COMPUTER AIDED TESTING OF ADAPTIVE RAMP CONTROL APPLICATIONS John Hourdakis and Department of Civil Engineering University of Minnesota 500 Pillsbury Dr. SE Minneapolis, MN 55455 Phone: (612) 625-8832, Fax: (612) 626-7750 Email:
[email protected]
Panos G. Michalopoulos Department of Civil Engineering University of Minnesota 500 Pillsbury Dr. SE Minneapolis, MN 55455 Phone: (612) 625-1509 Fax: (612) 626-7750 Email:
[email protected]
INTRODUCTION As the sophistication of traffic detection and control hardware increases, the need to improve freeway ramp control logic becomes evident, especially in view of increasing ATM S systems deployment. Since each freeway has its own characteristics, selecting the most effective control strategy and calibrating its parameters prior to implementation is problematic. A combination of these factors suggests the need for a systematic methodology for selecting and adapting the most appropriate control scheme for a particular situation. M ore often than not the ramp control strategies deployed were customized empirically and fine-tuned over a period of time. Even though empirical solutions can be effective, there is no assurance that they are the best for a particular freeway, while they take time to be fine-tuned and their deviation from optimality is unknown. Simulation is an obvious tool to shortcut the process but this rarely occurs in practice. Furthermore, simulators are at best designed to implement only a particular control strategy. Thus a flexible and uniform practical tool for selecting the best control strategy and optimizing it or developing and testing new concepts is currently lacking. In this paper a computer-aided approach is presented for testing, calibrating and evaluating any ramp control strategy desired for a particular freeway section or system including adjacent arterials (corridors). The methodology employs a versatile, easy to use microscopic simulator, which was selected after evaluating the most widely used ones. The selection criteria included ease of use, versatility, code availability and documentation, performance, and proven effectiveness through extensive employment in real projects. The simulator was enhanced to include an interface that allows integration of any user specified ramp control scheme. Enhancements were also made to automatically collect and feed demand patterns to the simulator. The entire simulation, database, and control logic package can be effectively used to estimate parameters and compare/evaluate ramp control strategies iteratively. New control strategies can also be implemented, tested, and calibrated by the user with relative ease. As a “test case“ application, the ramp control logic developed by the M innesota Department of Transportation (M nDOT) was implemented on a 24-km southbound segment of I-35W in M inneapolis and compared with the no-control alternative. In this manner the benefits of this ramp control strategy are quantified and presented. Improvements to the control strategy as well as testing of others are being planned with this methodology. 1
SIMULATOR SUMMARY AIM SUN2 (1,2) (Advanced Interactive M icroscopic Simulator for Urban and Non-Urban Networks), is a simulation package which is able to treat any complex geometry (such as interchanges, collectors-distributors, roundabouts. etc.) of realistic large-scale urban networks consisting of both freeway and surface streets. AIM SUN2 follows a microscopic simulation approach. This means that the behavior of each vehicle in the network is continuously modeled throughout the simulation period using several driver behavior models (car following, lane changing, gap acceptance, etc.). AIM SUN2 is a combined discrete-continuous simulator; this implies that there are some elements of the system (i.e. vehicles, detectors) whose states change continuously over the simulation time, which is split into short fixed time intervals, while there are other elements (i.e. traffic lights, entrance points) whose states change discretely at specific times. AIM SUN2 distinguishes between different classes of vehicles and drivers. It can also simulate incidents, conflicting movements and other complexities encountered in reality like weaving sections, HOVs, etc. AIM SUN2 can deal with any combination of roadway geometries. This includes urban networks, freeways, complex interchanges and intersections. Different types of traffic control can be accommodated. This includes pre-timed or actuated signals, unsignalized intersections and ramp control. The vehicle behavior models are functions of several parameters that allow modeling of different types of vehicles, i.e. cars, buses, trucks, etc. The simulator provides detailed statistical outputs for all M OEs and traffic variables, such as flows, speeds, travel times, delays etc. These can be global (for all the simulation period) or periodic. These measurements can be aggregated for the entire system or desegregated for sections and turnings. The output is quantitative and graphical including a drawing of the network and an animated representation of the vehicles. As an enhancement to the original simulation package for the purposes of research conducted at the University of M innesota, a series of tools were created to assist in the automation of simulation input data. Specifically, one of the major problems in using simulation is the copious and time-consuming task of entering initial and boundary conditions. To minimize this effort software was prepared to store data from 3000 detectors, collected by M nDOT’s Traffic M anagement Center (TM C), in a relational database. This demand tool can produce initial and boundary conditions for any modeled section by accessing the above database. In this manner a procedure that used to take days for entering the demand patterns is now accomplished in minutes.
