due to increasing fuel prices (e.g.,
if commuting long distances). Collective transportation
(CT), e.g., public transport systems, provides a partly solution
to these issues. Yet, CT does not support door-to-door
transportation hence reducing convenience; it might be
limited in off-peak hours, and it is still a cost factor when
travelling long distances. A solution to these issues is ride
sharing, an evolving form of CT making alternative transportation
more affordable. In this paper we present a modular,
context-aware ride sharing platform. We aim at enhancing
convenience, reliability, and affordability of different
forms of ride sharing by means of context data. Additionally
our approach supports an easy server- and client-side
expansion due to the modular platform structure.

Kurzfassung (sekundär)

In densely populated urban areas high amounts of traffic
pose a major problem, which affects the environment,
economy, and our lives. From a user’s perspective, the
main issues include delays due to traffic jams, lack of parking
space and high costs due to increasing fuel prices (e.g.,
if commuting long distances). Collective transportation
(CT), e.g., public transport systems, provides a partly solution
to these issues. Yet, CT does not support door-to-door
transportation hence reducing convenience; it might be
limited in off-peak hours, and it is still a cost factor when
travelling long distances. A solution to these issues is ride
sharing, an evolving form of CT making alternative transportation
more affordable. In this paper we present a modular,
context-aware ride sharing platform. We aim at enhancing
convenience, reliability, and affordability of different
forms of ride sharing by means of context data. Additionally
our approach supports an easy server- and client-side
expansion due to the modular platform structure.