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Chapter 4: Conclusions and Future Directions In this project, Dr. Douglas Gettman at Kimley-Horn and Associates (KHA), Dr. Henry Liu at the University of Minnesota, and Dr. Montasir Abbas at Virginia Tech performed experimental research on the mitigation of oversaturated traffic conditions on arterials and networks with the support of a number of graduate students and staff. Dr. Alex Skabardonis provided advisory support. This research was divided into five experimental areas: ⢠Diagnosis and quantification of the type and cause of oversaturated conditions ⢠Development of a methodology for testing and evaluating the relative performance of mitigation strategies on specific scenarios ⢠Development of tools to relate diagnosis of conditions with mitigation strategies ⢠Testing of mitigation strategies on real-world scenarios ⢠Distillation of the findings into a rational guide for practitioners to diagnosis and select the appropriate mitigation strategies The research focused on identifying traffic control strategies that can be implemented by traffic signal systems using traditional actuated-coordinated fixed-parameter signal timing plans, time-of-day scheduling, or traffic-responsive plan selection methods. Definitions and Diagnosis A substantial set of definitions was identified during the project to develop a taxonomy for describing oversaturated scenarios. This taxonomy includes spatial extent (approach, route, network), duration (intermittent, persistent, pervasive), causation (demand, incidents, timings, etc.), recurrence, and symptoms (storage blocking, starvation, etc.). The basic definition of an oversaturated condition starts from the presence of an overflow queue on a traffic movement after the termination of the green-time for that movement. From this basic concept, higher level definitions were developed for approaches, phases, routes, arterials, and networks. To diagnose the severity of an oversaturated condition a methodology was developed to estimate queue length from high resolution second-by-second occupancy data from advance detectors and second-by-second phase timing data. This methodology allows measurement of queues that grow substantially upstream of the detector location. In addition, two quantitative measures of oversaturation intensity were developed. These measures (TOSI and SOSI) quantify the relationship between the length of the residual queue at an intersection approach or movement with the available green-time. SOSI measures how much green-time is wasted when vehicles cannot move due to downstream blockage and TOSI measures how much green-time is spent Operation of traffic signal systems in oversaturated conditions Page 277
dissipating overflow queuing from the previous cycle. Oversaturated routes, arterials, intersections, networks, and so on are then defined as having TOSI and/or SOSI > 0 on the constituent approaches and movements at the same time. The characteristics of how TOSI and SOSI change over time were described in Task 2 in field tests in Minneapolis on TH55. Development of Management Objectives and Characterizing Oversaturated Conditions Scenarios In Task 3, we defined three broad operational objectives of signal timing strategies: 1. Minimizing (user) delay 2. Maximizing throughput 3. Managing queues The minimize delay objective drives strategies and operational principles that assume undersaturated operation and encapsulates all objectives that might be considered to be effective during undersaturated operation. This includes (a) minimizing the delay at a single intersection and (b) minimizing delay in a network or series of intersections on a travel route. Minimizing user delay is not appropriate when the situation is oversaturated, particularly since it is no longer possible to avoid phase failures. However, minimizing total vehicle delay was found to be an acceptable objective during the loading regime of an oversaturated scenario. In some situations, maximizing throughput was also appropriate during the loading regime. An objective of maximizing throughput de-emphasizes the individual userâs experience and focuses more heavily on the performance of the system as a whole. Timing strategies designed with this objective in mind tend to punish lighter movements to the benefit of the greater (system) good. This is done by moving heavier phases for longer amounts of time more frequently than by the typical cycle-failure minimizing methods of actuated control. Strategies that maximize throughput can be categorized as: ⢠Strategies that make best use of the physical space (e.g., lag heavy left turns; run closely-spaced intersections on single controller). ⢠Strategies that make best use of green-time in the cycle (e.g., prevent actuated short greens, separate congested movements from the uncongested ones, phase reservice). ⢠Strategies that reduce the negative impact of other influences (e.g., bus and pedestrian movements) on the overall ability of the signal system to process vehicle flows. Throughput maximization strategies have the goal of either increasing input to a system that has spare storage capacity, increase output from a system that is severely limited in storage capacity, or Operation of traffic signal systems in oversaturated conditions Page 278
