Pedestrian System Research Articles

Optimized Right-Turn Pedestrian Collision Avoidance System Using Intersection LiDAR

The incidence of right-turning pedestrian accidents is increasing in South Korea. Most of the accidents occur when a large vehicle is turning right, and the main cause of the accidents was found to be the driver’s limited field of vision. After these accidents, the government implemented a series of institutional measures with the objective of preventing such accidents. However, despite the institutional arrangements in place, pedestrian accidents continue to occur. We focused on the many limitations that autonomous vehicles, like humans, can face in such situations. To address this issue, we propose a right-turn pedestrian collision avoidance system by installing a LiDAR sensor in the center of the intersection to facilitate pedestrian detection. Furthermore, the urban road environment is considered, as this provides the optimal conditions for the model to perform at its best. During this research, we collected data on right-turn accidents using the CARLA simulator and ROS interface and demonstrated the effectiveness of our approach in preventing such incidents. Our results suggest that the implementation of this method can effectively reduce the incidence of right-turn accidents in autonomous vehicles.

ANALYSIS OF THE CROWD MANAGEMENT AND PEDESTRIAN MOVEMENT DURING HAJJ PILGRIMAGE ON MAKKAH

The Hajj pilgrimage, the world's largest annual mass gathering, poses significant challenges in crowd management and pedestrian movement due to the sheer number of participants and logistical complexities. This study emphasizes the need for effective strategies to ensure the safety of millions of pilgrims in Mecca, Saudi Arabia. By reviewing literature and analysing pedestrian movement systems, it identifies key bottlenecks and safety risks, especially during high-density periods like Nafra day. Field observations from 2019 to 2024, including the post-COVID-19 era, offer insights into crowd behaviour and the effectiveness of management strategies. The study highlights the importance of integrating advanced simulation tools with urban design to optimize pedestrian pathways and prevent overcrowding, contributing to Saudi Vision 2030's goals of enhancing the pilgrimage experience and ensuring participant safety.

IMPLEMENTATION OF PEDESTRIAN SAFETY SYSTEMS IN THE URBAN INFRASTRUCTURE OF KHARKIV

The article focuses on pedestrian safety issues within the urban infrastructure of Kharkiv City in Ukraine and the implementation of modern protection systems on roads. It examines approaches to organising pedestrian zones and safety islands, with particular emphasis on the RS PSS system, designed to enhance pedestrian safety on roads with high traffic intensity. Significant attention goes to the growing number of road traffic accidents (RTAs) at pedestrian crossings and main city highways. From 2018 to 2024, an analysis of accident hotspots took place based on statistical data. This analysis led to the creation of a map of RTAs, which helped identify the most dangerous areas for pedestrians and determine the priority locations for safety measures. One of the proposed solutions discussed is using bollards – protective posts that prevent vehicles from entering pedestrian areas. The article analyses the effectiveness of bollards in combination with other safety measures, such as safety islands and traffic management systems. In addition to bollards, landscape solutions that protect pedestrians and blend into the urban environment are also mentioned. The main focus is on the RS PSS system, which consists of pedestrian slabs and safety islands manufactured using specialised technology. The system has been tested in various locations throughout the city and has demonstrated a significant reduction in RTAs after its implementation. The article illustrates the process of installing the system using the example of the reconstruction of a safety island on Liudviha Svobody Avenue. The use of modern equipment and technology ensured the rapid and efficient completion of the project. According to official data, the number of RTAs in this area decreased from 21 to 4 after completing the project. In conclusion, the implementation of new safety systems has had a positive impact on reducing accidents and improving pedestrian protection. There is also a noted need for further development and advancement of such systems and the integration of advanced technologies to enhance urban infrastructure. Keywords: pedestrian safety, pedestrian safety systems, safety islands, bollard, refuge.

