Comparative analysis of AMF, JSON and XML technologies for data transfer between the server and the client

Abstract

Freight trucks are an important component of the nation’s highway traffic. Due to their physical and operational characteristics, they can significantly impact traffic system performance, safety, and the travel experience of non-truck drivers. Methodological gaps exist in the literature on modeling car–truck interactions that do not result in crashes, especially those resulting from non-truck driver behavior. This paper focuses on the modeling of the behavior of non-truck drivers in the vicinity of trucks to capture these interactions. This is done by quantifying a time-dependent “discomfort level” for every non-truck driver interacting with trucks in the ambient traffic stream. The driver socioeconomic characteristics and situational factors that affect this discomfort are identified through a stated preference survey of non-truck drivers and a preliminary analysis of the survey data using a discrete choice model. A fuzzy logic based approach is proposed to determine the en-route time-dependent non-truck driver discomfort level. This is used in conjunction with the car-following and lane-changing logics of a traditional traffic flow model to generate a truck-following model and a modified lane-changing model in the vicinity of trucks. An agent-based freeway segment traffic flow simulator is constructed using the extended microscopic flow modeling logic. It provides a simulation-based framework to analyze alternative strategies to mitigate car–truck interactions. Experiments are conducted to analyze the sensitivity of the discomfort level to the causal variables, and evaluate the effectiveness of alternative mitigation strategies.