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- W659384295 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 growingdemand for the knowledge of cycling performance as well as cyclist behavior, which can bevaluable for both traffic planners and policy makers when it comes to the bicycle-relatedissues. The aim of this study, hence, is to investigate the cycling performance in detailand to further develop proper models which can be implemented in the microscopic cyclingtraffic simulation.The study was initiated with data collection in the summer of 2013 in Stockholm. Anumber of commuter cyclists were recruited and then provided with GPS devices to recordtheir daily cycling trips. The GPS devices were portable but qualified enough to measurecyclists’ 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 anda database was later established to manage the raw data. Prior to the data analysis, measurementnoise cancellation and profile smoothing were performed by implementing multipleprocessing 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 maintaina desired speed which varies depending on a number of factors, such as the cyclist’s owndemographics and the road grade. If a cyclist’s present speed was not corresponding toher present desired speed, she would accelerate or decelerate immediately. Based on thisassumption, the GPS data were classified into three parts, including dedicated datasets foracceleration profiles, deceleration profiles and cruising profiles. The profiles were analyzedstatistically and some significant cycling characteristics were founded. Moreover, mathematicalmodels were formulated to describe cyclists’ acceleration and deceleration behavior.The models were further estimated using the maximum likelihood estimator and evaluatedby several goodness-of-fit measures." @default.
- W659384295 created "2016-06-24" @default.
- W659384295 creator A5013446399 @default.
- W659384295 date "2014-01-01" @default.
- W659384295 modified "2023-09-26" @default.
- W659384295 title "Modeling of cyclists acceleration behavior using naturalistic data" @default.
- W659384295 hasPublicationYear "2014" @default.
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