An accurate smartphone-based indoor pedestrian localization system using ORB-SLAM camera and PDR inertial sensors fusion approach

Using one’s own smartphone for indoor navigation, especially for visually impaired individuals, can be a valuable and empowering tool to promote independence and safe mobility. Additionally, the integration of Pedestrian Dead Reckoning (PDR), which relies on smartphone sensors like accelerometers and gyroscopes, is a well-known and widely used approach in the field of indoor navigation. However, despite continuous improvements in sensor technology, addressing drift issues remains a principal problem in the effective implementation of PDR systems. On the other hand, although the use of Simultaneous Localization and Mapping (SLAM) technologies in smartphones for indoor navigation has also gained significant adoption in recent years, there are indeed challenges associated with SLAM, including low localization accuracy and potential tracking divergence. To overcome the above limitations, this paper presents a novel and promising solution for smartphone-based indoor localization, addressing the limitations of both PDR and SLAM through their synergistic integration. In fact, to correct the accumulated errors of the proposed localization system and thus improve the accuracy, our approach uses an ORB-SLAM camera and PDR inertial sensors via a Kalman filter to achieve back-end fusion. Experimental results show that our localization system significantly reduces the mean localization error by 62%, marking a considerable improvement over the standalone PDR and SLAM systems. Moreover, compared with state-of-the-art techniques, our system achieves high localization accuracy, with an improvement of 59%, making it highly suitable for today’s real-time applications, especially beneficial for visually impaired people.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Camera-in-the-loop based test scenario generation method for pedestrian collision avoidance system

The Pedestrian Collision Avoidance System (PCAS) of Intelligent Vehicle (IV) can be effective in preventing the occurrence of traffic accidents. However, the complicated operation environments introduce great challenges to the camera used by the PCAS. Therefore, the camera based PCAS should be fully tested and evaluated before deployment. The traditional simulation test for the camera based PCAS attempted to use geometric or physical simulation models, which have low reality and are suitable for the primary stage of the PCAS development. Camera-in-the-Loop (CIL) test is one of Hardware-in-the-Loop methods that embeds the real camera hardware into the virtual simulation system to test the camera. CIL can utilize the real hardware response while overcoming the common simulation weakness of fidelity. In this paper, we construct a CIL test platform, and propose the CIL based test scenarios generation and scenario parameter impact evaluation method for PCAS. First, we construct the CIL test platform whose image quality and functional confidence are both validated to prove CIL credibility. Second, the PCAS under the test is analyzed and the corresponding test scenario parameters are designed. In order to accelerate the test scenario generation, a Greedy Based Combination test method (GBC) based on the CIL is proposed. The Chi-square analysis and two-factor of variance analysis verification methods are used to analyze the influence of individual and multiple scenario parameters on the PCAS performance. The experiment results show that the GBC improves the test speed by 12 times compared to the traversal test, and the frequency ratio of each scenario parameter is no more than 3% different from that of the traversal test. Also, GBC has an equivalent ability to find the PCAS collision scenarios parameter combination to the traversal test.

Effects of driver’s braking behavior by the real-time pedestrian scale warning system

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian’s gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

Effect of moving walkway arrangement on unidirectional crowd flow characteristics

As moving walkways (MWs) are being used in larger airport terminals, train stations and so on, there is growing concern about their impact on the crowd movement. Some existing applications have shown that these passenger conveyors are not always capable of providing a better level of service, therefore, it is necessary to examine the effect of MWs on crowd dynamics thoroughly. In this work, we first construct more realistic numerical experimental scenarios and utilize a macroscopic continuum pedestrian model to simulate crowd movement in long corridors with the MWs under various conditions, including demand level, operation speed, position, landing area, and layout. Then, based on the comparison of experiment results such as total travel time, density distribution, queue size, and total crowd pressure, we analyze the effect of MWs on unidirectional crowd dynamics. From the results, we prove that the presence of a landing area between two segments of MWs can help to split the flow in the main direction and enhance the safety of crowd movement. Moreover, according to our research, compared to increasing the operation speed, modifying the position and layout of MWs is more effective and cost-saving in improving traffic efficiency. In summary, this paper investigates the effect of MWs and provides a better understanding to organizers when designing pedestrian conveyor systems.

