Proceedings - Istituto di Analisi dei Sistemi ed Informatica - Cnr

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Id: 087 A stochastic approach to delays at intersections Henk J. VAN ZUYLEN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 Id: 088 Tracking waves in the LWR model for a progressive dynamic traffic assignment calculation Vincent HENN, Thomas DURLIN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Id: 089 A comparison of macroscopic and microscopic approaches for simulating container terminal operations Giulio Erberto CANTARELLA, Armando CARTENÌ, Stefano de LUCA. . . . . . . . . . . . . . . . . 556 Id: 090 Object-oriented modeling and simulation of the transportation system Hanna SAWICKA, Jacek ZAK. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 Id: 091 Innovative technologies supporting the driving crew in degraded running conditions Giuseppe SCIUTTO, Marino LUCCHINI, Diego MAZZINI, Cristian VEGLIA. . . . . . . . . . . 567 Id: 092 Intersection modelling based on the second order traffic flow model (ARZ) Salim MAMMAR, Jean-Patrick LEBACQUE, Habib HAJ SALEM. . . . . . . . . . . . . . . . . . . . . . 570 Id: 093 Risk index model for real time prediction of potential traffic crash on urban motorway Habib HAJ SALEM, Christophe RAMANANJAONA, Jean-Patrick LEBACQUE, Salim MAMMAR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 Id: 094 Existence and stability of equilibriums in regional tax competition Megan M. KHOSHYARAN, Jean-Patrick LEBACQUE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Id: 095 Traffic flow modelling and inter-change vehicle spacing control in the Mont Blanc Tunnel Vincent HENN, Jean-Baptiste LESORT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Id: 096 A shockwave-based methodology for reliable estimation of origin-destination travel-time in urban areas Manoj K. JHA, Franklin OKONKWO, Wolfgang KUHN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 Id: 097 PIACON: Robust vehicle trajectory based arterial multi-criteria traffic signal control Andrzej ADAMSKI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 Id: 099 Modeling Activity choice distribution and road choice behaviour on a network with side constraints Tai-Yu MA, Jean-Patrick LEBACQUE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616 XVI
Id: 100 On the fractal geometry of the road network and its relevance to transport modelling Erel AVINERI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 Id: 101 Stochastic delay at traffic signals: influence of demand patterns Estelle CHEVALLIER, Ludovic LECLERCQ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 Id: 102 Car pooling clubs: viability of the concept applied to Lisbon's metropolitan area, using GIS and optimization techniques Gonçalo CORREIA, José Manuel VIEGAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Id: 103 A dynamic model of point-queue process Hilmi Berk ÇELIKOĞLU, Mauro DELL'ORCO, Ergün GEDIZLIOĞLU. . . . . . . . . . . . . . . . . 647 Id: 104 A dynamic node model with a quadratic travel time function based link model Hilmi Berk ÇELIKOĞLU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 Id: 105 Container stowage optimization with crane utilization balancing: modelling and heuristics Masoud HAMEDI, Ali HAGHANI, Saini YANG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Id: 106 The 2-period probabilistic TSP: a Markov decision model Giovanni ANDREATTA, Guglielmo LULLI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 Id: 107 Conflict resolution strategies in real time train traffic management Andrea D’ARIANO, Dario PACCIARELLI, Marco PRANZO. . . . . . . . . . . . . . . . . . . . . . . . . . 677 Id: 108 Urban transport: European experiences and the Hong Kong case Elisabetta VENEZIA, Justyna KARAKIEWICZ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 Id: 109 Auction based congestion pricing: The basic idea Dušan TEODOROVIĆ, Konstantinos TRIANTIS, Yueqin ZHAO, Snežana MLADENOVIĆ. . 688 Id: 110 Dealing with uncertainties in transport policy making: a new paradigm and approach J.W.G.M. VAN DER PAS, D.B. AGUSDINATA, W. WALKER, V.A.W.J.MARCHAU. . . . . 694 Id: 111 Providing public participation in transportation decision using span (social participatory allocative networks) method Murat KARACASU, Şafak BİLGİÇ. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702 XVII
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 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 40 and 41: INTELLIGENT TRANSPORT SYSTEMS: ROLE
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
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[4] A. Bhave. Customer Satisfaction
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MODELING THE IMPACT OF TRAVEL INFOR
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Real travel time (random generated)
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Our schedule engine follows this. T
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HOME StartTime: 0 Duration: 571 Loc
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[15] Sun, Z., T.A. Arentze, and H.J
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prevailing traffic and road conditi
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3.2.2. Training of the network and
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Error (%) Error (%) Analytical mode
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method should be developed to help
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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
Page 332 and 333:
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
Page 382 and 383:
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.