CONTROL PLAN INTERFACE (CPI) DESCRIPTION The CPI is an interface that integrates AIM SUN2 with any external user defined ramp control logic. It facilitates the exchange of information between the simulator and an external control scheme. This is especially needed for simulating adaptive ramp control strategies, which use real time traffic measurements to determine current metering rates. Such measurements might be volume, occupancy, and speed on the mainline as well as queue-lengths on the ramps. The simulator provides the necessary measurements, which the CPI transfers to the external control logic. In its turn, the control logic calculates the new rates and transfers them to the simulator through the CPI. 2
In general, most of the simulators have been designed to include one or more known ramp control schemes. This allows the user to compare the available schemes within a single simulator, but it doesn’t allow comparison with other non-supported schemes. With the CPI, the user has the capability of easily programming any control logic without having to change the core of the simulator. AIM SUN2 is capable of communicating with an external application. The CPI enhances this ability by grouping the necessary information specifically needed for ramp control schemes. In addition, new functions were added to allow easier access to the simulator. This makes the job of the end user easier because he has more tools available to integrate his control logic with the simulator. Specifically, in the CPI the notion of detector stations was added, as most of the current ramp control strategies require measurements over all lanes of the mainline instead of lane by lane. Additionally, the user is now able to define the collection rate of measurements, which can now be different from the one specified in the simulator. For example, the simulator may collect lane-by-lane detector data every 30 seconds but the user’s ramp control logic could request and receive 5-minute detector station data. With the original external interface, the user’s logic has to be designed specifically for the network under consideration. With the CPI the user may access information at run-time about the road geometry, traffic detection and control devices as well as their mode of operation. This allows the user to design his logic in a more generic way and be able to use it on a variety of different networks. Finally, the implementation allows for customized output to be saved including information specific to the operation, effectiveness and general performance of the control logic.
IMPLEMENTATION In order to demonstrate the utility and relevance of TRAM LAB, in real life applications, the integrated ramp control strategy implemented by M nDOT was programmed and interfaced with AIM SUN2. Subsequently a 24-km section of I-35W in M inneapolis was simulated with AIM SUN2 and compared with the no-control alternative. Parameter optimization was not initially performed as it was assumed that this was already achieved through continuous fine-tuning during its long life span. As discussed later, using TRAM LAB future improvements of the control strategy are planned as well as comparison with alternate control schemes. With little or no control of traffic on entrance ramps, flow rates on the freeway will exceed capacity at critical bottleneck locations. The resultant shockwave activity (congestion) limits the flow on the freeway to below 1700 vehicles per lane per hour. The M innesota algorithm allows sustained flow rates on a controlled freeway of 2200 to 2400 vehicles per hour per lane. In what follows a brief description of this volume based real time control algorithm is presented.
INTEGRATED ADAPTIVE RAMP CONTROL ALGORITHM The M innesota control strategy (3) begins by dividing the freeway into zones. A zone is 3
defined as a freeway section, traveling in one direction, and is typically three to six miles in length. The beginning or upstream end of a zone is usually a free-flow area not subject to high incident rates. The downstream end of a zone is a bottleneck, where the demand to capacity ratio is highest on that freeway section. Lane drop locations, high volume entrance ramps, and high volume weaving sections are typical bottleneck locations. The zone control algorithm is built on the basic concept of balancing the volume of traffic entering the zone with that leaving the zone. All volumes of entering and exiting traffic are measured in real time every 30 seconds. When these total volumes are balanced, the density of traffic in the zone should remain within a narrow range. When the density of traffic in the zone is low, there is "space available" within the zone for additional entering traffic. The metering zone conservation equation can be expressed as: A + U + M + F = X + B +S A = Upstream mainline volume (veh/5 min); a measured variable U = Σ (Unmetered entrance ramp volumes) (veh/5 min); a measured variable M = Σ (Metered local access ramp volumes) (veh/5 min); a control variable F = Σ (Metered freeway to freeway access ramp volumes) (veh/5 min); a control variable X = Σ (Exit ramp volumes) (veh/5 min); a measured variable B = Downstream bottleneck capacity volume (veh/5 min); a constant S = Space available within the zone (veh); a computed volume based on occupancy of mainline detectors Stated as the sum of metered ramp volumes, the equation becomes: M+F=X+B+S-A–U Any measured variation in X + B + S - A - U is equaled by a controlled variation in M + F. Each individual variable in the above equation has atarget