both. At some point, however, no further revision to the signal timing will increase maximum throughput and queues will continue to grow until demand diminishes. The queue management objective applies when congestion is pervasive to the extent that additional green-time on a critical route will make the situation worse. Strategies that minimize total delay and maximize throughput were found to have similar performance in situations where queue management is a more appropriate objective. The only choice in these situations becomes the arrangement of the operation of signals within a network to prevent the queues from multiplying the problem. Traditional signal timing parameters (cycle, split, offset, etc.) are not particularly suited for operation during situations where queue management is the primary goal. Synchronizing the actions of multiple controllers in a system of intersections for the purpose of queue management is very difficult within the context of actuated-coordinated control by commanding patterns with different parameters. In the scenarios where signal timing plans were optimized for different objectives and were compared against each other, we approximated queue management strategies with timing plans (cycle, split, offset, sequence, etc.) that minimized the total degree of saturation on the critical routes. In tandem with these three operational objectives, we determined that there are three operating regimes during an oversaturated conditions scenario: Loading 1. Processing 2. Recovery During the loading regime, traffic volumes are increasing, route proportions are changing, and in the case of non-recurrent events, the triggering event has started. During loading, overflow queuing and other symptoms such as storage blocking and starvation begin to emerge. Early application of mitigation strategies can delay the onset of debilitating queuing. Early application of mitigation strategies is easier to conceptualize when the causal factors are recurrent. During the processing regime, the traffic volumes and route proportions are such that queues and congestion are not going to dissipate until either the traffic volumes are reduced, the route proportions are changed (i.e. drivers avoid the area, adjust their routes, decide to travel later, etc.), or both. This is the operational situation that many practitioners might characterize as there is nothing that can be done. Queue management strategies can be applied during this regime to help the system return to steady-state operation sooner than continuing to apply the normal operational strategies designed for undersaturated conditions. Queue management strategies might also be able to move more traffic that are not on the critical routes, since allocation of more green-time to the critical routes might actually make the problem worse. Operation of traffic signal systems in oversaturated conditions Page 279
During the recovery regime, traffic volumes and/or route proportions and/or restrictive downstream capacity (e.g. clearance of crash, removal of construction cones, reduction in traffic flow, etc.) have been adjusted so that the overflow queues begin to dissipate. In this regime of operation, mitigation strategies were found to be especially effective in returning the system to steady state sooner than continuing to apply normal operational strategies that assume undersaturated operation. The test cases performed in this research indicate that the recovery regime is where the most substantial performance improvements can be achieved by applying mitigations. In two of the test cases in Task 7, we explored the performance of timing plans designed for each of the operational objectives during each of the three operational regimes. It was found that there is significant value in applying different timing plans during the different regimes of a scenario. Mitigation Strategies We developed and tested mitigation strategies using three different methodologies. First, since little is published about traditional signal timing strategies for mitigating oversaturated conditions a research methodology was developed to compare the performance of traditional signal timing plans (cycle, splits, offsets, etc.) under different assumptions about the critical routes and operating regimes. This methodology compared mitigation strategies designed to maximize throughput, minimize delay, or manage queues against several realizations of critical route flows in two real-world test cases. This timing plan development framework and evaluation methodology extended previous work by Akcelik, Abu-Lebdeh, Lieberman, and Rathi. The principles by which the green-times, offsets, and objective functions were determined were extended from the principles developed by these previous works to address the inclusion of critical routes within the network and to comprehensively compare the mitigations on multiple objectives. The performance of each mitigation strategy was compared for both delay and throughput measures in a Pareto analysis to identify non-dominated strategies during each time period of a scenario. Typically it was found that no specific mitigation is optimal for both minimizing delay and maximizing throughput, and certainly