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SOME ISSUES CONCERNING THE USE OF SIMULATION IN ADVANCED TRAFFIC MANAGEMENT SYSTEMS Jaume Barceló* and David García< *Department of Statistics and Operational Research, Technical University of Catalunya, Pau Gargallo 5, 08028 Barcelona, Spain, Phone: +34-93-4017033, Fax: +3493-4015855, email: [email protected] < TSS-Transport Simulation Systems, Tarragona 110-114, 8015 Barcelona, Spain, Phone: +34.93-2296225, Fax: +34.93-2296226, e-mail: [email protected] ABSTRACT: Advanced Traffic Management Systems constitute one of the major envisaged applications of Intelligent Transport Systems. Various architectures and approaches have been proposed and tested in projects of R&D programmes of the European Commission in recent years, as for example ARTIS, KITS and ENTERPRICE, just to mention a few. An approach shared by all of them has been the conception of the system as an intelligent decision support system able of identifying the actual traffic conditions on the network from real time traffic measurements, make a diagnosis of the network state, identify potential or actual conflicts and propose strategies to prevent or alleviate the conflicts. In some cases the decision support functions have been based on evaluating by simulation the scenarios for the proposed management strategies. The simulation provides the human operator with a quantitative basis for the decision making process. A key question on using simulation in this context deals with the model building process in the changing conditions of the real-time decision making framework. This paper discusses some traffic management architectures that use the microscopic simulator AIMSUN2 for these purposes, and describes the structure of a translator that automatically builds the AIMSUN2 simulation model of the selected scenario from GEODYN, an ad hoc Geographic Information System purposely designed for these traffic management applications. Examples from ENTERPRICE project are presented and the quality of the results is discussed.

Key Words: Traffic Management, Simulation

1. INTRODUCTION: COMMON CONCEPTS FOR ADVANCED TRAFFIC MANAGEMENT ARCHITECTURES The architectures for the realisation of Advanced Traffic Management Systems (ATMS) have evolved in the last decade as a consequence of the research on the application of telematic technologies to manage traffic systems. This paper is based on the experience gained from the European projects whose acronyms are ARTIS (1994), KITS (1995), Barceló et al. (1996), (1998), ENTERPRICE (1999), and CAPITALS (1998). The interested reader can find many references to these and other similar projects in the Proceedings of the World Conferences on Intelligent Transport Systems published yearly since 1994 by ERTICO (http://www.ertico.com), or in the official web site for European R&D Projects (http://www.cordis.lu). The evolution has concerned namely the way in which the main functions of the system could be implemented, largely improved by the evolution of software and hardware technologies, but the main basic concepts on which these architectures rely are the same: •

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A real-time data acquisition system which main function is supplying through the traffic sensors the data that suitably processed will provide the information for the estimation of the traffic conditions on the network, the identification of conflicts,

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actual or potential, the diagnosis of the situation and the proposal of actions to alleviate, or hopefully avoid, the identified or foreseen conflicts. A module with traffic models to estimate the traffic conditions on the network. A short-term prediction module, with models to forecast the short-term evolution of the traffic conditions on a do-nothing scenario. A module usually constituted by a Knowledge Based System, with learning and reasoning capacity on the traffic problems according to the patterns of identification, prediction, local control actions and reasoning of general consistency. Complementarily, in the most advanced architectures, a module has been added, with capacity to evaluate the proposed actions prior to their implementation, and present the results to the human operator to support the decision making process.

From traffic detectors on the road Real-time Data Collection

Real-time Database

Historical Database

Traffic Models

Shortterm Prediction

ANALYSIS AND DIAGNOSIS OF THE NETWORK STATE (KBS)

RECOMMENDED TRAFFIC MANAGEMENT STRATEGIES

Scenario Simulation

ASSESSMENT OF STRATEGY IMPACTS To Operstor

Figure 1. Conceptual diagram for an ATMS Architecture Figure 1 depicts the conceptual diagram for a generic ATMS architecture in which the potential impacts of the strategies, recommended by the module that identifies the network state and makes the diagnosis, are assessed by simulation before being implemented by the human operator.

