decision support system for people evacuation: mobility demand and transportation planning,...
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1Progetto Progetto SICUROSICURO
Decision Support System Decision Support System for people evacuation: for people evacuation: mobility demand and mobility demand and
transportation planningtransportation planningF. Russo, C. Rindone, G. Chilà
Università Mediterranea di Reggio Calabria
XV Convegno Nazionale SIDT Rende, 9-10 giugno 2008INPUT 2010, Potenza, 13-15 Settembre 2010INPUT 2010, Potenza, 13-15 Settembre 2010
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I INTRODUCTION
II SW and DSSII.1 DEMANDII.2 EMERGENCY PLANNING
III CONCLUSIONS
CONTENTSCONTENTS
3Progetto Progetto SICUROSICURO
I INTRODUCTION
II SW and DSSII.1 DEMANDII.2 EMERGENCY PLANNING
III CONCLUSIONS
CONTENTSCONTENTS
4Progetto Progetto SICUROSICURO
study
in-de
pth
Time
Space
STRATEGIC TACTIC OPERATIVE
NAT
ION
ALREG
ION
ALLO
CAL
DIREC
TIONAL
PRAT
ICAB
LEFE
ASIB
ILE
Planning dimensions
I INTRODUCTIONI INTRODUCTION
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Invariante
Locale
Regionale
Nazionale
STRATEGIC TACTIC OPERATIVE
NATIONAL
REGIONAL
LOCAL
DIREC
TIONAL
PRAT
ICAB
LE
FEAS
IBLE
Time
Space
study
in-de
pth
External planning process
I INTRODUCTIONI INTRODUCTION
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Invariante
Locale
Regionale
Nazionale
STRATEGIC TACTIC OPERATIVE
NATIONAL
REGIONAL
LOCAL
DIREC
TIONAL
PRAT
ICAB
LE
FEAS
IBLE
Time
Space
study
in-de
pth
Internal planning process
I INTRODUCTIONI INTRODUCTION
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Objectives Constraints
Present situation
Strategies
Verify Objectives Constraints(EX ANTE)
Plan – Product
Future situation
Alternative scenarios SYSTEM O
F MO
DELS
Verify Objectives Constraints(EX POST)
Indicators (EX ANTE)
Indicators (EX POST)
Internal planning process
I INTRODUCTIONI INTRODUCTION
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R = p V NP probability
N exposure
V vulnerability
Risk components (Russo, Vitetta, 2007)
I INTRODUCTIONI INTRODUCTION
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Calamitous event
Preventiveinterventions
Calamitouseffects
Time
On goinginterventions
{Supply design {Demand management
Time for interventions
I INTRODUCTIONI INTRODUCTION
Decision Support System (DSS) and Software (SW) could assist decision makers before and during a calamitous event
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Source: Homeland Security Exercise and Evaluation Program (HSEEP), 2009
USA approach to emergency planning modelling
PLANNING DEVELOPMENT
TRAININGIMPROVEMENT
ACTIONS
EXERCISES
Ex ante evaluations
Ex post evaluations
I INTRODUCTIONI INTRODUCTION
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Source:
2009
I INTRODUCTIONI INTRODUCTION
USA approach to emergency planning modelling
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I INTRODUCTIONI INTRODUCTION
Logical Framework Approach (LFA) and Project Cycle Managment
Source: European Commission, 2004
Ex ante evaluations
Ex post evaluations
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P
PT
1) National law obliges Local Public Authority (P) to adopt emergency local plan
1) Local Public Authority (P) assign to Technician (T) activities to draw
2) The Technician (T) presents a proposal of plan to Local Public Authority (P)
P
4) Local Public Authority (P) submits to population (A) the proposal of plan
5) On the basis of remarks, Local Public Authority (P) approves plan
A
Actual emergency planning process
P: Political OrgansT: Technical OrgansA: Others Organs
I INTRODUCTIONI INTRODUCTION
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Objectives Constraints
Present situation
Strategies
Verify
Plan – Product
Future situation
Alternative Scenarios
SYSTEM O
F MO
DELS
Indicators
P
PT1
P A
PT2
results of SICURO project
System of Models in evacuation planning process
P: Political OrgansT1: Technical Organs (planner)T1: Technical Organs (analyst)A: Others Organs
I INTRODUCTIONI INTRODUCTION
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LOGFrame
LFA_INPUTSIF AND
LFA_ACTIVITIES IF AND
LFA_OUTCOMES IF AND
LFA_GOALS
THEN
LFA_OUTPUTS IF ANDTHEN
THEN
THEN
IndicatorsMeans of
verificationPlan descriptionExternal factors
Logical Framework Approach (LFA) for internal evacuation planning processLogical Framework Approach (LFA) for internal evacuation planning process
I INTRODUCTIONI INTRODUCTION
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Results of Safety of Users in Road Evacuation (SICURO Project) System of Models in evacuation planning process
Demand
Single Building
Supply
SupplyDemand
Emergency vehicles
Refuge’s area
Trips for categoriesof users, modes
and refuge’s areas
Evacuation times of singles buildings
Evacuation times of population
Evacuation times of weak users
Access times on refuge’s area
Present situation
(PR
EV
EN
TIV
E –
ON
GO
ING
) IN
TE
RV
EN
TIO
NS
Demand
Single Building
Supply
SupplyDemand
Emergency vehicles
Refuge’s area
Trips for categoriesof users, modes
and refuge’s areas
Evacuation times of singles buildings
Evacuation times of population
Evacuation times of weak users
Access times on refuge’s area
Future situation
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toward refuge’s area free (with or without user information on the system configuration) or targeted;
with different choice sets in relation to the alternatives: pedestrian, car, emergency vehicles, bus.
