Increasing the conspicuity of cyclists at night by using bicycle lights and clothing to highlight their biological motion to oncoming drivers

Reduced conspicuity is an important contributing factor to increased motor vehicle-bicycle crashes and cyclist fatalities at night. This study explored ways of making night-time cyclists more conspicuous to oncoming vehicles through cyclist clothing options and bicycle light position. Fifteen young participants (M = 29.1 ± 4.5 years) drove a closed-road at night and indicated when they first recognized a cyclist pedalling in place at the roadside, facing the approaching vehicle. The bicycle had a static forward-facing white light located either on the handlebars or the helmet and wore one of five clothing configurations: fluorescent vest, retroreflective vest, retroreflective ankle strips, retroreflective vest plus retroreflective ankle strips, retroreflective vest plus biomotion retroreflective strips. There was a significant effect of light position (p < 0.001) and clothing (p < 0.001), where recognition distances were longer when the light was helmet-mounted and when retroreflective strips were worn on the lower legs to highlight the cyclist’s biological motion compared to either of the vest conditions. There was also a significant interaction between light position and clothing (p = 0.007) such that the benefit of retroreflective strips was greater when the bicycle light was helmet-mounted rather than on the handlebars. Importantly, the benefits of leg markings were apparent even when positioned at the ankles alone. These findings highlight that retroreflective material is most useful for improving cyclist conspicuity at night when positioned on cyclists’ lower legs, particularly the ankles (highlighting the cyclist's pedalling motion), rather than on the torso, and have important practical implications for maximising cyclists’ conspicuity and hence safety on night-time roads.     

An investigation of behaviour and attitudes relevant to the user safety of pedestrian/cyclist shared paths

Bicycle/pedestrian shared paths are an increasingly popular solution to providing cycling infrastructure, despite evidence suggesting safety issues. Improved design and management of shared paths should be informed by understanding of bicyclist and pedestrian behaviour on shared paths, and of relevant safety initiatives (e.g. centreline marking). However, relevant research is lacking. Bicyclist/pedestrian passing events (n = 407) were observed on three relatively busy shared paths in Sydney, Australia – one of them without centreline marking. User characteristics, relevant behaviours and incidents (aggression, near-misses, crashes) were recorded. A tendency toward left-hand travel, as on Australian roads, was stronger for cyclists than for pedestrians, and where centreline was present. Cyclists were often estimated to travel above 10 km/h, a speed limit that has been suggested based on pedestrian safety considerations, but that would be unacceptable for long stretches of commuter cycling. Centreline was associated with lower estimated speeds. Cyclists typically adhered to their responsibility of giving way to pedestrians, but often passed on the left, passed too close, passed without slowing, or passed without warning (e.g. with a bell). Use of mobile telephones and mp3 players is common, particularly amongst pedestrians. Five near collisions were observed, and 53 survey participants reported 2 collisions and 13 near misses. Contributing factors appear to include path users using potentially distracting devices, or straying from the rules of thumb to keep to the left, and to overtake on the right.A survey of cyclists and pedestrians suggested that there are issues with perceptions of space ownership. These results represent an important contribution to the evidence-base for initiatives to improve the safety of shared paths.     

Cyclist efficiency and its dependence on infrastructure and usual speed

Bicyclists are a heterogeneous group, with varying abilities, traffic education and experience. While efficiency was identified as an important factor on utility bicycle trips, it might be traded for experienced safety, for example by choosing different pathways in a given situation, or by relinquishing one’s right of way. In a semi-controlled study with 41 participants, a grouping was made according to self-reported riding speed in relation to other cyclists. The participants cycled twice along a 3 km inner-city route, passing four intersections with different priority rules. The cyclists were free to choose how to negotiate the intersections. Speed and the traffic surroundings were recorded via gps and cameras on the bike of the participant and of a following experimenter. For each cyclist, the ‘base’ speed on undisturbed segments was determined as reference. Based on this, the efficiency in different types of intersections was computed per cyclist group. It turned out that infrastructural aspects, cyclist group and the presence and behaviour of interacting traffic influenced cyclist efficiency. Faster cyclists were delayed more when the infrastructure required a stop regardless of the traffic situation, like at a red traffic light or a stop sign. The members of the so-called ‘comfort cyclists’ group were delayed the most in a roundabout with mixed traffic, where many chose to get off their bike and walk. In a society working for equality of access to the transport system, it is recommended to develop solutions that consider and accommodate the behaviours of different cyclist groups when planning bicycling infrastructure.     