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2. THE MICROSCOPIC TRAFFIC SIMULATOR AIMSUN2 Microscopic traffic simulation has been the simulation approach used, not only due to its ability to capture the full dynamics of time dependent traffic phenomena, but also for being capable of dealing with behavioral models accounting for drivers’ reactions when exposed to Intelligent Transport Systems (ITS). Microscopic traffic simulators are simulation tools that emulate realistically the flow of vehicles on a road network. The main components of an advanced microscopic traffic simulation model are: • • • • •

An accurate representation of the road network geometry. A detailed modelling of individual vehicles’ behaviour including dynamic routes from origins to destinations and route choice models. An explicit and accurate reproduction of traffic control plans. Animated output of the simulation runs has proven to be not only a highly desirable feature but also a powerful analysis tool of the simulation results. Ability to deal with ITS systems, as for example adaptive traffic control systems, automatic incident detection systems, dynamic vehicle guidance systems, advanced traffic management systems, and so on.

Modeling in current microscopic simulation approaches is becoming a complex combination of the classical modeling concepts (car-following, lane change, gap acceptance, etc.), with computer modeling techniques based on Artificial Intelligence and other software engineering approaches (see for instance: Liu et al. (2000), Hernandez et al. (2000) and Hidas (2000)). This hybrid approach provides a sound framework to account for the behavioral aspects implied by ITS applications. The simulator used in the referenced European projects has been AIMSUN2 (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks), Barceló et al. (1999c). To properly perform the required functions AIMSUN2 has evolved from the classical microsimulation approach based on input flows at input sections in the model, and using turning proportions at intersections, to a Route Based microscopic simulation Barceló et al. (1995). In this model, vehicles are input into the network according to the demand data defined as a set of time-dependent Origin-destination matrices for each time interval and each vehicle type, and they drive along the network from their origin, following specific paths in order to reach their destination. In the Route Based simulation new routes are to be calculated periodically during the simulation, taking into account the current link costs that depend on the current traffic conditions. A Route Choice model is used, when alternative routes are available, to assign vehicles to the routes. Regardless of the Route Choice model used, there are two types of driver’s behavior with respect to the route assignment: Static and Dynamic, which refers to whether or not a vehicle can modify the actual path en-route as new paths become available during the trip. This route-based version of AIMSUN2 becomes a simulation platform for networks containing an ATMS/ATIS component in which traffic management centers provide real time information and the drivers react by possibly using different alternative routes. As ITS technology is being deployed Advanced Traffic Information Systems provide such information for Vehicle Guidance, next generation ATMS systems require this type of dynamic tools, Barceló et al (1999a).

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AIMSUN2 is embedded in GETRAM (Generic Environment for Traffic Analysis and Modeling) a simulation environment comprising a traffic network graphical editor (TEDI), a network data base, a module for storing results, and an Application Programming Interface to aid interfacing to other simulation models, assignment models or GIS, Barceló (2000). An additional library of DLL functions, the GETRAM EXTENSIONS, enables the system to communicate with external applications, as for example real-time control logic, adaptive ramp metering strategies, bus preemptive control logic, and so on. The functional structure of the system is depicted in figure 2. The GETRAM environment includes a function editor allowing the user to define his/her own Route Choice functions. The default function implemented in AIMSUN2 belong to the family of the Discrete choice functions, Ben-Akiva and Lerman (1991), Ben-Akiva and Bierlaire (1999) TEDI is a graphical editor for traffic networks. It has been designed with the aim of making the process of network data entry and model building user-friendly. Its main function is the easy entry of the network feeding the traffic simulators like AIMSUN2. To facilitate this task the editor accepts as a background a graphical description of the network area, in terms of a DXF file from a GIS or an AutoCAD system. In this way the detailed geometric model of the road network including any kind of freeways, highways, arterial etc. can be built subsequently into the foreground. These are the abilities that have been exploited to develop the applications described in this paper.