Page 390 and 391:
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
Page 414 and 415:
Individual contributions in Session
Page 416 and 417:
exceeded then the agent relies on i
Page 418 and 419:
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
Page 436 and 437:
[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
Page 442 and 443:
[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
Page 472 and 473:
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
Page 532 and 533:
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
Page 538 and 539:
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
Page 552 and 553:
community. The major function of tr
Page 554 and 555:
Table 2 The difference of the numbe
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#" ! $ #'.60% - # $ " "
Page 558 and 559:
## ∆ *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
Page 588 and 589:
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
Page 604 and 605:
In the heterogeneous Riemann proble
Page 606 and 607:
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
Page 612 and 613:
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
Page 638 and 639:
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
Page 654 and 655:
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): = ⎛
Page 678 and 679:
Figure 3 - Application of the progr
Page 680 and 681:
exceeding the maximum density. Ther
Page 682 and 683:
Here, n i p( t) 0 j, = when j>t; s,
Page 684 and 685:
The nodes rules component of the pr
Page 686 and 687:
3. Validity of proposed DNL model T
Page 688 and 689:
Under the set node configuration it
Page 690 and 691:
A DYNAMIC NODE MODEL WITH A QUADRAT
Page 692 and 693:
control variables such as link trav
Page 694 and 695:
♣ FW k : The set of links that ar
Page 696 and 697:
! ir á = 1 r" FW k (24) When the c
Page 698 and 699:
[3] V. Astarita. A continuous time
Page 700 and 701:
This paper focuses on the optimizat
Page 702 and 703:
to improve the operational efficien
Page 704 and 705:
THE 2-PERIOD PROBABILISTIC TSP: A M
Page 706 and 707:
N variables is solved, where N is t
Page 708 and 709:
approximating future costs using th
Page 710 and 711:
develop a MILP formulation and a br
Page 712 and 713:
one arc from each alternative pair,
Page 714 and 715:
URBAN TRANSPORT: EUROPEAN EXPERIENC
Page 716 and 717:
Well-developed and efficient public
Page 718 and 719:
In addition to these public systems
Page 720 and 721:
AUCTION BASED CONGESTION PRICING: T
Page 722 and 723:
3. Auction based congestion pricing
Page 724 and 725:
issues, and potential benefits of t
Page 726 and 727:
DEALING WITH UNCERTAINTIES IN TRANS
Page 728 and 729:
Figure 1 The policy analysis framew
Page 730 and 731:
uncertainties in the output of demo
Page 732 and 733:
• Pattern of system performance:
Page 734 and 735:
PROVIDING PUBLIC PARTICIPATION IN T
Page 736 and 737:
have 14, 13 and 6 criterions respec
Page 738 and 739:
6 Results of iterations Alternative
Page 740 and 741:
MINIMUM REDEPLOYMENT MODEL FOR DYNA
Page 742 and 743:
demand and unit busy probabilities,
Page 744 and 745:
3. The algorithm There have been va
Page 746 and 747:
DYNAMIC SET PARTITIONING APPLICATIO
Page 748 and 749:
4. The dynamic solution approach In
Page 750 and 751:
In Table 2 our in progress numerica
Page 752 and 753:
HANDLING DEMAND AND NETWORK CAPACIT
Page 754 and 755:
evacuation study may change the fin
Page 756 and 757:
3.1.1. P-Level Efficient Points Def
Page 758 and 759:
(Cell # 7 and #8) in the network ar
Page 760 and 761:
VALIDATION AND COMPARISON OF CHOICE
Page 762 and 763:
From the above, results of calibrat
Page 764 and 765:
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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