Existing studies regarding lane-changing behavior mainly focus on modeling lane-changing decisions. Lane-changing execution (LCE), which happens just after the adjacent gap has been accepted by the driver who wishes to change lanes, has not been much studied. This is especially true of the lateral movement aspect of a lane change, even though the LCE has considerable influence on driving safety and traffic simulation results and is also an indispensable part of the control design of an automatic vehicle. This paper proposes the first model for LCE in the lateral direction on the basis of empirical analysis using NGSIM data. After analysis of the data, two types of LCE, the continuous LCE (CLCE) and the discontinuous LCE (DLCE), were identified. A CLCE happens when the vehicles surrounding the lane-changing vehicle do not constrain the LCE behavior, and the entire LCE process is continuous. In contrast, a DLCE occurs when the lane-changing vehicle has to defer the lateral movement for a short time to make sure that changing lanes is safe. A model is proposed for each of the two types of LCE and then calibrated and validated. The results show that the proposed models can replicate real lane-changing execution behavior in the lateral direction with small errors.
The GPS-based travel survey is an emerging data collection method in transportation planning. The survey's application in trip mode detection has been explored in many studies. Most research on trip mode detection methods based on GPS data has been developed and tested with data collected from European and American countries. The methods cannot be easily adapted to Asian countries such as China, India, and Japan, which have much higher population densities, more complex road networks, and highly mixed travel modes during daily commuting. Furthermore, for trip segment division in multimode travel, existing algorithms use travel time and distance thresholds that are highly dependent on local travel behavior and lack universality across traffic environments. This paper proposes an innovative framework for detecting trip modes in complex urban environments. First, a smartphone application, GPSurvey, was developed to collect passive GPS trace data. Then a wavelet transform modulus maximum algorithm was developed for trip segment division. The algorithm has outstanding capabilities for identifying singularity features of a signal; this factor suits the task of detecting mode changes in a complex traffic environment. A neural network module was developed for mode detection on the basis of cell phone GPS location and acceleration data. The results indicate that the proposed method has promising performance. The average absolute detection error of mode transfer time was within 1 min, and the accuracy for detecting all modes was greater than 85%.
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