Estimating-the-Traffic-Impacts-of-Green-Light-Optimal-Speed-Advisory-Systems-Using-Microsimulation-.pdf (457.97 kB)
Download fileEstimating the traffic impacts of green light optimal speed advisory systems using microsimulation
journal contribution
posted on 2019-03-05, 14:21 authored by Cansu Gunsel, Marianna Imprialou, Lucy C.S. Budd, Craig MortonCraig MortonEven though signalised intersections are necessary for
urban road traffic management, they can act as bottlenecks and disrupt
traffic operations. Interrupted traffic flow causes congestion, delays,
stop-and-go conditions (i.e., excessive acceleration/deceleration) and
longer journey times. Vehicle and infrastructure connectivity offers the
potential to provide improved new services with additional functions
of assisting drivers. This paper focuses on one of the applications of
vehicle-to-infrastructure communication namely Green Light Optimal
Speed Advisory (GLOSA). To assess the effectiveness of GLOSA in
urban road network an integrated microscopic traffic simulation
framework is built into VISSIM software. Vehicle movements and
vehicle-infrastructure communications are simulated through the
interface of External Driver Model. A control algorithm is developed
for recommending an optimal speed that is continuously updated in
every time step for all vehicles approaching a signal-controlled point.
This algorithm allows vehicles to pass a traffic signal without stopping
or to minimise stopping times at a red phase. This study is performed
with all connected vehicles at 100% penetration rate. Conventional
vehicles are also simulated in the same network as a reference. A
straight road segment composed of two opposite directions with two
traffic lights per lane is studied. The simulation is implemented under
150 vehicles per hour and 200 per hour traffic volume conditions to
identify how different traffic densities influence the benefits of
GLOSA. The results indicate that traffic flow is improved by the
application of GLOSA. According to this study, vehicles passed
through the traffic lights more smoothly, and waiting times were
reduced by up to 28 seconds. Average delays decreased for the entire
network by 86.46% and 83.84% under traffic densities of 150 vehicles
per hour per lane and 200 vehicles per hour per lane, respectively.
Funding
This study was supported by the School of Architecture, Building and Civil Engineering, Loughborough University (UK) and Engineering and Physical Sciences Research Council (EPSRC).
History
School
- Architecture, Building and Civil Engineering