Literature Review on Ramp Metering

Zichuan Li, Xiaoli Sun05/21/2010

Outline

Introduction Ramp-metering algorithms

Local ramp-metering algorithms Coordinated ramp-metering algorithms

Summary References

Note: This literature review aims to introduce the general concept of ramp metering and focuses on local ramp metering algorithms.

Introduction

Definitions A ramp meter is a red-green (or red-green-yellow) traffic

signal on a freeway on-ramp that is used to regulate the flow of vehicles onto the freeway.

A ramp metering strategy is an important part of Advanced Traffic Management System (ATMS) which is a component of the Intelligent Transport System (ITS).

Objectives of applying ramp metering Break up on-ramp platoons to smooth out the merging of

traffic onto the freeway mainline so as to reduce bottlenecks caused by merging traffic.

Ensure that total flow on the freeway does not exceed capacity at downstream bottlenecks.

Some metered ramps have bypass lanes for high occupancy vehicles, allowing carpoolers and vehicles like buses to skip the queue and get directly on the highway.

Introduction

The first pre-timed ramp meter was installed in Chicago, IL in 1963. This first application involved a police officer who stopped traffic on an entrance ramp and release vehicles one at a time at a predetermined rate determined from a pilot detection program, so that the objectives of safer and smoother merging onto the freeway traffic were easier without disrupting the mainline flows. [2]

Since then ramp-meters have been systematically deployed in many urban areas including

Los Angeles, CA (1968); Minneapolis-St. Paul, MN (1970); Austin, TX (late 1970s, withdraw later); Seattle, WS (1981); Denver, CO (1981); Portland, OR (1981); Detroit, MI (1984); Etc.

Introduction

Main components Ramp-Metering Signal and Controller Advance warning signal Mainline detectors

Upstream detectors Downstream detectors

On-ramp detectors Check-in detectors Check-out detectors

Queue detectors (optional) HOV detectors (optional) Other optional components

Source:Pearson et al. (2001) [3]

Queue Detector

Freeway Local Road

Check-out Detector

Downstream Detectors

Upstream Detectors

HOV Detector

HOV Bypass Lane

Advance warning signal

Ramp metering signal

Check-in Detector

Ramp-metering algorithms

Ramp metering rate [3]

Typically ranges 4 -15 sec/veh; Lower than 4 sec/veh may confuse drivers since

it takes 2 seconds for vehicle to start up; Higher than 15 sec/veh may result in ramp

violation because of impatient waiting.

Classified by control philosophy

The pre-timed control allows vehicles to enter the freeway mainline at a pre-set rate based on time-of-day schedule. The control interval could range from 30 minutes to the entire period of peak hours. [3]

The traffic-responsive control uses real-time traffic data to determine the metering rate to better serve the current traffic characteristics. [55]

Actuated control operates based on traffic demands as registered by the actuation of vehicle detectors. The green time is a function of the traffic flow, and can be varied between minimum and maximum lengths depending on flows. Signals are traffic-responsive, but are not adaptive when the change of traffic condition is substantial.

Adaptive control uses real time data from detectors to perform constant optimizations on the signal timing plan for an arterial or a network. Signals can adapt to non-recurring congestion, incidents, events, or traffic demand growth over time, without needing to be reset.

Ramp-metering algorithms

Actuated Control

Adaptive Control

Pre-timed control

Traffic-responsive controlRamp Metering

Pre-timed ramp metering [3]

It is the simplest form of metering. Detectors may be installed on the ramp to actuate

and terminate the metering cycle, but the metering rate is fixed. The metering rate is determined on historical average traffic conditions.

No detector is needed on the freeway mainline. Pre-timed ramp metering benefits on incident

reduction from merging conflicts, but often leads to under-utilization of the freeway mainline capacity under time-varying traffic conditions and unnecessary ramp queuing and delays caused by over restrictive metering rates.

Example study: Linear programming method [17], etc .

Ramp-metering algorithms

Control philosophy of Actuated Control [54]

There are two basic components to the phase green time: the initial interval, the extendable interval.

