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Fuzzy Logic in Traffic Responsive Signal Control
Copyright © IFAC Transportation Systems Chania, Greece, 1997
FUZZY LOGIC IN TRAFFIC RESPONSIVE SIGNAL CONTROL
Tessa Sayers
Transport Operations Research Group University ofNewcastle upon Tyne, UK. NE1 7RU
Abstract: This paper first presents an introduction to the principles of fuzzy logic. There follows a description of a single intersection traffic responsive signal controller which uses fuzzy logic to support the decision process. Thirdly, there is an overview of a variety of ways in which fuzzy logic could usefully be incorporated into traffic signal control algorithms. Keywords: Fuzzy logic, fuzzy control, road traffic, traffic control
1. INTRODUCTION
the mapping of the individual discrete input and output values onto user-defined fuzzy sets.
Effective traffic responsive signal control in urban networks is an essential aspect of many systems designed to manage urban congestion. The signal controller must observe the ongoing traffic situation around the intersection and determine the appropriate green splits, stage sequences and cycle lengths. The effectiveness of the signal control depends on both the data available (quantity, quality and variety) and the use to which that data is pUL It is not an easy task, however, to combine large quantities of diverse input data, which are often nonlinearly related, in a system which preserves a useful degree of transparency and flexibility. The emergent methodology of fuzzy control can play a useful part in resolving this dilemma.
The simple building blocks of a fuzzy control system are fuzzy sets, which capture the significant categories of input and output values, and rulebases, which describe the relationship between inputs and outpuL These can be used to build a model which implements the desired non-linear mapping, and avoids unwanted "steps" in the output values caused by the simple use of thresholds in the input values. A thorough introduction to fuzzy control can be found in Lee (1990). 2. AN EXAMPLE OF THE USE OF FUZZY
LOGIC IN A TRAFFIC RESPONSIVE SIGNAL CONlROL APPLICAnON
1.1 Anatomy of a Fuzzy Logic Control System
The heart of a fuzzy logic control system is a set of rules which describe the relationship between the inputs and the output in qualitative " natural language" terms. As in a knowledge-based expert system, these rules provide an easily understood scheme for explaining the input/output mapping. In contrast to expert systems, however, a fuzzy logic rulebase can be relatively simple and concise, due to .
699
In this section, an application is described in which fuzzy logic techniques are incorporated into a "realworld" modular traffic responsive signal control system. A fuller discussion of the application can be found in Sayers, et aJ. (1996) . Unlike some of the prototype fuzzy traffic signal control systems such as those proposed by Pappis and Mamdani (1977) and Nakatsuyama, et al. (1985), the fuzzy logic control modules described below do not issue control directives such as "extend greentime for the current stage for n seconds" .
A fuzzy logic module uses data gathered during the previous cycle to provide an estimate of the greentime required by each signal group during the next cycle, thus enabling the framework signal plan to be adapted on a cycle by cycle basis. As in the case of the calculation of the signal group weights, different data are collected during greentime and redtime, such as the average degree of occupancy and arrival rate during greentime, and the maximum vehicle waiting time during redtime. The position of the detectors also influences the nature of the information they can provide. For example, a detector that is 100 metres before the stopline can give useful arrival rate information when the signal is red, whereas a detector at the stopline gives the maximum vehicle waiting time while the signal is red. These diverse data, once fuzzified, are used as inputs for the appropriate rulebases (e.g. for greentime data or redtime data) and the outputs are combined (in a conventional way) to give an estimate of the number of seconds of greentime required by each signal group during the coming cycle.
Rather, several fuzzy logic modules provide effective support for the established conventional control method. These modules form a bridge between the observation and control functions of the signal controller, converting the large quantities of raw data relating to the traffic flow on the junction approaches into succinct and meaningful measurements relating to each signal group (Phase). On the basis of these measurements, the controller is able to make both second-by-second and cycle-bycycle decisions about the apportionment of greentime.
