Abstract:
The exponential rate of increase in freeway traffic is expanding the need for accurate and realistic methods to model and predict traffic flow. Traffic modeling and simulation facilitate an examination of both microscopic and macroscopic views of traffic flows and are therefore considered one of the most important analytical tools in traffic engineering. This report presents a cellular automata model for traffic flow simulation and prediction (CATS). Cellular automata models quantize complex behavior into simple individual components. In this model, the freeway being simulated is discretized into homogeneous cells of equal length, and time is discretized into timesteps of equal duration. The CATS model allows users to define locations within the road topology where volume and density data will be calculated so that the model results can be compared to observed highway data.

Authors:
Dailey,D. J., Taiyab,N.

Keywords:
cellular automata, traffic modeling, dynamic simulation, prediction, control, ramp metering, research

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External Links: http://www.wsdot.wa.gov/research/reports/fullreports/537.1.pdf http://wsdot.wa.gov/Research/Reports/500/537.1.htm

Abstract:
A Fuzzy Logic Ramp Metering Algorithm was implemented on 126 ramps in the greater Seattle area. Two multiple-ramp study sites were evaluated by comparing the fuzzy logic controller (FLC) to the other two ramp metering algorithms in operation at those sites over a four-month period. At the first study site, the days when the FLC was metering had lower mainline occupancies and higher throughput volumes in comparison to the days when the Local Algorithm was metering. At the second study site, the days when the FLC was metering had mainline occupancies that were similar, queues that were shorter, and throughput that was similar to the days when the Bottleneck Algorithm was metering.

Authors:
Taylor,C. E., Meldrum,D. R.

Keywords:
ramp metering, fuzzy logic control, intelligent transportation systems, freeway operations, transportation management software

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Abstract:
A Fuzzy Logic Ramp Metering Algorithm was implemented on 126 ramps in the greater Seattle area. This report documents the implementation of the Fuzzy Logic Ramp Metering Algorithm at the Northwest District of the Washington State Department of Transportation.This programmer's guide contains the software design for the new and modified code, the integration procedure, the results of software regression testing, the test results of new functionality, a discussion of the performance evaluation software used, the algorithm's transferability to other regions, and recommendations for the future.Two other related reports cover the project's research approach, evaluation method, and the results of on-line testing of the Fuzzy Logic Ramp Metering Algorithm, as well as the algorithm design and tuning technique.

Authors:
Taylor,C. E., Meldrum,D. R.

Keywords:
ramp metering, fuzzy logic control, intelligent transportation systems, freeway operations, transportation management software

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Abstract:
A fuzzy logic ramp metering algorithm will address the needs of Seattle's freeway system and overcome limitations of the existing ramp metering algorithm. This project progressed toward implementing and testing a fuzzy neural ramp metering algorithm on-line at the Traffic Systems Management Center (TSMC) for the Washington State Department of Transportation's Northwest District. Improvements were made to neural network predictors to allow better generalization.Code was written for the fuzzy ramp metering algorithm and its interface with the pre-existing TSMC code. Of the new code written, approximately 95 percent of it was for the interface, and only 5 percent of it was for the ramp metering algorithm itself. Interfacing the fuzzy controller with the existing TSMC software required modification of 16 pre-existing files related to the ramp metering database, real-time skeleton, and ramp metering and data collector communications.A method was developed and code was written to directly send metering rates from the VAX to the 170 and to implement them, whereas previously only a metering rate adjustment had been possible. The operator interface was designed and code was written to enter fuzzy tuning parameters and fuzzy equations. The specifications for each new parameter were designed.Although this code was written, it has not yet been implemented on-line because of time constraints. Preparation for on-line implementation required more time that anticipated because of the unexpected complexity of the pre-existing TSMC code. On-line implementation and testing will proceed on a WSDOT/TransNow project that begins in September 1997.In addition to software design, further planning was necessary to ensure smooth implementation and quality performance. The testing plan was developed in greater detail to include software quality testing. Primary and backup study sites were chosen, and an evaluation technique was selected. A risk assessment plan was developed to mitigate future problems.

Authors:
Taylor,C. E., Meldrum,D. R.

