Speeding Research Articles

Energy Consumption Optimization for an Electric Delivery Vehicle

For nearly two centuries, electric drives have been used in transportation. Nevertheless, they were not always favored by designers. The century-long dominance of heat engines led to the disregard of numerous challenges associated with the operation of electric drive systems. One of these issues is the optimization of energy consumption by an electric vehicle. This publication proposes an electronic Energy Consumption Optimizer (ECO) that predictively uses information about the shape of the route and speed limits on its individual sections to control the motor speed and gear changes in the gearbox. This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. Simulation studies carried out using WLTP test cycles and cycles from real road routes showed the potential to decrease energy consumption for vehicle movement by approximately 10%.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Realizing the Calculation of a Fully Normalized Associated Legendre Function Based on an FPGA

A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth’s gravity field elements using ultra high-order gravity field coefficient models. In the surveying and mapping industry, researchers typically rely on CPU-based systems for these calculations, which leads to limitations in execution speed and power efficiency. Although modern CPUs improve instruction execution efficiency through instruction-level parallelism, the constraints of a shared memory architecture impose further limitations on the execution speed and power efficiency. This results in exponential increases in computation time as demand rises alongside high power consumption. In this article, we present a new computational implementation of an fnALF based on the ZYNQ platform. We design a task-parallel “pipeline” architecture which converts the original serial logic into a more efficient hardware implementation, and we utilize a redundant calculation layer to handle repetitive coefficient computations separately. The experimental results demonstrate that our system achieved accurate and rapid calculations. Under the only one geocentric residual latitude condition, we measured the computation times for spherical harmonic coefficient degrees of 360, 720, and 1080 to be 0.155922 s, 0.520950 s, and 1.401609 s, respectively. In the case of the multiple geocentric residual latitudes condition, our design generally yielded efficiency gains of over three times those of MATLAB R2020b implementation. Additionally, our calculated results were used to determine the geoid height in the field with an error of less than ±0.1m, confirming the reliability of our computations.

Influence of hydrofoil motion patterns on the hydrodynamic performance of undulating fin for biomimetic underwater robots

The undulatory propulsion of aquatic organisms like the black ghost knifefish offers high manoeuvrability and motion stability, making it a promising model for developing advanced biomimetic underwater robots. However, these robots are frequently hindered by limitations in speed and propulsion efficiency. Inspired by the energy benefits observed in fish schooling, this study introduces a cooperative propulsion system integrating the hydrofoil with the undulating fin to enhance propulsion performance through an investigation of their interaction across various motion patterns. Specifically, we investigate the effects of static, pitching, heaving, and their combination (pitching + heaving) of the hydrofoil on the hydrodynamic performance of the undulating fin. Our results indicate that a static hydrofoil slightly increases the fin's average thrust, while pure pitching marginally reduces it. The high-speed vortices generated by the hydrofoil during pure heaving significantly suppress the fin's thrust. In contrast, the combined motion of pitching and heaving substantially enhances propulsion performance. Thus, the combined motion of the hydrofoil can better enhance the hydrodynamic performance of the undulatory propulsion robot. This study provides a theoretical foundation for the optimized design of high-speed and stable undulating fin propulsion systems, offering valuable insights for developing underwater robots with improved operational capabilities.

Exploring factors influencing pedestrian accident severity: a multi-source study in the city of Berlin

Abstract Background Road safety remains a critical concern in Europe’s transport sector. In urban areas, where 40% of total road fatalities occur, pedestrians are particularly vulnerable, since according to road safety data accidents involving a pedestrian are 2.8 and 15 times more probable to be fatal than in rural areas and motorways respectively. In line with the Vision Zero concept and the European Commission’s directives, many EU members are taking steps to improve road safety. Accordingly, the identification of the key factors behind pedestrian accident occurrence and pedestrian accident severity in urban areas is ever more relevant. Methodology The proposed methodology employs traditional logistic regression models and artificial neural networks, using accident data from Berlin, Germany, sourced from the Berlin Open Data portal. The dataset comprises information on 3,257 accidents involving pedestrians in 2018 or 2019, including details about involved vehicles, accident details, and injury severity information. Additionally, the dataset was augmented with average network speed data from Uber Movement and road network information from Geographic Information System (GIS) applications. Results Formal analysis results indicated several factors as significant to accident severity, such as involvement of bicycle or heavy vehicles, lighting conditions, speed limit and accident type. Additionally, a comparison between modelling approaches shows a clear performance advantage of ANNs over statistical models. Research findings provide insight for various stakeholders working to enhance pedestrian safety in urban areas.

