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A BOTTOM-UP APPROACH TO IMPLEMENTING SUSTAINABLE TRAFFIC CONTROL
A BOTTOM-UP APPROACH TO IMPLEMENTING SUSTAINABLE TRAFFIC CONTROL Jos Vrancken1, Frank Ottenhof2 1
Faculty of Technology, Policy and Management Delft University of Technolog Delft 2
Trinité Automatisering B.V. Uithoorn
Abstract: This paper describes a bottom-up approach to road traffic control. The approach is intended as a supplement to the more traditional top-down approach to traffic control by traffic management centres applying traffic control measures according to control scenarios. The essence of this bottom-up approach consists of communicating road elements: nodes and links in the network measure their traffic state and communicate about it to other nodes and links. The combination of the two control mechanisms is a promising way of implementing the control part of Sustainable Traffic Management. The approach is currently being applied in two real-life projects: the DRIP control systems on the Dutch motorways A1, A6 and A9 and the project HARS: traffic control on the belt road around Alkmaar. Copyright © 2006 IFAC Keywords: Traffic control, control agents, bottom-up control 1. INTRODUCTION Road traffic management is applied in many countries for well-known purposes such as congestion reduction, traffic safety, reducing environmental damage and driving comfort. Road traffic control (RTC) is one of the main activities within road traffic management, next to demand management, law enforcement, incident handling and pricing. RTC is about influencing traffic streams. Almost all RTC-measures applied today are local, reactive measures: they attempt to solve a problem, such as congestion, after its occurrence, and, if they solve the problem at all, they solve it only locally, i.e. such that the network context is not taken into consideration. Practice shows that this has its limitations: in the case of congestion for instance, the problem is often only moved to another location in the network. An ambitious and fundamental step forward in traffic management is denoted by sustainable traffic management (Rijkswaterstaat, 2003). Among its goals are: - moving from the current, local and reactive traffic control towards proactive, network-level control; - making all traffic management authorities involved in a region, cooperate effectively; - having traffic control cooperate effectively with the other activities of traffic management, such as those mentioned above.
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
The cooperation of all traffic management authorities in a region is essential for sustainable traffic management. Therefore we consider all the road networks in a region, not just the motorways or just the urban network. The best known approach in traffic control is the one characterized by traffic management centres applying so-called control scenarios in response to current and predicted traffic states of the road network. A scenario is a control program that executes a coordinated series of individual traffic control measures, such as ramp metering, traffic lights, speed measures, warnings, route advice, etc. Scenarios are developed off-line and correspond to recurring patterns in the traffic state, such as the morning rush hours, or the weekend exodus. We will call this the top-down approach, the traffic mananement centre (the top) being the only entity allowed to take decisions. It has indispensable advantages, and by no means do we intend to replace this approach. The bottom-up approach, which is the main subject of this paper, is intended as a supplement to the top-down approach. The latter has a number of well-know limitations that all relate to the high complexity of traffic. For instance, the number of possible traffic states that would ideally require different treatment by the traffic management centre, is far too high to handle by means of a small number of scenarios. Moreover, the presence of
Page 473
human intelligence in the process, i.e. the drivers in the vehicles, makes predictability of future traffic states essentially harder than in the case of transportation processes that do not involve human intelligence, such as oil in pipelines or electricity in high-tension networks. Bottom-up traffic control can be done in several different ways. The core of each approach consists of a set of communicating agents. The most obvious set of agents are the vehicles (including the drivers). We currently witness the emergence of this control mechanism. More and more vehicles are equiped with communication and navigation devices. More and more of these devices are kept up-to-date about traffic state and can apply this information in on-trip route planning and navigation. Although this control mechanism is user-optimum oriented, it is generally considered as a beneficial development, even for the system-optimum, as it automatically leads to an improved spreading of traffic in time and over the network. The bottom-up control mechanism that we treat in this paper deals with a different set of agents, namely the road elements: nodes and links in the road network that