In ordinary conditions, the transportation demand can be simulated using the following sub - model:
Generation
Departure time
Distribution
Modal split
Route choice
with immediate or delayed approach, in relation to the time-gap available between t1 and t3
with free (with or without user information on the system configuration) or targeted departure;
free (with or without user information on the system configuration) or targeted.
In emergency conditions, the transportation demand can be simulated using the following sub - model:
I INTRODUCTIONI INTRODUCTION
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delayedimmediate
t0 initial instant at which we decide to plan;t1 time at which the time when the dangerous event will happen is known or supposed
forecasted; t2 time at which the threat occurs in the system; t3 time at which no evacuation action is possible;t4 time at which the dangerous event ceases its effects on the system.
Russo, Vitetta (2007)
Effect on the population
Mitigation actions
Possible Not possible
YesNot
time∆1
EFFECT IN THE TIME
e.g. time bombe.g. time bomb tsunamitsunamie.g. earthquakee.g. earthquake
∆3
Different demand models have to be specified, in relation to event types, which can be classified according to their effects in space and in time.
e.g. earthquakee.g. earthquake
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t0 time at which an hypothetical public decision maker decide to plan an evacuation from a considered area;
t1 time at which it’s possible to know when the hurricane will be in the considered area;
t2 time at which the hurricane reach the considered area ;
t3 time at which the hurricane starts its effects;
t4 time at which the hurricane ceases its effects on the population.
EXAMPLE: THE HURRICANE CASE
time∆1 ∆3
∆0 ≠0; ∆1 ≠0; ∆2 ≠0; ∆3 ≠0; ∆4 ≠0
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EXAMPLE
Example ∆0 ∆1 ∆2 ∆3
Tsunami ≠0 ≠0 ≠0 ~0
Hurricane ≠0 ≠0 ≠0 ≠0
Twin Towers ≠0 ≠0 ≠0 ≠0
Bomb ≠0 ≠0 ~0/≠0 ~0
Cistern ≠0 ≠0 ≠0 ≠0
Chemistry pollution ≠0 ≠0 ≠0 ≠0
Vulcan eruption ≠0 ~0 ≠0 ≠0
Earthquake ≠0 0 0 0
I INTRODUCTIONI INTRODUCTION
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A multy step approach to simulate demand in evacuation condition: user decisions & submodels
To evacuate or not?
By which transport mode?
Towards which destination?
By which path?
When?
GENERATION
DEPARTURE TIME
DISTRIBUTION
MODAL SPLIT
PATH CHOICE
I INTRODUCTIONI INTRODUCTION
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A multy step approach to simulate demand in evacuation condition: user decisions & submodels
To evacuate or not?
By which transport mode?
Towards which destination?
By which path?
When?
GENERATION
DEPARTURE TIME
DISTRIBUTION
MODAL SPLIT
PATH CHOICE
evacuation participation rates of evacuation zones
response curve, sensitive to the characteristics of the hurricane, time of day, type and timing of evacuation order
series of binary choices over time estimating a joint decision, generation with departure time, in the face of an oncoming hurricane
statistical approach (means and distributions)
I INTRODUCTIONI INTRODUCTION
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A multy step approach to simulate demand in evacuation condition: user decisions & submodels
To evacuate or not?
When?
GENERATION
DEPARTURE TIME
Sequential binary logit model
t,neU
t,nneU
)0UU0UUPr()UUUUPr(P 't,ee
't,nne
t,nne
t,ne
't,ee
't,nne
t,nne
t,ne
t >−≥−=>≥='t,n
e't,n
ne't UUU −=∆
])0UUPr(1[)0UUPr(P1t
1't
't,nne
't,ne
t,nne
t,ne
t ∏ ≥−−≥−=−
=)0UUPr( 't,n
ne't,n
e ≥−
Let and be the utility of a household n choosing to evacuate and not to evacuate, respectively, in time interval t, provided that the t interval was reached without evacuation. According to the random utility theory, the probability of a household evacuating in time interval t, ∀ t’≠t, is:
If the utility difference terms are independent in t, then:
where ], t’=1,2,…t, is the conditional probabilities of a household to evacuate in time interval t’ respectively, provided that the household has not evacuated yet
I INTRODUCTIONI INTRODUCTION
24Progetto Progetto SICUROSICURO
A multy step approach to simulate demand in evacuation condition: user decisions & submodels
To evacuate or not?
