Proceedings - Istituto di Analisi dei Sistemi ed Informatica - Cnr

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INTELLIGENT TRANSPORT SYSTEMS: ROLE OF SENSORS IN TRAFFIC MANAGEMENT Aman MOUDGIL 1 Gaurav MOUDGIL 2 Munish BANSAL 3 Abstract. Transportation Infrastructure is the backbone of every country. This is the gateway to economic development and subsequently, a healthy economy. Urbanization process in most of cities has reached an alarming stage, flooding the urban regions. Various attempts have been made to provide the necessary transport services and related infrastructure to meet the requirement. The rapid increase in transportation facilities has led to large amount of traffic problems including violating traffic rules leading to accidents (sometimes mat be fatal). This paper outlines the application of Information Technology in controlling transportation systems to solve many of the problems. 1. Introduction With the rising automobile industry, and the banks helping mankind in fulfilling their dreams, the number of automobiles has grown exponentially in the past few years. This has led to the fact that once considered a luxury, vehicles have now turned out to be a necessity and almost each and every person owns some vehicle or the other (be it a car, scooter, motorcycle, etc). This sharp increase in vehicles has led to large amount of traffic problems and the ones in a hurry prefer to skip traffic lights even when signaling Red. Now in order to help out the unlucky ones or rather to help the traffic authorities in managing this tumultuous traffic, the use of sensors at traffic junctions are proposed that should ease the working of the traffic authorities. The use of this technology should help the traffic management in the way that they will not have to strain their lives (as they have to nowadays) in keeping a continuous check on the disobedient freaks. 1 Department of Computer Science and Engg,Sector-26 Chandigarh-Pin:160019, aman_moudgil3000@yahoo.com 2 MS-SEMM University of California, Berkeley, gaurav_moudgil2000@yahoo.com 3 B.E-Civil Engg Punjab Engg. College,Sector-12 Chandigarh-Pin:160012, munishbansal29@yahoo.com 8
2. Sensors and Transducers Sensors and Transducers are devices used to measure mechanical, thermal, electrical and chemical changes occurring around. The sensor is responsive to changes in the quantity to be measured, for example, temperature, position, or chemical concentration. The transducer converts such measurements into electrical signals, which can be fed to instruments for the readout, recording, or control of the measured quantities. Sensors and transducers can operate at locations remote from the observer and in environments unsuitable or impractical for human beings. Some devices act as both sensors[1] and transducer[1]. A thermocouple has two junctions of wires of different metals; these generate a small electric voltage that depends on the temperature difference between the two junctions. A Thermistor is a special resistor, the resistance of which varies with temperature. A variable resistor can convert mechanical movement into an electrical signal. Specially designed capacitors are used to measure distance, and photocells are used to detect light. Other devices are used to measure velocity, acceleration, or fluid flow. In most instances, the electric signal is weak and must be amplified by an electronic circuit. For such a situation, use of SPZ & SPT Fiber optic traffic sensors[5] interfaced via MD-200[5] can be made. These sensors are sensitive to vertical pressure only but are very flexible and elastic. These sensors do not contain any metallic parts. Traffic sensors are not sensitive to environmental conditions and also can function smoothly under a wide range of temperature. Moreover unlike other types of sensors, these are quite simple to install. These sensors and many other Traffic control equipments are being manufactured by SIEMENS, which are the global leaders in Traffic Control and Regulation systems. SIEMENS have also developed Traffic control computers for controlling traffic signals under the names SITRAFFIC CENTRAL, ACTRA, and ICONS. These computer systems were developed in Germany, England and the U.S and provide specific solutions for all the requirements encountered in international transportation business. In fact SIEMENS manufactures total traffic control equipments right from Traffic Sensors to Traffic control computers to the maintenance and regulation of these equipments. Figure 1. Traffic Sensor 9
Page 3: Preface to the Proceedings of the E
Page 6 and 7: As for the Extra EURO Conference
Page 8 and 9: END-USERS COMMITTEE Government of A
Page 10 and 11: Id: 011 An activity-based systemof
Page 12 and 13: Id: 039 The influence of advanced t
Page 14 and 15: Id: 064 Strategic modelling and hie
Page 16 and 17: Id: 087 A stochastic approach to de
Page 18 and 19: Id: 112 Minimum redeployment model
Page 20 and 21: Id: 143 Towards mobile semantic gri
Page 22 and 23: 21. Alessandro BALDASSARRA, Univers
Page 24 and 25: 73. Paolo DELLE SITE, University of
Page 26 and 27: 125. Maria Alice Prudêncio JACQUES
Page 28 and 29: 177. Bruno MONTELLA, University of
Page 30 and 31: 228. Domenico SASSANELLI, DVT - Tec
Page 32 and 33: 279. Saini YANG, University of Mary
Page 34 and 35: To help fulfil the expectations of
Page 36 and 37: DO PEDESTRIANS EFFICIENTLY USE OVER
Page 38 and 39: The gray part in Figures 1 & 2 show
Page 42 and 43: Figure 2. MD-200 3. Proposal 3.1. I
Page 44 and 45: data to be visualized in a clear an
Page 46 and 47: 9. Would solve all the Traffic prob
Page 48 and 49: EFFECT OF EXTERNAL COST INTERNALISA
Page 50 and 51: where i ,a f is the transit user fl
Page 52 and 53: [7] L. D’Acierno, M. Gallo and B.