2.1 Support for decisions made during the running cycle. In the conventional traffic signal control method to be supported by fuzzy logic, each cycle runs according to a framework signal plan which defines windows during which certain signals may be red or green. These windows may overlap to a greater or lesser degree, giving the flexibility to start and end stages earlier or later in the cycle.
2.3 Initial test results
At these times during the running cycle, when the extension of the current stage is optional, the controller must make a real-time decision whether to terminate the current stage, and if so, which stage to switch next. A module using fuzzy logic to convert the available raw data relating to each signal group into a single "weight" for that signal group allows an evaluation of which stage requires greentime most urgently. Although the data used to calculate the signal group weight varies according to whether the signal group is currently green or red, the fuzzy module (known as the WSG module) provides a consistent output - relative signal group weight - for all signal groups, thus allowing them to be compared in a fairly straightforward manner.
To date, the controller has been tested in a simulation environment using the SIMULA microscopic traffic simulator and real complex junction topologies. Field trials are due to be carried out later in the year. The controller enhanced using fuzzy logic (FLenhanced controller) consists of the established conventional stage-based controller PDM with only the addition of the WSG module giving the relative weights of the signal groups (see section 2.1) as a basis for the decision whether to terminate or extend the currently green stage. The framework signal plan thus remains unchanged throughout the operation of the controller. The extent to which the fuzzy enhancement can alter the behaviour of the PDM controller is proportional to the number and size of the permitted stage overlaps defined in the framework signal plan as it is during these periods that the choice can be made of which stage should have precedence, based upon the signal group weights.
2.2 Support for decisions regarding the next cycle. The framework signal plan defines the minimum and maximum stage lengths for each cycle and also the possible stage sequences. In normal operation the framework signal plan remains constant during the periods when the traffic follows a certain pattern. For example, there may be a frameplan which has been developed to suit the morning peak and another to suit the evening peak. During these discrete time periods, the framework signal plan is not usually altered. However, it is possible to adapt the framework signal plan to suit the prevailing traffic conditions by estimating the number of seconds of greentime required by each signal group during the next cycle and stretching or shrinking the windows of the framework signal plan accordingly.
Tests comparing the sophisticated phase-based controller VS-PLUS and the FL-enhanced PDM, using similar framework signal plans and a number of different flow patterns and random seeds show that the FL-enhanced PDM consistently outperforms VS-PLUS, by an average of 2%. In order to put the two controllers being compared on an equal footing, it was necessary to constrain the framework signal plan to have only 3 overlapping periods of a total of 33 seconds during each llO second cycle. A more
700
of the raw data from the higher level control means that if the nature of the available data changes, it should be possible to cushion the control level from those changes, by adapting the fuzzy logic interface between the two levels. This is of particular relevance if different methods of vehicle detection are to be introduced (such as microwave, infra-red or video). These new detection methods result in the availability of different information about the traffic situation in addition to (or instead of) the conventional values associated with inductive loop vehicle detection. For each different combination of inputs, a fuzzy logic interface could be developed whose outputs present the same measurements to the controller. There would be a degree of independence between the controller and the input data, which would reduce the need to modify or redesign the underlying control strategy, thus facilitating the introduction of novel detection methods.
flexible framework signal plan could be expected to yield even better results. The addition of the module allowing the framework signal plan to be adapted would lead to an even greater degree of flexibility, and thus the possibility of adapting quickly to a wider range of traffic conditions.
3. POTENTIAL APPLICATIONS OF FUZZY LOGIC TO 1RAFFIC SIGNAL CONTROL Algorithms based on fuzzy logic can be incorporated into traffic signal control systems at different levels and can be applied to different problems within the signal control context. The earliest applications of fuzzy logic to traffic signal control have had an enormous impact on the development of fuzzy logic traffic signal control systems, and indeed fuzzy logic control systems in general. The drawbacks of these applications are that they operate in a highly stylised environment, such as the intersection of 2 one way streets, and require some inputs which are often not available (such as queue length, or prediction of vehicle arrivals) . This area of research has now matured to the extent that the focus is on the use of fuzzy logic in a real-world context, being implemented in actual traffic signal control systems, with all their complex and sometimes conflicting requirements.
