Search Results (127)

Urban green spaces (UGS) are essential for residents’ well-being, environmental quality, and social cohesion. However, previous studies have typically employed undifferentiated analytical frameworks, overlooking UGS types and failing to adequately measure the structural disparities of different UGS types within residents’ walking distance. To address this, this study integrates Gaussian Two-Step Floating Catchment Area models, Simpson’s index, and the Gini coefficient to construct an accessibility–diversity–equality assessment framework for UGS. This study conducted an analysis of accessibility, diversity, and equity for various types of UGSs under pedestrian conditions, using the high-density city of Shenzhen, China as a case study. Results reveal high inequality in accessibility to most UGS types within 15 min to 30 min walking range, except residential green spaces, which show moderate-high inequality (Gini coefficient: 0.4–0.6). Encouragingly, UGS diversity performs well, with over 80% of residents able to access three or more UGS types within walking distance. These findings highlight the heterogeneous UGS supply and provide actionable insights for optimizing green space allocation to support healthy urban development. Full article

We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations. Full article

Long-term and high-precision positioning is the key to the pedestrian indoor positioning method. The estimation methods relying only on the inertial measurement unit (IMU) itself lack external observations that can provide absolute information, and the cumulative error easily leads to the distortion of the calculated trajectory. In this paper, based on the Extended Kalman Filter (EKF) algorithm, the environmental magnetic field information is taken as the external observation quantity, and a positioning method combining inertial navigation and the magnetic field is proposed. The cumulative error is suppressed from both the yaw angle and pedestrian pose, and the overall navigation and positioning accuracy is improved. The experimental results show that the proposed fusion method greatly improves the suppression of yaw angle and displacement errors. In a total distance of 297.08 m, the yaw angle error is reduced from 11.043° to 4.778°, and the position error is reduced from 8.999 m to 0.364 m. The relative average error decreases from 3.02% to 0.12%. Full article

Large parks play a key role in the identity of urban public spaces and as destinations for residents’ urban walks, with the social benefits they provide being irreplaceable by other types of green spaces. This study examines the accessibility of large urban parks in Rizhao, China, focusing on spatial distribution, service equity, and optimization strategies. Using GIS-based walking route proximity analysis, the study identifies significant accessibility gaps in high-density urban areas. Rizhao is a typical coastal tourist city, selected as the study area due to its low level of urbanization and the underutilization of its natural resources. This study uses online map data to evaluate the service efficiency and supply–demand heterogeneity of large parks from multiple perspectives, proposing targeted, practical, and micro-intervention-based spatial measures based on typical case analysis. The results show that 70.52% of the population in the study area is served by park entrances within a 1500 m walking distance, indicating that a considerable portion of residents remain beyond a reasonable walking distance. In the context of urban renewal and sustainable development, this study proposes practical improvements to park accessibility, including suggestions for determining suitable locations for new large parks as a long-term goal, alongside low-cost interventions such as increasing park entrances to maximize the use of existing resources and optimizing pedestrian routes (including opening gated communities and adding crossing facilities) to improve park walking service catchment in smaller environments. This study provides insights for urban park renewal, retrofitting, and expansion, supporting accessibility measures in planning practices, and is expected to provide valuable references for urban managers and policymakers. Furthermore, the study suggests that policy adjustments are necessary to integrate green spaces into urban development more effectively, particularly in rapidly urbanizing areas. Full article

This study evaluates pedestrian continuity in Panama City, analyzing disruptions and the spatial relationship between crossings and transit stations. Using GIS and field validation, pedestrian networks were assessed based on their continuity, defined by well-maintained sidewalks and marked crossings, and discontinuities, caused by absent sidewalks, commercial infrastructure, service stations, and unmarked crossings. Two urban zones with contrasting layouts were analyzed: Zone A, characterized by a regular grid structure, and Zone B, marked by irregular planning. Results indicate that 67.55% of the study network maintains pedestrian continuity. Additionally, 46.79% of the measured distances between bus stops and formal pedestrian crossings exceed 100 m. The average length of continuous paths is 73.37 m in Zone A and 45.60 m in Zone B. Encroachments by businesses are the primary cause of fragmentation, and the study reflects an important impact of car-oriented urban infrastructures on discontinuities, such as service stations. These stations cause average disruptions of 34.69 m per station in Zone B and 27.56 m in Zone A. The research highlights the need for urban planning strategies to ensure pedestrian continuity, particularly in fragmented urban grids, and underscores the importance of an in-depth consideration of continuity in pedestrian network characterization studies. Full article

Pedestrian comfort is not only related to the health of the human body, but also directly impacts the efficiency and lifespan of building materials. Raised floors have more complex geometric characteristics and structural dynamic properties compared to traditional flooring, which necessitates an analysis of pedestrian comfort. This study explores four different configurations of raised floors in prefab bathroom units to evaluate their pedestrian comfort. Through theoretical and experimental modal analysis, the natural frequencies and potential deformation areas are identified, enabling the calculation of peak acceleration of the raised floors under walking excitation. The study then quantitatively assesses the pedestrian comfort of raised floors based on natural frequency and peak acceleration measurements, proposing an optimization design method using a multi-island genetic algorithm (MIGA). By adjusting parameters such as the support leg distance, beam shape, and support board surface layer thickness, an optimal balance between pedestrian comfort and heat transfer performance is achieved. Through the integration of simulation and experimental techniques, the study presents an efficient and cost-effective optimization design method for raised floors and explores the impact mechanism between design variables and pedestrian comfort performance. Full article

