Project Progress Report

Ramp Metering in Freeway System

Submitted To:

The 2014 Summer NSF REU Program

Sponsored By:

The National Science FoundationGrant ID No.: DUE – 0756921

College of Engineering and Applied ScienceUniversity of Cincinnati

Cincinnati, Ohio

Prepared By:

Emma Hand, Civil Engineering, University of CincinnatiJared Sagaga, Computer Science, University of Cincinnati

Isaac Quaye, Aerospace Engineering, University of Cincinnati

Report Reviewed By:

Heng Wei, PhD, PE, EITREU Faculty MentorAssociate Professor

Department of Civil and Architectural Engineering and Construction ManagementUniversity of Cincinnati

July 3, 2014

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Abstract

In 2013, traffic congestion cost commuters in the U.S. approximately $101 billion in lost time

and wasted fuel. Local governments and transportation agencies have used a variety of

mitigation strategies to reduce the cost of traffic congestion. This report examines one such

strategy, ramp metering. Using a modelling approach to evaluate the various types of metering,

we will be taking into consideration various components such as operational characteristics, the

ramp meter system in effect, and the amount of lanes on the on-ramp. The key element of

deploying a ramp metering system is to control the traffic entering the freeway mainline, with the

intent to reduce congestion and in turn reduce travel times and ensure the safety of motorists.

Data collected during this project along with previously gathered data will be used to create

traffic simulations using the micro simulation software VISSIM, and to determine the

effectiveness of the placement of these meters.

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Introduction

Ramp metering systems are traffic devices used to control traffic entering the freeway mainline.

Ramp meters are being used in states such as Arizona, Michigan, Minnesota and others, and have

been shown to successfully regulate the flow of traffic on the freeway in order to reduce

congestion, collisions and travel times to destinations. Different types of metering systems are

used in differing situations, and each has its own benefits and drawbacks, including maintenance

costs and ease of use. Ramp meters have been under development for decades, and have been

proven in many cases to be successful, with benefits far outweighing any drawbacks.

Departments of Transportation (DOT) in several states have performed tests which have proven

the effectiveness of ramp meters, and have included the voice of the public in meter installation

projects in order to gain the acceptance of residents and others who will be affected by the

metering systems. While ramp meters have been proven to be successful, there are some

limitations to these systems. They are very expensive to implement and maintain, and

complicated algorithms are used that will render ramp meters ineffective if there are any errors.

Isolated ramp meters fail to detect upstream or downstream traffic flows, which can result in

serious traffic congestions when there is sudden traffic change. Regardless of these limitations,

ramp meter strategies are expanding nationwide.

Background Literature Review

Ramp Meters in Different States

Several states around the country use ramp metering to control traffic, increase freeway safety,

reduce travel time and maximize freeway throughput. Some of these states include Minnesota

(433 ramp meters), Arizona (300 ramp meters), California (1,000 ramp meters), and Washington

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(280 ramp meters) (Minnesota Dept. of Transportation; Arizona Dept. of Transportation;

California Dept. of Transportation; Jacobson, Stribiak, Nelson, Sallman). The types of ramp

meters used vary from state to state. In Minnesota, depending on the ramp and traffic conditions,

either fixed time or responsive ramp meters are installed, and are monitored throughout the entire

state in a single system containing many sub-systems (Jacobson, Stribiak, Nelson, Sallman). In

Washington, responsive traffic meters and fuzzy logic are used to run the ramp metering system

statewide (Jacobson, Stribiak, Nelson, Sallman).

 

The introduction of ramp metering to Minnesota and Washington brought about several benefits,

but also presented each state with some difficulties during the implementation of the metering

systems. Some of the difficulties that were faced by each state’s Department of Transportation

about ramp metering include:

Poor performance in inclement weather or during special events

Vehicle queues force overrides causing the algorithms to restart

Staffing, training, and ramp metering implementation

Complete acceptance by the public

Poor marketing of its benefits and high reciprocity at a low expense

The Departments of Transportation in Minnesota (Mn/DOT) and Washington (WSDOT) are still

facing these issues today (U.S. Dept. of Transportation). With technology improving every day,

however, the difficulties are not as severe as when ramp metering was first introduced onto these

states’ freeways. These difficulties can also cause decisions on ramp metering installation to

take some time, especially if public input is needed to make the decisions. Washington includes

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the public in its ramp metering program, with public outreach and disseminating information

being vital to any planning of ramp metering installation (Jacobson, Stribiak, Nelson, Sallman).

Minnesota has also implemented this type of method into their future plans for proposed ramp

metering installations after performing an evaluation in 2001 on their ramp metering system in

Twin Cities.