By which transport mode?
Towards which destination?
By which path?
When?
GENERATION
DEPARTURE TIME
DISTRIBUTION
MODAL SPLIT
PATH CHOICE
disaggregate choice model for hurricane evacuation developed with post hurricane Floyd survey data collected in South Carolina in 1999
I INTRODUCTIONI INTRODUCTION
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A multy step approach to simulate demand in evacuation condition: user decisions & submodels
To evacuate or not?
By which transport mode?
Towards which destination?
By which path?
When?
GENERATION
DEPARTURE TIME
DISTRIBUTION
MODAL SPLIT
PATH CHOICE
disaggregate choice model for hurricane evacuation developed with post hurricane Floyd survey data collected in South Carolina in 1999
multinomial logit model to investigate the effect of risk areas in the path of a hurricane, and destination socioeconomic and demographic characteristics on destination choice behaviour.
path choice for emergency vehicle
I INTRODUCTIONI INTRODUCTION
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A multy step approach to simulate demand in evacuation condition: user decisions & submodels
To evacuate or not?
By which transport mode?
Towards which destination?
By which path?
When?
GENERATION
DEPARTURE TIME
DISTRIBUTION
MODAL SPLIT
PATH CHOICE
generation sub-model gives the level of demand in the study area according to the reference period and the population category
modal split sub-model gives the number of people using a given transport mode from a certain origin to a certain refuge area
distribution submodel gives the number of people choosing a given refuge area
SICURO RESEARCH PROJECT
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Distribution
Generation Modal choice
E ,r E k,r k kkd (h,c,m) d (h) p (c) p (m / c)
ξ ξ= ⋅ ⋅∑
Evacuation demand modelEvacuation demand model
Modal choice with distribution
Modal choice
Distribution E,r Ek,r k kkd (h,c,m) d (h) p (m) p (c /m)= ⋅ ⋅∑
E,r Ek,r kkd (h,c,m) d (h) p (mc)= ⋅∑
Residents
Occasional customers
Employees
Weak user
Teaching and student
EFFECT IN THE SPACE
EFFECT IN THE TIME
I INTRODUCTIONI INTRODUCTION
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RP data are not available for all dangerous events
models specified for hurricane evacuation, which are derived from observation of past evacuation behaviour, cannot be directly applied to other dangerous
events
prediction of user behaviour becomes essential, by:
evacuation trials SP (stated preference) surveys
RP data affected by the laboratory effect, like SP surveys with physical
verification
RP and SP approachesRP and SP approaches
I INTRODUCTIONI INTRODUCTION
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SP surveys allow us to simulate several emergency scenarios
SP surveys must be designed, defining:
Proposed scenarios must be realistic and clear, in order to limit distortions between real and stated behaviour.
In light of such considerations, SP surveys play a very important role and RP surveys during evacuation
trials may be viewed as physical checking SP data.
EmergencyEmergency scenariosscenarios
Attributes for each Attributes for each scenarioscenario
Variation in level of Variation in level of attributesattributes
Choice mechanismChoice mechanism
Period of referencePeriod of reference
TargetsTargets
Effects in time and in spaceEffects in time and in space
for each user category
RP and SP approachesRP and SP approaches
I INTRODUCTIONI INTRODUCTION
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In some evacuation conditions, the use of dynamic models is suggested.
Any Software (SW) or Decision Support System (DSS), for us knowledge, deals with this problem. We refer to Russo and Chilà (2007/c, 2008/b, 2010/a, 2010/b) for an analysis more complete of sequential dynamic approach (Gottman and Roy, 1990; Bakeman and Gottman, 1997).
I INTRODUCTIONI INTRODUCTION
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I INTRODUCTIONI INTRODUCTION
In this paper DSS and SW are analysed : •to specify, to calibrate and to apply demand model in evacuation conditions
•to support transportation planning in evacuation conditions
32Progetto Progetto SICUROSICURO
I INTRODUCTION
II SW and DSSII.1 DEMANDII.2 PLANNING
III CONCLUSIONS
CONTENTSCONTENTS
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SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
ALOGITALOGIT
HIELOWHIELOW
MMLM swMMLM sw
Some software used for demand model calibration
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ALOGITALOGIT
Some software used for demand model calibration
PREPAREPREPARE ESTIMATEESTIMATE APPLYAPPLY
the logit model is set up and the data are
prepared and checked
unknown coefficients appearing in the
model are estimated from the data
the model is tested and/or applied for
forecasting
The last version of Alogit allows the parameter calibration with revealed preference or stated preference data.
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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HIELOWHIELOW
Some software used for demand model calibration
It allows a multinomial or a hierarchical (nested) logic model to be estimated.
To improve the quality of estimated models, HieLoW provides the analyst with detailed statistical information.
Based on recently developed trust-region methods, the maximization algorithm of HieLoW explicitly exploits, when
needed, the non-concavity of the loglikelihood function. A tutorial helps beginners get familiar with HieLoW.
