Advanced Warning System Research Articles

The dynamics of frost formation on road surfaces: an experimental analysis

This study delves into the complexities of frost formation on road surfaces, a phenomenon that presents significant safety hazards in transportation due to its sudden emergence and unpredictable nature. Despite advanced meteorological warning systems for snowfall and freezing rain, black ice and frost remain difficult to predict and counteract. To address this, a controlled indoor simulation experiment was designed to investigate the characteristics of road surface frosting at low temperatures.Results from the experiment indicated that growth of the frost layer thickness follows a parabolic trend with extended freezing time yet the mass of the frost layer increases at a roughly linear rate. The data also revealed that lower temperatures expedite the phase transition of water vapor to ice, leading to faster increases in frost layer height. Additionally, the effects of air temperature and velocity on frost properties were examined. Interestingly, higher air temperatures facilitated rapid frost formation initially, but later stages displayed a plateau phase in the rate of accumulation. Furthermore, increased air velocity up to 1 m/s resulted in greater frost mass, but higher velocities diminished frost formation due to enhanced heat transfer.In conclusion, this study offers a detailed analysis of frost layer development under controlled conditions, providing valuable insights into the environmental factors influencing this hazardous phenomenon. The findings contribute to the understanding of frost dynamics on road surfaces, with implications for improving predictive models and developing effective countermeasures for road safety during icy conditions.

Machine learning-based modeling and fogging prevention strategies for ice rink environments

An ice rink designed as a specialized public building for ice sports, requires a precise indoor thermal environment to function optimally. Excess moisture in this environment leads to fogging issues. This study focuses on investigating an ice rink in Taiwan, employing machine learning-based modeling for fogging prevention. Utilizing logistic regression, we develop a fogging probability model, which serves as a pivotal advanced warning system for critical operational decision-making. Subsequently, we employ Deep Neural Networks (DNN) to predict the supply air temperature and humidity of two Air Handling Units (AHUs), extending this methodology to model the air conditions within the ice rink arena. The models achieve an impressive R2 of 0.94. Leveraging this predictive capacity, our study estimates fogging occurrences through a fogging line, exhibiting high classification accuracy of 96 %. Additionally, clustering analysis utilizing the k-means algorithm is conducted, serving as the initial condition for optimization. The optimization phase, employing a genetic algorithm, results in fogging prevention rates between 72 % and 85 % across clusters. This research indicates the efficacy of a machine learning-based approach in addressing fogging challenges, providing insight into effective preventive measures to maintain an optimal ice rink environment.

Are Warnings Suitable for Presentation in Head-Up Display? A Meta-Analysis for the Effect of Head-Up Display Warning on Driving Performance

Head-up display (HUD) warnings are gaining increasing attention with the growing importance of advanced warning systems in vehicles for improving driving safety. However, the effectiveness of HUD warnings is still controversial considering the various driving performance indexes, warning modalities, and driving scenarios used in previous studies. This study conducts meta-analyses of the relevant literature to examine whether HUD warnings (both unimodal HUD warnings and multimodal warnings with HUD) have advantages compared with other warnings (head-down display warnings, auditory warnings, tactile warnings, and multimodal warnings without HUD) for driver reaction times and driving quality. Overall, 33 articles were included in four meta-analyses. Additionally, the effects of the related moderator variables of scenario, mapping of warnings, and non-driving-related tasks (NDRTs) on the relationship between using HUD warnings and using non-HUD warnings was analyzed. The results show that using unimodal HUD warnings leads to faster reaction times compared with head-down display warnings but has no significant advantage on driving performance over auditory warnings in general and results in slower reaction times than using tactile warnings. However, results of the moderating effects suggest that using HUD warnings in non-critical scenarios and using high mapping HUD icons (i.e., HUD warnings with specific hazard information) are conducive to improving driving performance in comparison with other warning types. These results highlight the potential advantages of HUD in transmitting warning information. The future application and design of HUD warnings may need to focus on specific situations considering iconic mapping to an event.

Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH.

