Abstract:
Lanc-changing behavior models are important components of microscopic traffic
simulation tools. Particularly, heterogeneity of traffic can significantly affect lanechanging
behavior. The objective of this thesis is to analyze lane changing factors and
related decision making sequences to develop a framework for modeling the lanechanging
behavior in local traffic scenario. The intention is to provide an improved
lane-changing model with a generalized and flexible structure that will be capable of
providing the lane-changing behaviors of drivers in differing situations. The proposed
model is generalized to overcome to a certain extent the limitations of the existing
lane-changing models and fill the present gap in this arena. It is also intended to
provide means to train and validate the model parameters using local traffic and
behavioral data and also for cross-checking and calibration facilities. The proposed
modcl is built using the Adaptive Neuro-Fuzzy Interface System (ANFIS).
Various membership functions of lanc changing parameters were documented.
Assignment of mcmbership values for each class was done such that the obtained
output gave a realistic measure of the likely outcome of different factor combinations
to reflect truly as much as practicable driver levcl decision towards lateral movement
while moving in a powered-nonpowered mix traffic stream. To train the ANFIS
model the field data acquisition method was also highlighted which used varbalisation
technique to quantify drivcr's intention towards lateral movement. Using series of
input/output data set, the model constructs a fuzzy inference system (FIS) whose
membership function parameters are tuned (adjusted) in combination of a least
squares type of method and a backpropagation algorithm. This allows fuzzy systems
to leam from the data they are modeling. In this regard, the validation, checking and
calibration methodologies are also discussed. This model could be used as an
embedded tool for traffic simulation software, particularly to mimic lane changing
bchavior utilizing local data. This model could also be used for the estimation of risks
particularly for road accidents relating to lane change and also for assessing the
relationships among lane change maneuvers, traffic delay and congestion phenomena.