Keywords:
artificial neural networks (ANN), fuzzy logic controller (FLC), traffic data prediction, ramp metering, research

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Abstract:
A fuzzy logic ramp metering algorithm will address the needs of Seattle's freeway system and overcome limitations of the existing ramp metering algorithm. This project progressed toward implementing and testing a fuzzy neural ramp metering algorithm on-line at the Traffic Systems Management Center (TSMC) for the Washington State Department of Transportation's Northwest District. Improvements were made to neural network predictors to allow better generalization.Code was written for the fuzzy ramp metering algorithm and its interface with the pre-existing TSMC code. Of the new code written, approximately 95 percent of it was for the interface, and only 5 percent of it was for the ramp metering algorithm itself. Interfacing the fuzzy controller with the existing TSMC software required modification of 16 pre-existing files related to the ramp metering database, real-time skeleton, and ramp metering and data collector communications.A method was developed and code was written to directly send metering rates from the VAX to the 170 and to implement them, whereas previously only a metering rate adjustment had been possible. The operator interface was designed and code was written to enter fuzzy tuning parameters and fuzzy equations. The specifications for each new parameter were designed.Although this code was written, it has not yet been implemented on-line because of time constraints. Preparation for on-line implementation required more time that anticipated because of the unexpected complexity of the pre-existing TSMC code. On-line implementation and testing will proceed on a WSDOT/TransNow project that begins in September 1997.In addition to software design, further planning was necessary to ensure smooth implementation and quality performance. The testing plan was developed in greater detail to include software quality testing. Primary and backup study sites were chosen, and an evaluation technique was selected. A risk assessment plan was developed to mitigate future problems.

Authors:
Taylor,C. E., Meldrum,D. R.

Keywords:
artificial neural networks (ANN), fuzzy logic controller (FLC), traffic data prediction, ramp metering

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Abstract:
A fuzzy logic ramp metering algorithm will address the needs of Seattle's freeway system and overcome limitations of the existing ramp metering algorithm. The design of the fuzzy logic controller (FLC) reduced the sensitivity to sensor data, which frequently contains errors or noise. The rule base effectively balanced two opposing needs: to alleviate mainline congestion by restricting the metering rate, and to disperse the ramp queue by increasing the metering rate. To avoid oscillation between these two conflicting demands, the controller used inputs that were more descriptive of congestion levels, providing smooth transitions rather than threshold activations.Testing was performed using the freeway simulation software FRESIM. A multiple-ramp study site from Seattle's I-5 corridor was modeled using data such as freeway geometry, entry volumes, desired speeds, and driver behavior. To evaluate the FLC under a variety of conditions, entry volumes and incidents (such as a blocked lane or reduced capacity) were varied to create six test data sets. The performance of the FLC was compared to that of other available controllers, including clock, demand/capacity, and speed metering. The objective was to maximize total vehicle miles, maximize mainline speeds, and minimize delay/vehicle-mile while maintaining an acceptable ramp queue. For five of the six data sets, the FLC outperformed the other three controllers. In the FLC, sensors from the on-ramp were helpful in maintaining an acceptable ramp queue. Future work will involve on-line testing of the FLC.

Authors:
Taylor,C. E., Meldrum,D. R.

Keywords:
artificial neural networks (ANN), fuzzy logic controller (FLC), traffic data prediction, ramp metering, research

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Abstract:
This research project develops a fuzzy logic ramp metering algorithm utilizing artificial neural network (ANN) traffic data predictors. Considering the highly beneficial effects of ramp metering, such as reduced travel times and lower accident rates, optimizing metering rates is of great importance. The research objective is to overcome limitations of the current Seattle ramp metering algorithm, which reacts to existing bottlenecks rather than preventing them. An algorithm with predictive capabilities can help prevent or delay bottleneck formation. Hence, an accurate 1 -minute ANN prediction provides a powerful asset to the ramp metering algorithm. The research project divides into two stages: the ANN traffic data predictor and the fuzzy logic ramp metering algorithm. This research focuses primarily on the ANN traffic data predictors, but also lays the groundwork for the fuzzy logic ramp metering concepts and algorithm.The ANN predicts 1 minute in advance significantly better than previous techniques in the Seattle area, as well as demonstrates robustness to faulty loop detector data. A multi-layer perceptron type of ANN predicts congested mainline volume and occupancy for a station when given past values of volume and occupancy for that particular station and the adjacent upstream station. This data prediction provides an input to the fuzzy logic ramp metering algorithm. The ramp metering rate is then based on both current and predicted traffic flow. By considering the freeway as a control system instead of one section at a time, the new algorithm should avoid an oscillatory ramp metering rate, and achieve equilibrium more quickly and smoothly.