no strategy was uniformly dominant during each time period of a scenario with loading, processing, and recovery regimes. The Pareto front analysis also revealed the importance of identifying critical routes through a network of intersections. Mathematical approaches akin to O-D estimation were explored, but are not detailed in the final report. Significant differences in performance were observed for differing definitions of the critical routes in a complex scenario. Since (demand) volumes are not easily measurable during oversaturation, identification of critical routes was described as an ad-hoc inspection process in the resulting practitioner guidance. Additional research is needed in the area of critical route identification, particularly to bring together the concepts of measurement of TOSI and SOSI with traditional O-D estimation techniques. Operation of traffic signal systems in oversaturated conditions Page 280
In addition to this structured and mathematically-based development methodology, we tested a variety of potential mitigations including phase reservice, negative offsets, cycle time adjustment, left-turn treatment, phase sequence, dynamic lane allocation, and phase truncation using engineering judgment. After identifying the critical routes for a specific scenario, combinations of mitigations were tested on four different simulation test cases. All mitigation strategies were implemented as either scheduled timing plan changes or based on the detection of certain oversaturated conditions using the ifâ¦then software tool developed as part of this project. Detection of oversaturated conditions was tested with both traditional measures (detector occupancy) and the derived measures we developed in this project, TOSI and SOSI. This ifâ¦then tool is available from NCHRP for universal use. Finally, in addition to the multi-objective strategy development framework and the variety of investigations based on engineering judgment, we also developed a methodology for deriving green-time re-allocation directly from the quantitative TOSI and SOSI measures that were developed in Task 2. After measurement of TOSI and SOSI either with mathematical or observational methods, a two-pass heuristic algorithm was developed to calculate increases and decreases to green-time to drive TOSI and SOSI on an oversaturated route to as close to zero as possible. The potential effectiveness of this two-pass analytical procedure was demonstrated in Task 7. Iterative application (i.e. adaptive control for oversaturated route management) of the procedure seems a reasonable next step in extending the research in this direction. Practitioner Guidance In Task 6, we developed guidance for practitioners to identify mitigation strategies that apply to various oversaturated conditions. The guidance follows a systems engineering approach to problem resolution, starting with problem characterization. In the initial steps of this process, the goal is to answer several basic questions: ⢠How many intersections and directions of travel are affected? (Spatial extent) ⢠How long does the oversaturated condition last? How does it evolve over time? How does it dissipate during recovery? (Temporal extent) ⢠How frequently does the oversaturated condition occur? (Recurrence) ⢠What is the cause or causes of this oversaturated condition? (Causes and Symptoms) In subsequent steps, it is recommended that the practitioner identify the objectives and approximate regimes of operation such as the duration of the loading, processing, and recovery regimes by generating a dynamic map of how the queues grow and dissipate through the scenario. The dynamic map also serves to identify the critical routes through the network. The guidance provides a litany of mitigation strategies and identifies how each strategy applies to various oversaturated scenarios. Rules of thumb and design principles are provided for some of the mitigations, in particular identifying where measurements of TOSI and SOSI can be used to Operation of traffic signal systems in oversaturated conditions Page 281
identify appropriate actions. The guide concludes by specifying a generic systems-engineering-based process for deploying methodologies and evaluating their relative effectiveness. Requirements for on-line application of strategies using the logic tool developed in Task 4 are provided in the guide. Requirements for central system features, field controller features, additional detector stations, and other communications or field equipment for each type of mitigation are also detailed in the guidance. Significant future research and development will be needed to extend the existing guidance into a comprehensive tool for quantitatively determining mitigations in a cookbook fashion. Test Applications In Task 7, we applied methodologies developed in this project to six test networks. Two of the test networks (Reston Parkway in Herndon, VA and the Post Oak area in Houston, TX) were used in the development and testing of the methodology for developing mitigating strategies and testing those strategies using a multi-objective Pareto analysis. In the first test case (Reston Parkway), we considered the application of a single signal timing strategies for an entire oversaturated