Modelling cyclists' comfort zones from obstacle avoidance manoeuvres

Modeling collision avoidance maneuvers for micromobility vehicles

INTRODUCTION

In recent years, as novel micromobility vehicles (MMVs) have hit the market and rapidly gained popularity, new challenges in road safety have also arisen. There is an urgent need for validated models that comprehensively describe the behavior of such novel MMVs. This study aims to compare the longitudinal and lateral control of bicycles and e-scooters in a collision-avoidance scenario from a top-down perspective, and to propose appropriate quantitative models for parameterizing and predicting the trajectories of the avoidance-braking and steering-maneuvers.

METHOD

We compared a large e-scooter and a light e-scooter with a bicycle (in assisted and non-assisted modes) in field trials to determine whether these new vehicles have different maneuverability constraints when avoiding a rear-end collision by braking and/or steering.

RESULTS

Braking performance in terms of deceleration and jerk varies among the different types of vehicles; specifically, e-scooters are not as effective at braking as bicycles, but the large e-scooter demonstrated better braking performance than the light one. No statistically significant difference was observed in the steering performance of the vehicles. Bicycles were perceived as more stable, maneuverable, and safe than e-scooters. The study also presents arctangent kinematic models for braking and steering, which demonstrate better accuracy and informativeness than linear models.

CONCLUSIONS

This study demonstrates that the new micromobility solutions have some maneuverability characteristics that differ significantly from those of bicycles, and even within their own kind. Steering could be a more efficient collision-avoidance strategy for MMVs than braking under certain circumstances, such as in a rear-end collision. More complicated modeling for MMV kinematics can be beneficial but needs validation.

PRACTICAL APPLICATIONS

The proposed arctangent models could be used in new advanced driving assistance systems to prevent crashes between cars and MMV users. Micromobility safety could be improved by educating MMV riders to adapt their behavior accordingly. Further, knowledge about the differences in maneuverability between e-scooters and bicycles could inform infrastructure design, and traffic regulations.

The relationship between cycle track width and the lateral position of cyclists, and implications for the required cycle track width

INTRODUCTION

Sufficient cycle track width is important to prevent single-bicycle crashes and collisions between cyclists. The assumptions on which the minimum width is based in guidelines is founded on only a few studies. The aim of the present study is to investigate the relationship between cycle track width and lateral position of cyclists.

METHOD

We conducted an experiment to evaluate the lateral position of cyclists along cycle tracks with different widths (Study 1). Participants cycled on an instrumented bicycle with a LIDAR to measure their lateral position. Five conditions were defined: cycle track width of 100 cm, 150 cm and 200 cm without interaction, and cycle track width of 150 cm and 200 cm with an oncoming cyclist simulated by a parked bicycle. The cross-sectional Study 2 is based on the collected lateral position measurements at cycle tracks with varying width reported in Dutch studies since 2010.

RESULTS

The experimental Study 1 with 24 participants shows that an increase in cycle track width causes cyclists to ride further away from the verge and keep more distance from an oncoming cyclist. The cross-sectional Study 2 was based on lateral position measured at 33 real-life Dutch cycle tracks. Study 2 yielded similar results, indicating that doubling pavement width increases lateral position by some 50%. Study 2 shows that, compared with a solo cyclist without interaction, a right-hand cyclist of a duo and a cyclist meeting an oncoming cyclist ride around 30% closer to the verge.

CONCLUSIONS

The wider the cycle track, the more distance cyclists maintain from the verge. Cyclists ride closer to the verge due to oncoming cyclists.

PRACTICAL APPLICATIONS

Given a cyclists' lateral position while meeting, common variations between cyclists' steering behavior, and vehicle width and circumstances, a cycle track width of 250 cm is needed for safe meeting maneuvers.

A UWB/INS Trajectory Tracking System Application in a Cycling Safety Study

This paper focuses on the safety issue for cyclists and pedestrians at unsignalized intersections. The cycling speed needs to be calmed when approaching the intersection. This study proposes and deploys an integrated portable ultra-wideband/inertial navigation system (UWB/INS) to extract cycling trajectories for a cycling safety study. The system is based on open-source hardware and delivers an open-source code for an adaptive Kalman filter to enhance positioning precision for data quality assurance at an outdoor experimental site. The results demonstrate that the system can deliver reliable trajectories for low-mobility objects. To mitigate accident risk and severity, varied cycling speed calming measures are tested at an experimental site. Based on the trajectory data, the statistical features of cycling velocities are evaluated and compared. A new proposed geometric design is found to be most effective when compared with conventional traffic signs.