Authors:
Meldrum,D. R., Taylor,C. E.

Keywords:
artificial neural networks (ANN), fuzzy logic control (FLC), freeway traffic prediction, ramp metering

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Abstract:
To mitigate increasing traffic congestion and to improve highway safety, state departments of transportation have come up with some innovative strategies for optimizing the efficiency of congested freeway sections. Two such strategies are freeway-to-freeway ramp metering and mainline metering. Freeway-to-freeway ramp metering involves installing traffic signals (either on their side of the roadway or overhead) on the ramps found at freeway-to-freeway interchanges. Mainline metering involves installing traffic signals (usually overhead) on the mainline of a freeway. This paper examines some examples of freeway-to-freeway ramp metering in the United States, namely, in Minnesota and California. The advantages and disadvantages of freeway-to-freeway ramp metering are discussed. This paper then describes the only known operating example of mainline metering in the United States. Implementation and operational issues of mainline metering are discussed. The paper suggests that a complete and thorough analysis should take place prior to the installation of any freeway-to-freeway or mainline metering system. This analysis is needed to ensure that safety is maintained and that environmental concerns are addressed. The suggested policy on freeway-to-freeway ramp metering is as follows: 'Install meters on freeway-to-freeway ramps where system performance and efficiency will be improved.' The suggested policy on mainline metering is as follows: 'Install mainline meters on freeways approaching bottleneck locations where analysis indicates that improved traffic operations will result.' Guidelines for both metering types are listed in the paper.

Authors:
Klastorin,T., Pivo,G., Pilcher,M., Carlson,D., Hyman,C., Hansen,S., Hess,P., Thatte,A.

Keywords:
traffic surveillance and control, traffic congestion, ramp metering, TRB

Abstract:
The current project addressed two major weak points of the existing WSDOT Ramp Control System. One weak point in the system is the fact that it reacts to the problem (congestion), rather than preventing the problem. The other weak point in the system is its reliance on detector data that may be in error. Both of these problems can be minimized by developing methods to accurately predict short-term traffic data. By predicting the onset of congestion early enough, the ramp metering system can act to prevent or delay occurrence of the problem. Also, if a detector has failed or is malfunctioning, the data from the detector can be estimated from short-term predictions based on neighboring detectors. At the beginning of the current project, the researchers had hoped that the same model would provide a basis for both forecasting congestion (for predictive ramp control) and replacing erroneous data (predicting actual values). However, the best method for filling in missing detector data turned out to be multivariate time series analysis. Several pattern recognition and time series models were tested for further development. In both cases, the simpler models turned out to be the best choices, and in both cases, further model testing and development were recommended. The research on both model types continues in follow-up studies that are expected to lead to incorporation of these models in the new TSMC computer system.

Authors:
Nihan,N. L., Knutson,K. L.

Keywords:
bridge and construction, turner proposal, concrete bridge, fatigue, overload, replacement costs, remaining life, route assessment, maximum moments, maintenance, design loads, continuity, traffic surveillance and control, ramp controls, freeway management, traffic flow forecasts, forecast models, ramp metering

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Abstract:
The current project addressed two major weak points of the existing WSDOT Ramp Control System. One weak point in the system is the fact that it reacts to the problem (congestion), rather than preventing the problem. The other weak point in the system is its reliance on detector data that may be in error. Both of these problems can be minimized by developing methods to accurately predict short-term traffic data. By predicting the onset of congestion early enough, the ramp metering system can act to prevent or delay occurrence of the problem. Also, if a detector has failed or is malfunctioning, the data from the detector can be estimated from short-term predictions based on neighboring detectors. At the beginning of the current project, the researchers had hoped that the same model would provide a basis for both forecasting congestion (for predictive ramp control) and replacing erroneous data (predicting actual values). However, the best method for filling in missing detector data turned out to be multivariate time series analysis. Several pattern recognition and time series models were tested for further development. In both cases, the simpler models turned out to be the best choices, and in both cases, further model testing and development were recommended. The research on both model types continues in follow-up studies that are expected to lead to incorporation of these models in the new TSMC computer system.