Design Strategies to Improve Metro Transit Station Walking Environments: Five Stations in Chongqing, China

While transit-oriented development (TOD) has been widely adopted in urban design alongside the expansion of urban metro transit, the creation of pedestrian-friendly environments has often been overlooked during implementation. This has resulted in a lower walking advantage around metro transit stations. To address this issue and encourage walking and public transport use in metro transit station areas, this study undertook a quantitative comparative analysis of the pedestrian environment in five Chongqing metro transit station areas. The analysis focused on three key dimensions: “comprehensive evaluation”, “basic scale”, and “structural quality”. The comprehensive evaluation considered factors such as the pedestrian catchment area ratio, POI kernel density distribution, and crowd agglomeration. The basic scale dimension comprised floor area ratio, building density, pedestrian road density, and the quantity of station entrances and exits. Finally, structural quality factors included land use type mixing degree, POI function mixing degree, intersection connectivity, median street length, pedestrian route directness, and green view index. Based on these analyses, this study proposes a series of pedestrian environment design strategies including land use and transportation. The strategies for land use advocate for “developing compact and diverse land use”, “strengthening attraction of station center”, “positioning large projects on the edge”, “restricting private transportation capabilities”. The strategies for transportation consist of “increasing pedestrian road density”, “traffic calming organization”, “subdivision of road types”, and “three-dimensional pedestrian traffic system”. These strategies aim to create a more humanized and environmentally friendly pedestrian environment, proactively rise to the challenge of climate change, thereby cultivating sustainable urban development.

The application of deep learning in autonomous driving

Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected in its own advantages, but also in its ability to lead the development direction of intelligent transportation in the future, helping to promote the development of intelligent transportation systems, realize the intelligent interconnection between vehicles and vehicles, vehicles and road infrastructure, and further enhance the safety and convenience of traffic travel. Therefore, autonomous driving technology is a significant innovation that will bring a better and more convenient future for mankind. In this study, a method based on sensor internal and external parameter calibration transformation matrix, road target detection algorithm, automatic vehicle detection method, pedestrian intention prediction technology and automatic pedestrian recognition system are analyzed, which are applied to object detection and obstacle avoidance. This article provides a good overview of the field of autonomous driving.

Pedestrian Collision Avoidance in Autonomous Vehicles: A Review

Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community over the last ten years. For this purpose, we propose a classification of studies in the literature in terms of the following: (i) pedestrian detection methods, (ii) collision avoidance approaches, (iii) actions, (iv) computing methods, and (v) test methods.

Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety

Rapid advances in technology and shifting tastes among motorists have reworked the contemporary automobile production sector. Driving is now much safer and more convenient than ever before thanks to a plethora of new technology and apps. Millions of people are hurt every year despite the fact that automobiles are networked and have several sensors and radars for collision avoidance. Each year, many of them are injured in car accidents and need emergency care, and sadly, the fatality rate is growing. Vehicle and pedestrian collisions are still a serious problem, making it imperative to advance methods that prevent them. This paper refines our previous efficient VANET-based pedestrian safety system based on two-way communication between smart cars and the cell phones of vulnerable road users. We implemented the scheme using C and NS3 to simulate different traffic scenarios. Our objective is to measure the additional overhead to protect vulnerable road users. We prove that our proposed scheme adds just a little amount of additional overhead and successfully satisfies the stringent criteria of safety applications.

