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Decision Support System for people evacuation: mobility demand and transportation planning, diFrancesco Russo, Corrado Rindone, Giovanna Chilà
 

Decision Support System for people evacuation: mobility demand and transportation planning, diFrancesco Russo, Corrado Rindone, Giovanna Chilà

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Sesta Conferenza Nazionale in Informatica e Pianificazione Urbana e Territoriale

Sesta Conferenza Nazionale in Informatica e Pianificazione Urbana e Territoriale

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  • The work concerns transport and land use strategic planning. Planning dimensions concern time (for implementing the plan). space (area affected by the plan) and detail (in planning decisions).
  • The work concerns transport and land use strategic planning. Planning dimensions concern time (for implementing the plan). space (area affected by the plan) and detail (in planning decisions).
  • To follow the exercises approach, in literature, a set of methods to organise and to realise real experimentations are available For instance in USA, a set of procedures to implement evacuation exercises are developed.
  • In relation to these effects, several emergency scenarios can be defined.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.
  • RP surveys include preferences inferred from observations of a decision maker's actual choices, in relation to real contexts. SP surveys represent the stated behaviour of users in relation to hypothetical contexts. During evacuation trials, RP data can be obtained, even if they are affected by the laboratory effect, because each user participating in evacuation trials knows that he/she runs no real danger. Therefore, RP surveys during evacuation trials are a statement of behaviour in emergency conditions, similar to SP surveys with physical verification. SP surveys allow us to simulate several emergency scenarios, which can differ in user category, in the effect in space and time of the dangerous event. Proposed emergency scenarios must be characterized by the description of: period of reference; targets; effects produced in time and space. Possible distortions can be removed by comparing SP data with observed flows during evacuation trials.

Decision Support System for people evacuation: mobility demand and transportation planning, diFrancesco Russo, Corrado Rindone, Giovanna Chilà Decision Support System for people evacuation: mobility demand and transportation planning, diFrancesco Russo, Corrado Rindone, Giovanna Chilà Presentation Transcript

  • Decision Support System for people evacuation: mobility demand and transportation planning F. Russo, C. Rindone, G. Chilà Università Mediterranea di Reggio Calabria XV Convegno Nazionale SIDT Rende, 9-10 giugno 2008 INPUT 2010, Potenza, 13-15 Settembre 2010
    • I INTRODUCTION
    • II SW and DSS
          • II.1 DEMAND
          • II.2 EMERGENCY PLANNING
    • III CONCLUSIONS
    CONTENTS
    • I INTRODUCTION
    • II SW and DSS
          • II.1 DEMAND
          • II.2 EMERGENCY PLANNING
    • III CONCLUSIONS
    CONTENTS
  • study in-depth Time Space STRATEGIC TACTIC OPERATIVE NATIONAL REGIONAL LOCAL DIRECTIONAL PRATICABLE FEASIBILE Planning dimensions I INTRODUCTION
  • STRATEGIC TACTIC OPERATIVE NATIONAL REGIONAL LOCAL DIRECTIONAL PRATICABLE FEASIBLE Time Space study in-depth External planning process I INTRODUCTION Invariante Locale Regionale Nazionale
  • STRATEGIC TACTIC OPERATIVE NATIONAL REGIONAL LOCAL DIRECTIONAL PRATICABLE FEASIBLE Time Space study in-depth Internal planning process I INTRODUCTION Invariante Locale Regionale Nazionale
  • Objectives Constraints Present situation Strategies Verify Objectives Constraints (EX ANTE) Plan – Product Future situation Alternative scenarios SYSTEM OF MODELS Verify Objectives Constraints (EX POST) Indicators (EX ANTE) Indicators (EX POST) Internal planning process I INTRODUCTION
  • R = p V N Risk components (Russo, Vitetta, 2007) I INTRODUCTION Evacuation Resistence Prevention
  • Calamitous event Preventive interventions Calamitous effects Time On going interventions { Supply design { Demand management Time for interventions I INTRODUCTION Decision Support System (DSS) and Software (SW) could assist decision makers before and during a calamitous event
  • Source: Homeland Security Exercise and Evaluation Program (HSEEP), 2009 USA approach to emergency planning modelling PLANNING DEVELOPMENT TRAINING IMPROVEMENT ACTIONS EXERCISES Ex ante evaluations Ex post evaluations I INTRODUCTION
  • Source: 2009 I INTRODUCTION USA approach to emergency planning modelling