Transforming farming with intelligence: Smart vibration monitoring and alert system

During the evolution towards digital agriculture, the pivotal role of tractor riding necessitates a focus on improving operator performance and well-being. While most research has centered around vibration analysis, tangible solutions to control elevated vibration levels remain rare. The study aims to introduce an intelligent ThingSpeak-Enabled IoT (Internet of Things) solution that provides real-time monitoring and generates prompt warning alerts for tractor operators when vibrations exceed safe thresholds. The initial phase involved the real-time measurement of WBV (whole-body vibration) and SEAT (seat effective amplitude transmissibility). Following this, the secondary phase encompassed the analysis and validation of the system in cases where WBV and SEAT exceeded the recommended limits. The experimental design comprised 135 trials by systematically varying tractor ride parameters, including average speed (m/s), average depth (m), and pulling force (kN) levels. Daily vibration exposure response ranged from 0.43 m/s² to 0.87 m/s² with a mean exposure of 0.64 m/s2, surpassing the EAV (exposure action value) threshold of 0.5 m/s². The SEAT values ranged between 91.37 and 133.08 with a mean of 108.35, that indicates insufficient seat isolation capacity, i.e., < 100. Statistically, the study ascertained a significant influence of average speed and average depth WBV and SEAT responses at a 5% significance level. It underscores the potential efficacy of altering speed and depth parameters to attenuate vibration exposure levels. Further, the effectiveness of the system was tested through the automatic transmission of warning alerts via emails, text messages, and flashing red LED light on the IoT system. This critical feature provides considerable utility for tractor operators to adjust ride settings, ensuring that the ride remains within safe vibration limits. Furthermore, adopting such an advanced warning system in tractor manufacturing signifies a pioneering step towards sustainably enhancing operator well-being.

Sensor Based Flood &amp; Landslide Pre-Alert System

The proposed system is an advanced early warning system designed to mitigate disasters such as landslides and floods by providing crucial minutes of warning. It consists of a wireless sensor network deployed on the slopes of landslide and flood-prone mountainsides. The network utilizes a Node MCU topology, and for demonstration purposes, a two-node system is suggested. The nodes in this system are equipped with various sensors to gather essential data. They utilize a tipping rain sensor to measure precipitation and an accelerator to detect potential landslides by monitoring module position changes. Additionally, there's a humidity sensor continuously monitoring surface temperature changes. These sensor nodes transmit their data to a central base station, which acts as a receiver. Both the public and the base station are equipped with Node MCU microcontrollers to facilitate communication. The base unit, positioned at a strategic location, such as the district collector's office, utilizes a long-range transmitter to communicate with the master unit. When the sensor nodes detect significant landslide or flood activity within a specific time frame, indicating a potential flash flood, this critical information is transmitted to the master unit. The master unit responds by activating an alarm and promptly sending messages to relevant officials and the general public through Node MCU microcontrollers connected to it. This swift communication allows for timely evacuation and response measures to minimize the impact of the disasters.

Advancing Applied Nonlinear Real-Time IoT System for Disaster-Resilient Dam Management through Water Level Monitoring Systems

The nonlinear dynamics of water management in dams are critical to mitigating floods and guaranteeing a long-term water supply for neighboring towns. With the integration of sensor networks, this project seeks to create an advanced monitoring and warning system that can dynamically regulate water levels. By utilizing the Internet of Things (IoT), the system aims to provide flood protection in the neighborhood a nonlinear component. Water levels are closely monitored by a number of sensors, which send out alerts via GSM to allow for prompt communication. Information is effectively sent via the system to the cloud, where application software effortlessly delivers it to users. By using a nonlinear strategy, this project not only satisfies requirements but also serves as a strong safety net, reducing possible losses in the event of major water-related problems.

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Floods are one of the most lethal natural disasters. It is crucial to forecast the timing and evolution of these events and create an advanced warning system to allow for the proper implementation of preventive measures. This work introduced a new graph-based forecasting model, namely, graph neural network sample and aggregate (GNN-SAGE), to estimate river flooding. It then validated the proposed model in the Humber River watershed in Ontario, Canada. Using past precipitation and stage data from reference and neighboring stations, the proposed GNN-SAGE model could estimate the river stage for flooding events up to 24 h ahead, improving its forecasting performance by an average of 18% compared with the persistence model and 9% compared with the graph-based model residual gated graph convolutional network (GNN-ResGated), which were used as baselines. Furthermore, GNN-SAGE generated smaller errors than those reported in the current literature. The Shapley additive explanations (SHAP) revealed that prior data from the reference station was the most significant factor for all prediction intervals, with seasonality and precipitation being more influential for longer-range forecasts. The findings positioned the proposed GNN-SAGE model as a cutting-edge solution for flood forecasting and a valuable resource for devising early flood-warning systems.

Decoding nociceptor-DC dialogues

Neuro-immune interactions link physiological and immune responses in host defense. Hanč etal.1 report that nociceptors attract dendritic cells (DCs) via the chemokine (C-C motif) ligand 2 (CCL2), initiate a "sentinel" DC program via the neuropeptide calcitonin gene-related peptide (CGRP), and enhance DC inflammatory responses through direct connections. These neuroimmune units integrate nociceptors' rapid responsiveness with DCs' immune coordination, functioning as an advanced warning system.

