Electric Bicycle Research Articles

Portable Electric Bicycle Drive

A portable electric bicycle drive system is a compact, removable motor unit designed to convert a standard bicycle into an electric one. It typically includes a small electric motor, a battery pack, a controller, and a mechanism for throttle or pedal-assist, providing supplemental power to aid in pedaling. These systems are easily installed and removed, allowing users to switch between manual and electric cycling as needed. Ideal for commuters and recreational riders, they make it easier to tackle challenging terrain and long distances. The motor can be mounted on the rear wheel, front wheel, or crank set, offering flexibility for various bike types. With features like water resistance, customizable power modes, and regenerative braking, portable electric bicycle drives are becoming an increasingly popular, cost-effective, and eco-friendly solution for urban and outdoor cycling. Key Words: Portable Electric Bicycle Drive. Lithium- ion battery, BLDC Motor, Speed Controller

Is polytrauma really just a simple accident? Recurrent characteristic of polytrauma

BackgroundPolytrauma was defined as a severe traumatic injury and believed that it was a sudden and unpredictable incident. Is polytrauma really just a simple accident? In order to comprehensively answer this question, the study sought to determine the frequency and specific risk factors associated with polytrauma recidivism, while also comparing the initial and subsequent injury events among recidivists.MethodsA multicenter, retrospective cohort study was conducted at four Advanced Trauma Centers’ emergency surgery or traumatic intensive care units (TICUs) between August 2020 and July 2023. A total of 2490 consecutive trauma patients who met the criteria consecutively were recruited and analyzed in the study. Risk factors for recurrent polytrauma were identified through the use of logistic regression analysis. A nomogram was created using the results from a multivariate logistic regression analysis and the rms package in R.ResultsIn polytrauma, the rate of recidivism was 44.6% (672/1507), then recidivists were predominantly male (80.4%) and frequently within the 45 to 54 year-old age range (51.3%). Recidivists in polytrauma patients overall had a median time to reinjury of 27 months, as measured by the interquartile range (IQR). The polytrauma patients in the recent traumatic event were often more severe than that in the initial event, as patients had higher ISS scores and lower GCS scores (P < 0.01). Moreover, polytrauma recidivism were associated with poorer prognosis and increased healthcare costs. Polytrauma patients with the specific characteristics were found to have a higher likelihood of experiencing a subsequent recurrence, including being male (OR = 3.82,95% CI: 2.21–6.83), aged 45–54 years old (OR = 2.62,95% CI: 2.13–6.32),experiencing sleep deprivation (OR = 2.38,95% CI: 1.32–4.25), working in construction (OR = 2.72,95% CI: 1.44–5.42), working as delivery staff (OR = 3.65,95% CI: 1.51–7.96) and being involved in an electric bicycle collision (OR = 2.85,95% CI: 1.31–5.64).ConclusionPolytrauma recidivism is associated with a high recurrence rate, poorer clinical outcomes, and elevated healthcare costs. Key predictive markers for recidivism include being male, aged 45–54 years, experiencing sleep deprivation, employment in construction or delivery roles, and involvement in electric bicycle collisions. These findings highlight polytrauma recidivists as a critical target for primary prevention efforts. Public health strategies should prioritize tailored interventions to reduce recidivism, aiming to mitigate morbidity, mortality, and associated healthcare burdens in this high-risk population.

Exploring the travel behavior of household-owned electric bikes with the consideration of nonlinear effects of factors with the aid of the generalized additive mixed model

Electric bikes (E-bikes) reduce traffic burden in developing countries and provide a lightweight travel option for households. Few studies examined nonlinear effects of factors on e-bike travel behavior. This study adopts the generalized additive mixed model to explore the travel behavior of household-owned e-bikes with the consideration of nonlinear effects of factors based on data from Zhoukou, China. The study confirms that there is a significant nonlinear effect of many factors on the travel behavior of household-owned e-bikes, with the nonlinear effects varying by factors. The lower the number of household bicycles, the higher the travel frequency of household-owned e-bikes. An increase in the number of household cars leads to a decrease in the travel frequency of household-owned e-bikes, and this effect becomes larger as the number of cars increases. The number of household-owned e-bikes is positively correlated with travel frequency, but the correlation weakens after more than 3 e-bikes. There is a negative correlation between the travel frequency of household-owned e-bikes and the number of persons older than 6 when it exceeds 4. The effect of the number of persons older than 6 on the travel frequency of household-owned e-bikes gradually increases when the number of persons older than 6 exceeds 3. Finally, this study puts forward policy suggestions regarding enhancing the attractiveness and competitiveness of household-owned e-bikes. These findings could advance the understanding of the role of e-bikes in households and serve as references for policymakers and planners.

