ON THE MULTIDISCIPLINARY DYNAMICS OF TRAFFIC SCIENCE
J A Michon
Abstract
J A Michon
Abstract
This paper considers the dynamic aspects of the role psychology plays, or might play, as one of the pillars supporting the framework of the traffic sciences. The basic position is that, like other branches of science, traffic science is subject to an alternating movement, first away from and then again towards the acceptance of or mental processes as determinants of behavior. Since the science of psychology precisely studies these internal processes, it's perceived significance in the field as such is traffic safety varies depending on the phase of this title movement: sometimes it appears to provide guidance while its service at other times as no more than a (perhaps not even very convenient) aid. The two basic types of models that psychologist normally used for the description and explanation of behavior are the input-output models and the cognitive processing models. The input-output models (also known as stimulus-response models) or close relatives of the conventional models one will see traditionally applied in the various domains of traffic science, such as the widely used traffic stream models are the travel demand models. As such the input-output approach is almost devoid of psychological content, even when it is pursued by psychologists themselves. Cognitive (information processing) models, on the other hand, make detailed assumptions about how perceptions, decisions, and actions of travelers come about, in a way that has, until recently, hardly ever been considered in modeling practice. The current trend in traffic and transportation policy towards greater attention for lifestyle and the other personal determinants of travel puts a greater demand on such detailed processing models.
OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This paper considers the dynamic aspects of the role psychology plays, or might play, as one of the pillars supporting the framework of the traffic sciences. The basic position is that, like other branches of science, traffic science is subject to an alternating movement, first away from and then again towards the acceptance of or mental processes as determinants of behavior. Since the science of psychology precisely studies these internal processes, it's perceived significance in the field as such is traffic safety varies depending on the phase of this title movement: sometimes it appears to provide guidance while its service at other times as no more than a (perhaps not even very convenient) aid. The two basic types of models that psychologist normally used for the description and explanation of behavior are the input-output models and the cognitive processing models. The input-output models (also known as stimulus-response models) or close relatives of the conventional models one will see traditionally applied in the various domains of traffic science, such as the widely used traffic stream models are the travel demand models. As such the input-output approach is almost devoid of psychological content, even when it is pursued by psychologists themselves. Cognitive (information processing) models, on the other hand, make detailed assumptions about how perceptions, decisions, and actions of travelers come about, in a way that has, until recently, hardly ever been considered in modeling practice. The current trend in traffic and transportation policy towards greater attention for lifestyle and the other personal determinants of travel puts a greater demand on such detailed processing models.
Key concepts: Perception, Multidisciplinary approach, Cognition, Behavioural sciences, Psychology, Cognitive psychology, Computer science, Data science