scenario. In the second test case, we explicitly considered the three regimes of the scenario in applying a sequence of three signal timing plans during the three operational regimes. Two other networks (TH55 in Minneapolis, MN and downtown Pasadena, CA) were used in the development and testing of strategies directly related to TOSI and SOSI. TH55 was used to prove and refine the concepts of TOSI and SOSI and to test the forward-backward procedure (FBP) in a relatively simple situation. The Pasadena, CA downtown network was used for developing and testing the FBP in a more stressing and complicated routing scenario. . Finally, two other test cases (an arterial in Surprise, AZ and a small network in Windsor, ON) were used to test a variety of mitigation strategies using engineering judgment and to apply the guidance methodology developed in Task 6. The Windsor, ON network was also used to demonstrate the application of the ifâ¦then on-line mitigation strategy selection tool. All of the test applications were done in simulated using Vissim with either the RBC or the Virtual D4 traffic controller. While route proportions and demand flows were changed over time, no dynamic traffic assignment was used, i.e. vehicles in the simulation did not react to the congestion conditions to change their route, change their destination, or forgo travel. Test Cases for the Multi-Objective Pareto Analysis Two scenarios were used to develop and test the strategy development methodology. These test cases focused on recurrent, daily congestion and oversaturation on an arterial and then in a more complicated network. The first test case applied a single mitigation timing plan to the entire oversaturated scenario and the second test case applied a sequence of three timing plans during the scenario to address the loading, processing, and recovery regimes. This methodology for developing mitigation strategies focuses on the identification of critical routes in a network. Operation of traffic signal systems in oversaturated conditions Page 282
From the identification of these critical routes and the approximation of the arrival demands on the routes, an optimization problem is solved to obtain a range of feasible cycle, split, offset values that meet an operational objective (minimize delay, maximize throughput, or manage queues). These solutions were then evaluated in Vissim to determine the differences in the performance of each combination of mitigation strategies for both delay and throughput measures. Non-dominated solutions were then identified using Pareto analysis. Different mitigation strategies are non-dominated at different times during the scenario and tend to result in clustering of similar mitigations during the three regimes of operation. The first test case analyzed an oversaturated scenario on Reston Parkway in Herndon, VA. This scenario is an arterial that intersects with the heavily traveled Dulles Toll Road. Combinations of cycle, splits, and offsets designed for operation in oversaturated conditions were tested. In this test case, the loading, processing, and recovery regimes were not explicitly considered in the timing plan design process. Only one timing plan was applied during the entire duration of the scenario. The timing plans were then combined with either upstream metering on the critical route or with phase reservice for the northbound left turn at the critical interchange. In both cases it was found, in general, that short cycle lengths (e.g. 100s) with close to simultaneous offsets would minimize total system delay. Medium-length cycle times (e.g. 140s) were found to maximize throughput. Strategies that were optimized to maximize throughput and combined with upstream metering generally decreased total delay by 20% and increased total throughput by 15%. Strategies that were optimized to minimize total delay and combined with upstream metering could reduce delay by up to 40%, but increased throughput by only 7%. Strategies that were focused on maximizing throughput and combined with phase reservice decreased total delay by 27% and increased total throughput by 22%. Strategies that were focused on minimizing total delay with phase reservice could reduce delay by up to 63%, but increased throughput by only 5%. This test case illustrated the importance of considering different objectives and focusing signal timing plan development on mitigating conditions for a given or assumed set of critical routes. In the second test case in the Post Oak area of Houston, TX, the three regimes of operation were explicitly considered in the timing plan development process. The same Pareto front evaluation procedure was applied to determine the differences in performance of each combination of mitigation strategies under different assumptions about the critical routes through the network. Two combinations of critical routes were evaluated. The first considered the combination of critical routes that pass through and into the network area. The second considered the combination of critical routes that result from travelers inside of the network area that are departing the area from the numerous parking facilities inside the network. Since this test case was the most complex and large-scale scenario of those that were tested in the project, each combination of critical routes was overlaid with each other to identify the critical movements and approaches throughout the network. Timing plans were then designed with consideration of these Operation of traffic signal systems in oversaturated conditions Page 283