Uses of Ramp Metering

According to the FHWA (Federal Highway Administration),

- Ramp meters are used to regulate and reduce traffic volume on freeways by spreading out

queues of vehicles over a period of time.

- Reduced traffic congestions are a result of traffic meters, increasing freeway speeds and

thereby improving travel time and travel time reliability.

- Metering systems help lessen crashes on freeways around the entrance ramps, in effect

ensuring the increased safety of motorists.

Types of Ramp Metering

There are different types of ramp metering systems that are put in place, each with its own

benefits and limitations.

1. Fixed Ramp Metering

Fixed Ramp Meter systems, also called Pre-timed Ramp Meter systems, operate on a fixed ramp

cycle, or period of time that the meter goes through the colors red and green. This pre-timed

meter cycle is based off of data from past traffic conditions, assuming the patterns of these

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conditions are fairly constant from day to day (Kang). Different meter cycles are used depending

on how many cars are intended to pass through. A system designed to break up platoons, or

large groups of vehicles, has a cycle between four to seven seconds, with meter rates lasting just

long enough for one or two cars to pass through at a time. During times of the day when traffic

is not expected to be as busy, cycles last ten seconds or more allowing three or more vehicles to

pass at a time. In the present study, the cycle used for the single lane on ramp will be four

seconds and the cycle for the two lane ramp will be 6.56 seconds.

1.1 Benefits

Fixed ramp meter systems have low maintenance costs, and provide drivers with a reliable

pattern to which they can easily adjust (Kang). The pre-timed systems provide benefits

associated with reduced congestion. Travel time and side-swipe accidents that happen when

incoming traffic merges onto the mainline freeway are lessened, while throughput, or the lack of

traffic stand-stills, is increased.

1.2 Limitations

Fixed meter cycles do not respond to any traffic conditions on the freeway mainline, remaining

unaltered even if there are sudden fluctuations in the traffic. There can also be a problem with

fixed meter systems when the amount of vehicles on the on-ramp reaches capacity, because

traffic enters the on-ramp at a greater rate than the meter allows traffic to access the freeway.

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2. Traffic Responsive Ramp Metering

The meter cycles of responsive ramp meters are based on real-time measurements from sensors

installed along the freeway network shown in Fig. 1. The mainline loops give the occupancy of

the mainline traffic. The passage loop gives the count of vehicles that pass through the meter.

The demand loops relay to the meter when there are vehicles waiting to enter the freeway; when

these sense no vehicles, the meter remains red. The queue loop senses how many vehicles enter

the on-ramp and whether it is full. All of these factors contribute to the meter’s determining of

the meter rate. These meter systems can be either local or coordinated. The local ramp metering

method uses measurements from an area around a single ramp whereas the coordinated ramp

metering system uses data from the entire network. Sites have differing traffic conditions, and

based on these conditions an apt algorithm is used. Depending on the algorithm used,

coordinated responsive systems either sense only traffic from local arterials, streets by the

highway, or communicate with meter systems on adjacent ramps. (Papamichail I., and

Papageorgiou, M.)

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Figure 1. Responsive ramp metering system diagram

2.1 Benefits

The local and coordinated ramp metering strategies have proven to be more efficient than the

fixed traffic ramp meters because of the sensors that communicate between the freeway and the

ramp meters. There are no backups onto local arterials due to the sensor at the entrance to the

on-ramp. Sensors detecting the current traffic conditions on the mainline freeway allow the

meter to change its rates accordingly. The traffic responsive strategy is a better regulator of

traffic from the ramp joining the freeway.

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2.2 Limitations

Coordinated ramp meter systems are extremely complex, and thus very expensive to install and

maintain. Isolated systems do not take into account the traffic upstream or downstream of the

on-ramp. This can cause problems if sudden traffic changes occur further out than the isolated

system senses, and the ramp meter allows too much traffic to enter the freeway. (Papamichail I.,

and Papageorgiou, M.)

Goals and Objectives

The objectives of this project are to investigate and understand the effectiveness of ramp

metering placements, and determine the number of installments needed on a stretch of highway.

The effects of single and two lane ramp metering implementation will be observed, through data

collected from I-275 and US-42 Lebanon Road during peak periods, simulations created by the

group in VISSIM, and information gathered from several other sources. The research tasks to be

undertaken are completing data processing of the GPS and video data collected on the I-275 and

US-42 Lebanon road and completion of VISSIM software training. We estimate that the data

collections and processing will be completed by the end of the 4th week and VISSIM training will

be completed by the end of the 5th week. This will allow us to begin simulations during the 6th

and 7th weeks on the software and to investigate the effects of placing ramp meters on the sites of

interest on the I-275. We will provide progress reports on our training and findings in our

biweekly reports and presentations along the way as we work towards completing our final

technical paper, poster, and PowerPoint presentation.