value (denoted by t ). The zone conservation equation written in the target volume form is : Mt + Ft = Xt + Bt + S t - At - Ut . All variables, except S t , are assigned a one-hour volume derived from the detector data. This volume is the median value from fifteen days, of the highest floating sixty-minute flow rate. The volumes for each variable are expressed as five-minute flow rates. The value of St is set to zero, indicating the target condition has no space available in the zone. When these target volumes are placed in the equation, an exact balance may not appear. For this reason, a minor adjustment to the incoming volume (At ) is made to balance the equation. The values in this balanced equation are the target volumes. The controlled variables M + F are expressed as the products of the target volumes and a series of factors. The metering factors for local access ramps range from 0.5 to 1.5, and for freeway-to-freeway ramps, they range from 0.75 to 1.25. For the most restrictive rate, M = 0.5 M t and F = 0.75 Ft . Each metered ramp is assigned six metering rates. On local access ramps, these rates over a five-minute time period would correspond to 1.5, 1.3, 1.1, 0.9, 0.7, and 0.5 times the target volume. On freeway-to-freeway ramps, rates over a five-minute 4
period are 1.25, 1.15, 1.05, 0.95, 0.85, and 0.75 times the target volume. The selection of which rate to use is then determined by a comparison of the measured variables (X + B + S A - U) to a series of thresholds. The AM and PM peak periods used by M nDOT are defined to bracket the heavy work trip times on weekdays. Each zone is designated an AM zone, a PM zone, or both. Zones are assigned to be metered when the expected flow rate at the bottleneck is equal to capacity for one or more hours during the peak period. The AM peak has a turn on period (6 AM to 7 AM ) during which ramp meters will turn on individually or in groups when calling for a restrictive rate 5 or rate 6. Turn on is not initiated when less restrictive rates are called for because typical flow early in the peak is characterized by dramatic swings in volume under open flow conditions. Once ramp metering has begun, the rate used will be variable between rate 1 and rate 6. The mandatory metering period (7 AM to 8 AM ) is used for all AM zones. During the third or turn off time period (8 AM to 9:30 AM ), a ramp meter will turn off when the arrival rate of vehicles falls and the ramp empties. Every 5 minutes for each ramp, the volume recorded downstream of the metering signal is compared to the number of greens (possible car releases) displayed during that 5 minutes. When the measured volume falls below 90% of the number of possible car releases, that ramp meter is turned off. The PM peak is also in three parts. The turn on period is 2 PM to 3:30 PM . The mandatory metering is 3:30 to 5:30 PM . The turn off period is 5:30 PM to 7:00 PM.
EVALUATION OF RAMP CONTROL LOGIC PERFORMANCE
5
From Downtown
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31ST STREET
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35 STREET
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LYNDALE
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76TH STREET I - 494
106
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60 STREET TH 62
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113 STREET
I-35W ND
122 STREET TH13 Figure 1. Test Site Map: Southbound I-35W
TEST SITE DESCRIPTION
Following discussions with the M nDOT engineers’ in-charge of the TM C, a 24-km long section of I-35W going south was selected for testing purposes. This section was specifically chosen as it includes most of the common geometry configurations found in the Twin Cities. This section begins at Downtown M inneapolis and ends at the interchange with Highway 13. It includes 20 exit and 22 entrance ramps that are controlled during PM peak hours. Four entrances are freeway-to-freeway ramps, carrying very high volumes in the range of 1200 veh/hr with long spillback queues. The geometry includes 6 weaving sections and also has a lane drop section. The test site is divided into three zones and has three bottleneck locations. It also has a single HOV lane from I-494 interchange to Highway 13 that is about 10 km long. The total experiment was based on data collected during a 60-day period during M ay and June 1999. The larger amount of this period was used to calibrate the simulation model parameters. After it was deemed that the simulator was working as close as possible to real life conditions, one day’s worth of data from the above period was used on the evaluation described in this paper. This day was specifically chosen because it did not involve any malfunctioning detectors or non-recurring congestion due to incidents. The test period extends for 6 hours during the PM peak period, specifically from 2 to 8 PM. The experiment consisted of two test cases, one involving normal congestion levels and the other where the previous demands were uniformly increased by 20%. In both cases, we assumed a traffic composition of 95% cars and 5% trucks. In each case, two simulations were performed with and without ramp control. The M OEs that were collected include Total Travel Time (TTT) in veh-hrs and Total Delay (TD) in veh-hrs separately for the mainline 6
and the ramps and Total Travel (TT) in veh-km for the whole network. Point specific statistics were collected for the 3 bottleneck sections. Each of the above results was averaged over 30 simulations in order to minimize random fluctuations.