critical movements and considered a sequence of timing plan changes at the beginning of the processing regime and then at the beginning of the recovery regime. The timing plan strategies considered green flaring, phase reservice, negative and simultaneous offsets, and harmonic (2:1 and 3:1) cycling at many of the intersections in the network. The strategy development process directly considered development of timing plans that were focused on one of the three regimes of the scenario, loading (minimize delay or maximize throughput), processing (manage queues), or recovery (maximize throughput). A pre-processing step to identify the timing plan among all of the plans that were developed that performed best during each regime of operation for either minimizing delay or maximizing throughput. This optimization process also identified the recommended points in time for switching between the three timing plans during the scenario. Notably, the switching points for the (maximize throughputï manage queuesï maximize throughput) strategy and the (minimize delayï manage queuesï maximize throughput) strategy were different times. These two sequences of timing plans were then tested in the simulation and compared with the baseline operation of the network. The baseline operation with a single timing plan with a common cycle for all intersections throughout the network. In both strategies, the cycle time of the mitigations was reduced during the processing regime (from 150s to 100s or 90s) and then slightly increased during the recovery regime (from 150s to 160s). In addition, approximately half of the intersections in the network were double-cycled during the processing regime (80s or 75s cycle time) in order to manage the growth and interaction of long queues on the short network links in the interior of the network. Both strategies were found to provide modest 5-10% improvements over the baseline strategy for total delay. For total stops and average stops per vehicle, the (minimize delayï manage queuesï maximize throughput) strategy combination showed small detriments and the (maximize throughputï manage queuesï maximize throughput) strategy improved both measures over the baseline. For total throughput, the (minimize delayï manage queuesï maximize throughput) strategy produced improved throughput on approximately 1/3 of the intersections and decreased throughput on the remaining intersections when compared to the baseline. The (maximize throughputï manage queuesï maximize throughput) strategy produced improved throughput on approximately 2/3 of the intersections and decreased throughput on the remaining intersections when compared to the baseline. The detriments to throughput at the intersections with reduced performance were less significant than the detriments produced by the (minimize delayï manage queuesï maximize throughput) strategy. Those locations with throughput reductions were typically at the locations where SOSI was non-zero (portions of the green-time were wasted because no vehicles could move). In addition, significant throughput improvements were found at the intersections that were double-cycled. Operation of traffic signal systems in oversaturated conditions Page 284
Test Cases for Direct Application of TOSI and SOSI to Re-allocate Green-Time In the test applications for direct application of the TOSI and SOSI measures, we found significant improvements were possible. In the TH55 in Minneapolis, MN application, the TOSI and SOSI values were used to identify offset and green-time re-allocation recommendations to improve the throughput performance. This test case could represent both recurrent and/or non-recurrent oversaturation. In this case, which had one oversaturated approach, it was demonstrated that direct measurement of TOSI and SOSI and adjustment of the green-times on the arterial using the FBP could drive the resulting TOSI and SOSI measurements to zero. In the test case using the Pasadena, CA downtown network, two scenarios were tested. The first was a single oversaturated route in one direction on an arterial. The FBP was developed and applied using the average TOSI and SOSI values measured along the route in the do nothing condition. The resulting adjustments to the green splits along the oversaturated route resulted in a 30% improvement to the throughput along the route. Notably because of the downstream blocking conditions along portions of the route, some of the green-time splits were actually reduced, but the throughput performance along the route was still increased. In the second test, two intersecting oversaturated routes were generated (one southbound and one westbound). The same FBP was applied to calculate the green-time adjustments along both routes. In this test, the average throughput for southbound route showed no appreciable improvement but the westbound route was improved by 10%. In both of the test cases, modifications to cycle time, phase sequence, and other mitigation approaches