Analysing crash severity on expressways in India using statistical and ML approaches

The high injury severity of traffic crashes on Indian expressways is a significant concern for road safety experts, although studies dedicated to this critical issue are limited. The present study investigates the factors influencing crash severity using multinomial logit (MNL), decision tree (DT) and random forest (RF) models on a dataset of 2,747 crashes on three selected expressways. The dependent variable, crash severity, had four injury severity categories: fatal, severe, minor, and property damage only (PDO). Various explanatory variables, included traffic and speed characteristics, temporal and geometric characteristics, primary contributing factors, crash type, and the vehicles involved. Synthetic minority oversampling (SMOTE) and randomise class balancing (RCB) techniques were also employed to tackle the class imbalance issue in the dataset. The predictive performance of models was evaluated using classification accuracy and Kappa value. The RF model showed the highest predictive accuracy on the RCB dataset. The key findings highlight the critical need for enforcement of speed limits and entry restrictions, lane discipline, and improvement of design deficiencies such as provisions of truck laybys and bus bays. These measures can help in policy-making and engineering improvements to enhance road safety on expressways in India.

Electrophoretic video display based on image semantic segmentation

AbstractElectrophoretic displays (EPDs) are popular in consumer electronics, like e‐readers and tags, for their paper‐like visuals and minimal power consumption. However, EPDs struggle to rapidly switch to target grayscales due to the inherent limitations in the response speed of their display particles. Video halftoning technology achieves a smoother video grayscale display, but it also inevitably reduces the image quality and affects the viewing experience. Therefore, this paper proposes an EPD driving method based on image semantic segmentation. The method could distinguish the dynamic region and the static region accurately. For static areas, high‐definition driving waveforms were used to ensure the image display accuracy. For the dynamic area, dynamic target and dynamic text were processed by different algorithms, respectively, and the speed‐driving waveforms were used to maintain the fluency and clarity of the video display. With the method we proposed, the display quality for EPD in complex video playback was optimized significantly. Compared to DBS refresh techniques, the SSIM had increased from 4% to 82% and the PSNR had increased from 2.56 dB to 10.89 dB with our method, significantly enhancing the video playback performance of EPDs.

Drivers’ long-term crash risks associated with being ticketed for speeding

Introduction: This research analyzes the relationship between police-issued tickets for speeding and the crash risk of those drivers, in New Zealand, between 2015–2019. Method: The main data are constructed through data-matching license details of crash outcomes with all officer-issued tickets for speeding between 2015–2016 (N = 534,935). The sub-group of drivers that accumulate tickets is compared to a coarsened exact matched set of drivers of the same age. Results: There is a strong relationship between the number of tickets a person has in a two-year period (2015–16) and the likelihood of a crash outcome (2017–2019). However, the accumulation of tickets is not the best predictor of crash likelihood. A combination of the excess in speed and the accumulation of tickets increases the relative odds of a subsequent crash. These results are discussed considering the threshold at which New Zealand criminalizes alcohol-relating offending (notionally 4.2 times the base rate crash risk). The same rate of elevated crash risk exists when a driver has one ticket for being 10 km/h over the speed limit and has another speeding ticket within two years.

Speed limits to fluctuation dynamics

Fluctuation dynamics of an experimentally measured observable offer a primary signal for nonequilibrium systems, along with dynamics of the mean. While universal speed limits for the mean have actively been studied recently, constraints for the speed of the fluctuation have been elusive. Here, we develop a theory concerning rigorous limits to the rate of fluctuation growth. We find a principle that the speed of an observable’s fluctuation is upper bounded by the fluctuation of an appropriate observable describing velocity, which also indicates a tradeoff relation between the changes for the mean and fluctuation. We demonstrate the advantages of our inequalities for processes with non-negligible dispersion of observables, quantum work extraction, and the entanglement growth in free fermionic systems. Our results open an avenue toward a quantitative theory of fluctuation dynamics in various non-equilibrium systems encompassing quantum many-body systems and nonlinear population dynamics.