A glossary and a permanent contextual help system are also included to facilitate the user's work.
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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MMLM swMMLM sw
Some software used for demand model calibration
The software realized by Train includes the following files:
mxlmsl.m is the code that the user runs; the user specifies the model within this code; doit.m is a script that is called up at the end of mxlmsl.m; it checks the data,
transforms the data into a more useful form, performs the estimation and prints results;check.m is a function that checks the input data and specifications; it provides error
messages and terminates the run if anything is found to be incorrect;loglik.m is a function that calculates the log-likehood function and its gradient; this
function is input to Matlab's fminunc command, which is part of Matlab's Optimization
Toolbox; this function calls llgrad2.m;llgrad2.m is a function that calculates for each person the probability of the chosen
alternatives and the gradient of the log of this probability;
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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MMLM swMMLM sw
Some software used for demand model calibration
The software realized by Train includes the following files:
der.m is a function that calculates the derivative of each random coefficient with respect
to the model parameters;makedraws.m is a function that creates the standardized draws that will be used in the
run, based on the specifications given by the user in mxlmsl.m; trans.m is a function that transforms the standardized draws into draws of coefficients;data.txt is an ascii file of data on vehicle choice; the data and its format are described
within mxlhb.m;myrunKT.out is the output file of running maxlhb.m with no modifications.
This software allows parameter calibration considering RP and SP surveys.
SW AND DSS FOR DEMAND SW AND DSS FOR DEMAND
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Several commercial Decision Support Systems (DSS) are available to
evaluate transport demand.
Generally, these belong to the GIS (Geographic Information Systems)
software class.
GIS software integrates maps with their respective information or attributes.
Through its ability to link spatial data (maps) and non-spatial data (attribute
information) in one location, GIS provides a framework for efficient data
storage and data retrieval, intuitive display of information in a spatial
context, and combining various types of information so that the data may be
analyzed further.
Referring to demand model evaluation, GIS can be subdivided into two
main classes:
GENERIC GIS GENERIC GIS SOFTWARESOFTWARE
TRASPORTATION GIS TRASPORTATION GIS SOFTWARESOFTWARE
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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GENERIC GIS GENERIC GIS SOFTWARESOFTWARE
generic GIS software are developed and implemented in several fields (marketing,
planning, business analysis, transport, and so on)
Among the software belonging to the first class, we recall:
MapInfo, a powerful Microsoft Windows-based mapping and geographic analysis application from experts in location intelligence.
ArcInfo, the first GIS software available on the market
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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generic GIS software are developed and implemented in several fields (marketing,
planning, business analysis, transport, and so on)
Among the software belonging to the first class, we recall:
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
OmniTRANS, which provides a versatile working
environment for multimodal transport planning and modelling; it offers an
integrated software platform for visual display of models
and graphical presentation of results, strong project
management tools to assist in managing all of the information
associated with model scenarios.
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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generic GIS software are developed and implemented in several fields (marketing,
planning, business analysis, transport, and so on)
Among the software belonging to the first class, we recall:
Emme/2, which is a graphical software tool for multimodal
transportation planning, which allows the transportation network to be modelled and assigns the traffic
generated under a given set of conditions
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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generic GIS software are developed and implemented in several fields (marketing,
planning, business analysis, transport, and so on)
Among the software belonging to the first class, we recall:
PTV, which is a software suite for transportation planning and operation
analyses
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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generic GIS software are developed and implemented in several fields (marketing,
planning, business analysis, transport, and so on)
Among the software belonging to the first class, we recall: TransCAD, designed specifically for use by transportation professionals to store, display, manage, and analyze transportation data; TransCAD combines GIS and transportation modelling capabilities in a single integrated platform, providing capabilities that are unmatched by any other package; TransCAD can be used for all modes of transportation, at any scale or level of detail.TransCAD provides:a powerful GIS engine with special extensions for transportation; mapping, visualization and analysis tools designed for transportation applications; application modules for routing, travel demand forecasting, public transit, site location and area management;
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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DSS belonging to the second class generally include comprehensive tools for:
TRIP GENERATIONTRIP GENERATION
TRIP DISTRIBUTIONTRIP DISTRIBUTION
MODE SPLITMODE SPLIT
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
45Progetto Progetto SICUROSICURO
DSS belonging to the second class generally include comprehensive tools for:
The goal of trip generation is to estimate the number of trips, by purpose, that are produced or
originate in each zone of a study area. Trip generation is performed by relating frequency of trips to
the characteristics of the individuals, the zone and the transportation network.
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
TRIP GENERATIONTRIP GENERATION
In some cases, there are two primary tools for modelling trip generation:
Cross-Classification, which separates the population in an urban area into relatively homogeneous groups based on certain socio-economic characteristics; average trip production rates per household or individual are then empirically estimated for each classification;
Regression Models, which allow evaluation and application of multivariable aggregate zonal models and disaggregate models at the household or individual level.