The controller described in section 2 and in Sayers et al. (1996) is designed with practical considerations in mind. It is intended to be useful in a wide range of urban intersections and also to be able to be implemented within a short time.
3.2 Control o/transportation networks The application of fuzzy logic to the control of intersections in a network has been addressed in a number of publications. Chiu and Chand (1993) present a distributed form of network control in which each intersection controller requires only local data and signal timings from adjacent intersections. The control strategy follows the SCATS paradigm, with the dual aim of maximising the highest degree of saturation by adjusting cycle length, whilst balancing the degree of saturation on all approaches by adjusting the stage split In addition to the use of fuzzy decision rules to adjust the cycle length and stage split, an offset adjustment is also calculated using fuzzy logic, to provide some co-ordination between adjacent intersections. Their approach seems promising, as does that of Tzes et al. (1995) , who present a network control strategy exemplified by a star-shaped network with a central critical intersection and four outer intersections. The outer intersections are controlled independently by fuzzy logic controllers for isolated intersections, with the critical intersection responding in a cycle-free manner to the traffic approaching it from the outer intersections. However, when the critical intersection is in danger of becoming oversaturated, it is allowed to override the autonomous control of the outer intersections in order to prevent a "blockage situation". The override control from the critical intersection is also implemented using fuzzy logic.
There are many different ways in which fuzzy logic could be useful in the traffic signal control arena and some are outlined below.
3. J Integration 0/ Fuzzy Logic into Conventional Control Algorithms At a level which would probably require little change to existing traffic responsive signal control systems, fuzzy logic modules could be integrated into conventional algorithms, as a substitute for functions describing complex and/or non-linear relationships between variables, such as the relationship between gap, occupancy and traffic density. The advantage of using fuzzy logic is that it is transparent and easy to modify, without sacrificing the complexity of the relationship modelled or the accuracy of the model.
In the traffic signal controller described in section 2 the data level is separated from the control level using an interface based on fuzzy logic. In this example the available raw detector and signal data are converted into consistent., succinct and meaningful values relating to each signal group by means of appropriate fuzzy logic rulebases and these measures can form the basis of the main signal control decisions at a higher level. This separation 701
3.3 Fuzzy Logicfor Prediction
3.5 On-line adaptation offuzzy systems
Many traffic control systems contain a predictive element, such as predictions of link travel time, vehicle flow or queue length. Fuzzy logic can also be used for predictive purposes, where inputs derived from historic data can be combined with each other and with actual data to predict future values of the variable in question. Historic data could be recent (e.g. measured flow over the last 3 cycles) or more long term (e.g. average flow for the appropriate time-of-day and day-of-week based on longer periods of observation). Palacharla and Nelson (1995) present a fuzzy neural network method for on-line travel time estimation using aggregated occupancy and flow data received from conventional inductive loop detectors.
Many fuzzy control systems include an element of on-line self-tuning, thus refining the fuzzy set definitions and/or rulebases to improve performance. This is usually within the context of a closed-loop control system, where feedback enabling the performance of the controller to be monitored is constantly available. A traffic signal controller does not have real-time performance evaluation measures readily available to it and therefore it would seem that off-line optimisation of the fuzzy system parameters is the only practical way to fine tune the effectiveness of this kind of controller. It is conceivable however that different rules, set or indeed control strategies could give better results in differing traffic scenarios and the appropriate mode of operation could be switched in response to the recognition of a changing traffic scenario, in realtime. This is effectively the approach adopted by Tzes, et al. (1996) and Nakatsuyama et al. (1984), where a change to a more co-ordinated system is switched when the traffic reaches a certain density. The definition and recognition of different traffic scenarios could be driven by a conventional algorithm, by fuzzy logic, or by the use of a Kohonen neural network, as demonstrated by Van der Voort, et al.(1996).
3.4
Optimisation and evaluation of sets and rulebases
One criticism often levelled at the principle of fuzzy logic control is the subjectivity of the fuzzy set definitions and the rulebases. These are the fundamental building blocks of a fuzzy logic control system and as such determine its performance. Much work has been carried out on the derivation and optimisation of these components.