  • I INTRODUCTION Logical Framework Approach (LFA) and Project Cycle Managment Source: European Commission, 2004 Ex ante evaluations Ex post evaluations
  • P P T 1) National law obliges Local Public Authority (P) to adopt emergency local plan
    • Local Public Authority (P) assign to Technician (T) activities to draw
    • 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 Organs T: Technical Organs A: Others Organs I INTRODUCTION
  • Objectives Constraints Present situation Strategies Verify Plan – Product Future situation Alternative Scenarios SYSTEM OF MODELS Indicators P P T1 P A P T2 results of SICURO project System of Models in evacuation planning process P: Political Organs T1: Technical Organs (planner) T1: Technical Organs (analyst) A: Others Organs I INTRODUCTION
  • LOGFrame LFA_INPUTS IF AND LFA_ACTIVITIES IF AND LFA_OUTCOMES IF AND LFA_GOALS THEN LFA_OUTPUTS IF AND THEN THEN THEN Indicators Means of verification Plan description External factors Logical Framework Approach (LFA) for internal evacuation planning process I INTRODUCTION
  • Results of Safety of Users in Road Evacuation (SICURO Project) System of Models in evacuation planning process Demand Single Building Supply Supply Demand Emergency vehicles Refuge’s area Trips for categories of 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 (PREVENTIVE – ON GOING) INTERVENTIONS Demand Single Building Supply Supply Demand Emergency vehicles Refuge’s area Trips for categories of 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 I INTRODUCTION
  • 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 t 1 and t 3 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 INTRODUCTION
  • delayed immediate t 0 initial instant at which we decide to plan; t 1 time at which the time when the dangerous event will happen is known or supposed forecasted; t 2 time at which the threat occurs in the system; t 3 time at which no evacuation action is possible; t 4 time at which the dangerous event ceases its effects on the system. Russo, Vitetta (2007) Effect on the population Mitigation actions Possible Not possible Yes Not time  1 EFFECT IN THE TIME e.g. time bomb tsunami e.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. earthquake I INTRODUCTION
  • t 0 time at which an hypothetical public decision maker decide to plan an evacuation from a considered area; t 1 time at which it’s possible to know when the hurricane will be in the considered area ; t 2 time at which the hurricane reach the considered area ; t 3 time at which the hurricane starts its effects ; t 4 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 I INTRODUCTION
  • EXAMPLE I INTRODUCTION 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
  • 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 INTRODUCTION
  • 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 INTRODUCTION
  • 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 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 INTRODUCTION
  • 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 INTRODUCTION
  • 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 INTRODUCTION
  • 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 I INTRODUCTION
  • Distribution Generation Modal choice Evacuation demand model Modal choice with distribution Modal choice Distribution Residents Occasional customers Employees Weak user Teaching and student EFFECT IN THE SPACE EFFECT IN THE TIME I INTRODUCTION
  • 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 approaches I INTRODUCTION
  • 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. Emergency scenarios Attributes for each scenario Variation in level of attributes Choice mechanism Period of reference Targets Effects in time and in space for each user category RP and SP approaches I INTRODUCTION
  • 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 INTRODUCTION
  • I 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
    • I INTRODUCTION
    • II SW and DSS
          • II.1 DEMAND
          • II.2 PLANNING
    • III CONCLUSIONS
    CONTENTS
  • SW AND DSS FOR DEMAND ALOGIT HIELOW MMLM sw Some software used for demand model calibration
  • ALOGIT Some software used for demand model calibration PREPARE ESTIMATE APPLY 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 DEMAND
  • HIELOW 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 DEMAND
  • MMLM 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 DEMAND
  • MMLM 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
  • 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 SOFTWARE TRASPORTATION GIS SOFTWARE SW AND DSS FOR DEMAND
  • GENERIC GIS SOFTWARE 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 DEMAND