Authors:
Nihan,N. L., Cabrera-Gonzalez,I.

Keywords:
bridge and construction, turner proposal, concrete bridge, fatigue, overload, replacement costs, remaining life, route assessment, maximum moments, maintenance, design loads, continuity, traffic surveillance and control, ramp controls, freeway management, traffic flow forecasts, forecast models, ramp metering

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Abstract:
The current project addressed two major weak points of the existing WSDOT Ramp Control System. One weak point in the system is the fact that it reacts to the problem (congestion), rather than preventing the problem. The other weak point in the system is its reliance on detector data that may be in error. Both of these problems can be minimized by developing methods to accurately predict short-term traffic data. By predicting the onset of congestion early enough, the ramp metering system can act to prevent or delay occurrence of the problem. Also, if a detector has failed or is malfunctioning, the data from the detector can be estimated from short-term predictions based on neighboring detectors. At the beginning of the current project, the researchers had hoped that the same model would provide a basis for both forecasting congestion (for predictive ramp control) and replacing erroneous data (predicting actual values). However, the best method for filling in missing detector data turned out to be multivariate time series analysis. Several pattern recognition and time series models were tested for further development. In both cases, the simpler models turned out to be the best choices, and in both cases, further model testing and development were recommended. The research on both model types continues in follow-up studies that are expected to lead to incorporation of these models in the new TSMC computer system.

Authors:
Nihan,N. L., Babla,M. D.

Keywords:
bridge and construction, turner proposal, concrete bridge, fatigue, overload, replacement costs, remaining life, route assessment, maximum moments, maintenance, design loads, continuity, traffic surveillance and control, ramp controls, freeway management, traffic flow forecasts, forecast models, ramp metering

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Abstract:
This report documents an operational analysis of 1-405 HOV facilities. The primary objectives of this analysis were (1) to provide information that could assist in the development of a coordinated plan for the 1-405 high-occupancy vehicle (HOV) lane system to ensure that the existing and planned HOV facilities worked together and that transitions between facilities occurred smoothly, and (2) to survey the 1-405 commuters as a means of understanding their perceptions of HOV lane operations and constraints on the ability of single-occupant vehicle (SOV) commuters to rideshare.The analysis included an overview of HOV lane operations in the United States, a public opinion survey of commuters who primarily lived and worked east of Lake Washington, results of focus groups with workers who lived in east King County, transportation modeling centering on the 1-5 corridor, traffic analysis of HOV lane options, a cost-effectiveness analysis, and the results of a symposium that presented and discussed the results of the project.

Authors:
Ulberg,C., Erickson,K.

Keywords:
HOV lanes, mode choice, public opinion, ramp metering, weaving analysis, transportation modeling, survey methodology

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Abstract:
The current project addressed two major weak points of the existing WSDOT Ramp Control System. One weak point in the system is the fact that it reacts to the problem (congestion), rather than preventing the problem. The other weak point in the system is its reliance on detector data that may be in error. Both of these problems can be minimized by developing methods to accurately predict short-term traffic data. By predicting the onset of congestion early enough, the ramp metering system can act to prevent or delay occurrence of the problem. Also, if a detector has failed or is malfunctioning, the data from the detector can be estimated from short-term predictions based on neighboring detectors. At the beginning of the current project, the researchers had hoped that the same model would provide a basis for both forecasting congestion (for predictive ramp control) and replacing erroneous data (predicting actual values). However, the best method for filling in missing detector data turned out to be multivariate time series analysis. Several pattern recognition and time series models were tested for further development. In both cases, the simpler models turned out to be the best choices, and in both cases, further model testing and development were recommended. The research on both model types continues in follow-up studies that are expected to lead to incorporation of these models in the new TSMC computer system.