3.6 Combining Fuzzy Logic and Neural Networks
Park, et al. (1994) describe the use of genetic algorithms to optimise the fuzzy set definitions and the rulebase (known as the fuzzy relation matrix, in the context of the New Fuzzy Reasoning Method) with good results based on a simple example. When considering traffic signal control systems, however, it is no simple matter to determine how each variation in fuzzy sets and rulebases should be evaluated. Usually, proposed traffic signal control systems are evaluated using simulation, due to the complexity of the interactions between traffic flows and the signals. The optimisation would thus become an extremely lengthy process, especially if several traffic flow scenarios were examined and several random seeds chosen for each chromosome generated during the GA optimisation process. 00line optimisation of fuzzy controllers has also been carried out using simulated annealing, another heuristic optimisation method (see Huyghe and Hamam (1995», and this would be subject to the same drawbacks as the use of genetic algorithms. However, as these types of optimisation would be off-line, the processing time required need not be an insuperable obstacle, especially with the availability of high speed processors and parallel computing.
It is also possible to combine fuzzy logic with neural networks, in which a fuzzy logic system is implemented using a neural network. This system has the advantage that it can be trained if data is available, ensuring that it is consistent with known data and not purely subjective. Also the neural network used is transparent, with nodes and weights having a known significance, thus avoiding the "black-box "phenomenon distrusted by many. No examples of this approach to traffic signal control are known to the author at the current time although the technique is widely used in other control applications.
4. CONCLUSION
Fuzzy logic is a powerful, adaptable and accessible tool which can facilitate the building of sophisticated traffic responsive signal control systems in many different ways. The use of fuzzy logic is much more widespread in other control applications and the advances in its use that have been made in these contexts could usefully be investigated in the context of traffic signal controL In the final analysis, fuzzy logic will have to compete with the myriad of other signal control techniques, and will not gain acceptance just because of its novelty, but the fact
702
Road. In: IFAC-World Congress, Preprints, Budapest 1984, pp. 13·18. Palacharla P.Y. and P.c. Nelson (1995). On-line Travel Time Estimation using Fuzzy Neural Network. The Second World Congress on Intelligent Transpot Systems '95, Yokohama pp. ll2 - ll6. Pappis c.P. and E.H. Mamdani (1977). A Fuzzy Logic Controller for a Traffic Junction.
that it has become an established methodology in other areas of process control which require consistently high performance and robustness indicates that it is likely to have a positive contribution to make to the field of traffic signal control.
ACKNOWLEDGEMENTS
IEEE Transactions on Systems, A1an, and Cybernetics, SMC-7, pp. 707 • 717. Park D., A. Kandel and G. Langholz (1994). Genetic-based New Fuzzy Reasoning Models with Application to Fuzzy Control. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, No. 1, pp. 39·47. Sayers T.M ., M .G.H. Bell, Th. Mieden, and F. Busch (1996). Traffic responsive signal control using fuzzy logic • a practical modular approach. Proc., EUFlT '96, Aachen, Germany, September 2-4, 1996. Tzes A. , W.R. McShane and S. Kim (1995). Expert Fuzzy logic Traffic Signal Control for Transportation Networks. Institute of Transportation Engineers 65th Annual Meeting, Denver USA 1995, pp. 154 - 158 Van der Voort M., M. Dougherty and S. Watson (1996). Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. Transportation Research· C, Vol. 4, No. 5 pp. 307·318
The author acknowledges the help and financial support provided by Siemens AG.