GETRAM

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API Library

GIS AIMSUN2 AIMSUN2 Kernel GETRAM Extensions

Simulated Data

Pre-simulator Simulator

Costs Routes

Shortest Routes Component User Interface

Control & Management Actions

External Applications

Figure 2: Conceptual structure of GETRAM/AIMSUN2

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3. AN EXAMPLE OF USING AIMSUN2 IN ATMS ARCHITECTURE: ENTERPRICE As has been mentioned in the introduction, ENTERPRICE, an European R&D Project completed one year ago, was one of the leading European Projects in the 4th Framework Programme of the European Union designing, implementing and demonstrating an ATMS application. The core of the proposed ATMS was MOTIC, which stands for MO (Mobility Analysis) and TIC (Traffic Information Centre). Figure 3, taken from the reports of the European Project ENTERPRICE (1999), illustrates how the conceptual architecture, proposed in the introductory section, and depicted graphically in Figure 1, has been implemented in the MO part of the MOTIC traffic management and information system. The traffic analysis function for Traffic Management is based on two main MOTIC components: The Scenario Generation and the Scenario Simulation. Three main software pieces are integrated in the Scenario Generation module: a GDF/GETRAM Translator, a Scenario Translator and an Origin-Destination (OD) trip matrix estimation tool, called ODTOOL. The GDF/GETRAM Translator automatically translates the information on the road network geometry stored in an Informix database into the GETRAM representation required to build an AIMSUN2 simulation model. The Scenario Translator completes automatically the information required to run the AIMSUN2 simulation model for the selected scenario. A main piece of information is the O/D matrix, information provided by the ODTOOL and automatically integrated into the Scenario Generation.

MO-part architecture long-/mid-term strategy update Graphical User Interface D O

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real-time TIC data Historical traffic data

Geographic data

Planning data (control strategy)

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Scenario Simulation AIMSUN2 microsimulation Model

Qualitative analysis ) (Knowledge Bases)

Analysis Quantitative & Evaluation analysis (statistics)

Bus Dati Software Software Data Bus

Geographic DataBase (Network Model)

Evaluation DataBase (Scenarios, results, ...)

Figure 3: AIMSUN2 in MO-part Architecture The Scenario Simulator runs the AIMSUN2 simulation model for the selected scenario. For this purpose an ad hoc version of AIMSUN2 has been integrated into MOTIC. This version is

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able of reading the output from the Scenario Translator, accept complementary inputs from the Database, accept MOTIC commands and disseminate the simulation output statistics in the format required by other ENTERPRICE Tools. It is composed of two elements: a library and an application to test the library. The library is included in AIMSUN2 simulator, so AIMSUN2 can translate on the fly the scenario without any user intervention. The logic scheme showing how the process works is depicted in figure 4. The translator generates automatically the GETRAM geometric model of the road network, the translator ensures a high quality one-to-one mapping, TEDI can then be used for a first phase quality checking of the quality and correctness of the Database contents. Before running the simulation model AIMSUN2 performs a testing process to check other aspects of the correctness of the transport network representation, as for example, connectivity, completion of the turning and group definitions, existence of paths between all the O/D pairs, etc.

GIS (6)

GDF (1)

COMPLEMEN TARY DB(2)

(GEODYN)

TRANSLATOR (3)

INTERMEDIA TE GETRAM DB (4)

TEDI (5)

GETRAM DB(7)

AIMSUN2

Figure 4: Logic of the Translation/Simulation Process The TRANSLATOR (3) is the automated process that builds the intermediate GETRAM model of the Road Network under consideration. The GDF-GETRAM TRANSLATOR developed in ENTERPRICE was focused on a particular representation of GDF(1) in an INFORMIX Database supporting an ad hoc GIS, called GEODYN (6), for that project. The experience gathered in ENTERPRICE showed that not all the data and information required by the traffic models performing the main functions of the ATMS is included in the GDF Files or GIS. Some of this information is not currently available in GDF (i.e. number of lanes,

