Journal of Cycling and Micromobility Research最新文献

Cyclists prefer to use infrastructures that separate them from motorized traffic. Using a traffic light to segregate car and bike flows, with the addition of bike-specific green phases, is a lightweight and cheap solution that can be deployed dynamically to assess the opportunity of a heavier infrastructure such as a separate bike lane. To compensate for the increased waiting time induced by these new phases, we introduce in this paper a deep reinforcement learning solution that adapts the green phase cycle of a traffic light to the traffic. Vehicle counter data are used to compare the DRL approach with the actuated traffic light control algorithm over whole days. Results show that DRL achieves better minimization of vehicle waiting time at every hours. Our DRL approach is also robust to moderate changes in bike traffic. The code used for this paper is available at : https://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists

Electric pedal-assist bikes (e-bikes) are an emerging technology that aims to enhance cycling by incorporating battery-powered motors activated while pedalling. To promote cycling effectively, it is crucial to understand the factors that influence cyclists’ route choice behaviour. This study investigates individual route choice behaviour among cyclists, taking into account their bike type (i.e., e-bikes and regular bikes). Data collected through a stated preference (SP) survey in Finland is analysed using discrete choice models to compare the differences between e-bike and regular bike users’ route choice behaviour. The study also compares the outputs of multinomial and mixed Logit models for both e-bike and regular bike users to address the impact of error correlation in SP data. Furthermore, by employing a classification approach, the study examines the differences between the expected and actual behavioural changes upon using e-bikes, referred to as the expectation–reality gap, in terms of route choice behaviour. Our research findings highlight certain factors that consistently promote cycling among both regular bike and e-bike users, specifically, low interaction with traffic, fewer intersections, and the presence of separated bike facilities. Also, our findings imply that the SP survey is well-designed to capture the preferences of the individuals. Hence, the observations are not severely correlated, i.e., errors can be assumed to be independently and identically distributed. Furthermore, we show that regular bike and e-bike users with similar characteristics do not share similar beliefs regarding the effects of e-bikes on their cycling habits.

Cycling is good for health and the environment, and urban traffic planning is increasingly focused on making cycling easier and safer for everyone. Cycling in rural areas also has a high potential for increased cycling, but fundamental knowledge about rural road cycling is missing. This study aims to investigate the prevalence of rural cycling and the perceptions of cyclists and motorists on rural roads, especially in overtaking situations. Cross-sectional data from three surveys with 1899 respondents in total were used, whereof one survey provides results representative for Swedish adults in terms of age, gender, income, educational level and geographical regions. In total, 38 % of the respondents use rural roads as cyclists or pedestrians, 10 % never use rural roads and the remaining 52 % use rural roads as motorists only. Out of the latter, 44 % stated that they would like to cycle on rural roads, but do not do so for various reasons. The way overtaking manoeuvres are experienced differs between motorists with and without cycling experience. Qualitative analyses revealed which factors make cyclists uncomfortable during overtaking manoeuvres on rural roads. These should be considered when developing recommendations for appropriate overtaking manoeuvres. Examples of application are legislation, motorist information campaigns, driver coaching and future vehicle automation systems.

In Sweden, the transport sector accounts for 32% of greenhouse gas emissions, with passenger cars contributing to 62% of these. In this context, electric bikes, commonly known as e-bikes, have emerged as a promising solution for reducing carbon emissions in the transport sector. This paper explores the potential of e-bikes in substituting passenger car trips and reducing transportation-related emissions. To achieve this objective, we use a synthetic population in the Västra Götaland (VG) region, Sweden, with daily activity schedules and simulate an average weekday of travelling with e-bikes instead of their private cars. For assessing the potential for e-bike substitution, the current literature often relies on trip-level analysis, which does not adequately consider people’s daily travel-activity plans, resulting in an unrealistic estimation of replaceable trips and their carbon emissions reduction. Combining an e-bike speed model by agents’ characteristics and an open-source routing engine, our simulation identifies potential car trips that can be replaced with e-bikes, considering all activities and the travel between them for an average weekday. The simulation results suggest that e-bikes could replace 57.6% of car trips. Building on this, we explore the potential reduction in greenhouse gas emissions from car trips taken by residents in the study area. If the top 70% of feasible car users, ranked by shortest to longest daily travel distances, switch to e-bikes, emissions could be reduced by 10.1% compared to 2018 levels. If all feasible car users adopt e-bikes, a reduction of up to 22.8% in emissions could be achieved, representing the upper limit presented by our study. The findings also reveal that males under 40 years old provide the highest e-bike substitution rates in their daily activity schedules, and in areas with a high population density, replaceable car trips are more common than in rural areas. This research provides valuable insights into e-bike substitution and its impact on emission reduction. It contributes to the existing literature through its modelling approach that realistically considers individuals’ socio-demographic characteristics and daily activity schedules when assessing the substitution potential.