Time-optimal control of a solid-state spin amidst dynamical quantum wind

Time-optimal control holds promise across the full spectrum of quantum technologies, where the rapid generation of unitary gates and state transformations is crucial to mitigate decoherence effects. In practical scenarios, quantum systems are always immersed in an external time-dependent field or potential, either owing to the inevitable influence of the environment or as a sought-after effect for enhanced coherence. The challenge then lies in finding the time-optimal approach to navigate quantum systems amidst dynamical ambient Hamiltonians, a pursuit that has proven elusive thus far. We showcase the implementation of arbitrary quantum state transformations and a universal set of single-qubit gates under a background Landau-Zener Hamiltonian. Leveraging the favorable coherence properties of timedomain Rabi oscillations, we achieve velocities surpassing the Mandelstam-Tamm quantum speed limit and significantly lower energy costs than those incurred by conventional quantum control techniques. These findings highlight a promising pathway to expedite and economize high-fidelity quantum operations.

High performance P300 spellers using GPT2 word prediction with cross-subject training

ABSTRACT Amyotrophic lateral sclerosis (ALS) severely impairs patients’ ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain–computer interface (BCI) offers an alternative communication method by interpreting a subject’s EEG response to flashing characters presented on a grid interface. This paper addresses the common speed limitations encountered in training efficient P300-based multi-subject classifiers by introducing innovative ‘across-subject’ classifiers. We leverage a combination of the second-generation Generative Pre-Trained Transformer (GPT2) and Dijkstra’s algorithm to optimize stimuli and suggest word completion choices based on subjects’ typing history. Additionally, we employ a multi-layered smoothing technique to accommodate out-of-vocabulary (OOV) words. Through extensive simulations employing random sampling of EEG data from multiple subjects, we demonstrate significant speed enhancements in typing passages containing rare and OOV words. These optimizations result in approximately 10 % improvement in character-level typing speed and up to 40 % improvement in multi-word prediction. We demonstrate that augmenting standard row/column highlighting techniques with layered word prediction yields close-to-optimal performance. Furthermore, we explore both ‘within-subject’ and ‘across-subject’ training techniques, showing that speed improvements are consistent across both approaches.

An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer

One of the important applications of real-time crash prediction analysis lies in the field of proactive traffic management, where instantaneous crash risk evaluation and dynamic decision-making are prerequisites. This research proposes an integrated and advanced real-time crash risk prediction framework for Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) implemented freeways considering their operational periods. Statistical methods are utilized to identify the significant crash contributing factors (related to traffic, roadway geometry, and weather conditions) and explain their relationships with crashes. Time-Embedded Transformer models are proposed to predict the likelihood of real-time crash events. The sensitivity and false alarm rate of the proposed AM peak model are found to be 0.76 and 0.27, respectively, whereas the values are 0.78 and 0.24, respectively for the PM peak model. Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. Practitioners and policymakers could apply this method to obtain a more accurate real-time crash likelihood estimation, identify important crash precursors, dynamically update algorithms, and enhance safety aspects while operating traffic management strategies on freeways.

Reduce Speed Limits to Minimize Potential Harm and Maximize the Health Benefits of Street Trees

Urban greening is threatened by the concern that street trees increase traffic-related injury/death. Associations between all serious and fatal traffic crashes and street tree percentages were examined in Sydney, Australia. Associations were adjusted for confounding factors relating to driver behavior (speeding, fatigue, and use of alcohol) and road infrastructure, including alignment (e.g., straight, curved), surface condition (e.g., dry, wet, ice), type (e.g., freeway, roundabout), and speed limit. Models indicated that 10% more street trees were associated with 3% and 20% higher odds of serious or fatal injuries and 20% tree collisions on roads of any speed, respectively. However, further analysis stratified by speed limit revealed contrasting results. Along roads of 70 km/h or greater, 10% more street trees were associated with 8% higher odds of serious or fatal injury and 25% higher odds of death. Comparable associations were not found between street trees and serious or fatal injuries along roads below 70 km/h. Reducing speed limits below 70 km/h saves lives and may mitigate risks of serious or fatal traffic accidents associated with street trees, enabling greener, cooler, healthier cities.