Scope of Study

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The main focus of this research will be on the traffic conditions at a single lane on-ramp without

a ramp meter, and both single and two lane on-ramps with ramp meters.

The selection of the sites should meet the following criteria:

1) There are elevated locations nearby for placing the camcorder to capture the traffic.

2) Location should be busier in the peak hours than the normal flow of freeway.

Based on the above criteria, the I-275 and US- 42 Lebanon road intersection is the chosen study

site selected for the current research. Video data will be collected and post-processed using a

traffic counter, and a GPS device will be utilized while driving on a given stretch of the road

during peak hours of the day. Traffic flow on the freeway mainline, on-ramp, and arterials will

be observed, and both a single and two lane ramp implementation will be investigated.

Information gained from the observations of these on-ramps will be analyzed in order to

determine which type of ramp metering is most effective and efficient. The gathered data will be

used to create simulations of possible real-world scenarios in VISSIM. The simulations will also

aid in the investigation of future implementation of ramp metering.

Materials and Methods

Research Training

Training on background information for ramp metering was given by our GRA. We received

reading materials and presentations on the origin, progress and status of ramp metering across

the country. We learned how to use the GPS and traffic data collector, and to interpret data

retrieved from these devices. We also received preliminary training on VISSIM and aim to

complete training in the coming weeks.

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Data Collection

Our team received hands-on training from our faculty mentor and GRA on data collection

methods, which included field work on I-275 to observe the sites of interest between the

Mosteller and Reed Hartman exit ramps. Fig. 2 shows our team at the study site around the

Sharonville exit ramp. We observed the loop detectors that were installed on the ramps and the

loop detector stations that communicated with these detectors. Fig. 2.1 shows a loop detector

station. Fig. 3 displays a GPS device used for tracking and storing information of the route

taken, after which we received training on how to retrieve data from the device for interpretation.

The GPS data collected gave us information on the travel time between the two sites of interest,

route taken, number of trips and time of day data was collected. Another data collection method

was the use of cameras which were installed on elevated surfaces to collect video data of traffic

on the ramps of interest. The cameras recorded traffic data all day thereby giving us traffic data

of the different peak periods in the day. After retrieving the videos from the cameras, we

watched the videos and with the use of the traffic data collector device (Fig. 4) we counted the

number of cars around the sites of interest. Also received training on how to represent the

collected information graphically to be able to understand the average travel time and the peak

hours of traffic.

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Figure 2. Observing the study site

Figure 2.1. Loop Detector Station

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Figure 3. GPS device

Figure 4. Traffic Data Collector

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Simulation

In order to create a simulation model in VISSIM, a network model is built by adding a scaled

aerial background image from a map of a selected route on a freeway. The background images

makes it possible to trace lanes, connectors and links to complete the design of the network

model. Before starting a simulation model, vehicle composition (types of vehicles to be placed

on the freeway), vehicle volume, route decisions and control signals have to be added to the

model to reflect the present traffic situation. After simulation parameters are set, the model is

ready to run simulations. Data such as travel time and link evaluation can be retrieved after a

simulation run. After changes such as placing meters and other control signals are made on the

network model, data changes can be compared and analyzed.

Validation and Calibration

As discussed earlier, traffic simulation models performed in VISSIM make use of traffic

demand, vehicle routing decisions, driver behavior, vehicle compositions, and control signals etc,

to aid in investigating a real world traffic condition on freeway systems. In order to be able to

have accurate results from VISSIM such as travel times from one point to another, the software

has to go through a calibration and validation process as shown in Figure 5. Calibration

parameters can be described in two categories, system calibration and operational calibration.

System calibration investigates input assumptions and operational calibration focuses on detailed

driver behavior features that affect overall traffic operations in the model. Calibration is

described as the adjustment of computer simulation model parameters to accurately imitate the

present conditions of the freeway system. Examples of adjustable parameters are vehicle

acceleration distributions, vehicle speed distributions, car following behavior, route decisions

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and lane change gap. The proposed calibration methodology includes three major stages; 1) base

model development, 2) planning of calibration approach, and 3) model calibration and

validation.

To shed more light on these adjustable parameters in the network model in order to better

reflect the present traffic conditions is described below.