Arterial Multi-Path Green Wave Control Model Concurrently Considering Motor Vehicles and Electric Bicycles

Arterial green wave control can effectively reduce the delay time and number of stops of the coordinated traffic flows. However, existing arterial green wave control methods mostly focus on motor vehicles and provide them with green wave bands, neglecting the electric bicycles that are widespread on the roads. In fact, electric bicycles have become an important tool for short-to-medium trips among urban residents because they are convenient, low-cost, and eco-friendly. To tackle this, an arterial multi-path green wave control model that considers both motor vehicles(cars and buses) and electric bicycles is presented in this paper. The presented model is formulated as a mixed integer linear programming problem. The optimization objective of the model is to maximize the sum of the green wave bandwidths for all coordinated paths of each traffic mode on all road segments. The key constraints of the presented model can be addressed by analyzing the relationships among the green wave bandwidth, coordinated path, common cycle time, offset, phase sequence, etc., to utilize the time–space diagram. The results of the numerical example show that compared with the traditional model for through motor vehicles (cars and buses), the total green wave bandwidths of cars, buses, and electric bicycles generated by the presented model at the entire arterial level has been increased by 36.8%, 47.9%, and 19.3%, respectively.

New approaches and performance analysis of on-demand delivery systems using buses

With the rapid growth of on-demand delivery, traditional third-party delivery services using electric bicycles face increasing challenges, including increased labor costs and safety issues. To address these issues, we propose two modes for on-demand delivery systems using buses. One is the “bus terminal + professional crowd shippers” (B&P) mode, which involves consolidating parcels from various origins at the bus transfer hub. From there, they are grouped and delivered by buses. The other is the “bus stop + crowdsourced passenger” (B&C) mode, which transports parcels directly from the origin to the destination by passengers without the need for consolidation and sorting at bus transfer hubs. Both modes fully utilize the surplus capacity of buses and employ resources from retail stores and third-party shippers. We evaluated these modes using key performance indicators (KPIs) such as labor costs, cost increases from en-route destination changes, and average delivery time per kilometer. This analysis was based on 18,349 delivery cases in downtown Ningbo, China. Through this evaluation, we identified the strengths, weaknesses, opportunities, and threats associated with each mode. Our findings reveal that both the B&P and B&C modes effectively reduce delivery costs and external impacts while maintaining a user experience and efficiency level comparable to traditional third-party delivery services for mid-to-long-distance (3–10 km) deliveries. However, the use of public transportation may limit the flexibility of the delivery mode. Based on these insights, we provide strategic recommendations for the further development and implementation of B&P and B&C modes.

Behaviour of motorcyclists and bicyclists before and after a road safety campaign, China.

To examine changes in red-light running and distracted riding among motorcyclists and cyclists before and after the 2020 implementation of the One Helmet, One Seatbelt campaign in China. We obtained 192 hours of film before (2019) and after (2021) implementation of the campaign in eight road intersections in Changsha. We calculated percentages and ratios of red-light running and distracted riding. To assess the associations between these traffic behaviours and the campaign, we used multivariable logistic regression models to calculate adjusted odds ratios (aOR). We filmed 5256 motorcyclists and cyclists in 2019 and 6269 in 2021. Red-light running decreased from 45.1% to 41.5% during this period (ratio: 0.92; 95% confidence interval, CI: 0.88-0.96), while distracted riding increased from 3.5% to 5.0% (ratio: 1.42; 95% CI: 1.19-1.69). After adjusting for covariates, male riders were more likely to run a red light compared to female riders (aOR: 1.28; 95% CI: 1.06-1.55). Red-light running was also more likely among electric bicycle riders (aOR: 1.46; 95% CI: 1.10-1.95) and motorcyclists (aOR: 1.47; 95% CI: 1.13-1.90) compared to traditional cyclists. All types of riders were less prone to run a red light during peak hours than off-peak hours (aOR: 0.85; 95% CI: 0.73-0.99). Distracted riding was more common on weekends compared to weekdays (aOR: 3.01; 95% CI: 2.02-4.49). China's national road safety campaign, which focuses on helmet and seatbelt use, was associated with reduced red-light running. Strict enforcement and targeted modifications could improve the campaign's effectiveness.

