Data Actualization Using Regression Models for ...

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CAPITALS/MADRID [1], SCOOT [13], SCATS [3], ICM AMS [12], TRIM [14],. ROMANSE [15]. The reason of these is the so called “no popularity” - there are still a.
Data Actualization Using Regression Models for Modeldriven Decision Support System in Transport Planning Irina Yatskiv (Jackiva)1 and Elena Yurshevich 2 1

Transport and Telecommunication Institute Riga, Latvia, Lomonosova 1, LV 1019 E-mail: [email protected] 2 Transport and Telecommunication Institute, Riga, Latvia, Lomonosova 1, LV 1019 E-mail: [email protected]

Abstract. A systematic approach to urban transport system planning and managing means the inclusion of a systematic monitoring system to collect the necessary data and periodically updating the DSS databases, as well as updating of models in their repositories. This should be supported by introduction of new data and information without changing and deleting the old. The authors proposed the application of regression analysis for data actualisation and new obtaining, and considered several task settings for realization of such approach. The proposed methodology focuses on the issues of data updating and preparation for modelling, consideration of model preparation and simulation scenarios including the analysis of the influence of the new solution implementation on the neighbouring fragments of the network. The approach has been approved using the simulation model for a fragment of Riga City. The offered procedures can be used in the frame of model-driven DSS and give the possibility to fulfill the process of the model actualisation faster and less expensive without loss of accuracy. Keywords: transportation system, decision-making, modelling, credibility, regression, procedure.

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Introduction

In the field of urban transport system (UTS) planning and management over the last 10 years, the decision support system (DSS) based on different concepts approach to modelling has been actively implemented. There are DSS designed for planning and management at different levels of decision-making. At the strategic level, decisionmaking is most often used in DSS through the macroscopic meta-model, as well as expert evaluation. In this case, DSS is used to furnish politicians with UTS-related solutions at a high abstraction level, without any particularization at a local level. At the tactical and operational level, decision-making is the most reasonable DSS based on the microscopic and mesoscopic modelling. Such kind of DSS is intended for planning at the level of individual fragments of UTS, traffic control, traffic light control, high-speed mode, etc. In addition, there are DSS designed for control and pre-

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vention of accidents and incidents on the roads and traffic control in specific situations. Especially, using of microscopic models (MM) repositories in model-driven DSS allows one to get a comprehensive idea of the upcoming changes on the global and local level of UTS and assess the impact on their subsequent implementation, and as a result to improve decision-making by the responsible person [1-3]. However, despite the obvious advantages, the using of MM as a part of DSS is studied poorly, and they are rarely used due to the complexity of the model structure and its application, as well as high requirements to the data [4]. The authors’ research [5, 6, 7] is devoted to several aspects of MM application as a part of DSS. There are provided several procedures that can be used for simplifying the MM application and that support the semi-automated processes of data preparation, actualisation and transition from databases to MM and back to DSS repositories. It will make the MM based DSS more attractive for decision makers at tactical and operational level of decision making regarding UTS planning, development and operation. The main attention in this article is paid to the problem of the data actualisation. There is demonstrated the idea of that problem solving by means of the models of different kind of applications and presented the results of this idea approbation with the example of the regression model (RM) application.

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Literature Review of UTS Model-driven DSS and the Experience of MM Application

The idea of model-driven DSS application for UTS planning, management and control was considered by different scientists (see the review in [8]). Juan de Dios Ortuzar in his monograph [2] describes the role of DSS in transportation system management and planning; he paid special attention to modeling application as a part of decision-making. Barcelo in [1] considered the DSS for the Madrid UTS management by using the models implemented in AIMSUN and GERTRAM software. Ulied and Esquius in [9] considered the framework of DSS meant for the European transport system management and control. Soo et.al in [10] presented the DSS framework providing a holistic framework to perform analytical assessments of integrated emergency vehicle pre-emption and transit priority systems. The leaders in DSS implementation are the U.S., Spain, Germany, and the UK. For instance, a new adaptive control DSS was described in the paper published in the magazine “Traffic Engineering & Control” [11]. This system is used in New York City and combines adaptive control of traffic lights in real-time mode and some operators who are involved in the process. Another example is the activities of ICM (Integrated Corridor Management) in San Diego (California, USA). Within the framework of the ICM implementation, a DSS is created which integrates both the inclusive communication system and the formulation of inter-jurisdictional agreements. At the heart of the DSS is simulation-based prediction system AIMSUN Online [12].

DSS used in today's practice of decision-making in transport planning are mainly based on macroscopic models – the use of microscopic models in this context is given little attention. There are only several examples of DSS that incorporate MM: CAPITALS/MADRID [1], SCOOT [13], SCATS [3], ICM AMS [12], TRIM [14], ROMANSE [15]. The reason of these is the so called “no popularity” - there are still a lot of unsolved and non-completely investigated problems, such as:  combined use of different types of models and the MM integration as a part of DSS;  inevitable obsolescence of the data and the models stored in repositories and DSS databases;  organization of the data and model actualisation in case of fragmentary measurements of the data in a real system (usually, there is considered the system with systematic data collection in a real system) or in case of their absence (for instance, consideration of a new solution);  etc. In authors’ opinion, these unresolved issues are the main reasons of why MM is rarely used in DSS. This paper is devoted to the last issue of those listed above.