REFERENCES Chiu S. and S. Chand (1993). Adaptive Traffic Signal Control Using Fuzzy Logic. Proc.,
IEEE International Conference on Fuzzy Systems 1993, pp 1371 - 1376. Huyghe E. and Y. Hamam (1995). Simulated Annealing for Fuzzy Controller Optimization: Principles and Applications. Proc. , IEEE conference on Man, Systems and Cybernetics, 1995, pp. 4509 ·4514. Lee C.C. (1990). Fuzzy Logic in Control Systems: Fuzzy Logic Controller - Parts I and TI. IEEE Transactions on Systems, Man , and Cybernetics, VoL 20, No. 2, pp. 404 - 435. Nakatsuyama M. , H. Nagahashi and N. Nishizuka (1984). Fuzzy Logic Phase Controller for Traffic Junctions in the One-Way Arterial
703
FUZZY LOGIC IN TRAFFIC RESPONSIVE SIGNAL CONTROL
Tessa Sayers
Transport Operations Research Group University ofNewcastle upon Tyne, UK. NE1 7RU
Abstract: This paper first presents an introduction to the principles of fuzzy logic. There follows a description of a single intersection traffic responsive signal controller which uses fuzzy logic to support the decision process. Thirdly, there is an overview of a variety of ways in which fuzzy logic could usefully be incorporated into traffic signal control algorithms. Keywords: Fuzzy logic, fuzzy control, road traffic, traffic control
1. INTRODUCTION
the mapping of the individual discrete input and output values onto user-defined fuzzy sets.
Effective traffic responsive signal control in urban networks is an essential aspect of many systems designed to manage urban congestion. The signal controller must observe the ongoing traffic situation around the intersection and determine the appropriate green splits, stage sequences and cycle lengths. The effectiveness of the signal control depends on both the data available (quantity, quality and variety) and the use to which that data is pUL It is not an easy task, however, to combine large quantities of diverse input data, which are often nonlinearly related, in a system which preserves a useful degree of transparency and flexibility. The emergent methodology of fuzzy control can play a useful part in resolving this dilemma.
The simple building blocks of a fuzzy control system are fuzzy sets, which capture the significant categories of input and output values, and rulebases, which describe the relationship between inputs and outpuL These can be used to build a model which implements the desired non-linear mapping, and avoids unwanted "steps" in the output values caused by the simple use of thresholds in the input values. A thorough introduction to fuzzy control can be found in Lee (1990). 2. AN EXAMPLE OF THE USE OF FUZZY
LOGIC IN A TRAFFIC RESPONSIVE SIGNAL CONlROL APPLICAnON
1.1 Anatomy of a Fuzzy Logic Control System
The heart of a fuzzy logic control system is a set of rules which describe the relationship between the inputs and the output in qualitative " natural language" terms. As in a knowledge-based expert system, these rules provide an easily understood scheme for explaining the input/output mapping. In contrast to expert systems, however, a fuzzy logic rulebase can be relatively simple and concise, due to .
699
In this section, an application is described in which fuzzy logic techniques are incorporated into a "realworld" modular traffic responsive signal control system. A fuller discussion of the application can be found in Sayers, et aJ. (1996) . Unlike some of the prototype fuzzy traffic signal control systems such as those proposed by Pappis and Mamdani (1977) and Nakatsuyama, et al. (1985), the fuzzy logic control modules described below do not issue control directives such as "extend greentime for the current stage for n seconds" .
A fuzzy logic module uses data gathered during the previous cycle to provide an estimate of the greentime required by each signal group during the next cycle, thus enabling the framework signal plan to be adapted on a cycle by cycle basis. As in the case of the calculation of the signal group weights, different data are collected during greentime and redtime, such as the average degree of occupancy and arrival rate during greentime, and the maximum vehicle waiting time during redtime. The position of the detectors also influences the nature of the information they can provide. For example, a detector that is 100 metres before the stopline can give useful arrival rate information when the signal is red, whereas a detector at the stopline gives the maximum vehicle waiting time while the signal is red. These diverse data, once fuzzified, are used as inputs for the appropriate rulebases (e.g. for greentime data or redtime data) and the outputs are combined (in a conventional way) to give an estimate of the number of seconds of greentime required by each signal group during the coming cycle.
Rather, several fuzzy logic modules provide effective support for the established conventional control method. These modules form a bridge between the observation and control functions of the signal controller, converting the large quantities of raw data relating to the traffic flow on the junction approaches into succinct and meaningful measurements relating to each signal group (Phase). On the basis of these measurements, the controller is able to make both second-by-second and cycle-bycycle decisions about the apportionment of greentime.
