evaluation of a self-organizing ambient intelligence based traffic system

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Evaluation of a self- organizing ambient intelligence based traffic system Hermann de Meer, Richard Holzer Chair for Computer Networks & Computer Communications University of Passau Germany

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Evaluation of a self-organizing ambient intelligence based traffic system. Hermann de Meer, Richard Holzer Chair for Computer Networks & Computer Communications University of Passau Germany. Overview. Modeling Socio-technical systems Quantitative measures Traffic scenario - PowerPoint PPT Presentation

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Page 1: Evaluation of a self-organizing ambient intelligence based traffic system

Evaluation of a self-organizing ambient intelligence based

traffic systemHermann de Meer, Richard Holzer

Chair for Computer Networks & Computer CommunicationsUniversity of Passau

Germany

Page 2: Evaluation of a self-organizing ambient intelligence based traffic system

Overview

1. Modeling Socio-technical systems2. Quantitative measures3. Traffic scenario4. Target orientation5. Emergence6. Real data measurements7. Conclusion

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Socio-technical systems

For the evaluation of socio-technical systems a mathematical model can be used. Ambient Intelligence (AmI) based smart environments:

Ability to monitor user actions. Adjust their configuration and functionality accordingly. System reacts to human behavior while at the same influencing it.

Feedback loop Tight entanglement between the human and the technical system.

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Socio-technical systems

SOCIONICAL Large scale integrating project funded by the EU in FP7 (ICT). Duration: 1.2.2009-31.1.2013 Development and evaluation of methods and tools for modeling, simulation, and prediction of global

properties and emergent phenomena in large scale socio-technical systems. Focuses on AmI based smart environments. Partners:

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Universität PassauBeacon Tech LTD Universität Linz London School of Economics and Political Science Eidgenössische Technische Hochschule Zürich Vereiniging voor Christelijk Hoger Onderwijs Wetenschappelijk Onderzoek en Patientenzorg

Akademia Górniczo-Hutnicza im. Stanis#awa Staszica w Krakowie Julius-Maximilians Universität Würzburg Fraunhofer Gesellschaft zur Förderung der angewandten Forschung E.V. Sociedad Iberica de Construcciones Electricas Sa SmartCare Srl Technische Universität München Martin-Luther-Universität Halle-Wittenberg Civil Protection Department - Ministry of Home Affairs

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Modeling approaches

Modeling

Micro-level modeling Macro-level modeling

Behavior of each entityMacro states are aggregated

micro states

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Quantitative measures

Time: Continuous/discreteState space: Continuous/discreteBehavior: Deterministic/stochasticLocal rules: •Local differential equations•Automata

Time: Continuous/discreteState space: Continuous/discreteBehavior: Deterministic/stochasticGlobal rules:•Differential equations•Recurrence equation•Transition probabilities/rates

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Socio-technical systems

After building the model, evaluation methods have to be chosen: Analytical evaluation methods Simulation

For analyzing self-organizing properties of a socio-technical system, quantitative measures have been developed.

Quantitative measures are defined on the micro level to describe global properties.

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Quantitative Measures

The following quantitative measures are based on information entropy H:Levels of• emergence

• Are there any coherent patterns induced by local interactions?• autonomy

• How much control data from external entities are needed to keep the system running?

• global state awareness• How much information does a single entity have about the global state

(averaged over all entities)?

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Quantitative Measures

The following quantitative measures are based on fitness functions:Levels of• target orientation

• Is the high-level goal that the system designer had in his mind, reached by the system?

• adaptivity• Is the high-level goal also reached after changes in the environment (new

control data from external entities)?• resilience

• Is the high-level goal still reached after unexpected events in the system (e.g. break down of nodes, attacks by an intruder, …)?

Although quantitative measures are defined analytically on the micro level, it is usually too difficult to calculate them analytically, so they are approximated by simulations.

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Emergence

To measure the level of emergence [0, 1]

of a system, we consider the values in the communication channels k K between the entities and compute the dependencies.

[0, T] 1 high level of emergence (many dependencies)

[0, T] 0 low level of emergence (few dependencies)

At time t we compare the information contained in all channels with the sum of the information contained in each single channel:

t = 1 – (H(Conft|K) / kK H(Conft|k))

where Conft is the global state containing all local states and all values on the communication channels at time t and Conft|K is the valuation of communication channels at time t and H denotes the information entropy.

Level of emergence of the whole system in the interval [0, T]:

[0, T] = Avg(t), Time average value of the map : [0, T] [0, 1]

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Global state awareness

To measure the level of global state awareness [0, 1]

the initial states are partitioned according to the equivalence relation induced by a property of interest.