The initial interval is determined by the specified minimum green and the variable initial operation. The controller will hold the phase in green for the duration of the initial interval regardless of demand or conflicting calls.

During the extendable interval, the phase will be allowed to terminate (i.e., gap out) if the vehicle headways exceed the specified vehicle extension (passage) time.

However, the phase must terminate at the end of the extendable interval even when there is demand (i.e., max out). The duration of the extendable interval is either calculated from the specified maximum green or can be directly specified.

If the traffic demand is substantially high, the green phase terminates at the maximum green time (e.g., max out), in that case the actuated controller operates no different to the pre-timed controller and loses the flexibility to respond to fluctuating traffic flow.

Ramp-metering algorithms

Ramp-metering algorithms

Adaptive control [55]

Adaptive control systems are currently the most advanced and complex control systems available.

They receive real-time data through detectors, then uses an online computer to create an optimal timing plan.

Adaptive control works well for areas with high rates of growth, where timing plans would need to be updated frequently.

Some adaptive control strategies have an explicit objective function linking to its control strategy. Example objective functions

Minimizing total travel time Maximizing system throughput

Classified by geometric extent

In local ramp metering algorithms, the metering rate is determined based on local traffic conditions.

Volume-based metering uses the freeway main volume at the upstream and the downstream capacity to compute the metering rate.

Occupancy-based metering determines the ramp-metering rate based on the occupancy of the downstream freeway mainline, and uses feedback regulation to maintain a pre-specified occupancy.

The coordinated ramp metering measures and controls several ramps as a system to optimize traffic over an area.

Ramp-metering algorithms

Local ramp-metering

Coordinated ramp-meteringRamp Metering

Coordinated

Cooperative

Competitive

Integrated

BOTTLENECK algorithm [29]

SWARM algorithm [34,35]

METALINE [36]

FUZZY LOGIC algorithm [37, 38, 39, 11]

NEURAL NETWORK algorithm [40]

Zone control [25, 26]

Coordinated ALINEA

DYNAMIC algorithm [45]

LQ (linear-quadratic) feedback control algorithm [18, 46, 47, 48, 49]

SUCCESIVE OPTIMIZATION algorithm [50, 51]

LP (linear programming) algorithm [41, 42, 43, 44]

Local

Volume-based

Occupancy-based

Demand-capacity strategy [3, 5, 6 , 16]

Congested pattern control [27]

ALINEA [7]

Local fuzzy logic control [9, 10, 11]

Iterative-learning [28]

Local neural network control [12, 13, 26]

Ramp Meteringalgorithms

Pre-timed

HELPER algorithm [32]

LINKED-ramp algorithm [33]

Ramp-metering algorithms

Both local and coordinated algorithms [53]

Bottleneck Compass Dynamic metering control FLOW Helper Linked Neural Control RAMBO SWARM (System Wide Adaptive Ramp Metering). ZONE

Local ramp-metering algorithms- Demand-capacity strategy

The ramp meter is triggered when the upstream flow exceeds a predetermined threshold or the downstream flow is below a predetermined threshold.

The metering rate is the difference between the upstream flow measured in the previous time interval and the downstream freeway capacity.

The metering rate is truncated if it drop below the Rmin.

R(t)=C-V(t-1)R(t): metering rate, i.e., number of vehicles allowed to enter the freeway in time interval t;

C: downstream freeway capacity;

V(t-1): upstream flow in time interval t-1.

Source: [3, 5]

R(t)

CV(t-1)

Local ramp-metering algorithms- Demand-capacity strategy

The demand-capacity strategy (1975) is the earliest traffic-responsive ramp metering control algorithm implemented in field sites.

It actually uses the short-term “historical” (i.e., the previous interval) data to determine the current metering rate. So it is not able to “predict” the traffic conditions and select metering rate before congestion occur.

It is an “open-loop” control strategy, hence it is sensitive to disturbance.

A “open-loop” controller is also called “non-feedback” controller which uses only the current state and its model to compute the output of the controller and does not use feedback to determine if the output achieves its desired goal.