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
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DSS belonging to the second class generally include comprehensive tools for:
Trip distribution models are used to predict the spatial pattern of trips or other flows
between origins and destinations. DSS provide numerous tools with which to perform trip
distribution, including procedures to implement growth factor methods, apply previously-
calibrated gravity models, generate friction factors and calibrate new model parameters.
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
TRIP DISTRIBUTIONTRIP DISTRIBUTION
SW AND DSS FOR DEMANDSW AND DSS FOR DEMAND
47Progetto Progetto SICUROSICURO
Mode choice models are used to analyze and predict the choices that individuals or groups of individuals make in selecting the transportation modes that are used for particular types of trips.
Typically, the goal is to predict the share or absolute number of trips made by mode. Software provides procedures for calibrating and applying mode choice models based on multinomial and nested logit models, and may be pursued at either a disaggregate or aggregate zonal level.
Estimation of the parameters in the nested logit and multinomial logit model is performed by the method of maximum likelihood, which calculates the set of parameters that are most likely to have resulted in the choices observed in the data.
DSS belonging to the second class generally include comprehensive tools for:
Mode choice models are used to analyze and predict the choices that individuals or groups of individuals make in selecting the transportation modes that are used for particular types of trips.
TRANSPORTATION TRANSPORTATION GIS SOFTWAREGIS SOFTWARE
MODE SPLITMODE SPLIT
SW AND DSS FOR DEMAND SW AND DSS FOR DEMAND
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I INTRODUCTION
II SW and DSSII.1 DEMANDII.2 EMERGENCY PLANNING
III CONCLUSIONS
CONTENTSCONTENTS
49Progetto Progetto SICUROSICURO
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
for project management
for specific component
generic to analyze transportation system in ordinary condition adopted for emergency condition
Logical Framework Approach Project Cycle Management
Project Cycle Management and Logical Framework Approach
specific to analyze transportation system in emergency condition
50Progetto Progetto SICUROSICURO
for project management
• Microsoft Project® (Microsoft, 2007)
• SmartDraw ® (SmartDraw.com, 2009) and Microsoft Visio® (Microsoft, 2007)
• Logical Decisions® for Windows, applied in Decision Analysis
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
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Project Cycle Management
• Project Facilitator®, vers. 1.4 (Live Application Technology, 2004)
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
for project management
52Progetto Progetto SICUROSICURO
Logical Framework Approach
• LogFrame for Windows, vers. 1.0 (Maizemoor International, 2008)
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
for project management
53Progetto Progetto SICUROSICURO
Project Cycle Management and Logical Framework Approach
• TeamUP-PCM® (TEAM Technologies, Inc., 2008)
Stakeholder Analysis
Trees Analysis (Problem Analysis, Objectives Analysis)
Program and Project Structure
Conflict Analysis
Logical Framework
Schedule (WBS, Responsibility Matrix, Gantt Chart, CPM)
Performance Tracker (Output & Purpose Milestones)
Performance Budget
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
for project management
54Progetto Progetto SICUROSICURO
for specific component generic to analyze transportation system in ordinary condition adopted for emergency condition
– Macroscopic simulatione.g. EMME/2, TransCAD, VISUM, CUBE
– Mesoscopic simulatione.g. DYNASMART-P
– Microscopic simulatione.g. INTEGRATION, CORSIM
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
55Progetto Progetto SICUROSICURO
for specific component specific to analyze transportation system in emergency condition
• MASSVAC (MASS eVACuation)
• OREMS (Oak Ridge Evacuation Modeling System)
• ETIS (Evacuation Traffic Information System)
• HURREVAC (HURRicane EVACuation)
• SLOSH Model (Sea, Lake, and Overland Surges from Hurricanes)
• HAZUS-MH (Multi-Hazards U.S. Software)
• CATS (Consequence Assessment Tool Set) / Joint Assessment of
