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Literature Review on Ramp Metering. Zichuan Li, Xiaoli Sun 05/21/2010. Outline. Introduction Ramp-metering algorithms Local ramp-metering algorithms Coordinated ramp-metering algorithms Summary References. Note:

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literature review on ramp metering

Literature Review on Ramp Metering

Zichuan Li, Xiaoli Sun


  • Introduction
  • Ramp-metering algorithms
    • Local ramp-metering algorithms
    • Coordinated ramp-metering algorithms
  • Summary
  • References


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

  • 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.
  • 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.

Downstream Detectors

  • 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

Upstream Detectors

Ramp metering signal

Check-out Detector

Check-in Detector

HOV Detector

HOV Bypass Lane

Queue Detector


warning signal


Pearson et al. (2001) [3]

Local Road


ramp metering algorithms
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.
ramp metering algorithms1
Ramp-metering algorithms
  • 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.

Pre-timed control

Ramp Metering

Actuated Control

Traffic-responsive control

Adaptive Control

ramp metering algorithms2
Ramp-metering algorithms
  • 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 algorithms3
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 algorithms4
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
ramp metering algorithms5
Ramp-metering algorithms
  • 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.

Local ramp-metering

Ramp Metering

Coordinated ramp-metering


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


Congested pattern control [27]




Local fuzzy logic control [9, 10, 11]

Local neural network control [12, 13, 26]

Iterative-learning [28]





BOTTLENECK algorithm [29]



SWARM algorithm [34,35]

HELPER algorithm [32]


LINKED-ramp algorithm [33]

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


NEURAL NETWORK algorithm [40]


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

DYNAMIC algorithm [45]

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

Zone control

[25, 26]


Coordinated ALINEA

ramp metering algorithms6
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
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.




Source: [3, 5]

local ramp metering algorithms demand capacity strategy1
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]



Local ramp-metering algorithms- ALINEA strategy


  • 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.



Source: [7, 14, 15, 16]


Local ramp-metering algorithms- ALINEA strategy

  • 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 local fuzzy logic control flc
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 flc1
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


ρ v




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

Source: [12, 13]

local ramp metering algorithms local neural network nn control1
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.

w (i,j)

w (j,k)

Input 1

Output 1

Input 2

Output 2

Input m

Output p

Source: [12, 13]

local ramp metering algorithms
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
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
  • 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.
  • Benefits and dis-benefits of deploying ramp metering strategies

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

  • 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.
  • 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].
  • 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.
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