Week 5 Lecture - Part 2.pdf

CVEN4405 Human Factors in Civil and Transport Engineering Week 5 lecture - Part 2 Micro-mobility, vulnerable road users, and philosophical aspects of road safety Dr Milad Haghani School of Civil and Environmental Engineering Week 5: Micro-mobility, vulnerable road users, & philosophical aspects o...

CVEN4405 Human Factors in Civil and Transport Engineering Week 5 lecture - Part 2 Micro-mobility, vulnerable road users, and philosophical aspects of road safety Dr Milad Haghani School of Civil and Environmental Engineering Week 5: Micro-mobility, vulnerable road users, & philosophical aspects of road safety *Micromobility is used to describe light travel devices that are either man powered or with electric support, where the electricity usually will not support the vehicle at higher speeds than 45 km/hr Cycling safety Bicyclist related accidents can be divided into the following five groups: 1. Collisions with motorised vehicles 2. Single accidents 3. Collisions with other bicyclists 4. Collisions with pedestrians 5. Other collisions (e.g., animals, open doors) • The most frequent accident type are single accidents. • Single accidents are often around 46%–94% of all reported bicycle accidents. • Among children, single accidents are in the range of 67%–94% of all bicycle accidents. • Collisions with motor vehicles constitute often between 2% to 45% of all bicycle related accidents. • The data seems to indicate that the majority of the bicycle related accidents are single accidents, as well as the majority of the serious injuries. • 35% of bicycle crashes leading to hospitalisation involved motor vehicles (average hospital stay of 9.2 days) and 65% were single-bicycle accidents (average hospital stay of 4.6 days). • Collisions with motor vehicles stands for majority of the fatalities, often in the range of 64%–100% of all cyclist fatalities. Cycling safety Cycling safety • The number of bicycle accidents is strongly related to exposure. • Bicycle accidents are frequent during months with favorable weather conditions. • Studies have also seen a correlation between both speed limit, estimated travel speed, speeding, mean travel speed and injury severity for bicyclists hit by motor vehicles (Höskuldur Kröyer, 2021). • Among fatally injured cyclists, 7%-21% were under the influence of alcohol. • Alcohol involvement was especially frequent in accidents during the evenings and nights. • Those who were under the influence of alcohol and/or drugs were more likely to suffer serious or fatal injuries. Cycling safety Common type of cyclist injuries: • Lower extremities • Upper extremities • Head/neck injuries • Head injuries were registered in between 72% and 77% of the fatal injuries. • In collisions with motor vehicles, the cyclists suffers head injuries both from impact with the car and the ground. • No surprise that the most well know safety equipment of cyclists is the bicycle helmet. • Meta-analyses have shown that the risk of fatal injuries, as well as head injuries and brain injuries was reduced considerable by wearing a helmet. Cycling safety Exposure as a risk factor: A region with a high level of cycling is likely to suffer more bicycle fatalities than a region with low level of cycling, everything else being equal. How increased exposure correlates with a higher number of fatalities. Cycling safety • Safety in numbers: Studies have shown that the accident rate at locations where there are many road users of a given type is usually lower than the accident rate at locations where there are few road users. • It was found that safety-in-numbers tends to be stronger for pedestrians than for cyclists, and stronger at the macro-level (e.g., citywide) than at the micro-level (e.g., in junctions). This can, with some simplification be interpreted that where there is greater volume of cyclists, each cyclist is safer. There are several theories as to why this relation exists. The most common ones are that this is due to: 1. Behavioral adaptation: more cyclists results in that those on motor vehicles are more aware of cyclists and hence adjust their behaviour, resulting in fewer accidents. 2. More cyclists will correlate with a better infrastructure and/or maintenance for bicyclists, that is, societies that have many cyclists are perhaps more likely to have a more developed bicycle infrastructure. 3. The individual and collective experience of the road users. More cyclists will result in increased general knowledge in cycling and higher skills. 