First-Order Macroscopic Traffic Models

Ferrara, Antonella; Sacone, Simona; Siri, Silvia

doi:10.1007/978-3-319-75961-6_3

Cited by 4 publications

(2 citation statements)

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“…In this study, the model's focus is narrowed down on discrete macroscopic characteristics, which lays emphasis on the overall behavior of vehicles over time. As well as the involved variables are discretized (both temporally and spatially) instead of using continuous variable, i.e., freeways are considered as a set of segments with defined lengths, and time is also divided into discrete intervals [34]. Subsequently, a generic integrated approach in Section Three is presented; as a matter of fact, the approach not only can be applied to modeling macroscopic road traffic flow, but it also illustrates the potential application of fuzzy cognitive maps in modeling complex and nonlinear systems, which are known notoriously as full of uncertainty and imprecision.…”

Section: Modelsmentioning

confidence: 99%

“…For this reason, FCM as a soft computing technique is presented to address networks of freeways included imprecision and uncertainty. These uncertainties from the macroscopic modeling point of view are mainly connected with road traffic flow, density, and approximate capacity associated variables that can increase the probability of a breakdown and shifting the free flow state of traffic to congested flow [11], [34]. According to the applied algorithm in the previous section, segments of each link (freeway) are assigned as the concepts (nodes) of the FCM, where calculated density defines their values.…”

Section: Inhibitory Causalitymentioning

confidence: 99%

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Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Amini¹,

F.²,

Mikulai³

et al. 2021

Infocommunications journal

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Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.

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Section: Modelsmentioning

confidence: 99%

Section: Inhibitory Causalitymentioning

confidence: 99%