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flow direction) as far as it is not required by the usual applications based on GDF. The design of GDF enables that it could be extended to account for this information Therefore a Complementary Database (2) providing the TRANSLATOR (3) with the missing information is required. Examples of missing items are the following: turnings at intersections, phases and timings at signalised intersections, detector location and detector types, location of Variable Message Sign (VMS) panels, libraries of available messages to be displayed at the VMS, etc.The inputs from the GDF and the Complementary Databases feed the TRANSLATOR that builds the primary GETRAM model which is stored in the Intermediate Database (4). The TEDI (5) set of graphic editors in GETRAM is the basis for the Consistency Tests to check the quality and correctness of the Database contents. This consistency test becomes the first step in the quality assurance process. Inconsistencies can be removed with the graphic utilities in TEDI. The output of the Consistency test will be a final GETRAM Database (7), which will support the rest of the process. The “Scenario Generation” is the graphically supported process followed by the operator to select the sub-network spanning the area contained in a graphical window to define the scenario that will be object of the detailed analysis. This scenario corresponds to the area in which the traffic conflicts have been identified, for which the Analysis and Evaluation Module in Figure 3 recommends strategies with the purpose of solving or alleviating those conflicts. The Scenario Generation process consists of the following steps: LEVEL 1 MAP OF THE WHOLE REGION

LEVEL 2 MAP OF THE SELECTED SCENARIO SUB-NETWORK GETRAM MODEL

GRAPHIC WINDOW (Scenario Generation)

Figure 5. Graphic initialisation of the Scenario Generation

1. The operator opens a graphic window on the screen displaying the map of the whole region to start the process. The system identifies and gets from the GETRAM Database (6) all the information concerning the sub-network spanning the area covered by the graphic window. This is depicted in Figure 5. 2. At this point the operator is allowed to change the value of some of the network parameter associated to management strategies or roadwork maintenance, as for example limit speeds on the sections and lane closures. 3. The identified information from the GETRAM Database is used by the system to build the GETRAM Scenario Model of the corresponding sub-network.. This is the model that will be simulated in AIMSUN2

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Traffic Management Strategies require to take into account explicitly traffic streams from origins to destinations, namely when re-routing is taken into account as part of the management strategy, and re-routing is based on information provided to the road users through VMS panels. The function of the simulation model is to provide the basis for a proper evaluation of the potential impacts of the proposed strategies, therefore the model has to be able of dealing with specific routes, alternative routes and information that could affect only a part of the road users. Those routes are affected by the re-routing but whose destinations are still reachable using the new routes. A proper way of modeling this effect is by means of a route-based microsimulation model The main input for a route-based microsimulation is a time sliced or time dependent OriginDestination matrix. Therefore an ad hoc estimation tool has been developed for the ENTERPRICE/MOTIC objectives: the O/D-Tool. This is an application that starting form an initial O/D target matrix (i.e. a previous matrix used for transport planning studies) and using traffic counts for given time periods, “adjusts” the target matrix in such a way that the traffic demand pattern for that time period could fit, acceptably well, the traffic variations identified through the traffic measurements. The adjusted O/D matrix for the selected scenario is the input for the AIMSUN2 microsimulation model of the scenario that has been previously automatically built by the Scenario Editor and the Automatic Translator. The traffic operator has available in this way simulation models of the selected scenario ready for testing the proposed traffic management strategies.