measure their own traffic state and communicate about it with other nodes and links. A node is any point in the road network where something changes: first of all crossings and entry and exit lane ramps, but also points where the number of lanes changes. A link is the road segment, in one direction, between two nodes. This approach is currently being implemented in two real-life case studies: the DRIP (Dynamic Route Information Panel) control system, deployed on the Dutch motorways A1, A6 and A9, and the HARS system for traffic control on the belt road around Alkmaar (Kernteam DVM Alkmaar, 2003). HARS is a Dutch abbreviation: Het Alkmaar RegelSysteem (the Alkmaar control system). The literature offers an abundance of approaches to traffic control. Most studies are simulation based. We mention only a few. In (Adler, et al., 2005) an agent-based approach can be found in which traffic is managed by negotiations between the different actors involved. (Yang and Recker, 2005) studies information distribution using vehicles as communicating and self-organizing agents. This method can be applied to make information, for instance about an incident, propagate faster than the traffic shock wave caused by the incident. In (Hegyi, 2004) an approach to traffic control based on a prediction model can be found. 2. BOTTOM-UP CONTROL MECHANISMS As mentioned above, the essence of a bottom-up control mechanism consists of an, often large, number of communicating instances or objects, usually called agents. Each agent has a relatively simple behaviour. Usually they all run the same
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
control program or one from a small number of different control programs. In general, agents are usually rather selfish, i.e. oriented at optimizing the situation for themselves. What matters is that the set of agents as a whole can also exhibit a certain behaviour, the so-called emergent behaviour, which can be beneficial for the whole (system-optimum) and hard to obtain by more traditional means. Essential for so-called bottom-up programming techniques, such as agent systems, neural networks or genetic algorithms, is that the results are hard to obtain in a traditional way. This holds to the extent that it is often hard to understand how the emergent behaviour is actually generated. If it were understood, then it would most probably be possible to program it in a more traditional way. So bottom-up programming techniques depend very much on short development cycles, on simulation and a lot of experimenting and tuning, but they can in principle make a contribution that is hard to obtain otherwise. Besides the vehicles-as-agents approach to bottom-up traffic management, one can also transform the road network into an agent system. This is the approach applied in the two case studies described below. In the road network, one can distinguish nodes and links, as defined above. So there are two types of agents: nodes and links. Both types of agents can be equiped with monitoring devices such that they can measure their traffic state. The agents can inform each other about their state and can send requests to each other. For instance, a link which tends to become overloaded can send a "reduce inflow" request to its upstream node. This node can either take appropriate measures itself (for instance, if the node is equiped with traffic lights, green times for the different outgoing links can be adapted) and/or it can forward the request to its incoming links. This will undoubtedly have an effect on traffic, and with proper tuning, it is likely that a desirable effect on the whole network can be found. Besides links and nodes, other network elements can also be represented by agents, such as road segments, routes and origin-destination pairs. Segments are the building blocks of links, routes are strings of connected links, and an origin-destination pair represents a set of routes with the same endpoints. The following two case studies illustrate these agents in more detail. 3. CASE STUDY 1: DYNAMIC ROUTE INFORMATION PANELS The first implementation of the described bottomup approach was done in a system of Dynamic Route Information Panels (DRIP's), currently deployed on the Dutch motorways A1, A6 and A9. DRIP's serve to inform drivers on the current traffic state of routes downstream of the DRIP. In case of congestion, drivers will thus know the extent of the congestion that they can expect. Knowing what to expect adds to
Page 474
the driver's comfort level. More importantly, route information enables drivers to change their route choice. Drivers are thus diverted away from the congested road. The extent to which this rerouting occurs is small though; it is often estimated that the amount of drivers that alter their route choice as a result of information provided by a DRIP is at most 15% (Kernteam DVM Alkmaar, 2003; Evaluation Rotterdam City, 2004; Craen et al., 2002). Nevertheless, DRIP's are a valued and widely deployed instrument. The following figure shows the network management hierarchy and how the hierarchy controls the DRIP's:
information concerning the tidal flow lane is given. To handle the bridge and tidal lane events a mechanism that assigns incidents to links is used. When the bridge opens an incident called 'Bridge open' is placed on the relevant links. Likewise, when the tidal lane closes an incident called 'Tidal lane closed' is placed on the relevant links. All sorts of incidents can be of effect on links, for example: the opening of a peak lane, a speed reduction, a traffic accident, etc. In response to a placed incident, links take appropriate actions. This means that they either execute measures themselves and / or communicate with other network elements to do so. In the DRIP case only the latter happens; DRIP's are under the command of OD-managers. Links publish information concerning their incidents. Routes subscribe to this information and publish it again themselves. Finally the OD-manager, who is a subscriber to this information, receives it and determines a relevant DRIP text to be displayed. 4. CASE STUDY 2: THE ALKMAAR PROJECT
Figure 1: Hierarchical DRIP control The bottom layer contains all the road segments. Each segment aggregates data from induction loops, updated every minute, into information about velocity, intensity and congestion state of the segment. Segments publish this aggregated information every minute. Links further aggregate and transform the information from their segments. The total length of the congestion on the link is calculated by adding up the lengths of the underlying segments that have a congested state. Links publish this information every minute. Routes subscribe to the link information and calculate the congestion length per routepart by adding up the congestion lengths of underlying links. Routes determine which informative text they wish to display on the DRIP depending on the specific combination of congestion lengths on the different routeparts. This text, together with the total congestion length, is published every minute. An origin-destination manager (ODmanager) subscribes to this information and commands a software component called DRIP control to put the text on the DRIP. Incidents Highway A1 has two features which set it apart from most other highways. It contains a moveable bridge over the river Vecht, which opens twice a day. It also features a tidal flow lane (the only one in the Netherlands). The states of both the bridge and the tidal flow lane must be represented on the DRIP's. When the bridge is open for ships to pass, the concerning DRIP's must show this. When the tidal flow lane is open the concerning DRIP's must show normal traffic information. When it closes a relevant text is displayed for ten minutes after which no more
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
Although not yet very sophisticated, the DRIP system effectively uses the network management hierarchy to control roadside equipment. The next project where the hierarchy will be used, is situated in and around Alkmaar. Alkmaar lies at the end of highway A9 and has a ring road that is often heavily congested. The network management hierarchy will serve in this project as a complement to regular dynamic traffic management solutions (especially control scenario's). Alkmaar has two types of actuators: traffic light systems (TLS's) and DRIP's. The DRIP's will be used for rerouting and informing drivers. The TLS's will be used as an instrument to change intensities of traffic flows. The DRIP's will be controlled in the same manner as in the A1-A6-A9 project described above. The functions of the OD-managers will however be enhanced and extended to enable more effective rerouting. Much traffic needs to pass over the ring. The use of the DRIPS enables RTC to influence the side which is used (e.g. East or West). If one side of the ring is (nearly) congested, traffic can thus be diverted to the other side. In the Alkmaar project links and nodes will play the most important role in traffic control; their cooperation will be responsible for controlling the TLS greentimes. The default settings for a TLS will be determined by signal schemes that are designed off-line, based on relevant traffic statistics. To make the system responsive to the actual and predicted traffic state, links will adjust TLS greentimes. Links have three possible sources of information they use to determine their traffic state: velocity-intensity measure points, the induction loops at TLS equipped crossings and a traffic simulation model called MADAM (ref.: Goudappel Coffeng B.V.). MADAM acquires the information from the TLS loops and velocity-intensity measure points and determines what the traffic state is on links that have no sensors
Page 475