were not considered. Both of these test cases indicated that there is promise in directly considering the quantitative measures of oversaturation intensity in the re-computation of green-time allocation. More research and development will be required to formulate more comprehensive analytical procedures that integrate this approach with the consideration of the loading, processing, and recovery phases of a specific oversaturated scenario; essentially solving for the green-time re-allocation as a rolling-horizon adaptive control problem. Test Cases for the Application of the Practitioner Guidance and On-line Evaluation of Mitigations Two additional test applications focused on following the process defined in the practitioner guidance. The first test case in Windsor, ON, evaluated mitigating strategies for handling oversaturated conditions on two critical routes (eastbound and northbound) competing for access to a single capacity-limited destination. In this test, a non-recurrent incident is generated at the entrance to a border-crossing tunnel. In normal operation, the westbound approach to the tunnel entrance has priority access to the tunnel since those vehicles are making a right turn into the critical link, however this route is not critical for the management of oversaturation. Six different mitigation strategies including metering, dynamic-lane assignment, phase omits, and green-time re-allocation were tested to see if more equitable allocation of green-time could be provided for the two critical routes with minor effect on the non-critical routes. This test was also envisioned to Operation of traffic signal systems in oversaturated conditions Page 285
explore improvement of performance to non-critical routes that were previously blocked by vehicles on the critical routes using the standard operations. This test case also demonstrated how the on-line calculation of TOSI and SOSI measures with ifâ¦then logic could be used to select appropriate mitigation strategies in a closed-loop manner. Typical locations for advance detection were used for the measurement of TOSI and SOSI and triggering the selection of a new plan. Initially it was conceived that the application of ifâ¦then rules for selecting timing plans would be less complicated for practitioners than the use of traditional target-based traffic-responsive methods. In straight-forward situations with only one or two critical oversaturated approaches this is true. When applying the approach to three, four, or more detection points, the truth table becomes onerous to generate manually. It was clear in these experiments that adaptive control algorithms are necessary to make appropriate decisions in complex situations. This is an important direction for future research and development. All of the mitigation strategies applied to this test case showed that more efficient use of available space could be achieved by applying metering and offset strategies to reduce the occurrence of SOSI > 0 along the critical route. The performance results for total travel time, delay, and throughput were largely inconclusive as most link-by-link performance was either not significantly different than the baseline, or performance improvements on some links were offset by performance detriments on other links. Because this scenario involves a single point of failure at the tunnel entrance, truncating phase green-time or omitting phases that have SOSI > 0 does not result in significant performance improvements for other approaches, since those movements are also blocked by the same downstream incident. It was found however that more equitable treatment for all routes could be obtained by disallowing right-turn-on-red and implementing gating on the non-priority routes. The final test case in Surprise, AZ was focused on a heavily traveled arterial with pre-planned special event traffic overlaid on P.M. peak traffic that is already near oversaturated. The special event occurs roughly in the middle of the arterial network and most event traffic approaches from the east. In normal operation, the westbound approach is heavily queued at the entrance to the special event facility. Eight different mitigation strategies were formulated and tested on this scenario. The mitigations included combinations of cycle time adjustment, green-time re-allocation, negative and simultaneous offsets, dynamic lane allocation, and double-cycling. The results for this scenario indicated that, in general, all of the mitigations outperform the baseline operation for both total travel time, throughput and delay measures. Improvements in the performance of the westbound oversaturated route were offset by detriments to the non-critical eastbound route. However, the mitigations provided more equitable performance in the two route travel times, whereas when applying the baseline strategy the performance of the critical route was three to four times worse than the non-critical route. The mitigation strategies showed considerable improvement over the baseline operation during the recovery regime. Most of the mitigating strategies improved the system recovery time by more than 20 minutes. Operation of traffic signal systems in oversaturated conditions Page 286