Combined synoptic and regional weather patterns affecting atmospheric Poaceae pollen concentrations in Sydney, Australia

Inhalation of grass pollen can result in acute exacerbation of asthma, prompting questions about how grass pollen reaches metropolitan areas. We establish typical atmospheric Poaceae (grass) pollen concentrations recorded at two pollen samplers within the Sydney basin in eastern Australia and analyse their correlation with each other and meteorological variables. We determine the effect of synoptic and regional airflow on Poaceae pollen transport during a period of extreme (≥ 100 grains m−3 air) concentration and characterise the meteorology. Finally, we tested the hypothesis that most Poaceae pollen captured by the pollen samplers originated from local sources. Fifteen months of daily pollen data, three days of hourly atmospheric Poaceae pollen concentrations and fifteen months of hourly meteorology from two locations within the Sydney basin were used. Weather Research Forecasting (WRF), Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) modelling and conditional bivariate probability functions (CBPF) were used to assess Poaceae pollen transport. Most Poaceae pollen collected was estimated to be from local sources under low wind speeds. Extreme daily Poaceae pollen concentrations were rare, and there was no strong evidence to support long-distance Poaceae pollen transport into the Sydney basin or across the greater Sydney metropolitan area. Daily average pollen concentrations mask sudden increases in atmospheric Poaceae pollen, which may put a significant and sudden strain on the healthcare system. Mapping of Poaceae pollen sources within Sydney and accurate prediction of pollen concentrations are the first steps to an advanced warning system necessary to pre-empt the healthcare resources needed during pollen season.

Comparison of sustainable flood risk management by four countries – the United Kingdom, the Netherlands, the United States, and Japan – and the implications for Asian coastal megacities

Abstract. Sustainable flood risk management (SFRM) has become popular since the 1980s. Many governmental and non-governmental organisations have been keen on implementing the SFRM strategies by integrating social, ecological, and economic themes into their flood risk management (FRM) practices. However, the justifications for SFRM are still somewhat embryonic, and it is not yet clear whether this concept is influencing current policies in different countries. This paper reviews the past and current flood management experiences from flood defence to SFRM in four developed countries to highlight lessons for coastal megacities in development. The paper explores recent strategies such as “Making Space for Water”, Planning Policy Statement 25 (PPS25), and the National Planning Policy Framework (NPPF) in the UK and “Room for the River” in the Netherlands, which were implemented to mitigate flooding, integrate FRM with sustainability concepts, and deliver sound FRM practice for future generations. In this context, the United States has also established a National Flood Insurance Program (NFIP), and in a different approach, Japan has developed an advanced flood warning and evacuation contingency system to prepare for climatic extremes. These case studies give good lessons in achieving long-term SFRM to deliver sound flood management practices considering socio-economic and environmental concerns. Most developing coastal megacities especially in Asia are still heavily reliant on a traditional hard-engineering approach, which may not be enough to mitigate substantial risks due to human factors (e.g. large population, rapid socio-economic growth, subsidence from excessive groundwater extraction) and natural factors (e.g. climate change including sea-level rise and land subsidence). It is clear that different countries and cities have their interpretation of SFRM, but this paper explores how policymakers can adopt “mixed options” to move towards long-term thinking about sustainability with social, economic, and environmental considerations.

Composite Imagery-Based Non-Uniform Illumination Sensing for System Health Monitoring of Solar Power Plants

Abstract Incidence of non-uniform illumination (NUI) degrades the affected solar photovoltaic modules temporarily. However, in worst case, it may also lead to permanent degradation. Thus, non-uniform illumination sensing in terms of encompassed area over solar photovoltaic module becomes important for sustainable power generation. An integrated framework by combining optical and thermal images for non-uniform illumination sensing and classification (static and dynamic) is proposed. The proposed technique detects hotspots along with identification of the nature of shading on the solar photovoltaic modules. Additionally, Hungarian Kalman filter is implemented for estimating the coverage of the non-uniform illumination-affected region including its abrupt shape. The proposed estimation technique also calculates the total loss in the output energy of the solar photovoltaic system due to non-uniform illumination. Overall, the proposed methodology develops a hybrid imagery-dependent advanced early warning system for large-scale solar power plants which are cost-effective and bypass multi-sensor data fusion to attain real-time application.