Electric Bicycle Battery Charging System Design Using Solar Panel

This solar-powered electric bicycle charging system design and development uses solar panels as a renewable energy source to charge the battery, which drives the electric motor. The system consists of four main components: solar panel, solar charge controller, battery, and electric motor. The solar panels are connected in parallel to generate 36V, which is then stored in the battery. The system is designed to optimize energy harvesting and reduce dependence on fossil fuels. Testing was conducted with research stages, including system design, hardware implementation, and testing. The results show that the system can charge the battery efficiently, with a charging time of 3 hours. The system was also tested under different conditions, including morning, afternoon, and night, to evaluate its performance. It can be concluded that the solar-powered electric bicycle charging system is a viable alternative to traditional fossil fuel-based systems. The system is environmentally friendly, reduces energy dependence, and provides a sustainable solution for transportation. The results of this research can be used to develop a more efficient and sustainable transportation system.

The Incidence and Severity of Pediatric Injuries Sustained by Electric Bikes and Powered Scooters: The Experience of an Urban, Tertiary Pediatric Emergency Department.

This study aimed to describe the incidence and severity of electrical bicycle (E-bike)- and power scooter (P-scooter)-related injuries and their secular trends among pediatric patients presenting to a pediatric emergency department (ED). This retrospective cohort study of patients aged <18 years who sustained E-bike and P-scooter injuries was performed between 2018 and 2023. We explored trends of severe trauma cases, ED visits, hospitalizations, and surgical interventions. Severity of trauma was rated by either an injury severity score (ISS) of >15 or the patient's need for acute care as defined by intensive care unit (ICU) admission, direct disposition to the operating room, acute interventions performed in the trauma room, and in-hospital death. Of the 1466 pediatric patients who presented to our pediatric ED following P-scooter and E-bike injuries, 216 (14.7%) were hospitalized, with a median age of 14.0 years (interquartile range, 10.5-16.0 years) and male predominance (69.0%). The number of ED visits increased 3.5-fold by study closure, with a parallel increase in hospitalizations, surgical interventions, and severe trauma cases. The relative percentages of severe trauma cases were not significantly different over time. Among hospitalized patients, 3 patients (1.4%) died and 9 (4.1%) required rehabilitation care. The incidence and severity of E-bike and P-scooter injuries and fatalities continue to increase within the pediatric population. Current personal and road safety regulations are providing inadequate in preventing these injuries, highlighting an urgent need for revision and stricter enforcement.

The Proposal of Integrated Actions in Transport as Part of a Climate Action Plan for UNICAMP

Since 2019, the State University of Campinas (UNICAMP) has been collecting data, preparing, and annually monitoring sustainability indicators submitted to the UI GreenMetric ranking, which guide improvement projects at the university in alignment with the Sustainable Development Goals (SDGs). This article aims to propose sustainable practices for managing urban mobility on its campuses, drawing inspiration from the experiences of national and international universities that ranked highest in the 2023 edition of the aforementioned ranking. The study employed both qualitative and quantitative research methods, using a descriptive approach combined with Deductive Content Analysis (DCA). This analysis was further validated by the Multi-Criteria Decision-Making (MCDM) method known as Fuzzy SWARA. Through this process, the study identified the most commonly adopted actions by universities: promoting the use of electric and/or conventional bicycles on campus (11.8%); projects in partnership with government, state, or municipal entities to develop express cycle paths and encourage bicycle use both on campus and in the city (9.2%); conventional bicycle rental on campus (9.2%); and electric car sharing on campus (9.2%). The proposals suggested for UNICAMP include encouraging carpooling, implementing a public transportation exemption or half-fare program for employees, promoting the use of bicycles while ensuring increased campus security, among other initiatives. This research is part of a proposal for a Climate Action Plan for the university, which aims to effectively integrate sustainability into the university's agenda and support the fight against the climate emergency through necessary adaptations.

Experimental modal identification of a pedestrian bridge through drive-by monitoring integrated with shared-mobility vehicles