Modelling yielding dilemma zone for drivers at non-signalised crosswalks

In developing countries like China, non-signalised crosswalks represent a considerable risk to pedestrian safety. This study introduces a “yielding dilemma zone” model to tackle the decision-making challenges encountered by drivers at these crosswalks. Field data collected via drones at three non-signalised crosswalks in Xi'an (China) facilitated the analysis of 1687 instances of driver yielding behaviour. The study employed Support Vector Machine (SVM), Artificial Neural Network (ANN), and Binary Logistic Regression (BLR) models to establish the boundaries of the dilemma zone. Additionally, it utilized surrogate safety metrics, such as the deceleration rate required to stop (DRS) and time to crossing (TC), to compare risks between dilemma and non-dilemma zones. Each modelling approach provided insightful findings. The SVM and ANN models identified the speed-distance dilemma zones, while the BLR model determined the multifactorial dilemma zones by considering characteristics such as pedestrian, vehicle, and traffic condition. The findings underscore the significance of the “yielding dilemma zone” for understanding driver-pedestrian interactions at non-signalised crosswalks. The innovative multi-model approach uncovered that pedestrian speed, vehicle type, vehicle approach speed, adjacent vehicle number, driving directions, and pedestrian crossing stage significantly influence this interaction. indicated a higher probability of collisions within dilemma zones compared to non-dilemma zones across all models. The study suggests that the SVM model could be instrumental in creating passive physical warning signs, while the ANN and BLR models could significantly contribute to real-time pedestrian and driver assistance systems. The “yielding dilemma zone” model, with its comprehensive approach, presents a promising avenue for enhancing pedestrian safety, intelligent transportation systems, and traffic management at non-signalised crosswalks.

Bio-Inspired Dark Adaptive Nighttime Object Detection.

Nighttime object detection is challenging due to dim, uneven lighting. The IIHS research conducted in 2022 shows that pedestrian anti-collision systems are less effective at night. Common solutions utilize costly sensors, such as thermal imaging and LiDAR, aiming for highly accurate detection. Conversely, this study employs a low-cost 2D image approach to address the problem by drawing inspiration from biological dark adaptation mechanisms, simulating functions like pupils and photoreceptor cells. Instead of relying on extensive machine learning with day-to-night image conversions, it focuses on image fusion and gamma correction to train deep neural networks for dark adaptation. This research also involves creating a simulated environment ranging from 0 lux to high brightness, testing the limits of object detection, and offering a high dynamic range testing method. Results indicate that the dark adaptation model developed in this study improves the mean average precision (mAP) by 1.5-6% compared to traditional models. Our model is capable of functioning in both twilight and night, showcasing academic novelty. Future developments could include using virtual light in specific image areas or integrating with smart car lighting to enhance detection accuracy, thereby improving safety for pedestrians and drivers.

Sensitivity of human-induced vibrations and comfort analysis to partially rigid STS joints in timber-to-timber composite floors

The study of floors vibration serviceability represents a well-known challenging task and has a critical role in comfort analysis, assessment and optimization of pedestrian systems in general. To this aim, several consolidated and/or simplified tools are available in the literature to address the most important aspects. In practice, however, most of assumptions for the analysis and verification are based on the use of rough mechanical models of joints and connections, which have indeed a primary role especially for composite floors. In this paper, the attention is given to timber-to-timber floors with inclined self-tapping screws (STS). A parametric Finite-Element (FE) numerical investigation is carried out by taking into account conventional walking features and a case-study timber-to-timber composite beam, well representative of a one-way wooden floor. A major advantage for structural and pedestrian modelling stages is taken from previous literature efforts, while large attention is spent for structural analysis and vibration serviceability assessment tasks, as a function of the STS mechanical parameters (first of all the secant stiffness). Comparative linear modal and non-linear time history analyses are carried out and addressed towards limit mechanical configurations for STS joints, in terms of vibration frequency considerations, as well as structural response and comfort assessment, based on classical performance indicators. The parametric numerical results are further discussed based on simplified methods provided by selected design standards. A critical analysis of major effects due to partial joint rigidity and floor performance classification is presented, both in terms of expected structural safety and comfort levels for similar systems.

