access icon free Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver–vehicle delay

In the past decades, different forms of car-following behaviour model have been intensively studied, proposed and implemented. These models are increasingly used by transportation experts to utilise for appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, an improved adaptive neuro fuzzy inference system (ANFIS) model is proposed to simulate and predict the car-following behaviour based on the reaction delay of the driver–vehicle unit. An idea is proposed to calculate the reaction delay. In this model, the reaction delay is used as an input and other inputs–outputs of the model are chosen with respect to this parameter. Using the real-world data, the performance of the model is evaluated and compared with the responses of other existing ANFIS car-following models. The simulation results show that the proposed model has a very close compatibility with the real-world data and reflects the situation of the traffic flow in a more realistic way. Also, the comparison shows that the error of the proposed model is smaller than that in the other models.

Inspec keywords: automobiles; traffic engineering computing; automated highways; fuzzy neural nets; inference mechanisms

Other keywords: ANFIS model; driver vehicle unit; transportation experts; adaptive neuro fuzzy inference system car following behaviour model; intelligent transportation systems; reaction delay; driver vehicle delay

Subjects: Knowledge engineering techniques; Neural computing techniques; Traffic engineering computing

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