Improving Jakarta’s Katulampa Barrage Extreme Water Level Prediction Using Satellite-Based Long Short-Term Memory (LSTM) Neural Networks

Jakarta, the capital region of Indonesia, is experiencing recurring floods, with the most extensive recording loss as high as 350 million dollars. Katulampa Barrage’s observation of the Upper Ciliwung River plays a central role in reducing the risk of flooding in Jakarta, especially flowing through the Ciliwung River. The peak flow measured in the barrage would travel 13–14 h to the heart of the city, providing adequate time for the government officials and the residents to prepare for the flood risk. However, Jakarta is continually pressed by the population growth, averaging 1.27% in the past 20 years. The constant growth of Jakarta’s population continually develops slums in increasingly inconvenient locations, including the riverbanks, increasing vulnerability to floods. This situation necessitates a more advanced early warning system that could provide a longer forecasting lead time. Satellite remote sensing data propose a promising utility to extend the prediction lead time of extreme events. In the case of this study, Sadewa data is used to predict the water level of Katulampa Barrage using long short-term memory (LSTM) recurrent neural networks (RNN). The results show that the model could predict Katulampa Water Level accurately. The model presents a potential for implementation and additional lead time to increase flood mitigation preparedness.

Assessment of drought, its causes and consequences in connection with local wisdom: a case study of Kohat basin drought-2017, Khyber Pakhtunkhwa, Pakistan

Extreme weather events are significantly increasing around the globe. Among these, drought is a unique environmental hazard. Its uniqueness is the length of time between the first indications and impacts upon the community. Droughts may occur for months, years and in some cases decades, affecting large geographical areas. This study aims to assess the occurrence, causes and impacts of drought regimes technically by using the Standardized Precipitation Index (SPI) technique and culturally by assessing the local people's indigenous knowledge. For this purpose, both primary and secondary data were collected. Historically recorded rainfall data were used to calculate SPI values to identify rainfall deficits for 3, 6 and 12 months timescales. Data about the indigenous knowledge was collected through focus group discussions and interviews with the community. Secondary data were collected from Agriculture Department, Irrigation department, BARANI research station and Pakistan Meteorological Department (PMD). The study results revealed that mild and moderate droughts are decreasing as the timescale increases. Contrary, severe and extreme droughts increase with the increase in timescales from 3 months to a year. Results also highlighted three extreme drought events recorded in the study area based on SPI-3 and SPI-6 timescales from 1954 to 2017. The number of extreme droughts based on SPI-12 is five in the selected timeframe. Furthermore, there was a significant reduction in agriculture production particularly in crops and vegetables during drought-2017. The findings of the study recommend that there is a need to improve the irrigation system. Wastage of floodwater should be controlled and conserved in small dams. Coordination amongst the stakeholders, advanced early warning system, awareness and planning at the local are significant measures to be taken in the study area to combat the problem.

The probabilistic solar particle event forecasting (PROSPER) model

The Probabilistic Solar Particle Event foRecasting (PROSPER) model predicts the probability of occurrence and the expected peak flux of solar energetic particle (SEP) events. Predictions are derived for a set of integral proton energies (i.e., E &gt; 10, &gt; 30, and &gt; 100 MeV) from characteristics of solar flares (longitude, magnitude), coronal mass ejections (width, speed), and combinations of both. Herein the PROSPER model methodology for deriving the SEP event forecasts is described, and the validation of the model, based on archived data, is presented for a set of case studies. The PROSPER model has been incorporated into the new operational advanced solar particle event casting system (ASPECS) tool to provide nowcasting (short term forecasting) of SEP events as part of ESA’s future SEP advanced warning system (SAWS). ASPECS also provides the capability to interrogate PROSPER for historical cases via a run-on-demand functionality.

IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum. Doi: 10.28991/esj-2021-SP1-011 Full Text: PDF

Multiple comparisons of precipitation variations in different areas using simultaneous confidence intervals for all possible ratios of variances of several zero-inflated lognormal models.

Flash flooding and landslides regularly cause injury, death, and homelessness in Thailand. An advancedwarning system is necessary for predicting natural disasters, and analyzing the variability of daily precipitation might be usable in this regard. Moreover, analyzing the differences in precipitation data among multiple weather stations could be used to predict variations in meteorological conditions throughout the country. Since precipitation data in Thailand follow a zero-inflated lognormal (ZILN) distribution, multiple comparisons of precipitation variation in different areas can be addressed by using simultaneous confidence intervals (SCIs) for all possible pairwise ratios of variances of several ZILN models. Herein, we formulate SCIs using Bayesian, generalized pivotal quantity (GPQ), and parametric bootstrap (PB) approaches. The results of a simulation study provide insight into the performances of the SCIs. Those based on PB and the Bayesian approach via probability matching with the beta prior performed well in situations with a large amount of zero-inflated data with a large variance. Besides, the Bayesian based on the reference-beta prior and GPQ SCIs can be considered as alternative approaches for small-to-large and medium-to-large sample sizes from large population, respectively. These approaches were applied to estimate the precipitation variability among weather stations in lower southern Thailand to illustrate their efficacies.

