Analysis of walking speeds and success rates on mid-block crossings using virtual reality simulation

Medina, Alberto M. Figueroa; Valdes-Diaz, Didier; Colucci-Ríos, Benjamín; Cardona-Rodríguez, Natacha; Chamorro-Parejo, Andrés

doi:10.1016/j.aap.2023.106987

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…To overcome this challenge, we propose a triggering function that utilises RSSI to estimate the distance travelled by a person in each path trajectory. Assuming that the paths taken by the person are plotted as shown in Figure 5, we can use the distance (d) between each node and the height (h) between the The Pythagorean theorem is used to predict the trajectory of a person taking path 1, which crosses the LoS at points a, b, and c with a walking speed of s. The average human walking speed is typically measured between 1 and 1.6 m/s, with a value of 1.3 m/s considered typical [32]. For example, if the spacing distance between nodes is d = 3 m and the middle distance between the transmitting (Tx) and receiving (Rx) positions is h = 6 m, a triangular shape with known parameters is developed.…”

Section: Tracking Area Verticallymentioning

confidence: 99%

“…The Pythagorean theorem is used to predict the trajectory of a person taking path 1, which crosses the LoS at points a , b , and c with a walking speed of s . The average human walking speed is typically measured between 1 and 1.6 m/s, with a value of 1.3 m/s considered typical [32]. For example, if the spacing distance between nodes is d = 3 m and the middle distance between the transmitting (Tx) and receiving (Rx) positions is h = 6 m, a triangular shape with known parameters is developed.…”

Section: System Structurementioning

confidence: 99%

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Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Abuhoureyah,

Yan Chiew,

Bin Mohd Isira

et al. 2023

IET Wireless Sensor Systems

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The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device‐free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine‐grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real‐time location‐tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location‐independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real‐world environments.

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Section: Tracking Area Verticallymentioning

confidence: 99%

Section: System Structurementioning

confidence: 99%

Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Abuhoureyah,

Yan Chiew,

Bin Mohd Isira

et al. 2023

IET Wireless Sensor Systems

View full text Add to dashboard Cite

show abstract

“…By examining data from Nanjing’s Xinjiekou area, Lu Jian and Ye Huiqiong [ 18 ] established elderly and young people’s average crossing speeds as 1.17 m/s and 1.29 m/s, respectively. Figueroa-Medina et al [ 19 ] pinpointed age and acceptable gap as the significant determinants of pedestrian crossing speed. Ku et al [ 20 ] employed a discriminative algorithm based on deep image learning to conduct a quantitative analysis of the safety and economic issues arising from traffic vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning

Cai,

Wang,

2024

Sensors

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Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

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“…We are not the first to propose VR as a medium for studying road-crossing. Factors of crossing sustainability that are attributable to static physical infrastructure have been examined in prior virtual crossing simulators [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In this paper, we instead focus on the under-examined themes of how dynamic signals (from pedestrians and vehicles, alongside static built infrastructure) factor into crossing sustainability.…”

Section: Introductionmentioning

confidence: 99%

Using Immersive Virtual Reality to Study Road-Crossing Sustainability in Fleeting Moments of Space and Time

Torrens,

Kim

2024

Sustainability

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Despite a history of year-by-year reduction in road-crossing harm and fatality in the United States, the trend reversed course in 2009 and road-crossing has grown more hazardous since. Within this tendency, there has been a marked uptick in risk to urban crossers who are neither children nor elderly. The age group in between these extremes represents a bulk of urban crossers, for whom theoretical explanations for crossing behavior that are focused on youth and senior crossing factors often do not apply. New insight is likely required to explain why the rate of crossing harm is growing for the 20–44 age group, but declining among the young and elderly. However, it is difficult to experiment with crossing scenarios in a real-world context, where significant dangers are present and for which the uniqueness of crossers and crossing sites is abundant. In this paper, we introduce an end-to-end system for examining crossing behavior using a unique combination of real human crossing behavior, made safe through the combination of agent-based models, motion capture, virtual geographic environments, and immersive technologies from virtual reality. We demonstrate that this combination of methods can be deployed to examine very high resolution and very high specificities of crossing scenarios and behaviors, with reach to individual crossers and their judgment over tiny windows of space and time. We demonstrate that the system can reproduce known effects from the theoretical literature and from existing case studies, while also generating huge swaths of empirical and diagnostically useful data on crossing actions, interactions, and reactions relative to fleeting events and phenomena of urban geography, traffic dynamics, and ambient pedestrian crowds. To prove the concept, we deploy the system to investigate crossing judgment behavior among the 20–44 age group.

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Analysis of walking speeds and success rates on mid-block crossings using virtual reality simulation

Cited by 10 publications

References 20 publications

Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning

Using Immersive Virtual Reality to Study Road-Crossing Sustainability in Fleeting Moments of Space and Time

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