Authors:
Nihan,N. L., Zhu,J.

Keywords:
bridge and construction, turner proposal, concrete bridge, fatigue, overload, replacement costs, remaining life, route assessment, maximum moments, maintenance, design loads, continuity, traffic surveillance and control, ramp controls, freeway management, traffic flow forecasts, forecast models, ramp metering

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Abstract:
The current project addressed two major weak points of the existing WSDOT Ramp Control System. One weak point in the system is the fact that it reacts to the problem (congestion), rather than preventing the problem. The other weak point in the system is its reliance on detector data that may be in error. Both of these problems can be minimized by developing methods to accurately predict short-term traffic data. By predicting the onset of congestion early enough, the ramp metering system can act to prevent or delay occurrence of the problem. Also, if a detector has failed or is malfunctioning, the data from the detector can be estimated from short-term predictions based on neighboring detectors. At the beginning of the current project, the researchers had hoped that the same model would provide a basis for both forecasting congestion (for predictive ramp control) and replacing erroneous data (predicting actual values). However, the best method for filling in missing detector data turned out to be multivariate time series analysis. Several pattern recognition and time series models were tested for further development. In both cases, the simpler models turned out to be the best choices, and in both cases, further model testing and development were recommended. The research on both model types continues in follow-up studies that are expected to lead to incorporation of these models in the new TSMC computer system.

Authors:
Nihan,N. L.

Keywords:
ramp controls, freeway management, traffic flow forecasts, forecast models, ramp metering

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Abstract:
The purpose of this project was to determine the feasibility and evaluate the usefulness of a predictive ramp-metering algorithm that anticipates bottlenecks (a bottleneck being a reduction in the traffic capacity of the freeway) one to two minutes before their occurrence. The predictive algorithm was tested on-line in the Washington State Department of Transportation's ramp-metering central computer. The predictive algorithms accuracy in predicting bottlenecks on-line was very good, with a correct prediction rate of almost 80 percent. The measured increase in volume and decrease in occupancy during a portion of the morning peak period showed that the predictive algorithm reduced the number and/or severity of bottlenecks on the freeway test section.

Authors:
Berg,D. B., Nihan,N. L.

Keywords:
traffic surveillance and control, ramp metering, ramp controls, predictive algorithm, traffic bottlenecks

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Abstract:
The Telecom Link established between the University of Washington and the WSDOT Traffic Systems Management Center (TSMC) was updated during this project in order to handle the shift made by the TSMC from a 1700 loop surveillance system to a 2200 loop system. Special computer programs were also written to provide specialized summary statistics for key stations. The new software allowed statistics for key stations to be collected and summarized during data transfer. The entire transfer retrieval system was streamlined during the Telecom project to reduce costs. Finally, freeway incident analysis was performed with a data set to demonstrate the use of the new system for TSM research.

Authors:
Nihan,N. L.

Keywords:
analysis, computer, computer program, cost, costs, data, data management, data transfer, freeway, freeway surveillance and control, incident, management, program, ramp metering, research, software, statistics, surveillance, systems, telecommunications, telecommunications link, traffic, traffic systems management, transportation systems management, TSM, Washington, WSDOT

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Abstract:
On June 5, 1985, a comprehensive, computer-controlled traffic management system (TMS) was implemented on a section of I-95 and I-395 in Northern Virginia. The roadway is a major commuter route into the District of Columbia. A before-and-after evaluation of the TMS was initiated by the Virginia Department of Transportation in the spring of 1983 in anticipation of a summer 1983 implementation. Due to a series of events, the TMS was two years late in being implemented, and data were not collected after its implementation until the spring of 1986. Accordingly, this study describes changes in travel characteristics between these two periods; it recognizes that several major events occurred that likely caused the changes.Changes in travel on local streets as well as on the interstate are described. Changes in traffic volumes, speeds, travel times, delays, vehicle miles of travel, vehicle hours of travel, and accidents are reviewed. Finally, information on incident detection and management is presented.

Authors:
Arnold,E. D.

Keywords:
incident management, ramp metering, traffic management, traffic surveillance, variable message signs