TEST RESULTS Before presenting the test results it should be recognized that due to the lack of sufficient data, the entire corridor was not simulated i.e. only the freeway and the ramps were included assuming no diversion. Delays were estimated by assuming a minimum speed of 10 mph above the posted speed limit in each section of the freeway, which varied from 45 to 55 mph and 45 mph on the ramps. With the improvements described earlier in this paper, the volume entry took under half an hour versus 72 hours manually. Table 1 summarizes the effectiveness of ramp control for normal and heavy congestion. As can be seen, TTT in the mainline decreased by 46% when control was introduced under normal congestion. This can be explained by the fact that with ramp control density remains below critical at the bottleneck. As a result, higher speeds were achieved. Total ramp delays increased substantially as expected but overall system TTT and delays were reduced by 34.61% and 61.78% respectively. For the heavy congestion case, the system TTT decreased by 24.39% and TD by 39.41%. Similar improvements were also realized in the remaining M OE’s (Table 1&2). In general, in both cases with control, higher speeds were achieved and the flow was smoother throughout the freeway as can be seen for the normal congestion case in Figure 2. The objective of the control algorithm is to maximize flow through the bottleneck of each zone. The graphs in figure 3, demonstrate that ramp control achieves its objective. The results of this testing simply confirmed that the ramp control strategy, improved the operating conditions on the freeway significantly on the overall system, especially with heavy congestion. This of course, was not unexpected. However, quantification of the results became a much easier task. The benefits in using TRAM LAB are that we can easily assess quantitatively the effects of continuously changing demand patterns without manual field measurements. Also easy calibration, modifying/improving existing and experimenting with new control algorithms is easier and more practical. As described in the next section such improvements are currently being planned.
Case MOEs Total Travel Time TTT (veh-hrs)
Total Delay TD (veh-hrs)
Normal Congestion No With Control Control Mainline 29037 15627
%Diff -46.18
Heavy Congestion No With %Diff Control Control 41231 27501 -33.30
Ramp 1948 System 30985 Mainline 16844
4634 20261 3128
137.86 -34.61 -81.43
3425 44656 29720
6263 33764 14124
82.86 -24.39 -52.48
Ramp
3667
292.36
2730
5535
102.77
935
7
Speed (km/hr) # of Stops Total Travel TT (veh-km)
System Mainline Mainline System
17779 59.6 2933764 698960
6975 88.55 266572 716591
-61.78 47.85 -90.91 2.52
32450 45 6163727 656250
Table 1. Ramp Control Strategy Evaluation Results
8
19659 -39.41 68 52.16 1913605-68.95 761870 16.09
CONCLUDING REMARKS Even though the example demonstrating the utility of TRAM LAB is simple, it has significant potential for testing existing or selecting the most suitable control strategy for specific freeways. It is also valuable for developing new integrated solutions, parameter calibration and algorithm optimization. The simplifications and functional enhancements made should aid in the widespread use of simulation in practice as well as improvements in ramp control strategies and other traffic management schemes. As mentioned earlier, the closeness of the tested control algorithm to optimality is unknown. Since, however testing has now become easier, a number of simulations can be performed in order to determine this as well as to optimize parameters quickly and efficiently. The control scheme presented assumes that the location of the bottleneck is fixed and known a priori. This is not always easy to determine and on the Twin Cities freeways this is often a tedious process that involves detailed analysis of traffic patterns to determine the actual bottleneck locations, which are time dependent. For this reason, it is currently planned to improve the control logic by dynamic variable zone determination based on real time bottleneck identification as well as developing new control schemes or testing others developed elsewhere in order to capitalize on recent ITS technological advances. Finally, it should be noted that what was evaluated in this paper was the ramp control algorithm presently working in the Twin Cities network. What wasn’t tested though is manual system overrides. Specifically, a human operator in the TM C observes actual traffic 9
conditions and has the ability to interfere with the algorithm whenever he deems it appropriate. After a series of observations of daily TM C operations, it was noticed that operators override the control algorithm frequently, specifically in cases of queue spillbacks and special events. The next step in this research will be to attach a user interface to the CPI in-order to allow manual interaction with the simulated ramp controllers, thus allowing the user to override the ramp control algorithm in the simulation. REFERENCES [1] Barceló, J., Ferrer J.L. and Grau R. “Microscopic Traffic Simulation for ATT Systems Analysis”. Research Report, Departament de Estadística e Investigación Operativa, Universitat Politecnica de Catalunya. April 1996. [2] Barceló, J., Ferrer J.L., Grau R., Florian M. and Le Saux E. “A Route Based Variant of the AIMSUN2 Microsimulation Model”. 2nd. World Congress on Intelligent Transport Systems, Yokohama, 1995. [3] Lau Rich P. “MnDOT Ramp Metering Algorithm”. Internal Report, Minnesota Department of Transportation, Minneapolis, Minnesota. 1996
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