of their own. In addition, MADAM predicts what the traffic states will be for the links in the next 30 minutes in blocks of 5 minutes. Links compare the information about their traffic state with a so called reference framework (Rijkswaterstaat, 2003). The reference framework defines criteria that the traffic state on the link should meet. If the link's traffic state deviates from the reference framework, or will deviate in the near future, links will take appropriate action. Links will communicate via intermediate nodes with other links and ask them to perform general services to induce a traffic state that meets the criteria. The first service to be implemented is called 'reduce outflow'. All links offer this service; when called upon, a link will reduce it's outflow by setting different TLS greentimes. If the link doesn't have a TLS (or any other way to implement the service) it will forward the service-call to its upstream neighbouring link(s). Also when the link calculates that the reduction in greentime (and thus in outflow) will make it violate its own reference framework, the link will send a corresponding 'reduce outflow' request to its predecessors. Nodes form the connections between links and basically serve as service-call relays; links actually send service calls not directly to other links but to their upstream node and that node forwards the call to the preceding link(s). If there is more than one preceding link the node will partition the request. In this partition the traffic states on the preceding links as well as their priority within the network is taken into account. Firstly, links that still have room, will be allocated as much of the service request as their buffer-capacity allows. Secondly, links with higher priority have to realize a smaller portion of the outflow reduction. 5. CONCLUSIONS The case studies have demonstrated that a bottom up approach to traffic control is a promising addition to the top-down approach. The main problem consists of the necessary configuration and tuning after deployment. Main advantages are certain effects on traffic that are hard to obtain otherwise, such as automatic spreading of traffic over the network. From a system development point of view, the approach has beneficial effects on the scalability of the system: a larger network can be accomodated with little extra complexity. 6. FUTURE RESEARCH The most important activity for future research on the bottom-up approach will consist of simulation experiments. It may seem awkward to do the real life implementation before extensive simulations have proven the validity of the approach. But in this case, there are several good reasons why the order has been reversed. The system for Alkmaar also implements the usual top-down scenario-based
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
approach, but it was important to take up the bottomup approach right from the start, otherwise there would be a serious risk that retro-fitting this approach in an existing and operational system might turn out impossible or prohibitively costly. The implementation also will produce the necessary data to feed simulation experiments, for instance about delay times in sensors and actuators, so the reliability of the simulation will be improved. Finally, the system structure is such that many adaptations, suggested by simulations, can still readily be implementend. Apart from finding more effective ways of configuring and tuning bottom-up traffic control systems, the cooperation with other control mechanisms, most notably the top-down approach and the mechanisms based on communicating vehicles, will be an important research issue. 7. ACKNOWLEDGEMENTS This research was based on the cooperation in the DRIP- and HARS-projects between Delft University of Technology, Trinité Automatisering B.V. and Goudappel Coffeng B.V. We express our gratitude to these companies and especially to Otto Krüse, Rolf Krikke, Marcel Westerman and Martie van der Vlist. This work was supported by the Next Generation Infrastructures Foundation and the Research Centre Next Generation Infrastructures, both situated in Delft, The Netherlands. REFERENCES Adler, J.L., G. Satapathy, V. Manikonda, B. Bowles, V.J. Blue (2005): A multi-agent approach to cooperative traffic management and route guidance, Transportation Research Part B, 39, pp.297-318. Craen, S. de and M. de Niet (2002) Possibilities and effects of Variable Message Signs, at: http://www.swov.nl/rapport/R-2002-13.PDF Evaluation Rotterdam City DRIP's (2004) at: http://www.maatregelencatalogus.nl/admin/uploa ds/7.pdf Goudappel Cofffeng B.V.: MADAM is a traffic simulation model, part of the OmniTRANS system. Information at: http://www.goudappel.nl/Site/basicsite.nsf/www VwContent/l2omnitrans.htm?OpenDocument Hegyi, A. (2004): Model Predictive Control for Integrating Traffic Control Measures, Trail Thesis Series. Katwijk, R.v. and P.v. Koningsbruggen (2002): Coordination of traffic management instruments using agent technology, Transportation Research Part C, pp. 455-471. Kernteam DVM Alkmaar (2003): Evaluation Dynamic Traffic Management Alkmaar, at: http://www.alkmaar.nl/gemeente/project/drip/32 M562_Brennmeijer_A4.pdf
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Klugl, F. , A. Bazzan and S. Ossowski (Eds., 2005): Applications of Agent Technology in Traffic and Transportation, Birkhauser Verlag. Rijkswaterstaat (2003): Handbook Sustainable Traffic Management, Rotterdam. Wolffram, S. (2002): A new kind of Science, Wolffram Media, 2002. Yang, X., W. Recker (2005): Simulation studies of information propagation in a self-organizing distributed traffic information system, Transportation Research Part C, to appear.