Scalable SOA-based Banyan optical space switch on InP membrane on silicon (IMOS)

Optical switches (OS) are vital for meeting modern data centers’ capacity and latency needs, overcoming the bandwidth and speed limitations that electronic switches encounter. To this end, this work proposes a semiconductor optical amplifier (SOA)-based OS on the IMOS, a unique platform with a combination of high refractive index contrast for compactness and monolithic active-passive integration for active functionalities, thereby enabling the creation of compact SOA-based OSes. An 8 × 8 Banyan SOA-based OS is integrated on a 4 × 4 mm2 area on the standard 4 × 6 mm2 IMOS cell. This marks the first application of the IMOS technology platform for SOA-based OSes and sets the stage for compact and large-scale monolithic photonic integrated OS circuits. The basic 2 × 2 switch module delivers a high optical signal-to-noise ratio (OSNR) and an extinction ratio (ER) above 45 dB. Employing NRZ-OOK routing on the 2 × 2 basic OS module, a 15 dB input power dynamic range (IPDR) is achieved within a 1 dB power penalty at 12.5 Gb/s. Data routing at higher data rates of 25 Gb/s and 40 Gb/s incurs power penalties of 0.8 and 1.4 dB, respectively. Furthermore, data routing for a 3-stage 8 × 8 switch operating at 25 Gb/s results in a power penalty of 1.2 dB. Compared to the 1-stage 2 × 2 switch, only a 0.4 dB additional penalty is observed despite incorporating 2 additional SOAs in the cascade. This indicates that the switch can scale to higher radix to accommodate multi-stage switches with numerous ports, large bandwidth, and fast speed, which is essential in modern large-scale data centers.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Enhancing Road Safety with Smart Infrastructure: Development and Implementation of Advanced Zebra Crossings and Speed Breaking

Abstract: This study addresses a critical issue in road safety by proposing innovative solutions to mitigate accidents caused by speeding and reckless driving, particularly in high-risk areas such as highways, hospitals, and schools. With road traffic accidents accounting for approximately 1.3 million fatalities annually, along with 20 to 50 million injuries or disabilities, the inefficacy of traditional traffic control systems is a growing concern. Factors such as poor visibility, adverse weather, and driver distractions further compromise the effectiveness of conventional signs and signals. This research introduces a "smart zebra crossing" system that integrates ultrasonic sensors and embedded LED lights to provide real-time alerts to both drivers and pedestrians, thereby enhancing visibility and safety, especially under low-light conditions. Additionally, a "smart speed breaking" system is proposed, which dynamically adjusts its height based on vehicle speed, detected through ultrasonic sensors, prompting drivers to decelerate when necessary. Both systems are linked to a central traffic management network, facilitating real-time adjustments in speed limits and providing alerts to connected vehicles. The integration of these technologies represents a significant advancement in road safety, offering an adaptive, intelligent solution for improving traffic management and reducing the risk of accidents, particularly in vulnerable zones.

Smart Zone-Based Vehicle Speed Retarding System Utilizing Radio Frequency (RF) in Electronic Vehicles

The proposed system aims in designing an efficient automatic wireless vehicle speed control in low-speed zones using RF communication. The advent of new high-speed technology and the growing computer capacity provided realistic opportunity for new robot controls and realization of new methods of control theory. This technical improvement together with the need for high performance robots created faster, more accurate and more intelligent robots using new robots control devices, new drivers and advanced control algorithms. The proposed method uses RF transmitter and receiver modules for establishing wireless communication, DC motors for the vehicle movement controller. The status of the project will display on LCD display. The controlling device of the whole system is a Microcontroller to which RF receiver module, DC motors are interfaced through a motor driver. Whenever the vehicle enters into a low-speed zone, the data from RF transmitter of low-speed zone will be received by the vehicle system. The microcontroller in the vehicle system automatically slows down the speed to the speed limit of that zone. The microcontroller checks the data with the program embedded in it and performs appropriate actions on the electric motors. The microcontroller is programmed using Embedded C language.

Nonlinear generalized piezothermoelasticity of spherical vessels made of functionally graded piezoelectric materials

The present study investigates the thermoelastic response of a heterogeneous piezoelectric sphere under thermal shock loading. Boundary conditions as well as loading are considered as symmetric; thus, the response of the sphere is expected to be symmetric. All of the properties of the thick-walled sphere, including mechanical, electrical and thermal properties, are considered dependent on the radial position, except for the relaxation time, which is considered a constant value along the radius. The governing equations of the sphere have been derived under heterogeneous anisotropic assumptions. The general form of the second law of thermodynamics, which is nonlinear in nature, and is called nonlinear energy equation is used. The number of the established equations is three, which includes the motion equation, energy equation and Maxwell electrostatic equation of Maxwell. These equations are obtained in terms of radial displacement, temperature difference and electric potential. The energy equation is derived based on Lord and Shulman theory with a single relaxation time. In the next step, by introducing dimensionless variables, the governing equations are provided in dimensionless presentation. Then these equations have been discretized using generalized differential quadrature method. Also, in order to follow the solution of the equations in time domain, Newmark method has been used. Since the system of equations is nonlinear, Picard algorithm is applied as a predictor-corrector mechanism to solve the nonlinear system of equations. Then numerical results are presented to investigate the propagation of mechanical, thermal and electric waves inside the heterogeneous sphere and also their reflection from the outer surface of the sphere. By examining the results, it can be seen that mechanical and thermal waves propagate with a limited speed, while the speed of electric wave propagation is infinite.