In the context of improving resilience in transport infrastructure, an emerging approach of indirect structural health monitoring is gaining attention, known as drive-by monitoring, instrumenting a vehicle with sensors to evaluate the bridges it crosses. However, their effectiveness has predominantly been investigated in ideal scenarios, with actual real-world applications being quite scarce. The research presented in this paper explores the feasibility of combining two routes: (1) fleet emissions reduction and (2) transport resilience enhancement, through drive-by monitoring integrated with shared mobility, including electric mobility scooter and bicycle. The information from drive-by data collected from shared mobility can give valuable information on critical transport infrastructure (e.g., bridges) and such a drive-by database has the potential for network level infrastructure condition assessment. In this paper, two different drive-by roadmaps are investigated subject to the flexibility of the bridges, namely partially or fully drive-by monitoring. To validate the proposed roadmaps, a full-scale pedestrian bridge was chosen for drive-by testing, where smartphone sensors and specialised accelerometers are mounted on shared mobility for data acquisition. Experimental results demonstrate that (i) smartphone sensing can provide data with similar accuracy compared to specialised accelerometers, (ii) bridge frequencies can be easily obtained from temporarily parked shared mobility, with a maximum relative error of 1.05%, (iii) both the bridge frequencies and operational deflection shapes are successfully extracted from the moving shared mobility by using variational mode decomposition and filtering techniques, and shared mobility's GPS data along with moving speeds are collected for potential vehicle positioning and drive-by database updating.

Low-cost urban heat environment sensing device with Android platform for digital twin

The proper monitoring of heat and meteorological variables is essential for the well-being of residents of metropolitan areas. It is challenging to configure spatial heat variations in complex urban environments, even though the temporal variation of urban heat flux has been measured at several designated monitoring stations. Neither the budget nor existing techniques for efficient urban heat monitoring are sufficient for a digital twin of the urban heat environment. As a result, we have developed a low-cost monitoring system that can be easily integrated into a portable pedestrian device, kickboard, or electric bike. With this system, citizens can collect information about urban heat, such as air temperature, surface temperature, relative humidity, barometric pressure, light intensity, and micro-geophysical features including topological aspects and mobile information (e.g., three-dimensional accelerations). Citizens can participate in daily scientific activities using these devices, which facilitate data acquisition and information exchange in urban digital twin environments.

INNOVATING IN URBAN DISTRIBUTION OF GOODS, SMALL CHANGES, BIG RESULTS FOR THE CITY

The conceptual article invites us to reflect on how innovation doesn’t always require huge investments or technological revolutions. Sometimes the most transformative changes come from simple ideas. It is in simplicity that we often find the greatest potential for change. Implementations such as optimizing routes, reorganizing delivery schedules or using electric bicycles. They show that accessible solutions can transform urban logistics. These small but modest steps, bring great benefits and contribute to sustainability. Likewise, the ability to reimagine our processes, reusing the strategic value of the resources we already have, is to see the familiar with new eyes, reusing the known to create something extraordinary. True innovation cannot flourish in isolation. This is where public-private collaboration becomes important. While companies can lead these efforts, support from the public sector is the direct way to create new opportunities. When we join forces, we generate an ecosystem of change that allows us to integrate these small advances. We believe that innovation is not just for the few, we all have the power to generate great results from small changes.

Severe multiple injuries in a 15-year-old boy with pelvic fracture complete anterior dislocation of the sacroiliac joint and rupture of the internal and external iliac arteries: A case report of a rare injury.

Anterior dislocation of the sacroiliac joint combined with pelvic fractures is relatively rare in clinical practice. It is often associated with hemodynamic instability and severe injuries to other regions, resulting in a complex condition, prolonged treatment duration, and high rates of mortality and disability. However, there are few reports in the literature describing the diagnosis and treatment of anterior dislocation of the sacroiliac joint. In this case, the patient sustained a pelvic fracture with anterior sacroiliac joint dislocation and rupture of both the internal and external iliac arteries following a motor vehicle accident, making it an even rarer and more challenging case to treat. Reporting such cases can enhance the understanding of the diagnosis and treatment of anterior sacroiliac joint dislocation with rupture of the iliac arteries and provide valuable references for similar cases. The patient was riding an electric bicycle and was hit by a small truck, resulting in a pelvic fracture, anterior dislocation of the sacroiliac joint, and rupture of the internal and external iliac arteries. The patient was diagnosed with open pelvic fracture (type C1.2), left complete anterior dislocation of the sacroiliac joint, left acetabular fracture, left internal and external iliac arteriovenous rupture. emergency room resuscitation, intensive care unit resuscitation, 6 surgeries and perioperative management. He has been discharged from the hospital for more than 1 year and was rechecked every month after discharge, the fracture has healed, there is no obvious pain and discomfort in and around the wound, he has been fitted with a prosthesis, and he is doing the walking function exercise. Pelvic fracture with anterior sacroiliac dislocation is clinically rare and critical, and is associated with large vessel rupture, severe organ damage, and high mortality and disability rates. Rapid restoration of pelvic stability and hemodynamic stability is the key to treatment. Rapid transfer to a tertiary trauma center, rapid examination through the green channel to clarify the diagnosis, close intensive care, and reasonable multidisciplinary teamwork for surgical intervention are all valuable experiences that we have concluded.

