Development of next generation ramp metering algorithm based on freeway density.
2011-02
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Development of next generation ramp metering algorithm based on freeway density.
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2011-02
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Abstract
Freeway ramp metering has been widely employed as an effective strategy to reduce
congestion and increase the freeway operational efficiency for over two decades. Over
the years, a number of isolated and coordinated metering strategies have been developed
and deployed various parts of the world. Minnesota, first through the Zone metering
algorithm, and later through its successor, the Stratified Zone Metering algorithm, has
been among the states that extensively use freeway on-ramp metering. Based on
MnDOT Regional Traffic Management Center (RTMC)’s recommendation, alternatives
for developing the next generation strategy to address limitations and substantially
enhance the performance of the currently deployed Stratified Ramp Metering strategy
were explored. Following a different approach, the Next Generation strategy was
developed by focusing on density rather than flow. This is because (as shown in earlier
research) while values of occupancy near capacity are quite stable, bottleneck capacity
has stochastic variations and a control strategy based on flow thresholds is likely to be
inefficient. This variability in the capacity flow would mean that a control strategy based
on flow thresholds would be likely to either under-load the freeway during the
uncongested regime of traffic flow, or overload the system after the occurrence of the
breakdown. While the former might lead to early onsets of congestion (congestion not
being delayed as much as possible since the full capacity of the system is not utilized),
the later might mean that the system is unable to recover from congestion efficiently
(due to an over-load on the system). Critical occupancy however, and therefore density,
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is known to have stable behavior at capacity. This suggests that using a density based
control approach can potentially enhance the overall performance of the system.
During the first part of the study we developed a methodology to estimate densities with
space and time based on data from loop detectors. The methodology is based on solving
a flow conservation differential equation (using LWR theory) with intermediate
(internal) freeway mainline boundaries, which is faster and more accurate than previous
research using only external boundaries. Capacity drop phenomenon is inherently
incorporated in the density estimation process, and the effect of the stochastic nature of
capacity flow is minimized by identifying bottleneck threats and zones based on critical
density values. Results compared with micro-simulation of a long freeway stretch show
that this model produces reliable and accurate results. We further extended this density
estimator using a two-value capacity (before and after the occurrence of a breakdown)
and we integrated it in the LWR formulation.
By carefully analyzing empirical data of active bottlenecks in the Twin Cities
Metropolitan Area we noticed that (i) there are many cases where capacity is
underutilized (4 min ramp delay constraint is misinterpreted by the algorithm) and (ii)
the system once congested is unable to return to a state of flow near capacity for too
long. One of the main reasons for the above inefficiencies is that capacity is considered
constant during all times at all bottlenecks. This is concluded based on two empirical
findings: (i) a significant capacity drop after the breakdown in many locations (varying
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0-15%) and (ii) the total capacity of a bottleneck (sum of mainline + on ramp) is a
function of the ratio of the two flows. More specifically, when ramp flows are higher the capacity is smaller (~5-10%). This happens very often in MN ramps because of the 4
minute constraint in ramp delays.
Instead of a layer-based algorithm, we proceed with a dynamic zone-based algorithm.
The whole freeway system is divided into zones, where the length of each zone is
dynamic and is estimated in real-time. Within each zone the metering rates are chosen
independently of conditions in other zones. The algorithm’s goal is to keep the car
density levels at all ramps below the congestion thresholds and not to allow low speeds
to occur in the mainline, by constraining the ramp delays. The ramp rates become
stricter when mainline density is close to the congestion threshold, and the ramp rates
increase when ramp waiting times are close to the ramp delay threshold. When it is not
possible to keep both uncongested because of high on-ramp and mainline demands, the
algorithm seeks to delay as long as possible the violation of both thresholds. The
effectiveness of the new control strategies has been assessed by comparison with the
current Stratified Zone Metering (SZM) version through microscopic simulation for the
H-169 site. Under the new control strategy the total travel time on the mainline
decreased by 1.5%, the ramp total travel time dropped by nearly 20%, the total system
(freeway and ramp) travel time decreased by about 3% and total delays decreased by
8%. This finding suggests that in this case the new strategy is very effective since it
reduces not only ramp delay, but also total system delay. The results clearly indicate that
the new control strategy is very effective in keeping ramp wait times below the maximum allowed and in reducing ramp delay time. Another interesting observation
made by analyzing the simulation results is that the new strategy substantially reduces
ramp queues, while the overall ramp delay for the peak period was reduced by nearly
30%.
The effectiveness of the newly developed control strategy is then assessed using the
AIMSUN traffic micro-simulator against the currently deployed strategy. The new
metering strategies is deployed on a simulated network and implemented using the
AIMSUN API module. The strategy is compared against the current strategy using
various measures of effectiveness and is found to succeed in delaying the onset of
breakdown, accelerating system recovery after breakdown, and improving the overall
freeway and ramp performances (through improved speeds and throughputs and reduced
delays and stoppages). A proposal for field implementation of the new strategy and of
comparison studies of performance based on ‘before’ and ‘after’ studies is suggested as a follow up for the study.
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University of Minnesota M.S. thesis. February 2011. Major: Civil Engineering. Advisor: Dr Nikolas Geroliminis. 1 computer file (PDF); x, 112 pages; appendices A-B.
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Srivastava, Anupam. (2011). Development of next generation ramp metering algorithm based on freeway density.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/104267.
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