2.1 Support for decisions made during the running cycle. In the conventional traffic signal control method to be supported by fuzzy logic, each cycle runs according to a framework signal plan which defines windows during which certain signals may be red or green. These windows may overlap to a greater or lesser degree, giving the flexibility to start and end stages earlier or later in the cycle.
2.3 Initial test results
At these times during the running cycle, when the extension of the current stage is optional, the controller must make a real-time decision whether to terminate the current stage, and if so, which stage to switch next. A module using fuzzy logic to convert the available raw data relating to each signal group into a single "weight" for that signal group allows an evaluation of which stage requires greentime most urgently. Although the data used to calculate the signal group weight varies according to whether the signal group is currently green or red, the fuzzy module (known as the WSG module) provides a consistent output - relative signal group weight - for all signal groups, thus allowing them to be compared in a fairly straightforward manner.
To date, the controller has been tested in a simulation environment using the SIMULA microscopic traffic simulator and real complex junction topologies. Field trials are due to be carried out later in the year. The controller enhanced using fuzzy logic (FLenhanced controller) consists of the established conventional stage-based controller PDM with only the addition of the WSG module giving the relative weights of the signal groups (see section 2.1) as a basis for the decision whether to terminate or extend the currently green stage. The framework signal plan thus remains unchanged throughout the operation of the controller. The extent to which the fuzzy enhancement can alter the behaviour of the PDM controller is proportional to the number and size of the permitted stage overlaps defined in the framework signal plan as it is during these periods that the choice can be made of which stage should have precedence, based upon the signal group weights.
2.2 Support for decisions regarding the next cycle. The framework signal plan defines the minimum and maximum stage lengths for each cycle and also the possible stage sequences. In normal operation the framework signal plan remains constant during the periods when the traffic follows a certain pattern. For example, there may be a frameplan which has been developed to suit the morning peak and another to suit the evening peak. During these discrete time periods, the framework signal plan is not usually altered. However, it is possible to adapt the framework signal plan to suit the prevailing traffic conditions by estimating the number of seconds of greentime required by each signal group during the next cycle and stretching or shrinking the windows of the framework signal plan accordingly.
Tests comparing the sophisticated phase-based controller VS-PLUS and the FL-enhanced PDM, using similar framework signal plans and a number of different flow patterns and random seeds show that the FL-enhanced PDM consistently outperforms VS-PLUS, by an average of 2%. In order to put the two controllers being compared on an equal footing, it was necessary to constrain the framework signal plan to have only 3 overlapping periods of a total of 33 seconds during each llO second cycle. A more
700
of the raw data from the higher level control means that if the nature of the available data changes, it should be possible to cushion the control level from those changes, by adapting the fuzzy logic interface between the two levels. This is of particular relevance if different methods of vehicle detection are to be introduced (such as microwave, infra-red or video). These new detection methods result in the availability of different information about the traffic situation in addition to (or instead of) the conventional values associated with inductive loop vehicle detection. For each different combination of inputs, a fuzzy logic interface could be developed whose outputs present the same measurements to the controller. There would be a degree of independence between the controller and the input data, which would reduce the need to modify or redesign the underlying control strategy, thus facilitating the introduction of novel detection methods.
flexible framework signal plan could be expected to yield even better results. The addition of the module allowing the framework signal plan to be adapted would lead to an even greater degree of flexibility, and thus the possibility of adapting quickly to a wider range of traffic conditions.
3. POTENTIAL APPLICATIONS OF FUZZY LOGIC TO 1RAFFIC SIGNAL CONTROL Algorithms based on fuzzy logic can be incorporated into traffic signal control systems at different levels and can be applied to different problems within the signal control context. The earliest applications of fuzzy logic to traffic signal control have had an enormous impact on the development of fuzzy logic traffic signal control systems, and indeed fuzzy logic control systems in general. The drawbacks of these applications are that they operate in a highly stylised environment, such as the intersection of 2 one way streets, and require some inputs which are often not available (such as queue length, or prediction of vehicle arrivals) . This area of research has now matured to the extent that the focus is on the use of fuzzy logic in a real-world context, being implemented in actual traffic signal control systems, with all their complex and sometimes conflicting requirements.