Page 54 and 55: EXPERIMENTAL STUDY ON DATA DRIVEN P
Page 56 and 57: Due to this uncertainty of bus arri
Page 58 and 59: Figure1. Flow of Data of the whole
Page 60 and 61: 5.1. Implementation Issue: Institut
Page 62 and 63: A MULTINOMIAL LOGIT MODEL FOR SERVI
Page 64 and 65: Some Logit models were proposed by
Page 66 and 67: service quality attributes. The uti
Page 68 and 69: [4] A. Bhave. Customer Satisfaction
Page 70 and 71: MODELING THE IMPACT OF TRAVEL INFOR
Page 72 and 73: Real travel time (random generated)
Page 74 and 75: Our schedule engine follows this. T
Page 76 and 77: HOME StartTime: 0 Duration: 571 Loc
Page 78 and 79: [15] Sun, Z., T.A. Arentze, and H.J
Page 80 and 81: prevailing traffic and road conditi
Page 82 and 83: 3.2.2. Training of the network and
Page 84 and 85: Error (%) Error (%) Analytical mode
Page 86 and 87: method should be developed to help
Page 88 and 89: to parking lots (T) are selected as
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4. Evaluation of two-phased parking
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[30]), in vehicle routing ([3], [19
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with: ∆τ t od ,l ∑ = ∆T (10)
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In terms of future research, we pro
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dell’informazione: Campi di appli
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Adler and Ben-Akiva are the first a
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Activity program Primary activity o
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primary activity PA in the primary
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A WEB BASED DATA SOURCE PLATFORM FO
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2.3. Process Layer The process laye
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DYNAMIC SELECTION OF FUZZY SIGNAL C
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Each of the fuzzy control strategie
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selection at the end of each extens
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PHYSICAL AND OPERATIONAL DESIGN IN
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∑ r∈Rw ∑ r∈R v h s l r h r
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As a first step in this study, the
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DSS FOR EMERGENCY MANAGEMENT ON MOT
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improve whole emergency system in t
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The three defined levels in the cer
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USING MECHANISTIC DESIGN PROCEDURES
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• General traffic inputs- Number
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Table 5 Sensitivity of the performa
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Figure 1 depicts the results for th
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6. Concluding Remarks The sensitivi
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a) Distribution of activities. For
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Figure 2. Multi-pole structure: the
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TOWARDS A SECURE RAILWAY TRANSPORT
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There are several reasons that peop
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For each potential countermeasure,
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SECURITY MANAGEMENT PROCESS Define
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OPTIMAL REROUTING DURING EMERGENCY
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Figure 1 Ring way network consistin
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Figure 3 Assignment during accident
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50% can be attributed to the stocha
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some hyper-network approaches. Simu
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Let V smk be the systematic utility
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RESEARCH ON USERS’ BEHAVIOR CHANG
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Item Date Subjects recruitment Sep.