TP5.2.3 Quality improvement project to reduce turn-around times to improve vascular theatre efficiency

AimOne of the factors influencing theatre efficiency is turn-around time (TAT). The aim of this QIP was to reduce turnaround times by 25% thereby reducing financial implications of theatre idle time.MethodsBaseline data was obtained from electronic theatre record system ‘Galaxy’ for the period October 2019 to February 2020. TAT (Time from the last patient going to recovery and the next one coming into the theatre) was measured and a period average was established. This QIP mapped processes and conducted interviews, to identify issues contributing to longer turnaround times. Interventions were then constructed and implemented over 6 weeks. ResultsOne of preventable causes of delay identified from staff interviews and personal observations was inadequate patient preparation by the ward. Preintervention percentage theatre utilisation was 86% and turnaround times was 51.7 minutes. A PDSA cycle was initiated focusing on advanced warning (30 minutes prior to the end of the previous surgery) from theatres to wards and advanced preparation from wards, using a newly designed ward-based checklist. After the first PDSA cycle there has been an improvement in TAT to 42.8 minutes, a decrease of 18.2%. Whilst this did not meet our goal of a 25% reduction, this remains significant. Unfortunately due to COVID -19 the second cycle has been delayed.ConclusionsAffordable and sustainable improvements will be needed in post COVID-19 recovery phase to tackle the backlog of surgeries. This project has demonstrated that advanced warning system can decrease turnaround times.

Advanced Warning System to Improve Safety at Train Grade Crossings

The severity of traffic accidents at train grade crossings has led to efforts in mitigating their potential damaging effects. Over the last 45 years, significant investment has been made at these crossings in the United States to reduce the number of accidents. Initially, these improvements reduced accidents, but in the last decade, the numbers have plateaued, even as additional safety investment has continued. Geospatial data from the Federal Railroad Administration was evaluated to ascertain the causes of the efficacy reduction of traditional safety measures. Temporal patterns were detected in diverse parameters that measure accident seriousness such as their frequency, financial damages, injuries, and deaths. An advanced warning system for motorist was proposed in this research to allow them enough time to change their schedules and driving routes, avoiding blockages caused by incoming trains. To avoid pitfalls from previous proposed systems the model will be initially implemented in locations with a high number of accidents and lower layout complexity for the railway, roads, and train grade crossings. Two railways in Texas were selected for the initial implementation of the system. The proposed pilot locations showed high potential for the development of the advanced warning system, in preparation for a broader effort to continue the improvement in safety at railway crossings.

Evaluation of driver performance with a prototype cyber physical mid-block crossing advanced warning system

Introduction: Using connected vehicle technologies, pedestrian to vehicle (P2V) communication applications can be installed on smart devices allowing pedestrians to communicate with drivers by broadcasting discrete safety messages, received by drivers in-vehicle, as an alternative to expensive fixed-location physical safety infrastructure. Method: This study consists of designing, developing, and deploying an entirely cyber-physical P2V communication system within the cellular vehicle to everything (C-V2X) environment at a mid-block crosswalk to analyze drivers’ reactions to in-vehicle advanced warning messages, the impacts of the advanced warning messages on driver awareness, and drivers’ acceptance of this technology. Results: In testing human subjects with, and without, advanced warning messages upon approaching a mid-block crosswalk, driver reaction, acceptance, speed, eye tracking data, and demographic data were collected. Through an odds ratio comparison, it was found that drivers were at least 2.44 times more likely to stop for the pedestrian with the warning than without during the day, and at least 1.79 times more likely during the night. Furthermore, through binary logistic regression analysis, it was found that driver age, time of the day, and the presence of the advanced warning message all had strong, significant impacts with a confidence value of at least 98% (p < 0.02) on the rate at which drivers stopped for the pedestrian. Conclusions: The results from this study indicate that the advanced warning message sent within the C-V2X had a strong, positive impact on driver behavior and understanding of pedestrian intent. Practical Applications: Pedestrian crashes and fatality rates at mid-block crossings continue to increase over the years. Connected vehicle technology utilizing smart devices can be used as a means for communications between pedestrians and drivers to deliver safety messages. State and local city planners should consider geofencing designated mid-block crossings at which this technology operates to increase pedestrian safety and driver awareness.