Project Summary and Directions for Further Research This goal of this project was to conduct research and develop guidance for practitioners in the application of mitigation strategies for oversaturated conditions. The body of knowledge in this area was quite limited when the project began. We focused our efforts on four topics: 1. Development of techniques to quantitatively characterize oversaturated conditions 2. Development of a technique for generating signal timing plan parameters and evaluating the effectiveness of those strategies 3. Developing an experimental tool for linking the quantitative measures of oversaturation to selecting of mitigating strategies in an on-line manner 4. Evaluating a wide range of strategies and providing evidence of effectiveness in a series of simulation experiments These activities were then used to generate a practical guide for selection and application of strategies to particular oversaturated situations. Much more research and development in this area is needed to establish understanding, generalize the guidance, and develop implementable procedures for analysis and application of strategies. The complexity of issues that must be considered during oversaturated conditions is still daunting for the development of closed-form solutions or cookbook type procedures. Some key take-away findings were identified during the project. The first key finding was that identifying the critical routes through a network of intersections is the first a critical first step in identifying appropriate mitigations. The methodology we developed for designing timing plans that explicitly consider critical routes showed promise that alternative formulation for the optimization process can result in significant gains in total system performance. In addition, this methodology is one of the first we know of that can optimize both the timing plan parameters of individual timing plans and the sequence and duration of application of those plans during a scenario. There is still much effort necessary to bring this complicated and experimental methodology closer to being able to be applied by a typical practitioner much like they might run a tool like Synchro or Transyt. The maximization of system throughput is only possible by equitable allocation of green-time to the critical routes and movements in the system. Once the oversaturated condition grows beyond just a single intersection, traditional operation with minimize delay strategies will only tend to exacerbate the situation further since these methods will over-allocate green-time to minor approaches. Furthermore, traditional thinking such as âmore green is always betterâ can work directly against the throughput objective since if the downstream link is already significantly queued, the upstream traffic will not be able to move anyway. It is not intuitively obvious how green-time can be re-allocated most appropriately in this situation. The second key development in this project was the development of the TOSI and SOSI quantitative measures and the development of the heuristic FBP to directly compute re-allocation of green-times from those Operation of traffic signal systems in oversaturated conditions Page 287
measurements. This process was developed and tested in an off-line manner in this project and shown to make improvements to an oversaturated scenario. These experiments point toward on-line application of the FBP to continually re-compute the green-time allocation in an adaptive manner. The final key finding of the project was that it is important to consider operating the system differently during the three regimes of operation during oversaturated conditions: ⢠Loading ⢠Processing ⢠Recovery During the loading regime, systems are best operated by continuing to minimize total delay or maximizing total throughput. When TOSI and SOSI become significantly > 0, strategies which manage queues on the critical routes are most effective. These strategies minimize the degree of saturation on critical routes in order to minimize SOSI and TOSI effects. Minimizing SOSI is the most important goal during the processing regime. Finally, in the recovery regime, strategies which maximize throughput are much more effective in clearing the overflow queues that were generating during the processing phase than other strategies. Figure 179 illustrates the difference between the total output processing rate of a mitigating strategy versus a no-mitigation baseline operation. In particular, note the substantial difference between the performances of the mitigation strategy with the baseline control strategy after the peak period ends. Operation of traffic signal systems in oversaturated conditions Page 288