Cyclists’ handheld phone use and traffic rule knowledge

Phone use is likely to distract cyclists and possibly increase crash risk. Therefore, handheld phone use among cyclists is forbidden by law in some countries, even though cyclists use compensatory strategies to attempt to mitigate distractions and related effects. Both demographic, environmental, and psychological factors have been associated with cyclists’ phone use. This study extends the existing literature by including traffic rule beliefs as an explanatory measure in predicting cyclists’ handheld phone use and additionally explores how well cyclists know these rules in different legislative contexts. Online questionnaire responses were collected in 2019 among 1055 cyclists living in Denmark (N = 568), where handheld phone use for cyclists was forbidden, and in the Netherlands (N = 487), where it was legal. Responses on phone use, traffic rule knowledge, cycling behaviour, demographic, and psychological measures were used to identify factors contributing to the likelihood of handheld phone use in three regression models; one for all respondents and one for each country. In the combined model, believing there are no rules on handheld phone use increased the likelihood of handheld phone use while cycling. Other significant factors were subjective norm, perceived behavioural difficulty, self-identity as a safe cyclist as well as demographic factors. The country-specific models found that male gender was only associated with more handheld phone use in the Netherlands, while believing there was no ban was only connected to an increase in the likelihood of using handheld phone in Denmark. Correct traffic rule knowledge was almost three times higher in Denmark, where handheld phone use was forbidden. The results identify subjective norms, potential overconfidence, and traffic rule awareness (when there is a ban) as relevant factors in reducing the likelihood of cyclists’ handheld phone use. Findings from country-specific models possibly point to a connection between culture and traffic rules. Future research should focus on underlying mechanisms and awareness of traffic rules.     

Drivers’ and cyclists’ safety perceptions in overtaking maneuvers

Drivers overtaking cyclists on rural roads are a safety concern, as drivers need to handle the interaction with the cyclist and possibly an oncoming vehicle. Improving the maneuver’s outcome requires an understanding of not only the objective, measurable safety metrics, but also the subjective, perceived safety of each road user. Previous research has shown that the perceived safety of the cyclist is most at risk at the passing moment, when driver and cyclist are closest to each other. However, to develop safety measures, it is necessary to know how both road users perceive safety, by understanding the factors that influence their perceptions during the overtaking maneuver. This study measured the perceived safety of drivers in a test-track experiment in Sweden and the perceived safety of cyclists in a field test in Spain. For both drivers and cyclists, we developed Bayesian ordinal logistic regression models of perceived safety scores that take as input objective safety metrics representing the different crash risks at the passing moment. Our results show that while drivers’ perceived safety decreases when there is an oncoming vehicle with a low time-to-collision, cyclists’ perceived safety is reduced by a small lateral clearance and a high overtaking speed. Although our datasets are heterogeneous and limited, our results are in line with previous research. In addition, the Bayesian models presented in this paper are novel and may be improved in future studies once more naturalistic data become available. We discuss how our models may support infrastructure development and regulation, policymaking, driver coaching, the development of active safety systems, and automated driving by providing a possible method for predicting perceived safety.     

