Perception of Safety and Liking Associated to the Colour Intervention of Bike Lanes: Contribution from the Behavioural Sciences to Urban Design and Wellbeing

Vera-Villarroel, Pablo; Contreras, Daniela; Lillo, Sebastián; Beyle, Christian; Segovia, Ariel; Rojo, Natalia; Moreno, Sandra; Oyarzo, Francisco

doi:10.1371/journal.pone.0160399

Cited by 17 publications

(11 citation statements)

References 13 publications

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“…How to deploy bike paths and bike lanes in order to create the optimal structure of bicycle infrastructures is becoming a trendy research topic. Indeed the number and the quality of bike facilities are positively related to the number of cyclists [ 4 , 5 ]. To this end, a fine understanding of cyclist behaviors is crucial to deploy bike facilities efficiently.…”

Section: Introductionmentioning

confidence: 99%

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

Magnana

Rivano

Chiabaut³

2022

PLoS ONE

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Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists’ route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In this paper, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists’ preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

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Section: Introductionmentioning

confidence: 99%

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

Magnana

Rivano

Chiabaut³

2022

PLoS ONE

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“…The way motor vehicles and bicycles are separated is also an important factor, although physical separation usually makes the rider feel safer [15], and it is difficult to implement separation at intersections. Colored pavement is a better alternative [16], as is advancing the stop line by 3-5 m, which can also reduce the interference of bicycles by motor vehicles [17].…”

Section: Introductionmentioning

confidence: 99%

Video Recognition for Analyzing the Characteristics of Vehicle–Bicycle Conflict

Xue¹,

Wang²,

Ge³

et al. 2021

Computers, Materials &Amp; Continua

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Vehicle-bicycle conflict incurs a higher risk of traffic accidents, particularly as it frequently takes place at intersections. Mastering the traffic characteristics of vehicle-bicycle conflict and optimizing the design of intersections can effectively reduce such conflict. In this paper, the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object, and T-Analyst video recognition technology was used to obtain data on riding (driving) behavior and vehicle-bicycle conflict at seven intersections in Changsha, China. Herein, eight typical traffic characteristics of vehicle-bicycle conflict are summarized, the causes of vehicle-bicycle conflict are analyzed using 18 factors in three dimensions, the internal relationship between intersection design factors and traffic conflicts is explored, and the guiding of design optimization based on the width of bicycle lanes and the soft separation between vehicles and bicycles is discussed. The results showed that colored paved bicycle lanes were better, performing better at a width of 2.5 m compared to 1.5 m. However, the colored pavement was not suitable for the entire road and had to be set at the position, at which the trajectories of a bicycle and motor vehicle overlapped. Thus, a 2.5-m-wide bicycle lane provides good safety. However, there are still defects in the existing safety indicators, so it is necessary to develop new indicators to reflect real vehicle-bicycle conflict situations more comprehensively.

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“…In Chile, coloured cycle lanes have been created with both different colours and different patterns (Vera-Villarroel et al, 2016). A survey was designed with pictures of three different patterns (full paint, chessboard-pattern or stripes) with the colour manipulated to five different colours (white, yellow, blue, red, or green), resulting in 15 different combinations.…”

Section: Introductionmentioning

confidence: 99%

“…All pictures were then shown with the same three questions regarding perceived safety, salience, and liking. They found that colour explained more of the variance than pattern and that red was consistently given higher scores on the dependent variables (Vera-Villarroel et al, 2016). The three items used were all focussed on the colour (e.g., "the colour of this bike lane intersection seems appropriate for my safety") and not on evaluations of the cycle lanes as a whole and if people ignored other aspects of the cycle lane this could have inflated any colour-specific effects.…”

Section: Introductionmentioning

confidence: 99%

Is Red the New Black? A Quasi-Experimental Study Comparing Perceptions of Differently Coloured Cycle Lanes

Karlsen

Fyhri

2020

Front. Psychol.

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Cities and road authorities in many countries have started colouring their cycle lanes. Some road authorities choose red, some blue, and some green. The reasoning behind this choice is not clear, and it is uncertain whether some colours are superior to others. The current study aims to examine whether coloured cycle lanes are viewed more positively than uncoloured lanes, and whether one of the typically chosen colours is perceived as safer and more inviting to cyclists or more deterring to motorists. Participants were invited to respond to a web survey. Respondents (N = 560) were from the four largest cities in Norway, both genders (42.1% women), and of a wide age range (79.6% between 30 and 70). Depending on cycling frequency, respondents were categorised as either motorist (n = 354) or cyclist (n = 206). All respondents rated different cycle lanes (uncoloured, blue, green, and red) on different attributes. The uncoloured lane was consistently rated least positively, with the lowest scores on visibility, perceived safety for both motorists and cyclist and how inviting it seemed to cycle in the lane. It was also estimated to be the lane that would experience the greatest degree of violation from motorists, in terms of driving or stopping in the cycle lane. The green and red lanes were consistently rated more positively than the blue lane, but whether green or red was preferred depended on whether the respondent had lived a place with coloured cycle lanes. People familiar with coloured cycle lanes, which in Norway are red, rated the red lane more positively than the green lane, while the opposite was true for people who were not used to coloured cycle lanes. The difference in ratings between different colours were similar to, or greater than, the difference from uncoloured to coloured, which implies that it not only matters that a lane is coloured, but also which colour it has.

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Perception of Safety and Liking Associated to the Colour Intervention of Bike Lanes: Contribution from the Behavioural Sciences to Urban Design and Wellbeing

Cited by 17 publications

References 13 publications

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

Video Recognition for Analyzing the Characteristics of Vehicle–Bicycle Conflict

Is Red the New Black? A Quasi-Experimental Study Comparing Perceptions of Differently Coloured Cycle Lanes

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