A mechanism to enhance bicycle conspicuity and visibility and increase detection distances: New insights into bicycle safety

Poor sensory conspicuity and poor visibility of bicycles are key factors that correlate strongly with bicycle-vehicle accidents. Although researchers have explored how to improve detection distances, i.e., the distances from which bicycles can be recognized by other road users, there is a dearth of research on ways to signal bicyclists' presence on the road. This study investigates how to enhance, at minimum cost, the level of visibility and sensory conspicuity of bicycles; it also considers ways to signal their presence to other road users, without necessitating any active behavior by bicyclists themselves. In the first study, the level of visibility of 6 rear-end components of bicycles was analyzed according to Adrian's model; the sensory conspicuity of these same components was analyzed via respondent perceptions in conditions of sunlight, twilight with no car headlights, twilight with car headlights, and night with car headlights. The level of visibility and sensory conspicuity of the 6 rear-end components were compared with considering angular size of the components under 4 lighting conditions. The level of visibility of the rear fender was good under sunlight and night-time conditions; in other conditions, the level of visibility was directly affected by painting the fender a silver color with reflectivity and also by the fender's angular size. However, the rear tire, among the 6 components tested, had a higher visible area when used with a short fender; it also produced rotational effects during riding conditions with no extra effort by the cyclists. In the second study, adhesive tape with specific patterns and 6 different color combinations were applied to the rear tire of a bicycle under the same lighting conditions, with the aim of creating a strong signal of the bicycle's presence for other road users. Among the 6 combinations, white stripes overlaid on the color red provide an optimal combination in terms of detection distance. The mean detection distance of white stripes on red in sunlight was 138.67 m, 94.67 m in twilight without car headlights, 94 m in twilight with car headlights, and 53.67 m at night with car headlights. In addition, this combination strongly signals the presence of the bicycle to other road users with no extra effort by the cyclists, thereby reducing the likelihood of drivers looking but failing to see bicycles. In sum, the study recommends that bicyclists install white stripes overlaid on red, in order to increase visibility and conspicuity and signal the presence of their bicycles, thereby reducing the likelihood of cyclist-vehicle collisions.     

Recognising customers' patterns of bus service patronage using a loyalty framework

With the aim of identifying typical characteristics of travellers, traditional segmentation approaches were based on socio-demographic variables. However, the approaches could not reveal the factors motivating individual behaviour. This result led to an emerging interest in psychological research models that are adhered to the decision-making process. Among various related theories, the concept of loyalty was attractive because the major purpose of establishing a loyalty concept is to recognise a customer's pattern towards a given service. However, there were few efforts aimed at determining market segments based on a loyalty framework. In addition, there was no consensus achieved on theoretical loyalty typology due to different empirical findings in different market contexts. This study aims to be the first loyalty-based attempt to provide an operational method of segmenting bus service market. Seeking practical implementation, another focus of this study is to determine typical characteristics of the market segments. Analyses that included cluster techniques were conducted on questionnaire data collected from 333 respondents in Hidaka city, Japan. A cross-classification between relative attitude and service patronage was successfully established, dividing the market into four segments. Segments of loyalty and no-loyalty were observed to be dominant over the remaining market. In contrast, the spurious loyalty segment was small and insignificant. An expansion of the latent loyalty segment was also observed when moving from the intention phase to the actual behaviour phase. Notably, not only demographic factors but also social awareness variables including environmental concern and elderly support were observed to be significant in distinguishing customer segments from one another.     

Modeling cyclists' facility choice and its application in bike lane usage forecasting

In some circumstances on streets equipped with new bike facilities, cyclists are not interested in using them. Instead, they continue to use shared spaces with pedestrians or motor vehicles. Thus, simply adding a bike facility does not guarantee that cyclists will switch to using it. Owing to the considerable development of bike facilities, the investigation of facility preference, particularly focusing on facility choice forecast, has become increasingly important. This study developed a model for predicting the facility choice of cyclists between on-street facilities (curb, traffic lane, and bike lane (BL)) and off-street facilities (sidewalks). Initially, the optimal model was selected using Bayesian Model Averaging method. Then, it was validated by both internal and external validations. Apart from the aforementioned factors, several other exogenous variables were also found to be significant predictors of bike facility choice, including the width of traffic lanes, existence of real-time stopping vehicle, type of bike, bus stop existence, and in-group cycling. Analysis of the relative importance of predictors indicated that bus stop existence, effective sidewalk width, and type of bike were the potential predictors. A framework for predicting BL usage, if it is present, was also developed. A test for the predictive performance of the application at a real site was carried out. By comparing predicted and actual BL usage figures, the analysis showed good predictive performance. The results of this study can help developers, planners, and designers to adopt reasonable investment decisions as well as better designs in developing new bike facilities.