4. Numbers in safety, that is, cyclists, to greater degree, choose their route from the safety of the route, or that this is partly a statistical artifact without any real relation. https://www.youtube.com/watch?v=xMilEK7YRmk https://www.youtube.com/watch?v=kf5bcCYvEt0 Cycling safety • There is limited research regarding what is the “real” cause of the safety in numbers effect. If it is a causal effect or simply a correlation. Should the explanation be 1 or 3, then we can expect that the measure of increasing the number of cyclists would have positive effect on the safety of each cyclist. Should the explanation be 2 or 4, then the reason has no direct causal relation to the number of cyclists. • There is no data available to determine if this will also apply to new micro-mobility devices. • Practitioners should not rely on this effect “solving” the bicycle safety problem for them but aim to improve the safety. Cycling safety: Self reports A psychological measure assessing cyclists’ anger experiences in traffic. • police interaction, • car interaction, • cyclist interaction, and • pedestrian interaction. https://www.youtube.com/watch?v=V1t3fX2SCzM https://www.youtube.com/watch?v=9jM1QGhUvNQ https://www.youtube.com/watch?v=9N5wibjmDA8 Cycling safety: Self reports Cycling safety: Experimental methods • Bicyclists’ perceived safety was studied in an immersive virtual reality experiment. • An experiment using a bicycle simulator combined with immersive VR. • Participants bicycled through five different environments with a bicycle simulator. • Participants felt safer when riding in the segregated bicycle path. Cycling safety • The validation study consisted of a within-subjects study design comparing participants riding on-road with riding in the simulator. • Absolute validity was established for measures of spatial positioning including average lane position, deviation in lane position and average passing distance from kerbside parked cars. • Relative validity was established for the average speed of cyclists and their speed reduction on approach to intersections. E-bike safety: Quasi-field experiments • The speed of e-cyclists has been measured to be somewhat higher compared to a regular bicyclist which might increase injury severity in accidents. • The most common e-bike accident type is single accidents. • Highlighting the influence of pedestrian crowdedness on e-bike navigation behaviour. • A fundamental relationship between e-bike speed and pedestrian crowdedness. • Comparison of passing vs meeting conditions for a mixed traffic. • Understanding microscopic behaviour of e-bike rider in shared mobility. • Modelled electric bike navigation comfort in different pedestrian crowding levels. Safer micro-mobility in shared traffic spaces A holistic, empirical and evidence-based approach Dr Milad Haghani Senior Lecturer Research Centre for Integrated Transport Innovation (rCITI) School of Civil and Environmental Engineering Project summary Micro-mobility–encompassing a range of lightweight vehicles such as (electric) bicycles and e-scooters– is becoming increasingly popular and its usage is expected to keep rising post pandemic, especially with cost-of-living expenses increasing (i.e., petrol) resulting in more people using alternative means of transport. The increase in the number of micro-mobility users has already introduced new road safety issues which will continue to escalate if not addressed. Of particular significance are the challenges of managing mixed traffic of micro-mobility users and pedestrians in shared spaces. If not understood and managed properly, such traffic settings could pose significant risks to safety and may result in the reduction of level of service/satisfaction for users in shared spaces. For example, in Victoria, 49 people have been admitted to the Alfred’s Trauma Centre in the past year as a result of incidents involving e-scooters and e-bikes, with 18 patients ending up in intensive care. With a government target of zero deaths and serious injuries on Australian roads by 2050, one cannot neglect the safety risks facing active transport users. Therefore, it is important to understand the dynamics of interactions between micro-mobility users and pedestrians, and to unmask the complexities and safety risks involved in such forms of mixed traffic. Such knowledge will offer insight essential for a range of applications, including regulation and legislation, facility design, and design of behavioural campaigns and user education. This project is taking a holistic and evidence-based approach to understand the complexities and nuances involved in pedestrians and micro-mobility user interactions. The study follows an empirical approach consistent of a variety of complementary methods (i.e., experimentation, field data analysis, surveys and computer simulations) to provide in-depth evidence-based knowledge and solutions to this issue. The outcomes of the project will provide essential knowledge and simulation tools that will highlight required areas of intervention. This can ultimately save lives and reduce serious injuries and enhance the level of service and satisfaction for all forms of active transport users. Aims and objectives 1. Provide the empirical evidence essential for understanding the complexities involved in interactions of pedestrians and micro-mobility users based on both field and experimental data. 2. Provide empirical understanding of the attitudes, behaviour, safety challenges and risk taking of escooter and bicycle users in shared spaces. 