5 research outputs found
Evaluation of Vehicle Assignment Algorithms for Autonomous Mobility on Demand
The term “Mobility” is gaining new perspectives.
Due to the paradigm shift driven by information technologies
and autonomous vehicles, on-demand mobility services have
experienced significant growth. Operating such a service
efficiently is a challenging task. Especially, assigning vehicles to
customers plays a vital role in this regard. To meet a
satisfactory level of service while keeping the operational costs
to a minimum requires efficient assignment strategies. Work
summarized in this paper utilizes several shared and nonshared assignment algorithms in order to propose a
methodology to assess the effects on the overall system
performance for an Autonomous Mobility on Demand system.
Selected algorithms are tested in a theoretical network with
real-world taxi data with the help of microscopic traffic
simulation software Simulation of Urban Mobility. Simulation
scenarios are generated for both varying demand levels and
increasing fleet sizes. Results suggest that for high demand
levels and small fleet sizes, shared algorithms outperform nonshared algorithms for every performance measure chosen: total
vehicle kilometers traveled, the ratio of empty fleet kilometers,
average passenger waiting time for pick up, and the number of
customers served in a period
Analysis on Effects of Driving Behavior on Freeway Traffic Flow: A Comparative Evaluation of Two Driver Profiles Using Two Car Following Models
Car-following (CF) behavior is the most abstract
form of driving action and, CF behavior modeling has been one
of the core aspects of traffic engineering studies for several
decades. The literature about CF behavior modeling is vibrant
and still evolving. Furthermore, the effect of CF models on the
traffic flow performance through case studies on different
traffic facilities is still being investigated. To shed light on this
matter, this study presents a microsimulation-based case study
considering a freeway stretch in Istanbul, Turkey, employing
two different CF models, i.e., Intelligent Driver Model (IDM)
and Wiedemann 99 through scenarios. Simulation of Urban
Mobility (SUMO) is utilized as the microsimulation
environment. Both CF models are calibrated according to the
measurements. Scenarios for the comparative evaluation are
setup based on the questions "What if German drivers used
this freeway stretch? How much would the traffic flow
performance change?" Using different case studies conducted
in German Freeways on the literature, simulation model
parameters are obtained for both models and, simulation
analyses are performed. Traffic flow performances are
evaluated based on the selected performance measures, such as
throughput and total travel time. According to the findings, it is
seen that results differ significantly between scenarios. We
elaborate on the differences obtained and discuss the
implications on different scenarios which are handled through
different CF models