  • 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 GIS 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 DEMAND
  • 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 GIS SOFTWARE SW AND DSS FOR DEMAND
  • 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 GIS SOFTWARE SW AND DSS FOR DEMAND
  • 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 GIS SOFTWARE SW AND DSS FOR DEMAND
  • DSS belonging to the second class generally include comprehensive tools for: TRIP GENERATION TRIP DISTRIBUTION MODE SPLIT TRANSPORTATION GIS SOFTWARE SW AND DSS FOR DEMAND
  • 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 GIS SOFTWARE TRIP 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 DEMAND
  • 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 GIS SOFTWARE TRIP DISTRIBUTION SW AND DSS FOR DEMAND
  • 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 GIS SOFTWARE MODE SPLIT SW AND DSS FOR DEMAND
    • I INTRODUCTION
    • II SW and DSS
          • II.1 DEMAND
          • II.2 EMERGENCY PLANNING
    • III CONCLUSIONS
    CONTENTS
  • SW 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
  • 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 PLANNING
  • Project Cycle Management
    • Project Facilitator®, vers. 1.4 (Live Application Technology, 2004)
    SW and DSS FOR EMERGENCY PLANNING for project management
  • Logical Framework Approach
    • LogFrame for Windows, vers. 1.0 (Maizemoor International, 2008)
    SW and DSS FOR EMERGENCY PLANNING for project management
  • Project Cycle Management and Logical Framework Approach
    • TeamUP-PCM® (TEAM Technologies, Inc., 2008)
    SW and DSS FOR EMERGENCY PLANNING for project management 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
  • for specific component generic to analyze transportation system in ordinary condition adopted for emergency condition – Macroscopic simulation e.g. EMME/2, TransCAD, VISUM, CUBE – Mesoscopic simulation e.g. DYNASMART-P – Microscopic simulation e.g. INTEGRATION, CORSIM SW and DSS FOR EMERGENCY PLANNING
  • 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 PLANNING
    • I INTRODUCTION
    • II SW and DSS
          • II.1 DEMAND
          • II.2 PLANNING
    • III CONCLUSIONS
    CONTENTS
  • CONCLUSIONS
    • 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
  • CONCLUSIONS
    • 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
  • Decision Support System for people evacuation: mobility demand and transportation planning F. Russo, C. Rindone, G. Chilà Università Mediterranea di Reggio Calabria XV Convegno Nazionale SIDT Rende, 9-10 giugno 2008 INPUT 2010, Potenza, 13-15 Settembre 2010
  • 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 EXPERIMENTATION
  • Melito Porto Salvo: Municipality of province of Reggio Calabria (Italy) III EXPERIMENTATION III.1 Definition of Evacuation Scenario and of Area of Study Area [m 2 ] 35,300,000 Population 10,483 Workers 2,432
  • STRATEGIC TACTIC OPERATIVE DIRECTIONAL PRATICABLE FEASIBLE NATIONAL REGIONAL LOCAL LCPP TIME Space study in-depth Melito Porto Salvo: Local Civil Protection Plan (LCPP) III EXPERIMENTATION III.1 Definition of Evacuation Scenario and of Area of Study Invariante
    • 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 EXPERIMENTATION III.1 Definition of Evacuation Scenario and of Area of Study
  • Melito Porto Salvo: area to test evacuation plan III EXPERIMENTATION III.1 Definition of Evacuation Scenario and of Area of Study Area [m 2 ] 42,990 Residents 255 Employees 225
  • Study area Zoning Urban area of Melito Porto Salvo - Province of Reggio Calabria (Italy) Residential building Public building Mixed building III EXPERIMENTATION III.1 Definition of Evacuation Scenario and of Area of Study Town of Melito Porto Salvo Study area Area (km 2 ) 35.30 0.04 Resident 10483 255 Employee 2432 225 Zone Area (m 2 ) Zone Area (m 2 ) 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 (m 2 ) 42990.37
  • 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 EXPERIMENTATION III.2 Survey
  • SURVEY – RP DATABASE Schools Public activities Private activities Families 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 Adress Number floors Number 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 EXPERIMENTATION III.2 Survey
  • Activity analysis Socio-economic analysis SURVEY – RP DATABASE III EXPERIMENTATION III.2 Survey 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 5 Agency 1 Public office 27 Furniture 3 School 1 Medicine and beauty 3 Totale 72 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 60 Employee, occasional customer and weak user Court 1 7 3 other 3 21 8 Mixed 28 262 99 Resident, weak user, employee and occasional customer
