کالیبراسیون کامپیوتر مدل ترافیک شبیه سازی خرد با شبکه های عصبی

ABSTRACT Calibration of microsimulation traffic model using neural network approach

The functioning of a traffic system is under the influence of various aspects of human behavior (Onieva, Milanés, Villagrá, Pérez, & Godoy, 2012). Studies show that the behavior of traffic participants is, among other things, territorially and culturally conditioned (Olstam & Tapani, 2004). Microsimulation models include variable behavior of drivers at a level of each particular entity, and the reality of modeling results depends on the initial choice of the model (Fang & Elefteriadou, 2005) and success of the calibration process (Transportation Research Bord, 2000).

Calibration of a traffic model is actually an adjustment of a model to a specific local traffic network and its users. Calibration is defined as the process of comparing and minimizing the differences between the modeling results and the real data obtained by counting and measuring in the local network (Transportation Research Bord, 2000). Validation of the model is an estimate of the success of the model calibration made through a comparison of indicators obtained from the calibrated model and the ones measured in traffic.

Analysis of bigger spacial and time scopes of a traffic network and prediction of a future state both need a larger number of models, including traffic demand models (Ettema, Arentze, & Timmermans, 2011), dynamic traffic distribution (Flötteröd, Chen & Nagel, 2012), daily mobility (Flötteröd, Bierlaire, & Nagel, 2011), flow prediction under typical and atypical traffic conditions (Castro-Neto, Jeong, Jeong, & Han, 2009) etc. It is realistic to expect that modeling of the selection of a travel mode and users’ routes is connected with the modeling of traffic (Park & Kim, 2001; Rickert & Nagel, 2001), economic and environmental indicators, both for real time modeling and for the prediction of the future traffic demand and its consequences. The degree of reliability of results of modeling a future state of the system is a subject to a debate (Dorothy, Ambadipudi, & Kill, 2006; Stevanovic´ & Martin, 2008), but scientific efforts are directed towards an adoption of reliability criteria in selecting tools in planning and optimization processes.

Prediction of selection of users’ routs on the microsimulation level (Flötteröd et al., 2011) and calibrations of traffic distribution model are not included. In this paper, the selection of users’ routs, which is the traffic distribution in an examined segment of the traffic network, is a user defined input parameter of the microsimulation model. The aim of this paper is to obtain a calibrated microsimulation model which is able to give good correlation with measured functional characteristics of a particular segment of the network in real traffic conditions.

Genetic algorithm (GA) is the most commonly used calibration algorithm for input parameters of the simulation model. Positive utilization experiences of GA are reported in the following examples: in the calibration of FRESIM model (Cheu, Jin, Ng, Ng, & Srinivasan, 1998), PARAMICS model, VISSIM and CORSIM model (Park, Won, & Yun, 2006) and in the calibration of a VISSIM model (Kim, 2006). Analysis of acceptable time gaps and determination of a critical time interval by means of Greenshields’ model (Cicu, Illotta, Bared, & Isebrands, 2011) represent a description of the calibration method of the VISSIM microsimulation model at roundabouts in New York.

For the purpose of calibrating a microsimulation traffic model, a new calibration method is analyzed. This paper shows results obtained by applying neural networks in the microsimulation traffic model calibration process.

The first traffic indicator, chosen in the calibration process, is the traveling time between measuring points. Measuring points were chosen in the real environment and connected to existing objects – a light pole at the beginning of the section and an electric supply pole at the end, because it was easier to enter them into the model layout that way. The traveling time is easily measurable in the field, and it is under the influence of the variable driver’s reaction time. In this paper, the mean traveling time is used in order to test the response of neural networks for the given indicator.

Furthermore, another easily measurable traffic indicator – queue parameters – is introduced in the calibration process. Validation is done for the traveling time and queue parameters for new sets of measured data. The findings of the comparison of modeled and measured data of the microsimulation traffic model, calibrated to one roundabout, are also shown, and the validation is done on another roundabout.

Prediction of traveling time, obtained by application of neural network (Park et al., 1999; Dia, 2001) can be analyzed in a broader context of traffic modeling, but a special emphasis is, in this case, used in microsimulation modeling.

The microsimulation model, selected to investigate the applicability of neural networks in the calibration process, is the VISSIM.1 Analyses are performed on the basis of results obtained by one-hour traffic simulations, corresponding to the duration of the data gathering in the field for every set of data measured in real traffic conditions.