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
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Faculty of Technology, Policy and Management Delft University of Technolog Delft 2
Trinité Automatisering B.V. Uithoorn
Abstract: This paper describes a bottom-up approach to road traffic control. The approach is intended as a supplement to the more traditional top-down approach to traffic control by traffic management centres applying traffic control measures according to control scenarios. The essence of this bottom-up approach consists of communicating road elements: nodes and links in the network measure their traffic state and communicate about it to other nodes and links. The combination of the two control mechanisms is a promising way of implementing the control part of Sustainable Traffic Management. The approach is currently being applied in two real-life projects: the DRIP control systems on the Dutch motorways A1, A6 and A9 and the project HARS: traffic control on the belt road around Alkmaar. Copyright © 2006 IFAC Keywords: Traffic control, control agents, bottom-up control 1. INTRODUCTION Road traffic management is applied in many countries for well-known purposes such as congestion reduction, traffic safety, reducing environmental damage and driving comfort. Road traffic control (RTC) is one of the main activities within road traffic management, next to demand management, law enforcement, incident handling and pricing. RTC is about influencing traffic streams. Almost all RTC-measures applied today are local, reactive measures: they attempt to solve a problem, such as congestion, after its occurrence, and, if they solve the problem at all, they solve it only locally, i.e. such that the network context is not taken into consideration. Practice shows that this has its limitations: in the case of congestion for instance, the problem is often only moved to another location in the network. An ambitious and fundamental step forward in traffic management is denoted by sustainable traffic management (Rijkswaterstaat, 2003). Among its goals are: - moving from the current, local and reactive traffic control towards proactive, network-level control; - making all traffic management authorities involved in a region, cooperate effectively; - having traffic control cooperate effectively with the other activities of traffic management, such as those mentioned above.
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
The cooperation of all traffic management authorities in a region is essential for sustainable traffic management. Therefore we consider all the road networks in a region, not just the motorways or just the urban network. The best known approach in traffic control is the one characterized by traffic management centres applying so-called control scenarios in response to current and predicted traffic states of the road network. A scenario is a control program that executes a coordinated series of individual traffic control measures, such as ramp metering, traffic lights, speed measures, warnings, route advice, etc. Scenarios are developed off-line and correspond to recurring patterns in the traffic state, such as the morning rush hours, or the weekend exodus. We will call this the top-down approach, the traffic mananement centre (the top) being the only entity allowed to take decisions. It has indispensable advantages, and by no means do we intend to replace this approach. The bottom-up approach, which is the main subject of this paper, is intended as a supplement to the top-down approach. The latter has a number of well-know limitations that all relate to the high complexity of traffic. For instance, the number of possible traffic states that would ideally require different treatment by the traffic management centre, is far too high to handle by means of a small number of scenarios. Moreover, the presence of
Page 473
human intelligence in the process, i.e. the drivers in the vehicles, makes predictability of future traffic states essentially harder than in the case of transportation processes that do not involve human intelligence, such as oil in pipelines or electricity in high-tension networks. Bottom-up traffic control can be done in several different ways. The core of each approach consists of a set of communicating agents. The most obvious set of agents are the vehicles (including the drivers). We currently witness the emergence of this control mechanism. More and more vehicles are equiped with communication and navigation devices. More and more of these devices are kept up-to-date about traffic state and can apply this information in on-trip route planning and navigation. Although this control mechanism is user-optimum oriented, it is generally considered as a beneficial development, even for the system-optimum, as it automatically leads to an improved spreading of traffic in time and over the network. The bottom-up control mechanism that we treat in this paper deals with a different set of agents, namely the road elements: nodes and links in the road network that measure their own traffic state and communicate about it with other nodes and links. A node is any point in the