Lateral Movement Decision Model for Powered Two-Wheelers in Taiwan

Abstract

The maneuvering of powered two-wheelers (PTWs) is non-lane-based, and the interactions between PTWs and other vehicles in mixed traffic are complex. The behavior of PTWs, particularly in developing countries, is not the same as that of passenger cars, which has been studied for several decades. This study contributes to the existing literature on PTWs in several ways. A real-world microscopic vehicle trajectory database has been digitalized from a video clip for a four-lane urban arterial in Taipei (Taiwan). From a modeling perspective, a dynamic discrete choice model is proposed to capture the lateral-shifting behaviors of PTWs in mixed traffic conditions. The model considers not only the positions of all surrounding vehicles but also riders’ perception of expected traffic conditions. The behavioral model is able to describe the sequential non-lane-based maneuvers of PTWs, including not following lane discipline, filtering, moving abreast of other vehicles in the same lane, oblique following, swerving, and overtaking from the same lane. The results indicate that a better model fit is obtained when taking into account the dynamic nature of the problem. The data and methods proposed have the potential to improve microscopic traffic simulation of mixed traffic with a large amount of PTWs. Practical applications are concerned with the improvement of road geometries and the definition of control strategies for mixed traffic flows.

Kinematic Characterization of Risky Riding Behavior of On-Demand Food-Delivery Motorcyclists in Taiwan

“…Compared to a car driver, whose movement is relatively more constrained within a traffic lane, a motorcyclist occupies less road space, can ride with another peer motorcyclist within the same traffic lane, and can also perform lateral movement more easily and even undertake weaving maneuvers. This finding is along the same lines as those in the current literature on mixed traffic composed of motorcycles and automobiles ( 33 , 45 , 50 ).…”

“…Following Lee et al (44), we defined a weaving maneuver in this study as when a vehicle k, which is at the side (either left-hand or righthand side) of another vehicle v at time = T, was behind the vehicle v at any time points in the past 5 s (Figure 2a). In this definition, the behindness is strictly defined by whether the central front end of vehicle k was located between the virtual range formed by the lines extended from the rear right end and rear left end of the leading vehicle v. It should be noted that we assumed a buffer distance of 0.5 m to enlarge the virtual range because motorcycles do not strictly follow traditional lane discipline and can travel alongside or weave by another vehicle in the same lane (45). To operationalize this analysis on the weaving maneuver, we derived weaving maneuver frequencies (i.e., how many weaving maneuvers were performed) and employed the chi-square test to compare weaving maneuver frequencies by driver group (i.e., ODFD versus non-ODFD motorcyclists) and by time (i.e., non-meal-peak hours and meal-peak hours).…”

Lane-Filtering Behavior of Motorcycle Riders at Signalized Urban Intersections

“…In addition to the aforementioned review of instrumented vehicle studies on driving behavior, several studies have collected the riding behavior of motorcycle riders using video cameras installed on road infrastructures and buildings, including maneuvering behavior along mixed traffic urban arterials [25][26][27][28]. However, only one study has been conducted on maneuvering behavior in queues at signalized urban intersections [5].…”

Predicting Choice of Filtering of Motorized Two Wheelers in Urban Mixed Traffic

“…A lane selection model was developed in the form of a multinomial logit model, which could predict correctly about 78% of the maneuvers. Other similar studies (12)(13)(14)(15) have also shown promising results, however, these studies have just focused on the lateral behavior of the MTWs, and paid little to no attention to modeling the longitudinal behavior which eventually strongly affects the filtering prediction by MTWs. There has been some research focusing on ''overtaking'' behavior of MTWs, which specifically includes ''on-the-fly'' maneuvers and ''oblique'' (or multi-stage) maneuvers (7,(16)(17)(18)(19)(20)(21).…”