The controller described in section 2 and in Sayers et al. (1996) is designed with practical considerations in mind. It is intended to be useful in a wide range of urban intersections and also to be able to be implemented within a short time.
3.2 Control o/transportation networks The application of fuzzy logic to the control of intersections in a network has been addressed in a number of publications. Chiu and Chand (1993) present a distributed form of network control in which each intersection controller requires only local data and signal timings from adjacent intersections. The control strategy follows the SCATS paradigm, with the dual aim of maximising the highest degree of saturation by adjusting cycle length, whilst balancing the degree of saturation on all approaches by adjusting the stage split In addition to the use of fuzzy decision rules to adjust the cycle length and stage split, an offset adjustment is also calculated using fuzzy logic, to provide some co-ordination between adjacent intersections. Their approach seems promising, as does that of Tzes et al. (1995) , who present a network control strategy exemplified by a star-shaped network with a central critical intersection and four outer intersections. The outer intersections are controlled independently by fuzzy logic controllers for isolated intersections, with the critical intersection responding in a cycle-free manner to the traffic approaching it from the outer intersections. However, when the critical intersection is in danger of becoming oversaturated, it is allowed to override the autonomous control of the outer intersections in order to prevent a "blockage situation". The override control from the critical intersection is also implemented using fuzzy logic.
There are many different ways in which fuzzy logic could be useful in the traffic signal control arena and some are outlined below.
3. J Integration 0/ Fuzzy Logic into Conventional Control Algorithms At a level which would probably require little change to existing traffic responsive signal control systems, fuzzy logic modules could be integrated into conventional algorithms, as a substitute for functions describing complex and/or non-linear relationships between variables, such as the relationship between gap, occupancy and traffic density. The advantage of using fuzzy logic is that it is transparent and easy to modify, without sacrificing the complexity of the relationship modelled or the accuracy of the model.
In the traffic signal controller described in section 2 the data level is separated from the control level using an interface based on fuzzy logic. In this example the available raw detector and signal data are converted into consistent., succinct and meaningful values relating to each signal group by means of appropriate fuzzy logic rulebases and these measures can form the basis of the main signal control decisions at a higher level. This separation 701
3.3 Fuzzy Logicfor Prediction
3.5 On-line adaptation offuzzy systems
Many traffic control systems contain a predictive element, such as predictions of link travel time, vehicle flow or queue length. Fuzzy logic can also be used for predictive purposes, where inputs derived from historic data can be combined with each other and with actual data to predict future values of the variable in question. Historic data could be recent (e.g. measured flow over the last 3 cycles) or more long term (e.g. average flow for the appropriate time-of-day and day-of-week based on longer periods of observation). Palacharla and Nelson (1995) present a fuzzy neural network method for on-line travel time estimation using aggregated occupancy and flow data received from conventional inductive loop detectors.
Many fuzzy control systems include an element of on-line self-tuning, thus refining the fuzzy set definitions and/or rulebases to improve performance. This is usually within the context of a closed-loop control system, where feedback enabling the performance of the controller to be monitored is constantly available. A traffic signal controller does not have real-time performance evaluation measures readily available to it and therefore it would seem that off-line optimisation of the fuzzy system parameters is the only practical way to fine tune the effectiveness of this kind of controller. It is conceivable however that different rules, set or indeed control strategies could give better results in differing traffic scenarios and the appropriate mode of operation could be switched in response to the recognition of a changing traffic scenario, in realtime. This is effectively the approach adopted by Tzes, et al. (1996) and Nakatsuyama et al. (1984), where a change to a more co-ordinated system is switched when the traffic reaches a certain density. The definition and recognition of different traffic scenarios could be driven by a conventional algorithm, by fuzzy logic, or by the use of a Kohonen neural network, as demonstrated by Van der Voort, et al.(1996).