How do different micro-mobility vehicles affect longitudinal control? Results from a field experiment

INTRODUCTION

While micromobility vehicles offer new transport opportunities and may decrease fuel emissions, the extent to which these benefits outweigh the safety costs is still uncertain. For instance, e-scooterists have been reported to experience a tenfold crash risk compared to ordinary cyclists. Today, we still do not know whether the real safety problem is the vehicle, the human, or the infrastructure. In other words, the new vehicles may not necessarily be unsafe; the behavior of their riders, in combination with an infrastructure that was not designed to accommodate micromobility, may be the real issue.

METHOD

In this paper, we compared e-scooters and Segways with bicycles in field trials to determine whether these new vehicles create different constraints for longitudinal control (e.g., in braking avoidance maneuvers).

RESULTS

The results show that acceleration and deceleration performance changes across vehicles; specifically, e-scooters and Segways that we tested cannot brake as efficiently as bicycles. Further, bicycles are experienced as more stable, maneuverable, and safe than Segways and e-scooters. We also derived kinematic models for acceleration and braking that can be used to predict rider trajectories in active safety systems.

PRACTICAL APPLICATIONS

The results from this study suggest that, while new micromobility solutions may not be intrinsically unsafe, they may require some behavior and/or infrastructure adaptations to improve their safety. We also discuss how policy making, safety system design, and traffic education may use our results to support the safe integration of micromobility into the transport system.

A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials

INTRODUCTION

Recent advances in technology create new opportunities for micro-mobility solutions even as they pose new challenges to transport safety. For instance, in the last few years, e-scooters have become increasingly popular in several cities worldwide; however, in many cases, the municipalities were simply unprepared for the new competition for urban space between traditional road users and e-scooters, so that bans became a necessary, albeit drastic, solution. In many countries, traditional vehicles (such as bicycles) may not be intrinsically safer than e-scooters but are considered less of a safety threat, possibly because-for cyclists-social norms, traffic regulations, and access to infrastructure are established, reducing the number of negative stakeholders. Understanding e-scooter kinematics and e-scooterist behavior may help resolve conflicts among road users, by favoring a data-driven integration of these new e-vehicles into the transport system. In fact, regulations and solutions supported by data are more likely to be acceptable and effective for all stakeholders. As new personal-mobility solutions enter the market, e-scooters may just be the beginning of a micro-mobility revolution.

METHOD

This paper introduces a framework (including planning, execution, analysis, and modeling) for a data-driven evaluation of micro-mobility vehicles. The framework leverages our experience assessing bicycle dynamics in real traffic to make objective and subjective comparisons across different micro-mobility solutions. In this paper, we use the framework to compare bicycles and e-scooters in field tests.

RESULTS

The preliminary results show that e-scooters may be more maneuverable and comfortable than bicycles, although the former require longer braking distances.

PRACTICAL APPLICATIONS

Data collected from e-scooters may, in the short term, facilitate policy making, geo-fencing solutions, and education; in the long run, the same data will promote the integration of e-scooters into a cooperative transport system in which connected automated vehicles share the urban space with micro-mobility vehicles. Finally, the framework and the models presented in this paper may serve as a reference for the future assessment of new micro-mobility vehicles and their users' behavior (although advances in technology and novel micro-mobility solutions will inevitably require some adjustments).

Influencing factors of observed speed and rule compliance of speed-pedelec riders in high volume cycling areas: Implications for safety and legislation