To suppress pedestrian positioning drift, a velocity constraint commonly known as zero-velocity update (ZUPT) is widely used. However, it cannot correct the error in the non-zero velocity interval (non-ZVI) or observe heading errors. In addition, the positioning accuracy will be further affected when a velocity error occurs in the ZVI (e.g., foot tremble). In this study, the foot, ankle, and shank were regarded as a triangular structure. Consequently, an angle constraint was established by utilizing the sum of the internal angles. Moreover, in contrast to the traditional ZUPT algorithm, a velocity constraint method combined with Coriolis theorem was constructed. Magnetometer measurements were used to correct heading. Three groups of experiments with different trajectories were carried out. The ZUPT method of the single inertial measurement unit (IMU) and the distance constraint method of dual IMUs were employed for comparisons. The experimental results showed that the proposed method had high accuracy in positioning. Furthermore, the constraints built by the lower limb structure were applied to the whole gait cycle (ZVI and non-ZVI). Full article

Town development, a crucial stage of urbanization, has been increasingly prioritized in recent sustainable socio-economic growth strategies. Vitality, especially the one measured by aggregate crowd behaviors, is widely recognized as a crucial development element. Conducting comprehensive assessments of the drivers of town vitality, particularly crowd vitality, is thus essential for addressing challenges and monitoring progress. This study examines representative towns in China and employs multiple datasets along with XGBoost-SHAP to investigate the mechanisms of development environment factors on aggregate crowd vitality. Key findings highlight the study’s novelty and broader implications: (1) The degree of industrial agglomeration is the most significant factor impacting the dependent measures, providing new data-driven insights into the role of economic clustering in town development. (2) Other indicators, such as the minimum distance to the town center, the enclosure, and car and pedestrian friendliness, can effectively predict town vitality, offering practical considerations for town planning. (3) Industrial innovation and diversification, rational planning of living circles, and enhancement of town conditions emerge as three crucial strategies for promoting urbanization. This study enhances empirical insights with strategies for addressing urbanization challenges, emphasizing how crowd data can be used to inform urbanization policies and planning practices, aiding urban planners in building more sustainable systems. Full article

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments. Full article

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections. Full article

A simple self-experiment allows for the measurement of individual pedestrian movements, aiming to understand the deviations from intended directions seen in heterogeneous human crowds. The method involves pedestrian self-observations using the GPS sensor of a smartphone, providing a unique perspective on individual behavior within a crowd and offering a means to evaluate average pedestrian speed. The study is focused on individual mobility in the context of a heterogeneous crowd rather than the behavior of a crowd composed of similar types of people. With this study, an important contribution to the understanding of interpersonal distances in heterogeneous crowds is made. Full article

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology. Full article

Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, trees, street furniture, and traffic signs) makes it the most economical complement to the cameras in the visible spectrum in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithms will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its average error in distance, the maximum errors, and the number of echoes per second it can provide. In this work, we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters. Full article

The purpose of this research is to investigate the thermal impact of urban green systems (UGS) (trees and living facades) and high albedo pavements on reducing the urban heat island (UHI) effect in London at the pedestrian street level. The research assesses the impact of UGS by suggesting practicable urban greenery-covering densities (25% and 50%) and using high albedo pavement in current and future climatic scenarios (2050 and 2080). This approach is intended to encourage pedestrians to walk longer distances for longer durations during the warmer months, following the Transport for London’s (TfL) 2017 Healthy Streets initiative. The research seeks to measure the advantages and assess the possible impact on the comfort and activities within urban streets. The study adopts a quantitative research design using ENVI-met modelling and questionnaires. Simulation results, the subject of this paper, confirmed that, across three climatic scenarios, the optimal UGS for thermal comfort is 50% trees followed by 25% trees, dependent on street orientation and solar access. Living facades (LF) with 25% and 50% covering had no discernible effect on the comfort of pedestrians, whereas high albedo pavement increases heat stress. Full article

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on a smart cane. The intelligent guide system relies on 2D LiDAR, an RGB-D camera, IMU, GPS, Jetson nano B01, STM32, and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on the cartographer algorithm, thus achieving simultaneous localization and mapping (SLAM). At the same time, through the improved YOLOv5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving, and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved YOLOv5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the omnidirectional wheels at the bottom of the smart cane and provide the smart cane with a self-leading blind guide function, like a “guide dog”, which can effectively guide the visually impaired to avoid obstacles and reach their predetermined destination, and can quickly and effectively identify the obstacles on the way out. The mapping and positioning accuracy of the system’s laser SLAM is 1 m ± 7 cm, and the laser SLAM speed of this system is 25~31 FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor environments. The improved YOLOv5 helps to identify 86 types of objects. The recognition rates for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25–26 FPS. Full article