Risk factors for bicycle-related overtaking, rear-end, and door crashes in the United Kingdom

Abstract Background Prior studies have offered valuable insights into risk factors associated with bicycle crashes at intersections. Nevertheless, limited research focuses on investigating three types of bicycle crashes that commonly occur on road segments: overtaking, rear-end, and door crashes. Methods In the present research, risk factors for these three types of crashes on road segments were examined by analyzing British STATS19 reports of crashes from 1991 to 2020. We calculated adjusted odds ratios (AORs) with 95% confidence intervals (CIs) for multiple risk factors using multivariate logistic regression models. The analysis included 127,637 bicycle crashes, categorized into 18,350 overtaking, 44,962 rear-end, 6,363 door, and 57,962 other crashes. Results Speed limits of ≥ 40 miles per hour (mph) (AOR = 2.238, 95% CI = 2.159-2.320), heavy goods vehicles (HGVs) as crash partners (AOR = 2.867, 95% CI 2.473-3.323), and elderly crash partners (AOR = 2.013, 95% CI = 1.937-2.092) were significant risk factors for overtaking crashes. Notable risk factors for rear-end crashes included midnight hours (AOR = 1.269, 95% CI = 1.190-1.354) and unlit darkness (AOR = 1.486, 95% CI = 1.404-1.573). Speed limits 20-30 mph (AOR = 16.185, 95% CI = 13.514-19.382), as well as taxis and private rental automobiles (AOR = 2.695, 95% CI = 2.310-3.145), were factors associated with door crashes. Further intriguing findings from our joint-effect analysis included higher probabilities for overtaking crashes in rural regions when older drivers and HGVs were crash partners (AOR = 2.93, 95% CI = 2.79-3.08 and AOR = 2.62, 95% CI = 2.46-2.78). Conclusions The mentioned risk factors have remained mostly consistent since our previous study was conducted in 2011. However, current research elucidated that certain factors have become more significant under specific circumstances. For instance, in rural areas, cyclists were more likely to be involved in overtaking crashes with heavy goods vehicles as crash partners. Key messages • Bicycle crash; Road segment; Door crash; Overtaking crash; Rear-end crash

Analysis of traffic injury trends, and strategies for road safety promotion in Lithuania

Abstract Every year, about 1.3 million people die on the world’s roads. The aim of the study was to analyze the mortality trends from traffic injuries, and to discuss strategies for the prevention of road traffic injuries and road safety promotion in Lithuania. A longitudinal study was conducted, and regression analysis was applied. The study showed that, when analyzing the changes in mortality from the main causes of death over 2009-2019, the greatest progress in mortality reduction was observed in the category of deaths from external causes; whereas, the number of deaths from diseases of the circulatory system did not change, and the number of deaths from malignant tumors increased slightly. In Lithuania, the death rate from traffic injuries per 100,000 inhabitants decreased significantly over a 50-year period (1971-2020) (in the whole population, and in the subgroups of men and women). In Lithuania, during the fifty-year period (1971-2020), pedestrian mortality rates decreased significantly in the whole population, and children and adolescents (0-14, 15-19, and 0-19 years). Over 1998-2020, road traffic injury deaths decreased significantly in children age groups 0-14, 15-19, and 0-19. After exploring the preventive strategies used for road safety promotion, it was revealed that the most effective results were achieved, when education, safe environment creation, and legislation with enforcement were used. It was important to create a safe engineering environment, to separate pedestrian and bicycle paths from high-speed traffic on roads, to improve the visibility of road users, to apply measures that reduce driver’s fatigue, and to combat the usage of mobile phones while driving. Preventive activities were aimed at alcohol control, speed limitation, education of safe behavior of drivers, promotion of safe cars operation. The usage of safety belts and safety seats, helmets for motorcyclists and cyclists were increased through legislative and educational measures.