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Transferring time Commuting time Tr
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4.2. Future tasks Only young people
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Percentage of people 80 60 40 20 0
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particular sound level. In this sen
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for the airports such as runways le
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A NEW METHOD FOR OFFSET OPTIMIZATIO
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q j-1 N j-1 n j-1 Cell j-1 N j N j+
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4. Optimization Algorithms The obje
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TRANSYT-7F (+10% delay) and the man
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spread out non-linear utility funct
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3. Application example Data source
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PDPC model estimation In order to i
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surpasses the lower bound of 35 % i
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follower pairs are introduced and a
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vehicle was not intentionally follo
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3. Concluding remarks and research
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2. the path choice from the alterna
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2.2. Generation of choice set For t
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set generated with the explicit app
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3.2. Generation of choice set 3.2.1
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A generic user n , travelling betwe
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[2] C. R. Bhat. Recent methodologic
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EXPLORING A GUIDANCE PROCESS FOR US
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The objective is to identify a set
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This impact can be evaluated (step
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etc.) and on queue discipline. The
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and actuated traffic signals. The f
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The results of the application of t
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In conclusion, after comparing the
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guarantees the attainment of perfor
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having direction of opposite march,
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The followings models propose, resp
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Where V 85c is the operating speed
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In order to support this statement,
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implementation of just local initia
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selection of measures to implement
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overall favourable achievements, bu
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Petty et al. (1997) focus on the tr
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t is the travel time for the itiner
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Hopkin, J., D. Crawford and I. Catl
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MNL model has long been considered
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Mendell and Elston and of Tang and
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An interesting extension of the mod
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• The newly-introduced multi-late
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surveillance data) and thus can be
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targets on signal level. Figure 3 (
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A GIS APPROACH TO OPTIMAL LOCALIZAT
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It is possible to determine the opt
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In particular, each record holds th
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START-UP LOST TIME AT URBAN TRAFFIC
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Phase 1 Phase 2 Phase 1 Phase 2 h(n
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n vehicles ≥6, n vehicles tot >14
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THE INFLUENCE OF ADVANCED TRAVELLER
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is very difficult to provide reliab
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Moreover to confirm the relative im
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[6] Lehtonen M., Kulmala R., Benefi
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0—`1u„|aM2l]^„Z€‚N\]o`buR
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Œ›.1]^ž#2+Qq]^`1]t0wve2Žä „
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a.1{À`buqN ]OR]^„3SMv[0wv‚~ a.
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ŒoqÞ >Ñ)%;"$oHxR]^¯¨Nlv†‘
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efficient transportation system, ne
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4. Goals Accomplished Three major g
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DIFFERENCES AMONG ROUTE FLOW SOLUTI
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Unchanged OD = 100% 90%−100% 70%
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EVALUATION OF ON-STREET PARKING SCH
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Figure 2. Virtual Reality Experimen
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Total 46 Gender Male: 32 Female: 14
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num of answers 25 20 15 10 others c
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part under investigation metallogra
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3.1. Hyperbolic filter Among differ
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pores shorter than a user fixed len
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customers are serviced exactly once
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problems. The interested reader is
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Problem Dethloff's Best Avg Soln Be
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[3] D.O. Casco, B.L. Golden, E.A. W
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work the software was tested on a
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v RT v P,1 Figure 2. Right-turn man
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where N is the number of the counte
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ANALYSIS ON THE MECHANISM OF CONGES
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3. Data Observation and Processing
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of the Moriguchi Line is a little s
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Heavy traffic volume Trigger of the
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ON THE CONVERGENCE OF WEIGHTING MET
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Ratio weighting and weighting with
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4.2. Hypothesis 2 HP2 is also suppo
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ANALYSIS OF THE PUBLIC TRANSPORT SU
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therefore better quality is receive
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Fig. 1 - Distribution of the Global
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chain. Then, the intermodal termina
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B. Processes The considered contain
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Figure 2 Simulation model 314
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[10] C. Degano, A. Di Febbraro, “
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and the crew duties are defined as
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espectively, the predicted and the
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WILLINGNESS TO PAY FOR ACCESS TIME
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Final Destination Located in Zone 1
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destination several discrete choice
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THE UNCERTAINTY OF DELAYS AT OPTIMA
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q ( t) = Pr( i = j+ a −d ) ∀ j
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The probability of having an extens
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TWO LANE HIGHWAYS - A MICROSCOPIC T
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acc un( t) un( t) un ( t + T ) = un
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speed (km/h) 100 80 60 40 20 0 0 20
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TIME WINDOWS FOR SCHEDULED TRIPS IN
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Station 1 1 2 a b (±2 minutes) c a
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Instance Time window: 1 2 3 4 5 6 S
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[4] ILOG. ILOG CPLEX v9.0 User’s
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We present here an exact optimizati
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To make bounding effective the algo
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A DYNAMIC ACTIVITY BASED TRAVEL DEM
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parameter vector, and a hill-climbi
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[2] Bowman, J.L., and Ben-Akiva. M.