Speed pedelecs (s-pedelecs) are electric bicycles offering pedal assistance up to 45 km/h. S-pedelecs may contribute to a more efficient and green traffic system. However, their potential to reach high speeds has raised road safety concerns. In the Netherlands a new legislation bans s-pedelecs from bicycle paths in urban areas. On the roads with a maximum speed limit of 50 km/h with adjacent bicycle paths, s-pedelec riders must use the roadways instead of the bicycle path. The impact of this legislation on the behaviour of s-pedelec riders and other road users as well as the possible consequences for road safety are yet unknown. Therefore, this naturalistic riding study investigated the safety-relevant behaviours of s-pedelec riders, i.e. speed characteristics while riding on the roadway, the extent of non-compliance with the ban on using bicycle paths, and speed and speed adaptation while using bicycle paths. Furthermore the study explored factors possibly influencing rider behaviour (the s-pedelec's motor-power, riders' beliefs and perceptions) as well as negative reactions of other road users encountering s-pedelec riders. 28 participants used a s-pedelec (a 350 W type or a 500 W type) for everyday trips for at least a fortnight. The s-pedelecs were equipped with two action cameras with integrated sensors and GPS. The results showed that mean speed on 50 km/h roadways was 31.8 km/h, which is far below the road's speed limit. The mean speed did not differ between s-pedelec types, but the speed distribution did. The '500 W riders' travelled 31.7% of the total distance in the 41-50 km/h speed band, as compared to 6.9% of the '350 W riders'. Furthermore the 500 W riders evaluated riding on the roadway more positively than the 350 W riders. On the roadway s-pedelec riders experienced signals of hinderance of the traffic flow (on average every 2 km) and negative reactions from drivers (on average every 27.5 km). As for non-compliance riders covered on average 22.5% of the distance on bicycle paths. The more the riders disagreed with the new legislation, the more distance they covered on the bicycle path. Mean speed on bicycle paths was 28.5 km/h, and it was significantly higher for 500 W riders than for 350 W riders. Speeds between 41 and 50 km/h were also far more common for 500 W riders (14.9% of the distance) than for 350 W riders (0.5%). Compared to the roadway 350 W riders reduced their speed on the bicycle paths to a higher extent (from 31.4 to 25.7 km/h) than 500 W riders did (from 31.9 to 30.5 km/h). The frequency of harsh braking of s-pedelec riders was low and did not differ between the roadway and the bicycle paths. In conclusion, s-pedelec riders in the Netherlands frequently ride on the bicycle paths although it is illegal. On the bicycle paths their speeds are much higher than those of conventional cyclists. On the 50 km/h-roadways, however, s-pedelec riders are apparently too slow for the traffic conditions. Overall, the speed profiles of 350 W types were better suited to the bicycle paths, whereas those of 500 W types to the roadways.

Speed characteristics of speed pedelecs, pedelecs and conventional bicycles in naturalistic urban and rural traffic conditions

To assess the potential impact of the higher speeds of pedal-assisted bicycles on safety, this study compared conventional bicycles, pedelecs and speed pedelecs (hereafter called s-pedelecs) on mean speeds, speed variability, harsh braking events (decelerations > 2 m/s²), and mean speeds above the speed limit (MSAL) in rural and urban areas in the Netherlands Data were collected in daily traffic, while the legal maximum speed for speed-pedelecs was 25 km/h, and pedelecs and s-pedelecs shared the infrastructure with conventional bicycles. Data were collected, using two-wheelers equipped with accelerometers and GPS. Personality factors - sensation seeking and risk taking - were measured with surveys. Regular commuters used one of the three bicycle types for two weeks. Participant bias was intentionally included by allowing participants to select a bicycle type of their preference, resulting in 12 conventional bicycle riders (71 % women), 14 pedelec riders (67 % women) and 20 s-pedelec riders (25 % women). S-pedelecs were much faster than conventional bicycles, amounting to a speed difference with conventional bicycles of 10.4 km/h in urban areas (M =28.2 km/h vs. 17.8 km/h) and of 13.2 km/h in rural areas (M = 31.4 km/h vs. 18.2 km/h). The speed differences between pedelecs and conventional bicycles were much smaller: 2.3 km/h in urban areas (20.1 km/h vs 17.8 km/h) and 4 km/h in rural areas (22.2 km/h vs. 18.2 km/h). Compared to conventional bicycles, s-pedelecs varied their speed to a greater extent and also braked harshly more frequently, showing a greater need for speed adjustment. These adjustments were larger at higher speeds. In contrast, pedelecs did not differ from conventional bicycles on speed variation. MSAL for s-pedelec riders differed by gender. For men the MSAL was 87 % on urban sections and 91 % on rural sections. For women, the MSAL was lower, respectively 23 and 69 %. None of the personality factors were associated with speed variability, harsh braking or MSAL. However, sensation seeking was associated with higher mean speeds on all three bicycle types. To conclude, pedelecs and conventional bicycles are similar in speed patterns, whereas the speed patterns of s-pedelecs differ significantly from the former two. The safety implications are discussed.