Measurement of the information of each node about the initial equivalence class (averaged over all nodes):

Level of global state awareness of the whole system in a time interval [0, T]:

[0, T] = Avg() Time average value of the map : [0, T] [0, 1]

[0, T] 1 means high level of global state awareness

(each node knows much about initial equivalence class)[0, T] 0 means low level of global state awareness

(each node knows few about initial equivalence class)

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t is a nondecreasing function of the time:Increasing local history leads to a decreasing entropy.

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Target orientation

Level of target orientation at time t:

TOt = E(bt(Conft)), where E(X) is the mean value of a random variable X, i.e. TOt is the average valuation of the global state

Conft at time t.

Before a new system is designed, we have a goal of the system in our mind:

The system should fulfil a given purpose.

Fitness function: In the model, the goal can be described by a valuation of configurations (either time dependent or time independent):

bt : Conf ! [0, 1] (Conf is the set of all global states)

TO[0, T] 1 means that the system mostly runs through many good configurations) high level of target orientation

TO[0, T] 0 means that the system mostly runs through many bad configurations) low level of target orientation

Level of target orientation of the whole system in a time interval [0, T]:

TO[0, T] = Avg(TOt) Time average value of the map TO : [0, T] [0, 1]

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Traffic scenario

Scenario: There was an accident on a highway Other cars slow down, when they are approaching the scene of accident. Possibility for AmI device:

Some cars may contain a device, which send information (e.g., current position) to other cars to improve safety.

The device can give advice to the driver (e.g., slow down) before he reaches the scene of accident Which rules for the AmI device leads to a high level of target orientation?

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Traffic scenario

Proposed system: If speed of a car is below a threshold v0, AmI device sends an alert to inform the cars behind. Alerts are forwarded backwards by other cars together with the position of the original sender. If the distance to the sender is smaller than a threshold sr (relevance distance), the adaptive-

cruise-control (ACC) is activated: Time headway is changed to a predefined value thw. Car stays in current lane until the merging point (100m before the accident) is reached.

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time headway

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Traffic scenario

For the analysis, we consider only a short road section (communication area for AmI alerts):

Outside of the communication area, the distance to traffic jam is larger than the threshold sr during the whole run of the simulation, so the alert signals would have no effect on the behavior.

The number of cars in this communication area varies from 10 to 140.

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beginning of road section end of road sectionrelevance area: 2382 m

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Target orientation

For the level of target orientation, we have different possibilities to define a "fitness function" b : Conf ! [0, 1] for the current state:#1 Based on the variance of the velocities of the cars.#2 Based on the variance of the time headways of the cars.#3 Based on the variance of the velocity change.#4 Based on the variance of the time headways change.#5 Based on the relative velocities from one car to the next car.#6 Throughput through the single lane area.

Variation of different system parameters: Equipment rate for AmI device: r { 0%, 10%, 50%, 100% } Traffic flow (vehicles per hour): f { 500, 1000, 1500 } Desired time headway in ACC: thw { 1.5, 1.8, 2.1 } Other system parameters (v0 = 30km/h, sr = 1000m) are constant.

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Target orientation

Measure #1:TO using variance of velocity vi of each car i

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level of target orientation

mean velocity

empirical variance

variance coefficient

K normalizing constant for the variance coefficient

nt number of cars in the system at time t

time [sec]

TOt

Parameters: r = 100%, f = 1500Variation of system parameter thw in ACC:

thw = 1.5 average TO[0, T] = 0.38thw = 2.1 average TO[0, T] = 0.39

T = 1800sec (time of the simulated system)

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Target orientation

Measure #2:TO using variance of time headway thwi of each car i

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level of target orientation

mean time headway

empirical variance

variance coefficient

K normalizing constant for the variance coefficient

nt number of cars in the system at time t

TOt

time [sec]

Parameters: r = 100%, f = 1500Variation of system parameter thw in ACC:

thw = 1.5 average TO[0, T] = 0.94thw = 2.1 average TO[0, T] = 0.95

T = 1800sec (time of the simulated system)

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Target orientation

In the analysis we investigated the influence of different types of system parameters: The time headway thw for the ACC is a system parameter which can be chosen by the system designer.

The aim of the analysis of this parameter is the optimization of the system with respect to predefined criteria. The equipment rate r can not be chosen by the system designer, but it expresses the proportion of cars, in which

the AmI device is implemented. The results can be used to analyze the influence of the equipment rate to the high level goal (traffic safety).

The traffic flow can not be chosen by the system designer either, but it describes the density of the traffic flow. The results of the analysis can be used to analyze different highways or different times of day (rush hours usually have a

higher value for this parameter).

