Therefore, from this perspective, reducing the vehicle speed before it entering the curve entry is an effective measure to regularize the track behavior of passenger cars.
These findings could provide a better understanding of the track behavior of passenger cars, judge the safety implications of driver behavior, and thus identify crash prone positioning and the potential mechanisms of head-on crashes, run-off-road and guardrail collisions.
Moreover, our findings may also be useful to automatic drive, which can provide helpful insight into the track behavior of passenger cars and track patterns recognition, and they could be useful in developing an anthropomorphic algorithm to simulate the driving habits of human drivers.
The model analyzes the microcosmic time behaviors of passengers and trains in detail, and is applied to different situations.
Second, we analyze different behaviors of passengers and drivers in participating taxi group rides, and explore the best incentives for TGR in order to maximize efficiency under optimal assignment.
And the behaviors of passengers with different social relation structures get micro-simulated with the utilization of hierarchical structure of personal contact network model, which clearly exhibits spatial clustering features of individual distributions.
This means that these models try to describe, analyze, and predict choice behavior of individual passengers or other decision makers (organization, household, shipper, etc).
Next, formulations are established based on AFC, timetable and train loading data, which are merged into a logit model of route choice behavior of subway passengers.