road network where something changes: first of all crossings and entry and exit lane ramps, but also points where the number of lanes changes. A link is the road segment, in one direction, between two nodes. This approach is currently being implemented in two real-life case studies: the DRIP (Dynamic Route Information Panel) control system, deployed on the Dutch motorways A1, A6 and A9, and the HARS system for traffic control on the belt road around Alkmaar (Kernteam DVM Alkmaar, 2003). HARS is a Dutch abbreviation: Het Alkmaar RegelSysteem (the Alkmaar control system). The literature offers an abundance of approaches to traffic control. Most studies are simulation based. We mention only a few. In (Adler, et al., 2005) an agent-based approach can be found in which traffic is managed by negotiations between the different actors involved. (Yang and Recker, 2005) studies information distribution using vehicles as communicating and self-organizing agents. This method can be applied to make information, for instance about an incident, propagate faster than the traffic shock wave caused by the incident. In (Hegyi, 2004) an approach to traffic control based on a prediction model can be found. 2. BOTTOM-UP CONTROL MECHANISMS As mentioned above, the essence of a bottom-up control mechanism consists of an, often large, number of communicating instances or objects, usually called agents. Each agent has a relatively simple behaviour. Usually they all run the same
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
control program or one from a small number of different control programs. In general, agents are usually rather selfish, i.e. oriented at optimizing the situation for themselves. What matters is that the set of agents as a whole can also exhibit a certain behaviour, the so-called emergent behaviour, which can be beneficial for the whole (system-optimum) and hard to obtain by more traditional means. Essential for so-called bottom-up programming techniques, such as agent systems, neural networks or genetic algorithms, is that the results are hard to obtain in a traditional way. This holds to the extent that it is often hard to understand how the emergent behaviour is actually generated. If it were understood, then it would most probably be possible to program it in a more traditional way. So bottom-up programming techniques depend very much on short development cycles, on simulation and a lot of experimenting and tuning, but they can in principle make a contribution that is hard to obtain otherwise. Besides the vehicles-as-agents approach to bottom-up traffic management, one can also transform the road network into an agent system. This is the approach applied in the two case studies described below. In the road network, one can distinguish nodes and links, as defined above. So there are two types of agents: nodes and links. Both types of agents can be equiped with monitoring devices such that they can measure their traffic state. The agents can inform each other about their state and can send requests to each other. For instance, a link which tends to become overloaded can send a "reduce inflow" request to its upstream node. This node can either take appropriate measures itself (for instance, if the node is equiped with traffic lights, green times for the different outgoing links can be adapted) and/or it can forward the request to its incoming links. This will undoubtedly have an effect on traffic, and with proper tuning, it is likely that a desirable effect on the whole network can be found. Besides links and nodes, other network elements can also be represented by agents, such as road segments, routes and origin-destination pairs. Segments are the building blocks of links, routes are strings of connected links, and an origin-destination pair represents a set of routes with the same endpoints. The following two case studies illustrate these agents in more detail. 3. CASE STUDY 1: DYNAMIC ROUTE INFORMATION PANELS The first implementation of the described bottomup approach was done in a system of Dynamic Route Information Panels (DRIP's), currently deployed on the Dutch motorways A1, A6 and A9. DRIP's serve to inform drivers on the current traffic state of routes downstream of the DRIP. In case of congestion, drivers will thus know the extent of the congestion that they can expect. Knowing what to expect adds to
Page 474
the driver's comfort level. More importantly, route information enables drivers to change their route choice. Drivers are thus diverted away from the congested road. The extent to which this rerouting occurs is small though; it is often estimated that the amount of drivers that alter their route choice as a result of information provided by a DRIP is at most 15% (Kernteam DVM Alkmaar, 2003; Evaluation Rotterdam City, 2004; Craen et al., 2002). Nevertheless, DRIP's are a valued and widely deployed instrument. The following figure shows the network management hierarchy and how the hierarchy controls the DRIP's:
information concerning the tidal flow lane is given. To handle the bridge and tidal lane events a mechanism that assigns incidents to links is used. When the bridge opens an incident called 'Bridge open' is placed on the relevant links. Likewise, when the tidal lane closes an incident called 'Tidal lane closed' is placed on the relevant links. All sorts of incidents can be of effect on links, for example: the opening of a peak lane, a speed reduction, a traffic accident, etc. In response to a placed incident, links take appropriate actions. This means that they either execute measures themselves and / or communicate with other network elements to do so. In the DRIP case only the latter happens; DRIP's are under the command of OD-managers. Links publish information concerning their incidents. Routes subscribe to this information and publish it again themselves. Finally the OD-manager, who is a subscriber to this information, receives it and determines a relevant DRIP text to be displayed. 4. CASE STUDY 2: THE ALKMAAR PROJECT
Figure 1: Hierarchical DRIP control The bottom layer contains all the road segments. Each segment aggregates data from induction loops, updated every minute, into information about velocity, intensity and congestion state of the segment. Segments publish this aggregated information every minute. Links further aggregate and transform the information from their segments. The total length of the congestion on the link is calculated by adding up the lengths of the underlying segments that have a congested state. Links publish this information every minute. Routes subscribe to the link information and calculate the congestion length per routepart by adding up the congestion lengths of underlying links. Routes determine which informative text they wish to display on the DRIP depending on the specific combination of congestion lengths on the different routeparts. This text, together with the total congestion length, is published every minute. An origin-destination manager (ODmanager) subscribes to this information and commands a software component called DRIP control to put the text on the DRIP. Incidents Highway A1 has two features which set it apart from most other highways. It contains a moveable bridge over the river Vecht, which opens twice a day. It also features a tidal flow lane (the only one in the Netherlands). The states of both the bridge and the tidal flow lane must be represented on the DRIP's. When the bridge is open for ships to pass, the concerning DRIP's must show this. When the tidal flow lane is open the concerning DRIP's must show normal traffic information. When it closes a relevant text is displayed for ten minutes after which no more
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
Although not yet very sophisticated, the DRIP system effectively uses the network management hierarchy to control roadside equipment. The next project where the hierarchy will be used, is situated in and around Alkmaar. Alkmaar lies at the end of highway A9 and has a ring road that is often heavily congested. The network management hierarchy will serve in this project as a complement to regular dynamic traffic management solutions (especially control scenario's). Alkmaar has two types of actuators: traffic light systems (TLS's) and DRIP's. The DRIP's will be used for rerouting and informing drivers. The TLS's will be used as an instrument to change intensities of traffic flows. The DRIP's will be controlled in the same manner as in the A1-A6-A9 project described above. The functions of the OD-managers will however be enhanced and extended to enable more effective rerouting. Much traffic needs to pass over the ring. The use of the DRIPS enables RTC to influence the side which is used (e.g. East or West). If one side of the ring is (nearly) congested, traffic can thus be diverted to the other side. In the Alkmaar project links and nodes will play the most important role in traffic control; their cooperation will be responsible for controlling the TLS greentimes. The default settings for a TLS will be determined by signal schemes that are designed off-line, based on relevant traffic statistics. To make the system responsive to the actual and predicted traffic state, links will adjust TLS greentimes. Links have three possible sources of information they use to determine their traffic state: velocity-intensity measure points, the induction loops at TLS equipped crossings and a traffic simulation model called MADAM (ref.: Goudappel Coffeng B.V.). MADAM acquires the information from the TLS loops and velocity-intensity measure points and determines what the traffic state is on links that have no sensors
Page 475