Catastrophic Events (JACE)
• MitigationPlan.com System
• Abbreviated Transportation Models (ATM)
SW and DSS FOR EMERGENCY PLANNINGSW and DSS FOR EMERGENCY PLANNING
56Progetto Progetto SICUROSICURO
I INTRODUCTION
II SW and DSSII.1 DEMANDII.2 PLANNING
III CONCLUSIONS
CONTENTSCONTENTS
57Progetto Progetto SICUROSICURO
CONCLUSIONSCONCLUSIONS
• Modelling tools implemented in SW and DSS to assist decision makers in preparing evacuation plans
• Relevant role of SW and DSS for transportation planning in emergency conditions
• real time• management of evacuation procedures
58Progetto Progetto SICUROSICURO
CONCLUSIONSCONCLUSIONS
• Advancements on mobility demand analysis and transportation planning
• Availability of SW and DSS for mobility demand and transportation planning
• SW and DSS for emergency planning • to collect and to represent data• to analyse relationships among data (models)
possibility to internalise the new negotiation among users, planners deciders and collectivity
• Future objectives• to develop structured planning and evaluation processes• to develop procedure and DSS able to simulate evacuation conditions
considering a dynamic sequential approach
59Progetto Progetto SICUROSICURO
Decision Support System Decision Support System for people evacuation: for people evacuation: mobility demand and mobility demand and
transportation planningtransportation planningF. Russo, C. Rindone, G. Chilà
Università Mediterranea di Reggio Calabria
XV Convegno Nazionale SIDT Rende, 9-10 giugno 2008INPUT 2010, Potenza, 13-15 Settembre 2010INPUT 2010, Potenza, 13-15 Settembre 2010
60Progetto Progetto SICUROSICURO
III.1 Definition of Evacuation Scenario and of Area of Study
III.2 Survey
III.3 Real Experimentation
III.4 Calibration and Validation of Proposed Models
III EXPERIMENTATIONIII EXPERIMENTATION
61Progetto Progetto SICUROSICURO
Area [m2] 35,300,000
Population 10,483
Workers 2,432
Melito Porto Salvo: Municipality of province of Reggio Calabria (Italy)
III EXPERIMENTATIONIII EXPERIMENTATION
III.1 Definition of Evacuation Scenario and of Area of Study
62Progetto Progetto SICUROSICURO
Invariante
STRATEGIC TACTIC OPERATIVEDIR
ECTI
ONAL
PRAT
ICAB
LE
FEAS
IBLE
NATIONAL
REGIONAL
LOCAL
LCPP
TIME
Space
study
in-de
pth
Melito Porto Salvo: Local Civil Protection Plan (LCPP)
III EXPERIMENTATIONIII EXPERIMENTATION
III.1 Definition of Evacuation Scenario and of Area of Study
63Progetto Progetto SICUROSICURO
• Simulation scenarios:
• a work day: 8.00-12.00
• a tank transporting dangerous goods is leaking (event type a)
• Mayor decides that surrounding area must be evacuated
• Categories of users in the evacuation area
• residents
• employees
• occasional customers
• teachers and students
• weak users
Melito Porto Salvo: verification applying system of models
III EXPERIMENTATIONIII EXPERIMENTATION
III.1 Definition of Evacuation Scenario and of Area of Study
64Progetto Progetto SICUROSICURO
Area [m2] 42,990
Residents 255
Employees 225
Melito Porto Salvo: area to test evacuation plan
III EXPERIMENTATIONIII EXPERIMENTATION
III.1 Definition of Evacuation Scenario and of Area of Study
65Progetto Progetto SICUROSICURO
Study area Zoning
Town of Melito Porto Salvo Study area
Area (km2) 35.30 0.04Resident 10483 255Employee 2432 225
Zone Area (m2) Zone Area (m2)
1 5091.50 7 3191.04
2 2869.96 8 3559.65
3 4064.14 9 5119.63
4 4885.16 10 3629.22
5 3801.01 11 3478.71
6 3300.35
TOTAL (m2) 42990.37
Urban area of Melito Porto Salvo - Province of Reggio Calabria (Italy)
Residential building
Public building
Mixed building
III EXPERIMENTATIONIII EXPERIMENTATION
III.1 Definition of Evacuation Scenario and of Area of Study
66Progetto Progetto SICUROSICURO
SURVEY
PRE-TEST
TEST
In order to calibrate the model we have carried:
directed to know socio-economic properties of the studying area
where an area with only public offices and one school is evacuated
where all the area are evacuated
directed to estimate habitual present user number and willingness user to evacuate
Revealed preferences before real
experimentation (RP)
Stated preferences before real experimentation (SP)
The data are recorded for the laboratory analysis. During the experimentation information have been founded with manual/automatic tools, 30
video cameras and interviewing evacuated user. From these surveys we can obtain variables for calibrating models.