Source: [3, 5, 6, 16]

controllerinput output

Os

The goal of the ALINEA strategy is to maintain the traffic density on the mainline equal to a pre-set value and optimize the traffic flow on the mainline.

The metering rate is based on the downstream mainline occupancy.

The metering rate is truncated if it exceeds a range [Rmin, Rmax].

In field experiments, it was found that ALINEA is not very sensitive to the choice of the KR. A value of KR = 70 vph was found to yield excellent results at many different sites.

O(t-1)

R(t)

R(t) = R(t-1) +KR[Os - O(t-1)]

R(t): metering rate, i.e., number of vehicles allowed to enter the freeway in time interval t;

KR: regulator parameter, KR>0, for example, KR=50-200 veh/hr;

Os: desired occupancy of downstream detector, usually critical occupancy, e.g., Os=20%;

O(t-1): downstream occupancy in previous time interval.

Local ramp-metering algorithms- ALINEA strategy

Source: [7, 14, 15, 16]

ALINEA (1991) is a “closed-loop” algorithm using “feedback” to determine the ramp metering rate for the subsequent periods, and attempt to predict operational problems before they occur. [7, 14, 15, 16]

The “feedback” means that the algorithm takes the traffic condition in previous time interval into account in order to predict current traffic condition and select metering rate.

ALINEA works better than demand-capacity strategy but it does not consider queue spill-back directly, so ALINEA has difficulty to balance freeway congestion and ramp queues when traffic becomes heavily congested.

Some optimized algorithms To enhance the efficiency to address specific issues which were not

covered by ALINEA. [4, 16]

FL-ALINEA (flow-based), UP-ALINEA (upstream-occupancy-based), UF-ALINEA (upstream-flow-based), X-ALINEA/Q (combination of strategies with ramp-queue control), etc.

Local ramp-metering algorithms- ALINEA strategy

controllerinput output

feedback

Local ramp-metering algorithms- Local fuzzy logic control (FLC)

Objective For example: maximize total distance traveled and

minimize total travel time and vehicle delay, while maintaining acceptable ramp queues.

Calculation procedure Step 1: fuzzification to convert the numerical input

variables into descriptive variables. Example input variables: upstream occupancy,

downstream occupancy, mainline speed, ramp queue occupancy, etc.

Step 2: rule evaluation to implement the control logic. “if … then…” rule base describes the control strategy.

Step 3: defuzzification to map the descriptive outcomes to a numerical output. The resulting control action is metering rate.

Source: [11]

Local ramp-metering algorithms- Local fuzzy logic control (FLC)

Fuzzy logic control strategy Advantages over traditional ramp-metering controllers [10, 11]

FLC does not require extensive system modeling. FLC utilizes partial or imprecise information. Hence, reduce

sensitivity to input measurement errors and missing data. FLC can compromise between conflicting objectives and

incorporate experts knowledge into control. Easy to tune algorithm without recompiling the code. Simulation results showed that FLC outperforms no metering,

pre-timed metering, demand-capacity metering and speed metering under various conditions.

Disadvantages Calibration of FLC parameters and fuzzy rules under various

traffic conditions. Some researchers proposed methods to adjust the parameters in

FLC to improve the performance. For example, Zhu (2008 [9]) used Particle Swarm Optimization (PSO) to adjust some important parameters in FLC.

Local ramp-metering algorithms- Local neural network (NN) control

Source: [12, 13]

Basic control logic Formulated the ramp

metering control as a nonlinear feedback control problem which composes of one or a number of feed-forward neural networks.