of their own. In addition, MADAM predicts what the traffic states will be for the links in the next 30 minutes in blocks of 5 minutes. Links compare the information about their traffic state with a so called reference framework (Rijkswaterstaat, 2003). The reference framework defines criteria that the traffic state on the link should meet. If the link's traffic state deviates from the reference framework, or will deviate in the near future, links will take appropriate action. Links will communicate via intermediate nodes with other links and ask them to perform general services to induce a traffic state that meets the criteria. The first service to be implemented is called 'reduce outflow'. All links offer this service; when called upon, a link will reduce it's outflow by setting different TLS greentimes. If the link doesn't have a TLS (or any other way to implement the service) it will forward the service-call to its upstream neighbouring link(s). Also when the link calculates that the reduction in greentime (and thus in outflow) will make it violate its own reference framework, the link will send a corresponding 'reduce outflow' request to its predecessors. Nodes form the connections between links and basically serve as service-call relays; links actually send service calls not directly to other links but to their upstream node and that node forwards the call to the preceding link(s). If there is more than one preceding link the node will partition the request. In this partition the traffic states on the preceding links as well as their priority within the network is taken into account. Firstly, links that still have room, will be allocated as much of the service request as their buffer-capacity allows. Secondly, links with higher priority have to realize a smaller portion of the outflow reduction. 5. CONCLUSIONS The case studies have demonstrated that a bottom up approach to traffic control is a promising addition to the top-down approach. The main problem consists of the necessary configuration and tuning after deployment. Main advantages are certain effects on traffic that are hard to obtain otherwise, such as automatic spreading of traffic over the network. From a system development point of view, the approach has beneficial effects on the scalability of the system: a larger network can be accomodated with little extra complexity. 6. FUTURE RESEARCH The most important activity for future research on the bottom-up approach will consist of simulation experiments. It may seem awkward to do the real life implementation before extensive simulations have proven the validity of the approach. But in this case, there are several good reasons why the order has been reversed. The system for Alkmaar also implements the usual top-down scenario-based
© 11th IFAC Symposium on Control in Transportation Systems Delft, The Netherlands, August 29-30-31, 2006
approach, but it was important to take up the bottomup approach right from the start, otherwise there would be a serious risk that retro-fitting this approach in an existing and operational system might turn out impossible or prohibitively costly. The implementation also will produce the necessary data to feed simulation experiments, for instance about delay times in sensors and actuators, so the reliability of the simulation will be improved. Finally, the system structure is such that many adaptations, suggested by simulations, can still readily be implementend. Apart from finding more effective ways of configuring and tuning bottom-up traffic control systems, the cooperation with other control mechanisms, most notably the top-down approach and the mechanisms based on communicating vehicles, will be an important research issue. 7. ACKNOWLEDGEMENTS This research was based on the cooperation in the DRIP- and HARS-projects between Delft University of Technology, Trinité Automatisering B.V. and Goudappel Coffeng B.V. We express our gratitude to these companies and especially to Otto Krüse, Rolf Krikke, Marcel Westerman and Martie van der Vlist. This work was supported by the Next Generation Infrastructures Foundation and the Research Centre Next Generation Infrastructures, both situated in Delft, The Netherlands. REFERENCES Adler, J.L., G. Satapathy, V. Manikonda, B. Bowles, V.J. Blue (2005): A multi-agent approach to cooperative traffic management and route guidance, Transportation Research Part B, 39, pp.297-318. Craen, S. de and M. de Niet (2002) Possibilities and effects of Variable Message Signs, at: http://www.swov.nl/rapport/R-2002-13.PDF Evaluation Rotterdam City DRIP's (2004) at: http://www.maatregelencatalogus.nl/admin/uploa ds/7.pdf Goudappel Cofffeng B.V.: MADAM is a traffic simulation model, part of the OmniTRANS system. Information at: http://www.goudappel.nl/Site/basicsite.nsf/www VwContent/l2omnitrans.htm?OpenDocument Hegyi, A. (2004): Model Predictive Control for Integrating Traffic Control Measures, Trail Thesis Series. Katwijk, R.v. and P.v. Koningsbruggen (2002): Coordination of traffic management instruments using agent technology, Transportation Research Part C, pp. 455-471. Kernteam DVM Alkmaar (2003): Evaluation Dynamic Traffic Management Alkmaar, at: http://www.alkmaar.nl/gemeente/project/drip/32 M562_Brennmeijer_A4.pdf
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Klugl, F. , A. Bazzan and S. Ossowski (Eds., 2005): Applications of Agent Technology in Traffic and Transportation, Birkhauser Verlag. Rijkswaterstaat (2003): Handbook Sustainable Traffic Management, Rotterdam. Wolffram, S. (2002): A new kind of Science, Wolffram Media, 2002. Yang, X., W. Recker (2005): Simulation studies of information propagation in a self-organizing distributed traffic information system, Transportation Research Part C, to appear.
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