Selection of Green Recycling Suppliers for Shared Electric Bikes: A Multi-Criteria Group Decision-Making Method Based on the Basic Uncertain Information Generalized Power Weighted Average Operator and Basic Uncertain Information-Based Best–Middle–Worst TOPSIS Model

This study introduces a novel multi-criteria group evaluation approach grounded in the theory of basic uncertain information (BUI) to facilitate the selection of green recycling suppliers for shared electric bikes. Firstly, a comprehensive index system of green recycling suppliers is established, encompassing recycling capacity, environmental sustainability, financial strength, maintenance capabilities, and policy support, to provide a solid foundation for the scientific selection process. Secondly, the basic uncertain information generalized power weighted average (BUIGPWA) operator is proposed to aggregate group evaluation information with BUI pairs, and some related properties are investigated. Furthermore, the basic uncertain information-based best–middle–worst TOPSIS (BUI-BMW-TOPSIS) model incorporating the best, middle, and worst reference points to enhance decision-making accuracy is proposed. Ultimately, by integrating the BUIGPWA operator for group information aggregation with the BUI-BMW-TOPSIS model to handle multi-criteria decision information, a novel multi-criteria group decision-making (MCGDM) method is constructed to evaluate green recycling suppliers for shared electric bikes. Case analyses and comparative analyses demonstrate that compared with the BUIWA operator, the BUIGPWA operator yields more reliable results because of its consideration of the degree of support among decision-makers. Furthermore, in contrast to the traditional TOPSIS method, the BUI-BMW-TOPSIS model incorporates the credibility of information provided by decision-makers, leading to more trustworthy outcomes. Notably, variations in attribute weights significantly impact the decision-making results. In summary, our methods excel in handling uncertain information and complex multi-criteria group decisions, boosting scientific rigor and reliability, and supporting optimization and sustainability of shared electric bike green recycling suppliers.

Modelling the complementarity and flexibility between different shared modes available in smart electric mobility hubs (eHUBS)

eHUBS are physical locations that integrate two or more electric shared mobility modes. As they provide transport users easier access to a wide range of transport modes, multimodal behaviour is expected to be more common. However, this issue has not been addressed in previous stated preference studies on mode choices involving innovative transport modes. In this study, multimodal behaviour is explicitly addressed both in measurement and in modelling by adopting the multiple discrete–continuous (MDC) modelling framework in contrast to discrete choice models. Instead of asking transport users to indicate the most preferred alternative, they were allowed to choose more than one alternative by allocating trips between several modes. This study aims to answer two questions: 1) whether there is complementarity between the multiple shared modes offered in eHUBS and 2) how would transport users adapt when one of the shared modes that they plan to use becomes unavailable. Using stated mode choice data of non-commuting trips from transport users whose current mode is driving a private car in Manchester, UK, several models under the MDC framework were estimated including Multiple Discrete-Continuous Extreme Value (MDCEV) model, mixed MDCEV model, and the extended Multiple Discrete Continuous (eMDC) model. The results show that there is complementarity between shared electric vehicle (EV) and electric bike (e-bike) offered in the eHUBS. In addition, the research show that the flexibility between those two shared modes is stronger than assumed in the MDCEV model, and common preference heterogeneity cannot fully account for this phenomenon.

Predicting and Analyzing Electric Bicycle Adoption to Enhance Urban Mobility in Belgrade Using ANN Models

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents’ awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade.

A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas

National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM2.5 and O3 concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM2.5 and O3 concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM2.5 and O3 concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R2 values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM2.5 and O3 concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM2.5 concentrations.

Impact of attitude, behaviour and opinion of e-scooter and e-bike riders on collision risk in Singapore