Figure 179. Comparison of output processing rates during recovery period It is quite clear that the mitigation strategy begins processing more of the overflow queues much more efficiently after the peak period input flows subside. The no-mitigation strategy continues to decrease in total system output for an additional 20 minutes and takes another 30 minutes before itsâ peak output processing rate is finally reached (approximately 30 minutes behind the mitigation strategyâs maximum output processing rate). Finally, the no-mitigation strategy returns the system to steady-state operation at least 10 minutes later than the mitigation strategy. All three of these metrics indicate that mitigation strategies designed to maximize throughput can be effective in improving total system output in the recovery regime. Of the three regimes, the largest performance improvements can be achieved by applying mitigations during the time when the system is recovering from the severe queuing. Directions for Future Research It is often said that good research generates more questions than provides answers. This project generated a number of significant directions for further research. While the goal of this project was to initially develop a guide for practitioners, there were simply too many unknowns in this area to distill a limited amount of benefits information into a guide or generate a prescriptive unified theory of operations. As such, the first future research need is to evaluate additional test cases that will illustrate the performance of certain mitigations in specific situations. Based on the wide variety of potential mitigations and combinations of mitigations, it simply was not possible to conduct an evaluation of 7 00 75 00 78 00 81 00 84 00 87 00 90 00 93 00 96 00 99 00 10 20 0 10 50 0 10 80 0 11 10 0 11 40 0 11 70 0 Time (Seconds) Game Day No Mitigation Resonant Cycle 20 minutes 30 minutes 10 minutes End of peak period flow Baseline operation continues to degrade Operation of traffic signal systems in oversaturated conditions Page 289
every type of treatment. While the real-world cases that were tested in this project are instructive, for the most part it is difficult to extrapolate specific results from a particular test to other scenarios. In order to fully develop a comprehensive guide, at minimum, an example application of each potential technique is needed. Much more emphasis of researchers that study signal systems issues is needed in this area. As has been discussed several times in the context of TRB Signal Systems Committee meetings, it could be helpful if some standard test bed networks and situations (hypothetical or real world) were developed for researchers to test methodologies and compare the results apples to apples. We propose that the networks tested in this project may be put into the public domain for testing methods and evaluation of strategies developed by other researchers. The second major need is to test and evaluate mitigating strategies in the real world. All of the tests that were performed during this project were done with simulation tools. It is well known that simulations have challenges in representation of real-world behaviors during oversaturation. Field testing and application of mitigations in real-world sites (among those that were tested in the simulation studies during this project) would certainly be a valuable research activity to follow this effort. The practitioner guidance could be greatly improved by development of additional ârules of thumbâ and more âcookbookâ type design principles for mitigations. This could not be achieved during this research due to the immaturity of knowledge in this area. Furthermore, the combination of mitigations into comprehensive strategies is still more art than science and the methodology developed in this project is too cumbersome and complicated for use by a typical practitioner. Development of an off-line analysis tool that can develop mitigation strategies in general network structures will be a valuable future research topic. Additional research is needed on the role of thresholds, persistence time, and recovery time in the measurement of TOSI and SOSI for selection of mitigations in an on-line manner. In this research, we selected what seemed to be common-sense values for these parameters but did not do any sensitivity analysis on the values of these inputs. Additional scenarios need also to be constructed and tested with the on-line tool for more comprehensive evaluation of the effectiveness of such a tool. In order to truly get such methods into real practice, system operators will have to âspecâ and procure such features in upgrades or new installations of ATMS. Finally, while it was found that real benefits can be achieved through application of fixed-parameter timing plans, it was clear that on-line adaptive feedback control methods would improve the operation of oversaturated systems. In particular, it appears that offsets and green-time on oversaturated critical routes need to be adjusted almost every cycle to mitigate TOSI and SOSI > 0. Negative offsets can be designed for a particular value of TOSI, but if the demand rate remains constant and the green-time is left constant, the queue length will continue to grow until the link is filled. Development of adaptive algorithms and logic that directly consider Operation of traffic signal systems in oversaturated conditions Page 290
oversaturated conditions in actuated-coordinated systems would be of benefit to the industry. The FBP could be extended to a rolling-horizon formulation to take another step in that direction. Much additional research would be necessary to extend the basic heuristic of the FBP to consider phase sequencing, cycle time, protected/permitted lefts, and so on. Operation of traffic signal systems in oversaturated conditions Page 291