3. Provide an empirical understanding of the attitudes, safety perceptions and protective behaviour of pedestrians in shared spaces. 4. Test the effectiveness of a broad range of soft and infrastructural interventions to improve safety and comfort levels of active transport users in shared spaces. 5. Develop an analytical modelling tool founded on empirical evidence that can replicate active transport user interactions in computer simulated settings, usable for various scenario testing purposes. Phase I: Field data analysis The CCTV footage of traffic on Pyrmont Bridge -as well as any other location with shared traffic and available CCTV footage- is analysed over a given time period to better understand: 1) The variation in the demand of micro-mobility traffic during times of day 2) The variation in the demand of micro-mobility traffic between weekends and weekdays 3) The frequency of encounters and close encounters between pedestrians and bicycle/scooter riders 4) The prevalence of bicycle/scooter riders at high speed 5) The prevalence of distracted pedestrians 6) The percentage of pedestrians in social groups The aim is to obtain preliminary insight and use the observations to formulate parameters of experiment design in Phase II. Phase 2: Controlled experiments Some of the required equipment: Experiment design • Pedestrian sample size: Approximately 100 participants to be recruited • Bike and scooter riders: Approximately 9-10 participants to be recruited • 25% of pedestrians will wear a heart-rate monitor • 10% of pedestrians will be distracted on their phone when the treatment involves distraction • 10% pedestrians will be listening to an auditory source (e.g., music) when the treatment involves distraction • The pedestrian crowd in all treatments will be composed of individuals as well as social groups of various sizes • Each day involves four hours of data collections plus time for registration, preparation of scenarios, refreshment breaks and catering (in total, 6 hours) Day 1: Observational Experiments Distraction Unidirectional - Distraction Same-Direction Scooter Traffic No Distraction Unidirectional - Distraction Opposing-Direction Scooter Traffic No Distraction Unidirectional Session I No Distraction Baseline (Pedestrian Traffic Only) Distraction Session III Pedestrian & Scooter Traffic Bidirectional No Distraction Bidirectional Mixed Traffic Session II Pedestrian & Bike Traffic Unidirectional - Distraction Same-Direction Bike Traffic No Distraction Unidirectional - Distraction Opposing-Direction Bike Traffic Bidirectional Mixed Traffic Session IV No Distraction Pedestrian & Scooter & Bike Traffic No Distraction Unidirectional - Distraction Same-Direction Bike & Scooter Traffic No Distraction Unidirectional - Distraction Opposing-Direction Bike & Scooter Traffic No Distraction Distraction No Distraction Distraction Bidirectional Mixed Traffic Distraction No Distraction Day 2: Interventional Experiments Session I Baseline (Pedestrian Traffic Only) Bidirectional with a certain percentage distracted No Intervention No Intervention Direction-Specific Lane Dedication (Mixed Traffic) Auditory signals from bike and scooter riders Session II (Soft Interventions) Pedestrian & Bike & Scooter Traffic Visual signals from bike and scooter riders Session III (Infrastructure Interventions) Pedestrian & Bike & Scooter Traffic Device Specific Lane Dedication (Non-Mixed Traffic) Traffic Calming Measures Direction-Specific Lane Dedication (Speed Regulation) (Mixed Traffic) + Combination of Soft Interventions Safe distance regulation Increased/decreased pathway width Combination of all soft interventions (resulting in different densities) Examples of Infrastructure Interventions/Modifications during Day 2 Experiments Future experiment (Electric) micro-mobility • • • Micro-mobility has become a catch-all term for several modes of transportation and is becoming popular for its convenience and environmental friendliness. It will play a huge role in the transition toward Smart Cities. The term includes fully or partially human-powered of small and lightweight electric-driven vehicles, for example, bicycles, e-bikes, e-scooters, human pods, e-skateboard, and other single-person vehicles as shown in the figures. Shared micro-mobility (Electric) micro-mobility • Since 2018 in particular in the United States and since 2019 in Germany, have been marked by a race toward electric micro-mobility. • Micro-mobility, where bikes and e-scooters provide a new way for residents to move throughout their communities. • The global electric scooters market size was estimated at USD 18.6 billion in 2019. Only the US micro-mobility