Conflict partners and infrastructure use in safety critical events in cycling – Results from a naturalistic cycling study

Accident statistics show that cyclists are at considerable risk of being involved in a crash. However, statistics based on police reports are often heavily biased towards on-road, bicycle–motor vehicle crashes. Crashes that do not involve motorised vehicles or that occur on other types of infrastructure are neglected. Naturalistic cycling methodology appears to be a promising approach to address these issues. The goal of this study was to identify and classify safety critical cycling events involving a variety of conflict partners and covering all types of infrastructure. Thirty-one participants in three age groups had their own bicycles equipped with a data acquisition system. Participants rode their modified bike as usual for a period of four weeks. Over 1600 trips were recorded overall. We were able to identify 77 safety critical events during the observation period. Only 43% of these events involved motorised vehicles as conflict partners. Conflicts with other cyclists and pedestrians accounted for about 57% of the situations. Likewise, less than 35% of the events occurred on-road. The data show that although motorised vehicles are still the single biggest threat to cycling safety, and roads still constitute one of the most crash prone types of infrastructure, the importance of crashes that do not involve motorised road users or occur not on-road should not be underestimated.     

Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning

The objective of this study is to model the microscopic behaviour of mixed traffic (cyclist-pedestrian) interactions in non-motorized shared spaces. Video data were collected at two locations of Robson Square non-motorized shared space in downtown Vancouver, British Columbia. Trajectories of cyclists and pedestrians involved in interactions were extracted using computer vision algorithms. The extracted trajectories were used to obtain several variables that describe elements of road users’ behaviour including longitudinal and lateral distances, speed and speed differences, interaction angle, and cyclist acceleration and yaw rate. The road users behaviour was modeled as utility-based intelligent rational agents using the finite-state Markov Decision Process (MDP) framework with unknown reward functions. The study implemented Inverse Reinforcement Learning (IRL) using two algorithms: the Maximum Entropy (ME) algorithm, and the Feature Matching (FM) algorithm to recover/estimate the reward function weights of cyclists in two types of interactions with pedestrians: following and overtaking interactions. Reward function weights infer cyclist preferences during their interactions with pedestrians in non-motorized shared spaces, and can form the key component in developing agent based microsimulation model for road users. Furthermore, the estimated reward functions were used to estimate cyclists’ optimal policy for such interactions. A simulation platform was developed using the estimated reward functions and the cyclist optimal policies to simulate cyclist trajectories for the validation dataset. Results show that the Maximum Entropy (ME) IRL algorithm outperformed the Feature Matching (FM) IRL algorithm, and generally provided reasonable results for modeling such interactions in non-motorized shared spaces, considering the high degrees of freedom in movement and the more-complex road users interactions in such facilities. This research is considered an important step toward developing a full Agent-Based Model (ABM) for road users in shared space facilities to evaluate the safety and efficiency of such facilities.     

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions.This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming.The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT.The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.     

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

The increasing prevalence of mobile phone usage while cycling has raised concerns, even though the number of cyclists involved in accidents does not increase at a comparable rate. A reason for this may be how cyclists adapt travelling speed and task execution to the current traffic situation. The aim of this study is to investigate speed adaptation among cyclists when conducting self-paced (initiated by the cyclist) vs. system-paced (initiated by somebody else) smartphone tasks in real 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 clearly show that cyclists use conscious strategies to adapt their speed to accommodate the execution of secondary phone tasks. Regarding tactical behaviour, it was found that cyclists kept on cycling in 80% of the system-paced cases and in 70% of the self-paced cases. In the remaining cases, the cyclists chose to execute the phone task while standing still or when walking. Compared to the baseline (17.6 ± 3.5 km/h), the mean speed was slightly increased when the cyclists listened to music (18.2 ± 3.7 km/h) and clearly decreased when they interacted with the phone (13.0 ± 5.0 km/h). The speed reduction profile differed between self-paced and system-paced tasks with a preparatory speed reduction before task initiation for self-paced tasks.In conclusion, when the cyclists had the chance they either stopped or adapted their speed proactively to accommodate the execution of the phone task. For self-paced tasks, the speed reduction was finalised before task initialisation, and for system-paced tasks the speed adaptation occurred in reaction to the incoming task. It is recommended to investigate whether the observed compensatory behaviour is enough to offset the possible negative effects of smartphone use.     

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.