DEMOGRAPHIC SURVEY AND CLASSIFICATION
directed to estimate habitual present user number and willingness user to evacuate
Revealed preferences during real
experimentation (SP with phisical check)
III EXPERIMENTATIONIII EXPERIMENTATION
III.2 Survey
67Progetto Progetto SICUROSICURO
SURVEY – RP DATABASE
SchoolsPublic activitiesPrivate activitiesFamilies
Buildings
Scholl staff and pupils
Public sector employees
Private sector employees
Residents
Sex; Age; Profession;Weak user or not;
Driving licence; Vehicle possession;
Habitually present in the morning hours;
Willing to evacuate or not
Number component
Number employees and occasional customers
Number employees and occasional customers
Number teachings and students
AdressNumber floorsNumber exit
Type
Sex; Age; Profession;Weak user or not;
Driving licence; Vehicle possession;
Habitually present in the morning hours;
Willing to evacuate or not
Sex; Age; Profession;Weak user or not;
Driving licence; Vehicle possession;
Habitually present in the morning hours;
Willing to evacuate or not
Sex; Age; Profession;Weak user or not;
Driving licence; Vehicle possession;
Habitually present in the morning hours;
Willing to evacuate or not
III EXPERIMENTATIONIII EXPERIMENTATION
III.2 Survey
68Progetto Progetto SICUROSICURO
Activity Number Activity Number
Clothes shop 4 Private office 10
Electronis, electrotechnis, mechanics, chemistry, car 6 Finance, insurance, credit 4
Food 8 Sport, free time, culture 5Agency 1 Public office 27Furniture 3 School 1Medicine and beauty 3Totale 72
Activity analysis
Socio-economic analysis
Building type Building number
Regular population
Occasional population
User category
Residential 23 89 0 Resident and weak user
Public
School 1 159 0 Teachers, pupils and weak user
Town hall 1 82 60Employee, occasional customer and
weak userCourt 1 7 3other 3 21 8
Mixed 28 262 99 Resident, weak user, employee and occasional customer
SURVEY – RP DATABASE
III EXPERIMENTATIONIII EXPERIMENTATION
III.2 Survey
69Progetto Progetto SICUROSICURO
Resident analysis
36%
43%
21%
Present Not present Not found/interviewed/contacted
Worker analysis
14%
41% 45%
Present Not present Not interviewed/contacted
User categories Percentage of residents (%)
Percentage of employees (%)
Present willing to evacuation simulation 8 34
Present not willing to evacuation simulation 13 11
Not present 36 14Not contacted 8 14Not interviewed 14 27Not found 21 0
SURVEY - SP DATABASE
III EXPERIMENTATIONIII EXPERIMENTATION
III.2 Survey
70Progetto Progetto SICUROSICURO
• 12/01/2007
Pre-Test
Evacuation of the school and of public buildings
• 1/03/2007
Test
Evacuation of the area of study
III EXPERIMENTATIONIII EXPERIMENTATION
III.3 Real Experimentation
71Progetto Progetto SICUROSICURO
MAXIMUM LIKELIHOOD
In Maximum Likelihood estimation the value of the unknown parameters are obtained by maximising the probability of observing the choices made by a sample
of users.
LEAST SQUARES
For given observed data, the least squares values of model unknowns are the values minimizing the sum of
squared deviations, comparing the data to model predictions.
A simple, important example is bivariate linear regression, where a straight line is fitted to n pairs of
measurements on two variables, an independent variable and a dependent variable.
III. EXPERIMENTATON
CALIBRATION PARAMETER
Modal split
Distribution
Generation
Generation
RP
SP
SP with phisical check
SP
SP with phisical check
III.4 Calibration and Validation of Proposed Models
72Progetto Progetto SICUROSICURO
PARAMETER GENERATION MODAL SPLIT DISTRIBUTIONMODAL SPLIT
WITH DISTRIBUTION
General General For employee group
For employee group For employee group
SOCIO –
ECONOMIC
LEVEL OF
SERVICE
CALIBRATED PARAMETERCALIBRATED PARAMETER
XX XX
XX XX XX
XX
III EXPERIMENTATIONIII EXPERIMENTATION
III.4 Calibration and Validation of Proposed Models
73Progetto Progetto SICUROSICURO
III EXPERIMENTATIONIII EXPERIMENTATION
Socio-economic parameter Generation Modal split Distribution Modal split with distribution
Resident Not resident
General For employee
group
For employee group
For employee group
% of actives over residents X
% of students over residents X
% of housewifes over residents X
% of retired people over residents X
% of residents younger than 14 and older than 5 years X
% resident younger than 19 and older than 15 years X
% resident younger than 24 and older than 20 years X
% resident younger than 65 and older than 25 years X
% resident overthan 65 years X
family number X
employee number X
teaching and scholl number X
Weak user number X
Dummy for employees of age below 45 years XDummy equal to 1 if the employee’s level is higher than 2, 0 otherwise X X X
Dummy equal to 1 if the employee’s level is higher than 3, 0 otherwise X X
Dummy equal to 1 if the user is a women, 0 otherwise X X
74Progetto Progetto SICUROSICURO
III EXPERIMENTATIONIII EXPERIMENTATION