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Conception of MM Traffic Volume Actualisation on the Base of Fragmentary Data Measuring

The main tasks of DSS are the following:  provision of storing and access to the data required for UTS planning and control tasks;  knowledge storing in knowledge bases and providing access to it;  models storing in the model repositories and organizing access to them;  ensuring the user interface to provide for data input by users;  ensuring manipulation with the models and data;  presentation of the output results in graphic and textual forms or in the form of generalized reports. MM used as a part of DSS for UTS planning provides for the following benefits and capabilities:  an opportunity of a more careful investigation of the causes of traffic congestion occurrence, and the possibility of locating UTS bottlenecks more precisely;  a possibility of conducting a more detailed analysis of the consequences of the proposed strategic decisions at a local level and refining them more in detail as follows: selecting interchanges of engineering solutions, optimizing the operating plan of traffic light controllers and organizing the traffic flow, regulating operation of public transport etc. Careful reproduction of UTS properties by MM involves with itself using of different kinds of data: about UTS network, traffic properties and its organization, public transport scheduling and routing, pedestrian flows organization etc. DSS should provide the communication between databases, data warehouses, data marts on the one

hand and MM on the other hand. A possible scheme of its organization is presented in Fig.1.

... Microsco Macrosc pic opic Models Models

Statistical Models

Internal Model Base

... Traffic Intensity

ODmatrices

TS parameters

Internal DB

Data Warehouse

Engineer

... Traffic Intensity

ODmatrices

TS parameters

External DB

Data Mart

Fig.1. Organization of access to the data and models

Unlike the macroscopic model, the MM application means the usage of wide range of different data. Taking into account that at the micro-level the traffic properties and UTS network configuration are changing faster than at macro-level, both the data in databases and the models in repositories are needed for a frequent actualisation. The traffic volumes especially require more frequent updating. Let us consider two common situations: 1. It is necessary to estimate the influence of new solutions on one UTS fragment on the traffic volume of neighboring. The researcher can have the microscopic models of considered UTS fragments, but he has not the possibility to connect them together to transmit the new volumes of traffic. 2. It is necessary to repeat the investigation of UTS fragment based on a previously created simulation model and with preliminary knowledge about the changed situation. In the first case the traditional solution is either expensive or does not exist at all. In the second case, it is possible either to conduct the repeated UTS survey implementation, or to apply the UTS macroscopic model as a source of data. The disadvantages of these approaches are the following: the first approach is very expensive and there is no possibility to implement it in UTS on constant basis, but the second one is possible only in case of such model existing. The paper presents the alternative conception, which is based on three main stages: 1. The conduct the fragmentary traffic flow volume measuring on some fragments of UTS. 2. To predict the new traffic flow volume on others UTS fragments using the fragmentary measurements of traffic and special approximation model. 3. To transfer the updating data regarding traffic volume to the input of MM and apply the simulation.

The key question of that approach: to find the approximation model that can describe the dependences between UTS fragments traffic flow volumes without detailed reproduction of UTS properties (Fig.2). The second requirement is very important because it is supposed that that kind of model will not be needed in actualisation as often as MM.

Fig.2. Illustration of the idea of approximation model application for the dependence between traffic volumes approximation

The authors proposed the idea to apply for that either regression models or artificial neural networks and there is expounded the idea of RM application in the offered paper. This approach has been developed and approved on simulation model that constructed in the frame of the project “Pedestrian and Transport Flows Analysis for Pedestrian Street Creation in Riga City” in 2011[5]. The holistic set of procedures for the use of RM for updating the existing traffic data and obtaining the new one were developed in [6].

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Data Actualisation for MM on the basis of Regression Models

Let us have at the time moment in the DSS models repositories the subset of models { } presented on Fig.3 and the data for these models collected at the fixed time moments . At the time moment the data may be needed in actualisation. Let us have a model for some fragment i of UTS created/updated at time . It is necessary to analyse the influence of throughput capacity of the fragment of UTS i on the other fragments lying at the same distance from the considered one. Let us denote the other fragment as j, , where k – is a number of crossroads located between crossroad i and j, k=2..n (Fig.3). It is necessary to simulate the new output traffic volumes from UTS fragment i and to estimate the influence of this traffic flow volume on the level of congestion of the other UTS fragment j using the corresponding simulation model. Several alternatives of possible situations have been formulated for planning the experiments and there was determined the set of factors for regression modelling. There were considered different types of streets, the distance between crossroads, the number of RM that can be used for that problem, etc. In general the plan of experiments was included 16 different set of factors (experiment scenario). The implementation of each experiment for regression modelling includes the following steps:  to form a sample of dependent and independent variables based on the average values of traffic flow volumes and the existing simulation model;

 

to check samples for homogeneity; to separate the generated samples into two subsamples: for RM creation and for checking their approximation quality;  to estimate the parameters of RM and to analyse the model quality;  to make the decision about the possibility of its application for the data actualisation based on the quality analysis results. The following statistics were used for the RM quality analysis: ̅̅̅̅ – adjusted coefficient of multiple determination, F-test – Fisher test, SEE – standard error of estimation; for the approximation quality testing: the root mean square error (RMSE), the root mean square normalized error (RMSNE), the 95% confidence interval (CI) and the 95% tolerance interval (TI). Let us consider two typical examples. The first example concerns the crossroads disposition which presented in Fig.3. The dependent variable is – the volume of output traffic flow from intersection N3. The independent variables are – the volume of input traffic flows of intersections N1, N2 and N3, accordingly. The analysis demonstrated that the number of independent variables can be decreased and the obtained results are presented in Table 1.

Fig.3. Scheme of the crossroads location Table 1. Characteristics of the quality of obtained RM for the case on Fig.3

̅̅̅̅ 0.73

F

p-level

SEE

RMSE

RMSNE

106.55