Evaluating interventions for phone distracted pedestrians in a virtual reality environment

The expert information processing system was proposed for a traffic signal system at a multimodal intersection with three types of traffic users: public transport (tram), pedestrians, and general traffic. The system considers the dynamics characteristics of the tram after it is detected by the entry detector of the intersection and is based on a set of fuzzy decision-making rules for decision-making on the time parameters adjustment of the traffic signal plan based on the compatibility of traffic direction signals and the importance of traffic requests. For general traffic signals, the solution is to extend or terminate the permissive signal for a certain group of traffic directions controlled by this signal. Decisions for tram signals, depending on the proximity of the planned permissive signal and the importance of the associated request, can affect both the time parameters of the immediate traffic signal plan and change the overall signal sequence and/or their duration. The conditional priority involves taking into account possible conflicts be-tween the decisions from the public transport signals or from public transport and general traffic signals, which are handled by the corresponding production rules proposed in the paper. The traffic light plan is changed iteratively in an acyclic manner, considering the degree of confidence of the decision obtained by a two-level fuzzy inference model based on the Mamdani algorithm, the second level of which is responsible for determining the signal sequence and the first level for their time parameters. The information processing model is implemented and validated using SUMO urban mobility simulation tool for moderate and saturated transport demands for a non-trivial intersection with synthetic traffic demand. The experimental results demonstrate the applicability of the system in conditions of a saturated transport demand, allowing to reduce the negative impact on the general traffic flows without a significant increase in the time losses of tram passengers compared to the adaptive unconditional way of implementing tram traffic light priority.

Background: Road traffic accidents have been one of the main causes of death worldwide. However, most of the crashes are both predictable and preventable. With the widespread use, mobile phone is considered as a major road safety risk. However, varieties of apps have recently been developed for improving the road traffic health and safety. Despite an increasing trend of these apps, there is no comprehensive analysis of their features and no taxonomy or classification of them based on the traffic safety theories or frameworks. The aim of this study was to explore characteristics of available mobile apps on the road traffic health and safety and classify them based on the Haddon’s Matrix. Methods: To conduct this research, a comprehensive and systematic review of the mobile applications developed for the road traffic health and safety was carried out through using the qualitative content analysis. Google Play was searched through using a combination of the keywords to retrieve the road traffic apps. In order to extract the app features, their description was examined, and their content was analyzed. Then they were classified in four main categories including Road Traffic Health & safety, Road Traffic Training, Road traffic Navigation, and Other Road Traffic apps. Finally, the Haddon’s matrix was applied to analyze and classify those mobile apps residing in two main categories of the traffic health & safety, and the traffic training. Haddon’s matrix is a relevant framework for structured analyses of traffic injury events. In the Haddon’s matrix, the contributions of human, vehicle/equipment and environmental factors to the injuries in the three phases pre-crash, crash and post-crash has been presented. Results: In this study, 916 mobile apps met the inclusion criteria and were included in the final analysis.19 subcategories were identified for classifying the included apps based on their features and functionalities. In total, 620 of the mobile apps were grouped on the basis of Haddon’s matrix. About 61.76% of these apps were categorized as the traffic health & safety group. Among the subcategories of the traffic health & safety group, the highest number of apps (194) was the real-time traffic alerting apps. Behavior based feed-backing subcategory was ranked the second among the traffic health and safety apps with 75 apps. When the researchers applied factors and phases of Haddon’s framework for analyzing the apps, it was found that the Haddon’s factors have been taken into account in the apps grouped into two main categories of the apps including traffic training and traffic health & safety applications. The highest percentage of Haddon’s matrix factor considered for the intervention through the apps was related to the physical & social environment (23.69%) in the traffic training category. The features of 259 apps were classified in the Event/ Driving phase of Haddon’s matrix. Most of the apps in this phase was related to the real-time traffic alerting subgroup. These apps performed in real time while driving. The lowest number of the apps in driving phase was observed in the behavior based feed backing subcategory, which inform drivers about their driving behavior in real-time. Sixteen apps had features that categorized them in both Pre-event / Pre-driving and Event / Driving phases. In addition, 20 apps were found to have feature for intervening in both Event / Driving and Post-event / Post –driving phases. All 235 apps in the traffic training category were in pre-event/ pre-driving phase, which requires users to use these apps before driving. Conclusion: Applying Haddon’s matrix on analyzing and classifying the traffic health and safety apps revealed strengths and weaknesses of the existing related mobile apps in terms of the factors that must be considered for intervention toward prevention and reduction of road traffic accidents as the main public health issue.