market is expected in 2030 to be worth between 200 and 300 billion US dollars. In Europe alone the market for shared e-scooter services is expected to reach at least 12 billion US dollars by 2025. • While there is a great deal of promise with these innovations, the emergence of micro-mobility comes with its own set of challenges and considerations for planners, residents, and local decision makers. • E-scooters are an urban trend in transportation as a way to get around. This class of mobility has truly taken off. From the different categories of electric vehicles, e-scooters were found to be the most disruptive due to their ride-share growth. E-scooters are convenient, cheap and comprise first and last mile options. • They have been deployed by start-up companies quite often without any communication between cities and the start-ups. Because of not communicating a plan regarding regulations with the local government, many scooter programs failed or were rolled back due to temporary bans. https://amp-abc-net-au.cdn.ampproject.org/c/s/amp.abc.net.au/article/101539846 https://www.9news.com.au/national/man-woman-injured-in-escooter-crash-manly-sydney/2c11a176-874a-44e7-87a9-75a6cb2b6c17 E-scooter safety • Dockless Electric Scooters (E-Scooters) have emerged as a popular micro-mobility mode for urban transportation. • This new form of mobility offers riders a flexible option for massive first-/last-mile trips. • Despite the popularity, the limited regulations of E-Scooters raise numerous safety concerns among the public and agencies. • Due to the unavailability of well-archived crash data, it is difficult to understand and characterise current status quo of E-Scooter-involved crashes. https://www.youtube.com/watch?v=WZHlDis_8-E https://www.youtube.com/watch?v=6MqgbQxkEm4 https://www.youtube.com/watch?v=GG0rMEoc-Dk https://www.youtube.com/watch?v=O6-stOhTz9U https://www.youtube.com/watch?v=1IFuLvw0szM E-scooter safety: Epidemiology of crashes and injuries • Trauma registry data from 9 urban trauma centers were queried for patients sustaining injury while operating an electric scooter • Injuries from electric scooter crashes are primarily to the head, face, and extremities, with approximately 1 in 5 patients requiring ICU admission and/or a surgical intervention. • During the 1-year study period, 87 patients required trauma surgeon care due to scooter-related injury, with a mean age of 35.1 years; 71.3% were male with 20.7% and 17.2% of patients requiring ICU admission and a surgical intervention, respectively. One patient died. E-scooter safety: Field methods • More privately-owned and fewer shared e-scooters one year after introduction. • Illegal behaviour on shared e-scooters fell. • Compliance by owner riders remained high. Type of variables recorded: • Type of e-scooter (private, Lime, Neuron) • Helmet use • Gender • Age group • Riding location • Time of day • Presence of passengers E-scooter safety: Survey/questionnaire (self report) methods • Face-to-face road survey (N = 459) in order to explore incident involvement history, driving attitudes and perceived risk among escooter users is Paris, France. Four risk factors: • Riding under the influence of alcohol • Riding under the influence of drugs • Using smartphone while riding • Riding in a group (accompanied) • • Male and young riders more likely to show risky behaviour. Longer trips associated with risk-taking behaviour. E-scooter safety: Alternative methods • • Analysing a set of reported crash data to describe the patterns of crashes related to E-Scooter Media reports were searched and investigated for constructing the crash dataset. Key crash elements: • • • Rider demographics Crash type Crash location • From 2017 to 2019, 169 E-Scooter-involved crashes were identified from the news reports across the US. Qualitative analysis highlighted certain key issues: • Helmet use • Riding under influence (RUI) • Vulnerable riders • Data deficiency E-scooter safety: Alternative methods E-scooter safety: Naturalistic experiments • Study the interactions between e-scooter riding and the environment settings through naturalistic riding experiments. • A mobile sensing system has been developed to collect data for quantifying the surrogate safety metrics in terms of experienced vibrations, speed changes, and proximity to surrounding objects. • E-Scooters can experience notable impacts from different riding facilities. Specifically, compared to bicycle riding, more severe vibration events were associated with E-Scooter riding, regardless of the pavement types. • Riding on concrete pavements was found to experience a multiple times higher frequency of vibration events when compared to riding on asphalt pavements of the same length. • E-Scooters are subject to increased safety challenges due to the increased vibrations, speed variations, and constrained riding environments. E-scooter safety: Alternative methods