Proceedings - Istituto di Analisi dei Sistemi ed Informatica - Cnr

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Item Date Subjects recruitment Sep. 1, 2005 (Thu) ~ Oct. 18, 2005 (Tue) Orientation Nov. 4, 2005 (Fri) Evaluation experiment Nov. 7, 2005 (Mon) ~ Dec. 16, 2005 (Fri) Follow-up hearing survey Dec. 8, 2005 (Thu) ~ Dec 16, 2005 (Fri) Table1. Experimental Schedule In this experiment, the introduction effects of the Tram Stop-Sharing on the subjects’ behavior during commuting to school have been verified based on the determination of the required time for schooling and transferring and the subjective evaluation of the transfer service and the public transport. The difference between the experimental condition of the before evaluation session and that of the after evaluation session is summarized in Figure 2 and the definitions of the commuting time and the transferring time in Figure 3. Route for the subjects at the before evaluation Walk to bus stop Wait for bus at bus stop Get on bus for Medical Center Tram Stop ‡@ Tram stop (Museum street) ‡@ ‡A Bus Stop ‡B ‡C Route for the subjects at the After evaluation ‡@ Take Toden, and got off at tram stop Wait for bus at tram stop Get on bus for Medical Center ‡@ Tram Stop ‡A Tram stop (Museum street) ‡B Bus Stop Figure 2. Experimental conditions for the before and after evaluations 132
Before evaluation After evaluation A. Time to leave home BDTime to arrive at tram stop Transferring time (D-B) CDTime to arrive at bus stop Transferring time (E-A) D. Bus departure time F. Bus departure time E. Arrival time at Medical Center Commuting time (E |A) Commuting time (E-A) Figure 3. Definitions of commuting time and transferring time 3. Estimation of introduction effects 3.1. Traveling time, transferring time and transferring time ratio The change of the commuting time, transferring time, defined in the chapter 3, and the transferring time ratio, as a indicator of the transfer service, between the before evaluation and the after evaluation have been examined. The transferring time ratio is defined as Formula (1). ∑∑ i S = (1) t′ ∑∑ i j j t ij ij S is the transferring time ratio. t ij and t’ ij are the transferring time and the commuting time for the subject called i on the day called j, respectively . Table 2 shows the change in *the traveling time, the waiting time and the transferring time ratio. *the transferring time, commuting time and transferring time ratio 133
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Preface to the Proceedings of the E
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As for the Extra EURO Conference
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END-USERS COMMITTEE Government of A
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Id: 011 An activity-based systemof
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Id: 039 The influence of advanced t
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Id: 064 Strategic modelling and hie
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Id: 087 A stochastic approach to de
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Id: 112 Minimum redeployment model
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Id: 143 Towards mobile semantic gri
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21. Alessandro BALDASSARRA, Univers
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73. Paolo DELLE SITE, University of
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125. Maria Alice Prudêncio JACQUES
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177. Bruno MONTELLA, University of
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228. Domenico SASSANELLI, DVT - Tec
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279. Saini YANG, University of Mary
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To help fulfil the expectations of
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DO PEDESTRIANS EFFICIENTLY USE OVER
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The gray part in Figures 1 & 2 show
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INTELLIGENT TRANSPORT SYSTEMS: ROLE
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Figure 2. MD-200 3. Proposal 3.1. I
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data to be visualized in a clear an
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9. Would solve all the Traffic prob
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EFFECT OF EXTERNAL COST INTERNALISA
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where i ,a f is the transit user fl
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[7] L. D’Acierno, M. Gallo and B.
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EXPERIMENTAL STUDY ON DATA DRIVEN P
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Due to this uncertainty of bus arri
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Figure1. Flow of Data of the whole
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5.1. Implementation Issue: Institut
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A MULTINOMIAL LOGIT MODEL FOR SERVI
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Some Logit models were proposed by
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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
Page 114 and 115: selection at the end of each extens
Page 116 and 117: PHYSICAL AND OPERATIONAL DESIGN IN
Page 118 and 119: ∑ r∈Rw ∑ r∈R v h s l r h r
Page 120 and 121: As a first step in this study, the
Page 122 and 123: DSS FOR EMERGENCY MANAGEMENT ON MOT
Page 124 and 125: improve whole emergency system in t
Page 126 and 127: The three defined levels in the cer
Page 128 and 129: USING MECHANISTIC DESIGN PROCEDURES
Page 130 and 131: • General traffic inputs- Number
Page 132 and 133: Table 5 Sensitivity of the performa
Page 134 and 135: Figure 1 depicts the results for th
Page 136 and 137: 6. Concluding Remarks The sensitivi
Page 138 and 139: a) Distribution of activities. For
Page 140 and 141: Figure 2. Multi-pole structure: the
Page 142 and 143: TOWARDS A SECURE RAILWAY TRANSPORT
Page 144 and 145: There are several reasons that peop
Page 146 and 147: For each potential countermeasure,
Page 148 and 149: SECURITY MANAGEMENT PROCESS Define
Page 150 and 151: OPTIMAL REROUTING DURING EMERGENCY
Page 152 and 153: Figure 1 Ring way network consistin
Page 154 and 155: Figure 3 Assignment during accident
Page 156 and 157: 50% can be attributed to the stocha
Page 158 and 159: some hyper-network approaches. Simu
Page 160 and 161: Let V smk be the systematic utility
Page 162 and 163: RESEARCH ON USERS’ BEHAVIOR CHANG
Page 166 and 167: Transferring time Commuting time Tr
Page 168 and 169: 4.2. Future tasks Only young people
Page 170 and 171: Percentage of people 80 60 40 20 0
Page 172 and 173: particular sound level. In this sen
Page 174 and 175: for the airports such as runways le
Page 176 and 177: A NEW METHOD FOR OFFSET OPTIMIZATIO
Page 178 and 179: q j-1 N j-1 n j-1 Cell j-1 N j N j+
Page 180 and 181: 4. Optimization Algorithms The obje
Page 182 and 183: TRANSYT-7F (+10% delay) and the man
Page 184 and 185: spread out non-linear utility funct
Page 186 and 187: 3. Application example Data source
Page 188 and 189: PDPC model estimation In order to i
Page 190 and 191: surpasses the lower bound of 35 % i
Page 192 and 193: follower pairs are introduced and a
Page 194 and 195: vehicle was not intentionally follo
Page 196 and 197: 3. Concluding remarks and research
Page 198 and 199: 2. the path choice from the alterna
Page 200 and 201: 2.2. Generation of choice set For t
Page 202 and 203: set generated with the explicit app
Page 204 and 205: 3.2. Generation of choice set 3.2.1
Page 206 and 207: A generic user n , travelling betwe
Page 208 and 209: [2] C. R. Bhat. Recent methodologic
Page 210 and 211: EXPLORING A GUIDANCE PROCESS FOR US
Page 212 and 213: 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
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2. Taxi planning model TP is modell
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Where the “Node flow conservation
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k Where θ is the step size in this
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ALLOCATION PLANNING OF HANDLING DEV
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and handling devices transfer times
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3. Solution approach In order to so
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From the previous calculation, orde
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UNIVARIATE AND MULTIVARIATE ARIMA M
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the portmanteau test statistic can
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provides a value of 0,11, i.e. acce
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emaining months have the same numbe
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CROSSING TIME MICRO-ANALYSIS: DIFFE
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The main characteristic of a zone i
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LEGENDA PERCORSI DI CALCOLO IN FUNZ
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Fig. 7 - Tempo di attraversamento v
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SIMULATION AND VALIDATION OF PARKIN
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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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