Modeling of cyclists acceleration behavior using naturalistic data
Abstract: Over the past few years, many cities have witnessed the increasing popularity of cycling, especially among ordinary commuters. Accordingly, there has also been a fast growing demand for the knowledge of cycling performance as well as cyclist behavior, which can be valuable for both traffic planners and policy makers when it comes to the bicycle-related issues. The aim of this study, hence, is to investigate the cycling performance in detail and to further develop proper models which can be implemented in the microscopic cycling traffic simulation. The study was initiated with data collection in the summer of 2013 in Stockholm. A number of commuter cyclists were recruited and then provided with GPS devices to record their daily cycling trips. The GPS devices were portable but qualified enough to measure cyclists’ position, speed and altitude with a time interval of one second. Before the winter, around 100 natural cycling trips made in the urban area of Stockholm were collected and a database was later established to manage the raw data. Prior to the data analysis, measurement noise cancellation and profile smoothing were performed by implementing multiple processing approaches, including the robust locally weight regression and the Kalman filtering. A cycling regime which separates the cyclist behavior into three different kinds (acceleration, deceleration and cruising) was constructed based on the data observation. According to this regime, a normal cyclist should always endeavor to achieve and maintain a desired speed which varies depending on a number of factors, such as the cyclist’s own demographics and the road grade. If a cyclist’s present speed was not corresponding to her present desired speed, she would accelerate or decelerate immediately. Based on this assumption, the GPS data were classified into three parts, including dedicated datasets for acceleration profiles, deceleration profiles and cruising profiles. The profiles were analyzed statistically and some significant cycling characteristics were founded. Moreover, mathematical models were formulated to describe cyclists’ acceleration and deceleration behavior. The models were further estimated using the maximum likelihood estimator and evaluated by several goodness-of-fit measures.
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