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Target orientation

For the analysis, we investigate the values of the measures for one variable parameter and two constant parameters: For fixed values thw and f and variable values for r we observe:

Measure #1 is often decreasing for increasing equipment rate r. Concerning measure #2, the results for a low input traffic flow f are nearly independent of the equipment rate r, but for f ≥ 1000, the measure yields

a strong increase for r = 100% compared to lower equipment rates. Therefore a high equipment rate yields a high level of target orientation with measure #2, if the input traffic flow is not too low.

For f ≥ 1000, measure #3 is monotone increasing with the equipment rate, so also in this case, a high equipment rate yields a higher level of target orientation.

Similar to measure #2, also measure #4 shows a strong increase for r = 100% compared to lower equipment rates, but only for the input traffic flow f = 1500.

Measure #5 is often decreasing for increasing equipment rate r. Measure #6 is often slightly decreasing for increasing equipment rate r.

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Target orientation

For the analysis, we investigate the values of the measures for one variable parameter and two constant parameters:

For fixed values f and r and variable values for thw we observe: While for the measures #1, #4 and #5 no monotone behavior can be seen, the measures #2 and

#3 are often increasing with thw, so usually a higher value for this system parameters leads to a higher value for the level of target orientation for the measures #2 and #3.

Measure #6 is slightly decreasing with increasing parameter thw, so a higher value for the parameter leads to a slightly smaller throughput.

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Target orientation

For the analysis, we investigate the values of the measures for one variable parameter and two constant parameters:

For fixed values thw and r and variable values for f we observe: The measures #1, #4 and #5 are decreasing for increasing input traffic flow f. The measure #2 is decreasing for increasing input traffic flow f if r ≤ 50%. The measures #3 and #6 are increasing for increasing input traffic flow f. A higher input

traffic flow obviously leads to a higher throughput.

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Emergence

How can the measure of emergence be applied in this scenario? Problem:

An analytic evaluation of the measure is impossible because of the high complexity of the system. Therefore, approximation methods based on simulation results are needed. Since the measure of emergence is based on the statistical entropy, probabilities have to be approximated by

relative frequencies. But the global state space is too large, such that a lot of simulations would be necessary to achieve accurate

results. Our approach: Use an aggregation technique on the state space to improve the usability of simulation results.

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Emergence

How can we define the partition on the global state space for aggregation? For the definition of the emergence, only the values on the communication channels are relevant. Internal states of the nodes can be ignored. Communication:

Only alert signals are sent. Content: Position of sender

New alert signal: If the car's speed is below a threshold Forwarded alert signal: If a signal is received from front, it is forwarded to the cars behind.

Possibilities for state aggregation: Consider each communication channel as a broadcast channel. Decompose the area of relevance into n fixed road sections. Aggregate all positions of one section into a single value, i.e. the content of the alert signal is the

section of the sender.

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Emergence

This approximation method has been tested for the following cases: r = 100%, f = 1500, thw = 1.5 r = 100%, f = 1500, thw = 2.1For both cases we used 20 simulation runs and n = 10 road sections.

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Emergence

thw = 1.5 thw = 2.1

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time [sec]

t

time [sec]

t

Average: [0, T] = 0.9766 Average: [0, T] = 0.9771

Emergence with aggregation

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Emergence

Interpretation of emergence: The level of emergence indicates the presence of global patterns in the system. Global patterns can be seen as dependencies between the communications of different nodes. A high value indicates high dependencies:

A newly generated alert signal is forwarded by other nodes, the communication data of different nodes are highly dependent. This is represented by a high value of emergence.

But: How accurate is the approximation method?

20 simulations might be not sufficient. Was n = 10 road sections a good choice? The aggregation leads to a decrease in entropy and to an increase for the level of emergence. What happens, if we calculate the emergence without aggregation in road sections?

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Emergence

thw = 1.5 thw = 2.1

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time [sec]

t

time [sec]

t

Average: [0, T] = 0.8843 Average: [0, T] = 0.8867

Emergence without aggregation

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Target oriented emergence

Depending on the situation, emergence can be a good or a bad property in a system. Traffic jam is a global pattern, which can be interpreted as an emergent phenomenon in the system. A homogeneous traffic flow around the accident scenario is also a global pattern, which can be

interpreted as an emergent phenomenon in the system.

We have to distinguish between emergence in general and target oriented emergence.