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AN AGENT-BASED APPROACH TO COMBINED
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2.1. Environmental Model As mention
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mental maps. A history based heuris
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[5] M. Bierlaire and E. Frejinger.
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• to evaluate the approaches of t
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10 seconds. The road user has an in
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FOOT SWITCH (Closing) TRAIN t (s) W
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for the vehicles. This is done in s
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(IVCSP1): ∑ min d∈D ∑ ∑ yij
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Furthermore, we denote a set of res
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ON THE POTENTIAL OF SOCIAL-PSYCHOLO
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5. Results and discussions A prelim
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Individual contributions in Session
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exceeded then the agent relies on i
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Considerations have to be given to
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capacity changes for road or non-mo
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incorporated to this integration. T
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A CONTRIBUTION TO PREDICTING THE MO
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( ) ( ) ( ) PM PV X ω V = α V (h,
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(up to 2 decimal points), while the
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There are only a few studies focuse
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ii) changes in number and severity
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It can be said that when the demand
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[3] N. Cavill, A. Davis. Cycling an
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The following objections for gap-ac
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As the third step, multiple regress
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[12] T. Aydemir, and S. Tanyel. Ço
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2. State of the art Nowadays, the i
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The methodology is based firstly on
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The network is composed of access/e
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The efficiency of the computational
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causing delay to the vehicles behin
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1,6 1,5 D_PCE 1,4 1,3 1,2 1,1 1 0 2
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A SEQUENTIAL MODEL BASED ON THRESHO
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(a) (b) Fig. 1 - Diagram of the pro
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probability density probability den
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− when the cost threshold is pass
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[11] S. Kurauchi, T. Morikawa. An e
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prediction approaches tackle this p
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implies that for aparticular depart
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5. Conclusions and discussion The r
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Again in [4], spillback congestion
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scale systems, leading to a very co
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This situation causes not only comp
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The “fail-to-access probability
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Figure 2. Underground network of Ce
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2005. Available from < http://www.r
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sovereignty - that is, that consume
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2.2. Public policy approaches to bo
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egistering and analyzing the speed
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ANALYSIS OF DEMAND FLUCTUATION ON U
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O is { s | ≤ s ≤ t} ≥ 0 for a
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longer distance increase at night.
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3.4. Demand variation owing to comm
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BETTER UNDERSTANDING THE POTENTIAL
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them to use transit responding to t
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Eight factors can be defined throug
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egular commute, which includes ques
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Select References Kemp, M. A. (1973
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Consequently, others important rela
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The most general form to solve the
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max z od k − d z od k od k < ε I
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1 2 3 4 5 1 0 800 500 1000 300 2 80
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A part from the results obtained fo
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[2] Cascetta E., Russo F. (1997). C
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2. Transportation system of Turkey
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5. The public survey To investigate
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6. Conclusions The traffic safety i
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TRANSPORT NETWORK DESIGN UNDER RISK
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2. Formulation A general transporta
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[11]). According to (9), if scenari
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Value of Time: ψ = €0.2/min Impr
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References [1] J. Abadie and J. Car
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(SITLUM). This integrates the state
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2.1.1. Sub-models within DELTA The
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Figure 3 SITLUM study area 4.1. Dat
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Figures 5 and 6 show summary indica
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The potential geographic area for d
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60,0% 50,0% 52,1% 40,0% Users 30,0%
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known or hub-based flag company des
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A STUDY ON THE EFFECT OF REMOVAL OF
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community. The major function of tr
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Table 2 The difference of the numbe
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#" ! $ #'.60% - # $ " "
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## ∆ *4,!*6,*1,! " $#*&
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#$%&% & ! " ( $"" ##"" "#" "%
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! "# $ %# ! &!'! (!
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A B) * $$$ ! ! ) !
