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Modeling of cyclists acceleration behavior using naturalistic data
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. , 76 p.
TSC-MT, 14-007
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-161745OAI: diva2:795377
Available from: 2015-03-16 Created: 2015-03-16

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