Abstract
Merging behavior is inevitable for drivers at on-ramp bottlenecks and a significant factor in triggering a traffic breakdown. Empirical data were collected by extracting trajectories from merging vehicles and adjacent vehicles at two on-ramp bottlenecks in Shanghai, China. These data included 58 normal (free-flow) lane changes (NLCs), 36 cooperative lane changes (CLCs), 135 forced lane changes (FLCs), and 188 unsuccessful lane changes (USLCs). The objective was to develop and compare five discrete choice models (two multinomial logit and three nested logit) to understand merging behavior at on-ramp bottlenecks better. Estimation results showed that the two-level nested logit model considering three merging types (NLC, CLC, and FLC) provided the best fit. The traffic flow condition (bottleneck), the time gap and the space gap of the lag vehicle, and the speed of the merging vehicle were key factors when choosing merging types. The resulting quantitative models can be used to perform a microscopic analysis of the breakdown mechanism and develop a traffic simulation model.
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