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-.10 J I & > =) D! H>==A;B&
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important to measure the conflict i
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3.3. PTTC indicator In a fixed coor
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(sec) 10 (km/h) (cm) 25 700 600 CON
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− − − framework and on the ot
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4. The transferability study This s
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stochastic aspects and it derives a
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δ(x) = 0 if x ≠ 0, δ(0) = ∞,
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queue probability x=0.833 0.14 prob
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and will be briefly reminded here i
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The integration of this equation in
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A COMPARISON OF MACROSCOPIC AND MIC
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points and simulate design scenario
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priorities and behavior, and events
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The analysis of a transportation sy
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transported each day. The statistic
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- ability to provide output data wh
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Currently, this aspect of safety re
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INTERSECTION MODELLING BASED ON THE
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In the heterogeneous Riemann proble
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Max ∑ ∑ 0 ≤ q Φ 0 ≤ q ≤
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4.1. Diverge node model results The
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Figures 6 and 7 represent the densi
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References [1] Lebacque J.P, Khoshy
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(safety index combined with a traff
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As regards cluster 3 (representing
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100 acc 13956 90 80 Occ(%), Risk In
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2. The model 2.1. Introduction The
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In other terms, the local first ord
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Figure 4 3. Existence and quality o
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A better way to approximate any pop
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• Calibrating motorists behaviour
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Let us further consider a truck dri
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A SHOCKWAVE-BASED METHODODLOGY FOR
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the problem under consideration is
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Solution Approach For the commuting
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56 52 48 44 40 36 32 28 24 20 16 12
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PIACON: ROBUST VEHICLE TRAJECTORY B
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TDS =(P, A, B): where P- denotes se
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3. PIACON vehicle trajectories base
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Table 2 .PIACON artery synchronizat
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MODELING ACTIVITY CHOICE DISTRIBUTI
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St. d d e od Min o ∑∫ fe( s, Ce
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1. initialize all links with small
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Figure 2. Evolution of quantity of
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ON THE FRACTAL GEOMETRY OF THE ROAD
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The transport network can be descri
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Figure 2. This network can be descr
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STOCHASTIC DELAY AT TRAFFIC SIGNALS
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The cumulative count curves method
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3.3. Intensity effect We compared d
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the average delay with the step dem
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References [1] R. Akçelik. HCM 200
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the other trips were done in SOV [1
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i. Hpath (i,k) is a feasible path i
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Objective function (LCPP): = ⎛
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Figure 3 - Application of the progr
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exceeding the maximum density. Ther
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Here, n i p( t) 0 j, = when j>t; s,
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The nodes rules component of the pr
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3. Validity of proposed DNL model T
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Under the set node configuration it
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A DYNAMIC NODE MODEL WITH A QUADRAT
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control variables such as link trav
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♣ FW k : The set of links that ar
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! ir á = 1 r" FW k (24) When the c
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[3] V. Astarita. A continuous time
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This paper focuses on the optimizat
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to improve the operational efficien
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THE 2-PERIOD PROBABILISTIC TSP: A M
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N variables is solved, where N is t
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approximating future costs using th
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develop a MILP formulation and a br
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one arc from each alternative pair,
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URBAN TRANSPORT: EUROPEAN EXPERIENC
