Modeling cyclist acceleration process for bicycle traffic simulation using naturalistic data

Cycling is a healthy and sustainable form of transportation. The recent increase of daily cyclists in Sweden has triggered broad interest in finding how policies and measures may facilitate the planning of bicycle traffic in the urban area. However, in comparison to car traffic, bicycle traffic is still far from well understood. This study is part of the research effort to investigate microscopic cyclist behavior, model bicycle traffic and finally build a simulation tool for applications in transport planning. In particular, the paper focuses on representing bicycle movements when the cyclist doesn’t interact with others. The cyclist acceleration behavior is modeled using naturalistic GPS data collected by eleven recruited commuter cyclists. After filtering the large amount of data, cyclist trajectories are obtained and acceleration profiles are abstracted. A mathematical model is proposed based on the dataset, and three model forms are estimated using the maximum likelihood method with Laplace and Normal error terms. While the model with more parameters shows superior performance, the simplified ones are still capable of capturing the trends in the acceleration profiles. On the other hand, the study also introduces social economic characteristics of cyclists to explain the model parameters and they show significant effects. However, the cyclist population being investigated in the study is still limited, and more convincing results can be obtained when the data collection effort is extended to larger population with more variable cyclist characteristics.     

Bicyclists’ visual strategies when conducting self-paced vs. system-paced smartphone tasks in traffic

Visual distraction among cyclists interacting with their mobile phones is a growing concern. Yet, very little research has actually investigated how cyclists apply visual strategies and adapt task execution depending on the traffic situation. The aim of this study is to investigate visual behaviour of cyclists when conducting self-paced (initiated by the cyclist) vs. system-paced (initiated by somebody else) smartphone tasks in traffic.Twenty-two cyclists completed a track in real traffic while listening to music, receiving and making calls, receiving and sending text messages, and searching for information on the internet. The route and the types of tasks were controlled, but the cyclists could choose rather freely when and where along the route to carry out the tasks, thus providing semi-naturalistic data on compensatory behaviour.The results show that the baseline and music conditions were similar in terms of visual behaviour. When interacting with the phone, it was found that glances towards the phone mostly came at the expense of glances towards traffic irrelevant gaze targets and also led to shortened glance durations to traffic relevant gaze targets, while maintaining the number of glances. This indicates that visual “spare capacity” is used for the execution of the telephone tasks. The task type influenced the overall task duration and the overall glance intensity towards the phone, but not the mean nor maximum duration of individual glances. Task pacing was the factor that influenced visual behaviour the most, with longer mean and maximum glance durations for self-paced tasks.In conclusion, the cyclists used visual strategies to integrate the handling of mobile phones into their cycling behaviour. Glances directed towards the phone did not lead to traffic relevant gaze targets being missed. In system-paced scenarios, the cyclists checked the traffic more frequently and intensively than in self-paced tasks. This leads to the assumption that cyclists prepare for self-initiated tasks by for example choosing a suitable location. Future research should investigate whether these strategies also exists amongst drivers and other road user groups.