  • SURVEY - SP DATABASE III EXPERIMENTATION III.2 Survey 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 14 Not contacted 8 14 Not interviewed 14 27 Not found 21 0
    • 12/01/2007
    • Pre-Test
    • Evacuation of the school and of public buildings
    • 1/03/2007
    • Test
    • Evacuation of the area of study
    III EXPERIMENTATION III.3 Real Experimentation
  • 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
  • CALIBRATED PARAMETER X X X X X X III EXPERIMENTATION III.4 Calibration and Validation of Proposed Models PARAMETER GENERATION MODAL SPLIT DISTRIBUTION MODAL SPLIT WITH DISTRIBUTION General General For employee group For employee group For employee group SOCIO – ECONOMIC LEVEL OF SERVICE
  • III 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 X Dummy 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
  • III EXPERIMENTATION Level of service parameter Generation Modal split Distribution Modal 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
  • MAIN OUTCOMES III EXPERIMENTATION Residents Occasional customers Employees Weak users School staff and pupils III.4 Calibration and Validation of Proposed Models Generation model Distribution Modal choice Present in the area Willing to evacuation simulation Refuge’s are fixed Refuge’s are not fixed Car Pedestrian Bus 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%
  • Calibration of resident generation model : PRESENT RESIDENT III EXPERIMENTATION Generation Spec. Parameters Value T-Student  2 1  FL % of actives over residents 0.13 2.82 0.99  NS % of students over residents 0.33 4.74  NC % of housewives over residents 1.00 42.36  NR % of retired people over residents 0.14 2.55 2  E2 % of residents younger than 14 and older than 5 years -0.09 -0.28 0.96  E3 % of residents younger than 19 and older than 15 years -0.83 -1.16  E4 % of residents younger than 24 and older than 20 years -0.32 -0.46  E5 % of residents younger than 65 and older than 25 years 0.80 8.36  E6 % of residents over 65 years -0.15 -0.51 3 m E,R Attendance coefficient 0.59 23.51 0.98
  • Calibration of resident generation model : PRESENT NOT - RESIDENT Calibration of resident generation model : USER WILLING TO EVACUATE ON THE PRESENT III EXPERIMENTATION Generation Generation User category  Employee 77 Occasional customer 80 School staff and pupils 92 Weak user 100 SP DATA RP DATA User category   Resident 39 / Employee 75 50 Occasional customer 67 / School staff and pupils 100 100 Weak user 100 100
  • Calibration of modal split model: TEST 01/03/2007 Alternatives: 1 Pedestrian, 2 Car III EXPERIMENTATION Modal split Parameters Alt Specific. 1 Specific. 2 Specific. 3  D Distance as the crow flies between origine and refuge’s area 2 -0.0072 (-0.3)  Car Dummy for car alternative 1 -0.8579 (-0.1)  TRP Time on pedestrian network from origine to the refuge’s area 1 -0.2881 -0.6924 -0.3049 (-1.0) (-0.4) (-0.9)  TRC 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  2 0.07 0.07 0.07
  • Calibration of modal split model: Town Hall Model, TEST 01/03/2007 Alternatives: 1 Pedestrian, 2 Car III EXPERIMENTATION Modal split Parameters Alt. Specific.1 Specific.2  WE1 Dummy for employees of age below 45 years 1 2.5660 3.0710 (1.70) (1.90)  DRC Distance on pedestrian network between origine and refuge’s area 1 -0.0025 -0.0027 (-1.60) (-1.90)  L2 Dummy if the employee level is higher than 2, 0 otherwise 2 0.2238 (0.20)  L3 Dummy if the employee level is higher than 3, 0 otherwise 2 1.1830 (0.70)  CW 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)  Women 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  2 0.45 0.47
  • Calibration of distribution model: Town Hall Model, TEST 01/03/2007 Alternatives: 1 Refuge’s area fixed by public decision maker (RAF), 2 Other refuge’s area (RAO) III EXPERIMENTATION Distribution Parameters Alt Specific. 1 Specific. 2  Women Dummy equal to 1 if the user is a women, 0 otherwise 1 2.850 (3.3)  L2 Dummy if the employee level is higher than 2, 0 otherwise 1 1.289 0.2657 (2.5) (0.4)  z10 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  2 0.14 0.35
  • Calibration of modal split with distribution model: Town Hall Model, TEST 01/03/2007 Alternatives: 1 Pedestrian with refuge’s area 1; 2 Pedestrian with refuge’s area 2; 3 Car with refuge’s area 1 III EXPERIMENTATION Modal split Distribution Parameters Alt Specific. 1  TRP,RA1 Time on pedestrian network from origine to the refuge’s area 1 1 -0.2688 (-1.4)  TRP,RA2 Time on pedestrian network from origine until to refuge’s area 2 2 -1.0260 (-1.5)  TRC,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  2 0.17