Enhanced indoor positioning through human-robot collaboration

AbstractIndoor positioning is a critical component for numerous applications and services. However, GNSS systems face challenges in delivering accurate positioning information in indoor environments. Current indoor positioning research primarily concentrates on enhancing the positioning performance of individual terminals through various techniques. As we transition into the Internet of Things (IoT) era, former indoor positioning methods need refinement. In this paper, we propose a novel indoor positioning method that leverages robots as mobile base stations to mitigate the problem of inadequate fixed base stations and aims to enhance positioning accuracy by incorporating pedestrian inertial navigation data. The process involves several steps. First, the mobile robots accurately determine their positions and performing coordinate transformations to ensure consistency with pedestrian coordinate systems. Then, pedestrians use the ranging information from these robots along with their smartphones’ sensors for multi-source fusion positioning. Finally, an Extended Kalman Filter (EKF) is applied to fuse the multiple sources of data, considering various sources of errors, to provide enhanced positioning performance. Experimental results demonstrate the effectiveness of this approach in addressing indoor positioning challenges. This method could benefit numerous scenarios involving robots, enhancing pedestrian positioning accuracy and overall system robustness. The paper provides a comprehensive exploration of this proposed method, its implications, and potential directions for future advancements.

Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System

Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System

A study on the current status of nationwide designation and operation of improving pedestrian convenience road system (Hokomichi) in the system's pioneering period

A study on the current status of nationwide designation and operation of improving pedestrian convenience road system (Hokomichi) in the system's pioneering period

Growth, yield and oil quality of adult pedestrian olive orchards grown at four different planting systems.

This study evaluated growth, yield and olive oil quality of mature pedestrian olive orchards. Trees of three Sicilian cultivars Calatina, Nocellara del Belice and Abunara were planted at four combinations of planting densities and training forms. Trees at 2 × 5m were trained to central leader (CLx2), those at 3 × 5m to free palmette (FPx3), those at 4 x 5 to globe vase (GVx4), and those at 5 x 5 to poly-conic vase (PVx5). 'Calatina' had the smallest trees in terms of trunk size in all growing systems, while canopy size of trees at higher densities was similar for all three cultivars. 'Calatina' was also the most growth efficient (m3 of canopy per cm2 of TCSA) and produced the least amount of pruning wood in the hedgerow systems (CLx2 and FPx3). Fruit yield per tree tended to be higher in more vigorous cultivars (Abunara and Nocellara) grown to 3D systems (GVx4 and PVx5), while 'Calatina' was the most yield efficient (kg of fruit per cm2 of TCSA) especially in the hedgerow growing systems. Fruit and oil yield per ha and average production value tended to be highest in CLx2 trees and lowest in GVx4 trees, with 'Calatina' showing the sharpest changes and 'Nocellara' the smallest changes among growing systems. According to the Jaen index, CLx2 tended to induce earlier fruit maturation, followed by PVx5, GVx4, and FPx3. The growing system did not affect oil fatty acid composition, while 'Calatina' had the highest amount of mono-unsaturated fatty acids and the lowest amount of saturated fatty acids. 'Abunara' oils exhibited the highest amount of total phenols in CLx2, while 'Calatina' and 'Nocellara' oils exhibited the highest amount in FPx3 and PVx5. Both, trans-2-hexenal ("cut grass" sensory note) and hexenyl acetate ("floral" sensory note) tended to be lowest in oils from trees grown at CLx2 and highest in those from trees grown at GVx4, showing a somewhat inverse relationship with fruit ripening degree. The outcome of the present study on mature pedestrian orchards shows that proper combinations of cultivars, planting densities, and training forms (canopy shape) may result in efficient intensive systems for growing olive in areas where super-high density systems cannot be profitable due to agronomic and environmental limitations (water shortage, steep sloping sites, small farm size, etc.). Pedestrian growing systems can also be used to exploit olive biodiversity by allowing the use of available local genotypes. For this reason, they may represent an effective and sustainable solution against unexpected climate changes and associated emerging diseases.