Car following behavior

This parameter sets the distance and the speed behavior of cars following lead cars. This ensures

that cars are not too close to each other thereby resulting in better lane changes and ramp exit

decisions. This parameter is based on the continued work of Wiedemann. The basic idea of his

model is that a driver can be in one of four driving modes (Wiedemann, 1991); free,

approaching, following, or braking. When a threshold based on speed and distance differences

between the lead and the following cars are crossed, the state changes.

Routing Decisions

A routing decision directs the vehicles in what routes to take. Whether the car will exit, merge

onto the freeway, join arterials, are all determined through the routing decision. There are four

types of routing decisions and routes, known as, static, partial, parking lot and managed lanes.

The static decision is the most common route decision in VISSIM. It has a start point and a

destination and uses a static percentage for each destination.

Priority Rules

In VISSIM, the right of way movements is determined with priority rules. Such priority rules

apply at the merging and diverging part of the traffic.

Desired Speed Changes

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This is used to determine the changes of free flow speed in VISSIM network. This is got from

site specific and speed limits rules. There are two ways of defining speed changes: Reduced

speed areas which is temporary and a more permanent change known as desired speed decisions.

Validation is the process of comparing simulated model results with the collected data

measurements to verify the accuracy of the simulation model. The main purpose of the validation

stage is to identify parameter settings in the simulation model which helps reach similar results

as the data collected from the field. It’s important to know that once a model is validated, it can

be used to analyze any future traffic situations which may include modifications to trip

distribution, travel demand, etc. The only time this model cannot be used for future traffic

scenarios is when significant changes has been made to the freeway.

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Figure 5. Calibration Methodology

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Results and Discussion

In Table 1, we see data of west bound and east bound average travel times on the I-275 from the

Mosteller road ramps to the Reed Hartman road ramps. For each trip, 3-6 loops were made

around the highway. The times displayed below are averages of these loops. The total average is

then taken from these times. The east bound route was divided into two sections and the west

bound route was also divided into two sections. This data was taken in the morning, afternoon

and evening to better understand the different travel times at different times in the day. The

mornings of May 21-May 26 we discovered that the average travel times from the Mosteller on

ramp on to the I-275 east, ranged from 00:45 sec and 00:50 sec on section 1 and 1:59 sec to 2:44

sec which average out to be 00:48 and 2:42 sec respectively. In the afternoon hours of May 19-

May 26 along this same route, the average of the travel times in section 1 was 00:52 sec and

section 2 was 2:19 sec; also in the evenings, travel times average at 00:46 sec in section 1 and

2:24 sec in section 2. After analyzing this data, we can see that there was very little variation in

travel times between these two destinations at different hours of the day. However, when we

consider travel times on the I-275 route heading west bound we discovered the averaged times

during the different hours of the day were slightly more. For sections 1 and 2 travel time

averages were 2:02 sec and 1:30 sec respectively, and for the afternoon, travel times were 2:01

sec and 1:32 sec respectively. Lastly, the evening times showed average times of 1:49 sec and

1:29 sec. After analyzing this information, we can see that there is increase in times compared to

the east bound travel times. This can be due to more volume of cars going west bound at the

different times in the day.

Table 1: Average East Bound travel times on I-275 between Mosteller and Reed Hartman ramps.

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MorningDate Average Time (min:sec)

Section 1 Section 25/21/14 00:50 02:095/22/14 00:50 03:305/23/14 00:47 03:005/24/14 00:47 01:595/25/14 00:50 02:515/26/14 00:45 02:44

Total Average 00:48 02:42Afternoon

5/19/14 01:05 03:005/20/14 00:50 02:245/21/14 00:52 02:395/22/14 00:55 02:005/23/14 00:53 01:305/24/14 00:45 02:215/25/14 00:49 02:405/26/14 00:47 01:56

Total Average 00:52 02:19Evening

5/21/14 00:47 02:495/24/14 00:48 02:045/25/14 00:45 02:295/26/14 00:45 02:12

Total Average 00:46 02:24

Points 1-2 (Section 1) Coordinates:Lat: 39.285, Lon: 84.417 to Lat: 39.287, Lon: 84.402 (approx.)

Points 2-3 (Section 2) Coordinates:Lat: 39.287, Lon: 84.402 to Lat: 39.285, Lon: 84.371 (approx.)

Table 2: Average West Bound travel times on I-275 between Mosteller and Reed Hartman ramps.

MorningDate Average Time (min:sec)

Section 1 Section 25/22/14 02:33 01:315/23/14 02:33 01:275/24/14 01:32 01:30

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5/25/14 01:33 01:305/26/14 01:58 01:30

Total Average 02:02 01:30Afternoon

5/19/14 01:50 01:305/20/14 02:01 01:415/21/14 02:07 01:325/22/14 02:15 01:325/23/14 02:27 01:335/24/14 02:07 01:315/25/14 01:31 01:315/26/14 01:52 01:26

Total Average 02:01 01:32Evening

5/21/14 01:27 01:325/24/14 02:05 01:255/25/14 02:10 01:285/26/14 01:32 01:30

Total Average 01:49 01:29

Note: West Bound morning has no input for 5/21/14 due to no usable data being collected for those segments.