Information collected on site Upstream & downstream

traffic flow rate per hour per lane (qu, q respectively)

Number of lanes of the freeway mainline (λ)

Section distance between upstream and downstream detectors (Δx)

Procedure to calculate traffic density:

Number of vehicles in the segment at time interval t+1 is:

Define vehicle concentration (section density) as:

Then the traffic density is:

))()()(()()1( kRkqkqtkNkN u

R(k)

ρ vqu(k)

△x

q(k)

))(

)()(()()1(

kRkqkq

x

tkk u

x

kNk

)()(

Local ramp-metering algorithms- Local neural network (NN) control

Input Traffic density of the section

Output Metering rate

Training procedure: Initialize parameters of the NN, i.e., initial the weights w(*,*). Train the NN, which means adjust weights w(*,*) using input and

output data. Finish training when the NN well maps the input with the output.

Source: [12, 13]

… … …

Input 1

Input 2

Input m

Output 1

Output 2

Output p

w (i,j) w (j,k)

Local ramp-metering algorithms

Both of ALINEA and neural network (NN) control algorithms are effective for moderate congestion but not for heavy congestion where queue spillback may occur. When queue spill-back occurs, these ALINEA and NN

algorithms simply apply an overriding metering rate and have difficulty to balance freeway congestion and on-ramp queues.

Coordinated ramp-metering algorithms

Cooperative ramp-metering Metering rates are first computed with the local traffic

information, then adjusted according to the conditions of the entire system.

Competitive ramp-metering Two metering rates are computed for each ramp, one is based

on local traffic conditions, and the other is based on system conditions, and the restrictive one is chosen

Integrated ramp-metering Local traffic conditions and system-wide traffic conditions are

both used to determine metering rates. Zone control strategy [25, 26]

It is a volume-based algorithm. This control divides the freeway into several zones and each contains no more than one on-ramp and thus operate the mainline at capacity.

Coordinated ALINEA

Summary

Ramp metering is a component of the Advanced Transportation Management System (ATMS) under the frame of ITS.

Ramp metering is one of the most direct, effective, and practical measures to manage freeway traffic if appropriately implemented.

In spite of positive impacts on freeway mainline flow, ramp metering potentially has significant negative impacts on traffic on ramps and local street network.

Summary

Benefits Dis-benefitsIncrease:Traffic safetyMainline travel time reliabilitySpeedCapacity usage/throughput

Reduce: CongestionTravel time DelayFuel consumption and emissionsAccident rate

Increase:Ramp delay and queue spill backFuel consumption and emissions on rampDivision/pressure on local diversion routesCosts of installation and MaintenanceEnforcementMode choice issuesEquity issuesPublic relations

Source: Rebecca, et al., 2001 [3]; Arnold, 1998 [1]

Benefits and dis-benefits of deploying ramp metering strategies

Summary

Local ramp metering algorithms Demand-capacity algorithm is the first actuated ramp metering

implemented on field site. But since it is an open-loop non-feedback algorithm, it is sensitive to disturbs and cannot predict traffic condition before congestion or occurs.

ALINEA and NN algorithms use feedback to maintain a desired level of occupancy, and both are effective on moderate traffic demand. But they have difficulty to balance freeway congestion and on-ramp queue under heavily congested traffic condition.

Fuzzy logic algorithm can handle missing or imprecise traffic data, and can compromise conflicting objectives. But parameters of the FL controller need calibration in order to reach the premium performance.

In summary, all the local ramp-metering strategies only responses to traffic around a single ramp and does not consider traffic conditions on other ramps or freeway mainline segments. They are suitable for localized problems.

Summary

Coordinated ramp metering algorithms With more cost, the coordinated ramp-metering

strategies looks at both of the local traffic conditions and the system-wide information, and is capable to prevent both freeway mainline congestion and ramp spillback.

In addition, some field experiments and many simulation studies have reported that coordinated metering strategies yield significant delay [31].

Summary

Recommendations for further research Extend the control boundaries to cover both the

freeway and its neighboring arterials, to better balance the freeway congestion, the ramp queue, and the local street congestion.

Coordinate ramp metering control with local road network traffic control system to improve the overall system. For example, reduce on-ramp traffic demand.

Integrate ramp metering system with the diversion routing system. For example, develop coordinated signal timing on local arterials in favor of diversion routes, etc.

Integrate ramp metering system with other control strategies. For example, speed control strategies.

References

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