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Well-developed and efficient public
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In addition to these public systems
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AUCTION BASED CONGESTION PRICING: T
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3. Auction based congestion pricing
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issues, and potential benefits of t
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DEALING WITH UNCERTAINTIES IN TRANS
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Figure 1 The policy analysis framew
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uncertainties in the output of demo
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• Pattern of system performance:
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PROVIDING PUBLIC PARTICIPATION IN T
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have 14, 13 and 6 criterions respec
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6 Results of iterations Alternative
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MINIMUM REDEPLOYMENT MODEL FOR DYNA
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demand and unit busy probabilities,
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3. The algorithm There have been va
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DYNAMIC SET PARTITIONING APPLICATIO
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4. The dynamic solution approach In
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In Table 2 our in progress numerica
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HANDLING DEMAND AND NETWORK CAPACIT
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evacuation study may change the fin
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3.1.1. P-Level Efficient Points Def
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(Cell # 7 and #8) in the network ar
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VALIDATION AND COMPARISON OF CHOICE
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From the above, results of calibrat
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50.0% 45.0% 40.0% ASA BUS = 1,770 3
Page 766 and 767:
2. Taxi planning model TP is modell
Page 768 and 769:
Where the “Node flow conservation
Page 770 and 771:
k Where θ is the step size in this
Page 772 and 773:
ALLOCATION PLANNING OF HANDLING DEV
Page 774 and 775:
and handling devices transfer times
Page 776 and 777:
3. Solution approach In order to so
Page 778 and 779:
From the previous calculation, orde
Page 780 and 781:
UNIVARIATE AND MULTIVARIATE ARIMA M
Page 782 and 783:
the portmanteau test statistic can
Page 784 and 785:
provides a value of 0,11, i.e. acce
Page 786 and 787:
emaining months have the same numbe
Page 788 and 789:
CROSSING TIME MICRO-ANALYSIS: DIFFE
Page 790 and 791:
The main characteristic of a zone i
Page 792 and 793:
LEGENDA PERCORSI DI CALCOLO IN FUNZ
Page 794 and 795:
Fig. 7 - Tempo di attraversamento v
Page 796 and 797:
SIMULATION AND VALIDATION OF PARKIN
Page 798 and 799:
is based on fuzzy logic. In fuzzy l
Page 800 and 801:
2.1.3. Models for parking off stree
Page 802 and 803:
described as a S shape Wiebe functi
Page 804 and 805:
The validation of searching models
Page 806 and 807:
Next step will regard the calibrati
Page 808 and 809:
COLLABORATIVE DEMAND AND SUPPLY NET
Page 810 and 811:
area, and an accurate overview of m
Page 812 and 813:
6. Conclusions Current scenario in
Page 814 and 815:
[20] Villa, A. Emerging trends in l
Page 816 and 817:
L eq is widely used since it allows
Page 818 and 819:
[4] Beruard B. (1986) Environmental
Page 820 and 821:
MODELLING URBAN TRAFFIC NETWORKS BY
Page 822 and 823:
uffer entity implementing an event-
Page 824 and 825:
3. Conclusion and further research
Page 826 and 827:
ased on such models. Various formul
Page 828 and 829:
Theory is said to be amenable to th
Page 830 and 831:
possibility distribution should be
Page 832 and 833:
What we see above is the possibilis
Page 834 and 835:
Williams, H. C. W. L. On the format
Page 836 and 837:
PETRI NET MODEL OF TRANSHIPMENT ACT
Page 838 and 839:
Activities in the yard area and the
Page 840 and 841:
conditions consequent to the event,
Page 842 and 843:
normal supervision of loading/unloa
Page 844 and 845:
When loading containers, Ch2 verifi
Page 846 and 847:
[10] G. Crainic, M. Gendreau and P.
Page 848 and 849:
transportation network instead of c
Page 850 and 851:
A crucial role in the evaluation of
Page 852 and 853:
Tv P Tv I Tr P Tc P Tr I Tc I (1) (
Page 854 and 855:
References [1] D. Ambrosino, A. Sci
Page 856 and 857:
private transportation on paths dev
Page 858 and 859:
2. Research Program The work progra
Page 860 and 861:
Starting from the availability of t
Page 862 and 863:
INFOMOBILITY AND LOGISTICS ON URBAN
Page 864 and 865:
- New algorithms for the optimal ma
Page 866 and 867:
of Milan), ATENA (Environment, Traf
Page 868 and 869:
flows. These are typically binary i
Page 870 and 871:
References [1] D. Ambrosino, A. Sci
Page 872 and 873:
[29] P. Dell'Olmo, and P.B. Mirchan
Page 874 and 875:
AN INTEGRATED INFORMATION AND MANAG
Page 876 and 877:
Transversally at the above-stated r
Page 878 and 879:
are generally “soft” measures t
Page 880 and 881:
At the moment, the CP software mana
Page 882 and 883:
L=25 km T=30’ C=4.5 € L=31 km T
Page 884 and 885:
The object of the research is to de
Page 886 and 887:
2.3. Microscopic simulation of traf
Page 888 and 889:
3.1. Pre-timed synchronization Sign
Page 890 and 891:
120 100 Delay [s/veh] 80 60 40 20 0
Page 892 and 893:
We are now aiming at exploiting the
Page 894 and 895:
Arterial travel time direction 2 [s
Page 896 and 897:
[15] Hunt P.B., Robertson D.I., Bre
Page 898 and 899:
That is, transit does not seem to b
Page 900 and 901:
The speed computed on the whole doo
Page 902 and 903:
RAIL STOP RAIL STOP Low density Hig
Page 904 and 905:
step requires not only the definiti
Page 906 and 907:
In Polish conditions Park and Ride
Page 908 and 909:
linguistic variable [4]. Values of
Page 910 and 911:
4.1. Section off P&R users among pr
Page 912 and 913:
Fig.4 Results of FIS - share of P&R
Page 914 and 915:
Number of potential P&R users: Volu
Page 916 and 917:
ITS FRAMEWORK ARCHITECTURE IN A MUL
Page 918 and 919:
made of several functional data flo
Page 920 and 921:
Functional Areas Acronyms 1 Provide
Page 922 and 923:
TOWARDS MOBILE SEMANTIC GRID FOR IN
Page 924 and 925:
esources and services become machin
Page 926 and 927:
dissemination as well as a semantic
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