Points 1-2 (Section 1) Coordinates:Lat: 39.286, Lon: 84.37 to Lat: 39.291, Lon: 84.388 (approx.)

Points 2-3 (Section 2) Coordinates:Lat: 39.291, Lon: 84.388 to Lat: 39.285, Lon: 84.415 (approx.)

Accomplished Tasks

Background literature review and reading assignments on ramp metering and its uses have been

completed by our team. Also completed are training on the use of the GPS device to gather

information, using the Q-travel software to retrieve the data saved on the GPS and the use of the

traffic data collector to count traffic collected on the video data collected. Preliminary training

has begun on the use of the VISSIM software to simulate traffic conditions on the highway being

investigated. The software however, relies on the data collected, processed and analyzed hence

we are working on processing the data to be used for simulation on VISSIM. Training on the

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VISSIM software involved first, building a network; this involved adding a background image

from google map which was zoomed in to about 50% and a snapshot taken. The snapshot of the

freeway taken was imported into VISSIM to aid in the drawing of lanes, connectors and links.

After which the user indicates what width and number of lanes was to be used; lanes ranged from

1-4 lane in this project. Upon completion of the lanes, the second step was to add vehicles; the

vehicle composition was determined for the different roads, whether it was a freeway, ramp or

arterials. Routing decisions were also used to guide the vehicles on the right flow of traffic.

Thirdly, controls such as stop and signal controls were added to the simulation to imitate a real

world traffic flow otherwise simulation without controls will result in errors in the program.

Upcoming tasks are to complete successful simulations of the traffic data gathered from the

video from the sites of interest on the I-275 from Mosteller to Reed Hartman rd. and to undertake

a field trip to the Ohio Department of Transportation infrastructure to statewide traffic

management center in Columbus to gather more information on traffic data collection and

analysis.

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References

Arnold, Jr., E. D. (1998). “Ramp Metering: A Review of the Literature,” Technical Assistance Report No. VTRC 99-TAR5, Virginia Transportation Research Council, Charlottesville, Virginia.

Department of Transportation, California (2007). "Why Are There Signals Installed at Some Freeway Onramps?" Why Are There Signals Installed at Some Freeway Onramps? Web. 16 June 2014. 

Department of Transportation, Minnesota (2011). "Ramp Meters." Minnesota Department of Transportation. Web. 16 June 2014.

DeWelles, Angela (2011). "ADOT Blog: Getting the Green Light: Valley Ramp Meters Now More Efficient." ADOT Blog: Getting the Green Light: Valley Ramp Meters Now More Efficient. Web. 16 June 2014. 

Federal Highway Administration (2013). "Ramp Metering Presentation." Localized Bottleneck Reduction Program. US Department of Transportation. Web. 16 June 2014.

Jacobson, Leslie N., Jason Stribiak, Lisa Nelson, and Doug Sallman (2006). "Chapter 11 - Case Studies." Ramp Management and Control Handbook. Washington, DC: U.S. Dept. of Transportation, Federal Highway Administration. 11-2-11-29. Print.

Kang, S., Gillen, D. (1999). “Assessing the Benefits and Costs of Intelligent Transportation Systems: Ramp Meters,” California PATH Research Report No. UCB-ITS-PRR-99-19, Institution of Transportation Studies, University of California, Berkley, California.Arizona Department of Transportation. (2003). Ramp Meter Design, Operations, and Maintenance Guidelines.

Papamichail I., and Papageorgiou, M. (2008). “Traffic-Responsive Linked Ramp-Metering Control,” IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 1, n.p.

State of California Department of Transportation. “Ramp Metering In Caltrans District 7 (Los Angeles and Ventura Counties).” 2005.

Yu, G., Recker, W., Chu, L. (2009). “Integrated Ramp Metering Design and Evaluation Platform with Paramics,” California PATH Research Report No. UCB-ITS-PRR-2009-10, Institution of Transportation Studies, University of California, Berkley, California.

Zongzhong, T., Nadeem, A. C., Messer, C. J., Chu, C. (2004). “Ramp Metering Algorithms and Approaches for Texas,” Transportation Technical Report No. FHWA/TX-05/0-4629-1, Texas Transportation Institute, The Texas A&M University System, College Station, Texas.

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