4. THE ODTOOL: A TOOL FOR THE ESTIMATION OF ORIGINDESTINATION MATRICES To simulate the traffic flows on the sub-network corresponding to the selected scenario for the current period of time one of the basic data input required is the Origin-Destination trip matrix for that period of time. That is the number of trips tij between each origin i, and each destination j for each time period. Origins and destination could lay in the borders of the area spanned by the network, that is the input and output gates defined by the border of the subnetwork, as well as in the area. This is the situation schematised in Figure 6 explained below. The underlying hypothesis on which is based the approach used to develop the procedure for automatically estimating and updating this input in ENTERPRICE, is that there is available a useful and relatively cheap source of information: the traffic counts for the previous period of time on a number of points on the network, either belonging to the sub-network or outside. In the case of the ENTERPRICE MOTIC these traffic counts come from the sensors located through the area. The density of sensors is usually not high enough so as to consider that all the flows in the network are known. Therefore there exists the need for an estimation of the traffic flows on sections where detectors are not available or because, due to existence of alternative routes counts from detectors cannot be directly extrapolated to some sections of the road network. An Origin-Destination based simulation is a sound method to make such estimations, and this requires an adjusted Origin-Destination Matrix. The Origin-Destination trip matrix estimation tool in the MOTIC, called ODTOOL is intended mainly for providing this information based on the following functionalities: a) Using an historical Origin-Destination trip matrix and on line traffic counts for the previous period on the whole network, the tool calculates an O/D trip matrix for that period so that when it is assigned the observed or counted traffic flows provide the best fitting under

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a least square criterion. The functionality can also be used for a sub-network if enough traffic counts are available in that sub-network. b) Given an O/D matrix for the whole area and a sub-network, it calculates the traversal O/D flows between gates defined by the border of the sub-network, that is it extracts from the global O/D matrix the sub-matrix corresponding to the selected sub-network. This subnetwork defines the scenario selected by the operator, where the traffic conflicts have been identified. This is illustrated graphically in figure 6. The so-called traversal matrix is the local O/D matrix for the shaded area inside the rectangle, spanned by a sub-network of the road network for the whole area. The traversal matrix is composed of the original Origins and Destination in the area plus some dummy origins and destinations, the input and output gates of flows into, from and through the area. In the figure I/Oi and I/Oj correspond to the i-th and j-th input/output gates, new dummy nodes, corresponding to the flows form centroid r to centroid s crossing the area, Ik is the k-th input gate for the flows with origin at centroid p, outside the area, finishing the trip inside the area, and On the n-th output gate, for flows generated at a centroid inside the area, leaving the area through this output gate and finishing the trip in centroid q outside the area. MACRO LEVEL (GLOBAL NETWORK)

I/Oi

I/Oj s

r

p

On

Ik

q

MICRO LEVEL (SUBNETWORK) AIMSUN2 MODEL

Figure 6: Traversal O/D matrix for a sub-area The core of this ODTOOL is a method based on a bilevel optimization method which description can be found in Spiess (1990), and Barceló and Casas (1999b). The algorithm can be viewed as calculating a sequence of O/D matrices that consecutively reduce the least squares error between traffic counts coming form detectors and traffic flows obtained by a traffic assignment using Restricted Simplicial Decomposition Algorithm. The calculation of the traversal matrix for a sub-area requires information about the routes used by the trips contained in the O/D matrix (dij). It requires the definition of the route and the trip proportions relative to the total trips dij used on each route originating at zone i and ending at zone j. This information is really difficult to handle and store in traffic databases, taking into account that the number of routes connecting all Origin-Destination pairs on a connected network can grow exponentially with the size of the network. This is the reason to use a mathematical programming approach based on a traffic assignment algorithm that is solved a each iteration without requiring the explicit route definition, that computes the traversal matrix during the network loading phase of the algorithm. The O/D matrix estimation tool developed for ENTERPRICE uses a Restricted Simplicial Decomposition Algorithm to

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perform the traffic assignment on which an optional traversal matrix calculator device can be switched on in order to provide the required traversal matrices for predefined sub-areas. The algorithmic scheme is the following: Step 1. The global O/D matrix available for the whole region is “time sliced” based on complementary information on the trip time distribution. Step 2. Generation of the Traversal Matrix for the selected scenario for the corresponding time period using the “time slice” of the global OD matrix for this period. Step 3. The Traversal Matrix for the time period is adjusted using an ad hoc version of Spiess (1990) bilevel optimization adjustment procedure which solves the following bilevel non-linear optimization problem:

Min F(v(g), vˆ) =

1 2 2 ∑ [v(g) a − vˆ a ] + ∑ [g i − gˆ i ]  2 a∈Aˆ i∈I 

v(g) = arg min ∑ ∫ s a ( x)dx va

a ∈A

0

∑h

s.t.

k

= g i , ∀i ∈ I

k ∈K i

h k ≥ 0,

∀k ∈ K i , ∀i ∈ I

va = ∑ ∑ δ ak h k ,

∀a ∈ A

i∈I k∈K i

where va(g) is the flow on link a estimated by the lower level traffic assignment problem with the adjusted trip matrix g, hk is the flow on the k-th path for the i-th O-D pair, and vˆ a is the measured flow on link a. I is the set of all Origin-Destination pairs in the network, and Ki is the set of paths connecting the i-th O-D pair. sa(va) is the volume-delay function for link a∈A. The algorithm used to solve the problem, based on a proposal by Spiess is heuristic in nature, of steepest descent type, and does not guarantee that a global optimum to the formulated problem will be found. The iterative process is as follows: At iteration k: k • Given a solution gi an equilibrium assignment is solved giving link flows vka , and k proportions pia satisfying the relationship v ka = ∑ p kia g ki , ∀a ∈ A i∈ I

Note: the target matrix is used in the first iteration (i.e. g 1i = g$ i , ∀i ∈ I ) • The gradient of the objective function F(v(g)) is computed. For a more realistic approach the gradient is based on the relative change in the demand, written as: g$ i for k = 0   F (g)   for k = 1,2,3, ... g ki +1 =  k  k ∂ 2  g i 1 - λ  ∂ g    i  gk    i (Then a change in the demand is proportional to the demand in the initial matrix and zeroes will be preserved in the process). • The gradient is approximated by

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∂ F2 (g) = ∑ h k ∑δ ak (v a − v$ a ), ∀i ∈ I $ ∂ gi k ∈K a∈A i

$ ⊂ A is the subset of links with flow counts). (where A • The step length is approximated as: ∑$ v 'a (v$ a − v a ) λ * = a∈A ∑ v '2a

where

$ a∈A

   v 'a = − ∑ g i  ∑ ∑δ ak (v a − v$ a )  ∑δ ak h k   k ∈K i a∈ A$  k ∈K i  i∈ I

. Step 4. This Adjusted Time Sliced O/D Matrix is the input to the Route Based AIMSUN2 Summarizing, the two main functions of the ODTOOL are the following: 1. The Traversal Matrix Calculator. For the sub-network spanning the sub-area of the global area and a given O/D trip matrix for the global area, the traversal matrix calculator module defines a system of input/output gates building a new zone system for the sub-area and calculates the traversal gate to gate trip matrix corresponding to the global O/D trip matrix, as described above. 2. The O/D matrix estimation Module. For a sub-network contained in a sub-area of the global area, the O/D matrix estimation module performs a matrix adjustment using traffic counts on points contained in the sub-area using the gate to gate system of the sub-network on a period of time and following the mathematical model described in the above referenced report. This is, the module estimates a gate to gate matrix for the sub-area.

5. PRELIMINARY CONCLUSIONS The process has been preliminary tested as part of the work in ENTERPRICE. Figure 7a depicts the whole motorway network of Hessen, and Figure 7b the sub-network of the Hessen Motorway network used as test bed scenario. Table 1 summarizes the results of the verification/validation of the base AIMSUN2 microsimulation model for the selected scenario. The first and fourth columns identify the detectors between Node1 and Node 2 in Figure 7b, the second and fifth columns the average measured flows at the detectors for 5-minute intervals over half an hour, and the third and sixth columns the average simulated flows at the same level of aggregation for the same period. The traffic situation corresponds to a severe recurrent traffic congestion in that part of the motorway network during the rush hour.