Active Mobility Devices (AMDs) such as electric scooters (e-scooters) and electric bikes (e-bikes) are increasingly used on shared paths and Park Connector Networks (PCNs) in Singapore, leading to frequent interactions between AMD riders, pedestrians, and cyclists. To ensure safety, it is crucial to understand the factors associated with collision risk related to these AMDs. To gain insights into the riders’ perspectives on the risk-taking behaviours and attitudes towards safety and sharing paths, a survey was conducted with 369 e-bike and 133 e-scooter riders across Singapore. The collected data was analysed to identify critical features of behaviour, attitudes, and opinions of e-scooters/e-bikes riders and their impact on perceived collision risk. Logistic Regression was used to select the most important behavioural features linked to collision risk, and significance of each was quantified by using the odds ratios in the chosen model. The results reveal that e-bike riders who regularly brake hard to avoid obstacles and highly value capacity of e-bike to carry goods face an increase in collision risk by 49.1% and 43.48% respectively. Those preferring quieter AMDs face 33.31% lower collision risk. Additionally, e-bike riders advocating for more traffic enforcement or the importance of slowing down when overtaking pedestrians face 20.69% and 38.84% lower collision risk respectively. E-scooter riders who manoeuvre quickly to dodge collisions or prioritize passenger-carrying capacity encounter a 142.25% and 67.43% higher collision risk, respectively. Furthermore, e-scooter riders willing to bend rules when not causing inconvenience to others face an increase in collision risk by 123.00%. These outcomes offer significant insights for the design and regulation of active mobility to safeguard all road users in a multi-modal transport environment.

Application of lean methodology in the optimisation of the production process of entrepreneurs

This paper provides a detailed overview of how Lean philosophy can be applied in the context of electric bicycle production to improve efficiency and reduce losses. The central part of the paper deals with the detailed application of these methods and techniques to the production of electric bicycles. Via a detailed analysis of existing processes and losses, the paper provides specific proposals for optimising the production process, improving quality control, better production planning, and encouraging continuous improvement (Kaizen). The case study provides a comparison between conventional and Lean production of electric bicycles, including a detailed quantification of losses and their ranking according to the magnitude of the impact on overall efficiency. Via specific application of the Lean philosophy, its tools and techniques, this paper shows how to achieve significant improvement in the efficiency of the production process, reduce losses and increase the competitive advantage in the market of electric bicycles.

A-198 ESG Opportunities Harnessed During Pathology Transformation

Abstract Background Synnovis (a SYNLAB business) is in the process of delivering one of the largest Pathology transformation programmes in the UK, moving to a seven-site hub and spoke model from 89 laboratories over a dispersed geography. This remit also presented an opportunity for SYNLAB to deliver on its Environmental Social Governance (ESG) ambitions, by embedding ESG principles into programme delivery. There are several examples within each ESG pillar that demonstrate the benefits to be leveraged during transformation. Methods SYNLAB’s vision is to deliver sustainable diagnostic services. The integration of ESG into both the decision-making process and transformation structure ties these ambitions into programme benefits realisation. As well as setting performance level targets, the delivery of ESG benefits provide measures of success throughout the transformation journey, motivating stakeholders and building confidence along the way. Results Environment: The efficient use of space and LEAN principle design has consolidated the fragmented laboratory services spanning 15,000m2 into a bespoke, cutting-edge Pathology Hub with 6 spokes (hospital based services). The new hub building was designed to achieve BREEAM Excellent standards which measure performance against criteria such as energy management and efficiency, sustainable design and materials. The hub’s energy is from renewable-backed sources, and we will achieve a 47% reduction in CO2 equivalent (CO2e) emissions by unifying a significant proportion of its legacy footprint in this way. Further energy savings will be achieved through the associated analytical equipment consolidation, including a decrease from 50 to 14 microtomes, a decrease of 29 to 15 ventilated benches and a reduction of over 200 fridges and freezers across the service. In addition, substantial CO2e emissions savings are also being delivered by moving to a greener logistics provider, utilising electric vehicles and bikes. 2023 saw such emissions fall by 60% across Synnovis, helping to keep London’s air cleaner. Social: The transformation programme enables the hand back of over 6,000m2 of real estate to Synnovis’ NHS partners to use for critical hospital services (the equivalent of over 150 operating theatres). Governance Both internally and externally, ESG is integrated within the Synnovis organisational culture. For example, internally it applies mandatory sustainability metrics to its solution selection processes. These influenced the selection of Abbott’s Indexor solution to support the transformation of Blood Sciences, delivering a saving of over 2.8 million plastic sample bags every year. Externally, SYNLAB works with its partners to manage this substantial and complex transformation programme. Risk management is assured through transparency, securing confidence in the good use of public funds, ensuring responsiveness to customer and partner feedback, and ensuring accountability for and the delivery of targeted healthcare outcomes. Conclusions The integration of SYNLAB’s ESG strategy within the transformation programme is delivering tangible benefits across all ESG pillars. It has highlighted that in addition to the core outcome of services transformation, there are many more opportunities that can be realised as a result of making ESG an integral part of the pathology transformation programme.