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Target oriented emergence

How about the emergence in our scenario?High emergence for both values of thw: thw = 1.5 average t = 0.9766

thw = 2.1 average t = 0.9771

Target orientation depends onthe fitness function:

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Measure #1:thw = 1.5 average TO[0, T] = 0.38thw = 2.1 average TO[0, T] = 0.39

Low target orientation

Measure #2:thw = 1.5 average TO[0, T] = 0.94thw = 2.1 average TO[0, T] = 0.95

High target orientation Target oriented emergence

emergence

target orientation

#1 #2

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Real data measurements

Questions: Can we apply the defined quantitative measures (target orientation, emergence) also on real data? Can we compare the results received from the simulations with the results received from real data?First we have to identify, which kind of real data is available.Data from the highway M30 in Madrid:

Detectors with sensors are located at the road. Each track of the road is sensed independently. Output of the detector:

Number of cars in the sensed track in a time interval of length 60sec. Average speed of the cars the sensed track in a time interval of length 60sec.

We have M30 data available of one day, when an accident occurred, so we have a scenario similar to the scenario already analyzed in the simulation.

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Real data measurements

But the defined measures can not be directly applied to the M30 data, because different detectors count only the cars, they can not identify them, so we have no trajectory of a car, the speed of a single car is not available, the time headway is not available, the cars do not have an AmI device, so the data covers only the case r = 0%.

To make the analysis of the simulation results comparable to the analysis of the M30 data, we have to adjust the measures, such that they can be applied to both types of data.

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Real data measurements

To be able to define a measure, which is applicable for both types of data, the simulation results must be aggregated in the same way: Time intervals of length 60sec are considered. In each time interval, the number of cars passing some specified positions (the positions of the detectors) is counted and the average

speed is calculated.

Measure for target orientation based on the variance of velocities: The number of cars detected in the time interval is used as a weight in the formula of the mean velocity t and the empirical variance t. Then the level of target orientation is defined as before:

This method is currently implemented by the University of Passau. The evaluation and comparison is planned for the next weeks.

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Conclusion

Quantitative measures can be used for the analysis and for the optimization the system parameters and the local rules. Level of target orientation:

For each setting, simulation runs lead to an approximation of the level of target orientation. The highest level of target orientation indicates the optimal values for the system parameters.

Level of emergence: Identification and analysis of the global patterns emerging from the local rules.

Measure of global state awareness Some drivers already know about the traffic jam near the accident. Other drivers are not aware of the situation. The measure for global state awareness can be applied to analyze the influence of different settings for system parameters:

Which system parameters can increase the global state awareness? Safety can be increased, if all drivers have maximal information about the global state.

Combination of measures: Target oriented emergence

) Improved safety in traffic

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Conclusion

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Quantitative measures provide a link from the micro level to the macro level:•They describe global properties of the system •They can be used for the analysis of complex systems.•They can be used for optimization of existing systems and for the design and engineering and of new systems.

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Thank you for your attention

Hermann de Meer

Chair for Computer Networks & Computer CommunicationsUniversity of PassauGermany

[email protected]

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• Richard Holzer and Hermann de Meer and Christian Bettstetter. On autonomy and emergence in self-organizing systems. IWSOS 2008 - 3rd International Workshop on Self-Organizing Systems, Vienna, Austria, December 10-12, 2008, Springer Verlag 2008

• Christopher Auer, Patrick Wüchner, and Hermann de Meer. The Degree of Global-State Awareness in Self-Organizing Systems. In Proc. of 4th International Workshop on Self-Organizing Systems (IWSOS 2009).  Zurich, Switzerland, December 9-11, 2009. Springer, LNCS.

• R. Holzer and H. de Meer. Quantitative Modeling of Self-Organizing Properties. In Proc. of 4th International Workshop on Self-Organizing Systems (IWSOS 2009). Zurich, Switzerland, December 9-11, 2009. Springer, LNCS.

• R. Holzer and P. Wüchner and H. de Meer. Modeling of Self-Organizing Systems: An Overview. In Workshop über Selbstorganisierende, adaptive, kontextsensitive, verteilte Systeme (SAKS 2010), Electronic Communications of the EASST, Vol. 27, 2010

• R. Holzer and H. de Meer. Methods for Approximations of Quantitative Measures in Self-Organizing Systems. In Proc. of 5th International Workshop on Self-Organizing Systems (IWSOS 2011). Karlsruhe, Germany, February 23-24, 2011. Springer, LNCS.

• Richard Holzer and Hermann De Meer. Modeling and Application of Self-Organizing Systems - Tutorial Paper. Proc. of the 5th Int'l IEEE Conference on Self-Adaptive and Self-Organizing Systems (SASO 2011) of IEEE Computer Society Press. 2011

• Matthew Fullerton, Richard Holzer, Hermann de Meer, Cristina Beltran Ruiz. Determining the Effectiveness of Ambient Intelligence within the Area of a Traffic Accident. Poster presentation at the 6th Int'l Workshop on Self‐Organizing Systems (IWSOS 2012)

References

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