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Node 2 Node 1

Figure 7a Motorway Network in Hessen Detector id. A661/29ON A3/67ON A661/34OS A3/68ON B43/38RN A3/64OS B43/34RS A661/19OS A5/83S A661/31ON A661/22ON B43/38RS A661/23ON

Figure 7b Selected scena

Real Count Sim Count Detector id. Real Count Sim Count 3 4 B43/39RS 2 4 3 7 A661/34ON 23 25 11 14 B43/42RN 6 1 4 7 A3/64ON 6 5 9 6 A3/61RN 11 5 5 14 A3/55ON 30 32 4 5 A3/25RN 10 8 20 22 B43/34RN 11 16 17 24 A3/65ON 6 10 6 3 A661/28ON 8 3 7 3 A661/23OS 4 4 4 5 A661/19ON 16 9 7 10 A5/83N 31 35

Table 1: Average measured and simulated flows The set of detector measurements represents the flow evolution along the stretch of motorway between the two selected nodes. The simulated series fits acceptably well the observed one as the graphics in Figure 8 make apparent. This visual perception is corroborated by the relatively low value of the Root-Mean-Square Error RMS = 4.03. A better quantitative estimate of the correspondence between the reality and the model is provided by the correlation coefficient, which value R2=0.8148 shows an acceptable degree of correlation between both series. The Variance Analysis for the regression identifies a Regression Line of equation X = β 0 + β 1 Xˆ where X are the observed values and Xˆ the simulated. β0 = 1.7939, and β1= 0.7735, with a p value of 2.87e-10 (practically 0) leading to accept the null hypothesis H0: β1>0. The quality of the regression between the simulated and the observed values becomes qualitatively apparent looking at the predicted values in Figure 8. On the other hand taking into account that the RMS criterion is a little bit misleading as it emphasizes the large errors, a more significant indicator on how similar the observed and simulated series are, are the Theil’s coefficients. The global coefficient U takes the value 12

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U=0.14 still within the acceptance limits, the bias proportion coefficient is UM=0.11, not very good but still acceptable, and the variance proportion coefficient is US=0.02 tell us that the simulated series adapts quite well to the variability of the observed series. Measured versus Simulated and Predicted Traffic Counts 40 35 30 25

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Figure 8: Measured, simulated and predicted traffic counts Taking into account that these values correspond to the simulation model build automatically by the translator, combining the information from the GIS and the Complementary Database, using default values for the simulation parameters, we can conclude that the preliminary verification/evaluation demonstrates the feasibility and correctness of the approach. Nevertheless with respect to the ODTOOL still further refinement work is necessary, namely with respect to the quality of the input target matrices and the detector measurements used to adjust the matrices. As has been pointed out by other researchers, Yang et al (1998), the number and layout of the traffic detectors play a crucial role. Unfortunately the current situation is that the detection layout has been set up thinking of the traffic control requirements which are not appropriate to reconstruct that mobility patterns. A careful research on the factors determining the accuracy of the estimates is undergoing.

6. REFERENCES ARTIS ( 1994), Proyecto piloto de Tecnologías Telemáticas Aplicadas al Transporte en el Área de Madrid, EU DGXIII, Project V2043, Final Report. J. Barceló, J.l. Ferrer, R. Grau, M. Florian, I. Chabini and E. Le Saux (1995), A Route Based variant of the AIMSUN2 Microsimulation Model. Proceedings of the 2nd World Congress on Intelligent Transport Systems, Yokohama. J.Barceló, J.Casas, E.Codina, A.Fernández, J.L.Ferrer, D. García and R.Grau, (1996), PETRI: A Parallel Environment for a Real-Time Traffic Management and Information System, Proceedings of the 3rd. World Congress on Intelligent Transport Systems, Orlando. J.Barceló, J. L Ferrer, D. García, M.Florian and E. Le Saux (1998),“Parallelization of Microscopic Traffic Simulation for ATT Systems Analysis”, in: Equilibrium and Advanced

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