3.4
Optimisation and evaluation of sets and rulebases
One criticism often levelled at the principle of fuzzy logic control is the subjectivity of the fuzzy set definitions and the rulebases. These are the fundamental building blocks of a fuzzy logic control system and as such determine its performance. Much work has been carried out on the derivation and optimisation of these components.
3.6 Combining Fuzzy Logic and Neural Networks
Park, et al. (1994) describe the use of genetic algorithms to optimise the fuzzy set definitions and the rulebase (known as the fuzzy relation matrix, in the context of the New Fuzzy Reasoning Method) with good results based on a simple example. When considering traffic signal control systems, however, it is no simple matter to determine how each variation in fuzzy sets and rulebases should be evaluated. Usually, proposed traffic signal control systems are evaluated using simulation, due to the complexity of the interactions between traffic flows and the signals. The optimisation would thus become an extremely lengthy process, especially if several traffic flow scenarios were examined and several random seeds chosen for each chromosome generated during the GA optimisation process. 00line optimisation of fuzzy controllers has also been carried out using simulated annealing, another heuristic optimisation method (see Huyghe and Hamam (1995», and this would be subject to the same drawbacks as the use of genetic algorithms. However, as these types of optimisation would be off-line, the processing time required need not be an insuperable obstacle, especially with the availability of high speed processors and parallel computing.
It is also possible to combine fuzzy logic with neural networks, in which a fuzzy logic system is implemented using a neural network. This system has the advantage that it can be trained if data is available, ensuring that it is consistent with known data and not purely subjective. Also the neural network used is transparent, with nodes and weights having a known significance, thus avoiding the "black-box "phenomenon distrusted by many. No examples of this approach to traffic signal control are known to the author at the current time although the technique is widely used in other control applications.
4. CONCLUSION
Fuzzy logic is a powerful, adaptable and accessible tool which can facilitate the building of sophisticated traffic responsive signal control systems in many different ways. The use of fuzzy logic is much more widespread in other control applications and the advances in its use that have been made in these contexts could usefully be investigated in the context of traffic signal controL In the final analysis, fuzzy logic will have to compete with the myriad of other signal control techniques, and will not gain acceptance just because of its novelty, but the fact
702
Road. In: IFAC-World Congress, Preprints, Budapest 1984, pp. 13·18. Palacharla P.Y. and P.c. Nelson (1995). On-line Travel Time Estimation using Fuzzy Neural Network. The Second World Congress on Intelligent Transpot Systems '95, Yokohama pp. ll2 - ll6. Pappis c.P. and E.H. Mamdani (1977). A Fuzzy Logic Controller for a Traffic Junction.
that it has become an established methodology in other areas of process control which require consistently high performance and robustness indicates that it is likely to have a positive contribution to make to the field of traffic signal control.
ACKNOWLEDGEMENTS
IEEE Transactions on Systems, A1an, and Cybernetics, SMC-7, pp. 707 • 717. Park D., A. Kandel and G. Langholz (1994). Genetic-based New Fuzzy Reasoning Models with Application to Fuzzy Control. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, No. 1, pp. 39·47. Sayers T.M ., M .G.H. Bell, Th. Mieden, and F. Busch (1996). Traffic responsive signal control using fuzzy logic • a practical modular approach. Proc., EUFlT '96, Aachen, Germany, September 2-4, 1996. Tzes A. , W.R. McShane and S. Kim (1995). Expert Fuzzy logic Traffic Signal Control for Transportation Networks. Institute of Transportation Engineers 65th Annual Meeting, Denver USA 1995, pp. 154 - 158 Van der Voort M., M. Dougherty and S. Watson (1996). Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. Transportation Research· C, Vol. 4, No. 5 pp. 307·318
The author acknowledges the help and financial support provided by Siemens AG.
REFERENCES Chiu S. and S. Chand (1993). Adaptive Traffic Signal Control Using Fuzzy Logic. Proc.,
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