Level of service parameter Generation Modal split DistributionModal split
with distribution
General General For employee group
For employee group
For employee group
Time on the pedestrian network from origine to the refuge’s area
X X
Time on the road network from origine to the refuge’s area X X
Distance as the crow flies between origine and refuge’s area X X
Distance on the pedestrian network between origine and refuge’s area
X
Dummy origine for zone 10 X
75Progetto Progetto SICUROSICURO
MAIN OUTCOMESMAIN OUTCOMES
III EXPERIMENTATIONIII EXPERIMENTATION
Generation model Distribution Modal choice
Present in the area
Willing to evacuation simulation
Refuge’s are fixed
Refuge’s are not fixed
Car PedestrianBus or
Emergency vehicles
37% 39% 84% 16% 80% 20%
77% 75% 84% 16% 80% 20%
80% 67% 84% 16% 80% 20%
92% 100% 100% 100%
100% 100% 100% 100%
Residents
Occasional customers
Employees
Weak users
School staff and pupils
III.4 Calibration and Validation of Proposed Models
76Progetto Progetto SICUROSICURO
Spec. Parameters Value T-Student ρ2
1
αFL % of actives over residents 0.13 2.82
0.99αNS % of students over residents 0.33 4.74
αNC % of housewives over residents 1.00 42.36
αNR % of retired people over residents 0.14 2.55
2
αE2% of residents younger than 14 and older than 5 years -0.09 -0.28
0.96
αE3% of residents younger than 19 and older than 15 years
-0.83 -1.16
αE4% of residents younger than 24 and older than 20 years
-0.32 -0.46
αE5% of residents younger than 65 and older than 25 years
0.80 8.36
αE6 % of residents over 65 years -0.15 -0.51
3 mE,R Attendance coefficient 0.59 23.51 0.98
Calibration of resident generation model: PRESENT RESIDENT
III EXPERIMENTATIONIII EXPERIMENTATION
Generation
77Progetto Progetto SICUROSICURO
User category λ (%)Employee 77Occasional customer 80School staff and pupils 92Weak user 100
Calibration of resident generation model: PRESENT NOT - RESIDENT
SP DATA RP DATAUser category ξ (%) ξ (%)Resident 39 /Employee 75 50Occasional customer 67 /School staff and pupils 100 100Weak user 100 100
Calibration of resident generation model: USER WILLING TO EVACUATE ON THE PRESENT
III EXPERIMENTATIONIII EXPERIMENTATION
Generation
Generation
78Progetto Progetto SICUROSICURO
Calibration of modal split model: TEST 01/03/2007
Parameters Alt Specific.1
Specific.2
Specific.3
βD Distance as the crow flies between origine and refuge’s area
2 -0.0072
(-0.3)
βCar Dummy for car alternative 1 -0.8579
(-0.1)
βTRP Time on pedestrian network from origine to the refuge’s area
1 -0.2881 -0.6924 -0.3049
(-1.0) (-0.4) (-0.9)
βTRC Time on the road network from origine to the refuge’s area
2 -1.0590 -1.186 -0.9961
(-0.90) (-0.90) (-0.80)
Initial Likelihood -30.4985 -30.4985 -30.4985
Final Likelihood -28.3578 -28.3219 -28.3512
ρ2 0.07 0.07 0.07
Alternatives: 1 Pedestrian, 2 Car
III EXPERIMENTATIONIII EXPERIMENTATION
Modal split
79Progetto Progetto SICUROSICURO
Calibration of modal split model: Town Hall Model, TEST 01/03/2007
Parameters Alt. Specific.1 Specific.2
βWE1 Dummy for employees of age below 45 years 1 2.5660 3.0710
(1.70) (1.90)
βDRC Distance on pedestrian network between origine and refuge’s area
1 -0.0025 -0.0027
(-1.60) (-1.90)
βL2 Dummy if the employee level is higher than 2, 0 otherwise 2 0.2238
(0.20)
βL3 Dummy if the employee level is higher than 3, 0 otherwise 2 1.1830
(0.70)
βCW Dummy equal to 1 if the user use the car to go to work, 0 otherwise
2 0.3494 0.3042
(0.60) (0.50)
βWomen Dummy equal to 1 if the user is a women, 0 otherwise 1 1.7770 1.9390
(1.30) (1.30)
Initial Likelihood -14.56 -14.56
Final Likelihood -7.95 -7.68
ρ2 0.45 0.47Alternatives: 1 Pedestrian, 2 Car
III EXPERIMENTATIONIII EXPERIMENTATION
Modal split
80Progetto Progetto SICUROSICURO
Calibration of distribution model: Town Hall Model, TEST 01/03/2007
Parameters Alt Specific.1
Specific.2
βWomen Dummy equal to 1 if the user is a women, 0 otherwise
1 2.850
(3.3)
βL2 Dummy if the employee level is higher than 2, 0 otherwise
1 1.289 0.2657
(2.5) (0.4)
βz10 Dummy origine for zone 10 2 1.264 1.483
(2.8) (3.9)
Initial Likelihood -39.5094 -39.5094
Final Likelihood -34.1374 -25.8085
ρ2 0.14 0.35
Alternatives: 1 Refuge’s area fixed by public decision maker (RAF), 2 Other refuge’s area (RAO)
III EXPERIMENTATIONIII EXPERIMENTATION
Distribution
81Progetto Progetto SICUROSICURO
Calibration of modal split with distribution model: Town Hall Model, TEST 01/03/2007
Parameters Alt Specific.1
βTRP,RA1 Time on pedestrian network from origine to the refuge’s area 1
1 -0.2688
(-1.4)
βTRP,RA2 Time on pedestrian network from origine until to refuge’s area 2
2 -1.0260
(-1.5)
βTRC,RA1 Time on road network from origine to the refuge’s area 1
3 -1.9670
(-1.3)
Initial Likelihood -40.6487
Final Likelihood -33.5616
ρ2 0.17
Alternatives: 1 Pedestrian with refuge’s area 1; 2 Pedestrian with refuge’s area 2; 3 Car with refuge’s area 1
III EXPERIMENTATIONIII EXPERIMENTATION
Modal split
Distribution