BackgroundThe prevalence of long-term or chronic conditions that limit activity and reduce quality of life in young people aged 10-24 years is rising. This group has distinct health care needs and requires tailored support strategies to facilitate increasing personal responsibility for the management of their condition wherever possible, as they mature. Mobile phone and tablet mobile technologies featuring software program apps are already well used by young people for social networking or gaming. They have also been utilized in health care to support personal condition management, using condition-specific and patient-tailored software. Such apps have much potential, and there is an emerging body of literature on their use in a health context making this review timely.ObjectiveThe objective of this paper is to develop a systematic review protocol focused on identifying and assessing the effectiveness of mobile phone and tablet apps that support young people’s management of their chronic conditions.MethodsThe search strategy will include a combination of standardized indexed search terms and free-text terms related to the key concepts of young people; long-term conditions and mobile technology. Peer-reviewed journal articles published from 2003 that meet the inclusion and exclusion criteria will be identified through searching the generated hits from 5 bibliographical databases. Two independent reviewers will screen the titles and abstracts to determine which articles focus on testing interventions identified as a mobile phone or tablet apps, and that have been designed and delivered to support the management of long-term conditions in young people aged 10-24 years. Data extraction and quality assessment tools will be used to facilitate consistent analysis and synthesis. It is anticipated that several studies will meet the selection criteria but that these are likely to be heterogeneous in terms of study design, reported outcomes, follow-up times, participants’ age, and health condition. Sub-group analyses will be undertaken and where possible meta-analyses will take place.ResultsThis review will synthesize available knowledge surrounding tablet and mobile phone apps that support management of long term physical health conditions in young people. The findings will be synthesized to determine which elements of the technologies were most effective for this population.ConclusionsThis systematic review aims to synthesize existing literature in order to generate findings that will facilitate the development of an app intervention. The review will form the first phase of development and evaluation of a complex intervention as recommended by the United Kingdom Medical Research Council. The knowledge gained from the review will be verified in subsequent phases, which will include primary qualitative work with health professionals and young people with long term conditions as research participants. Young people living with long-term conditions will be involved as co-researchers and consumer advisors in all subsequent phases to develop and evaluate an app to support the management of long-term physical health conditions.Trial RegistrationPROSPERO International prospective register of systematic reviews: CRD42014015418; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42014015418#.VRqCpTpnL8E (Archived by Webcite at http://www.webcitation.org/6XREcWqQY).

In this project, the ESP8266 microcontroller and LEDs are utilized to create a system that effectively controls traffic flow during emergency situations. Two modules, an emergency vehicle module, and a traffic module, communicate with a server. The server receives signals from the emergency vehicle and triggers the corresponding traffic lights to turn green and yellow in the direction of the vehicle. Simultaneously, the traffic lights in other directions turn red and yellow, providing a clear path for the emergency vehicle. To enhance safety and awareness, a mobile application is developed. Users between the emergency vehicle and the traffic signal can install the app, which alerts them of the approaching vehicle. Real-time updates, sound alerts, and vibrations are provided to ensure the public is aware and can take appropriate actions. An additional app is designed for the emergency vehicle driver, allowing manual control over traffic signals when they are positioned too closely together. This ensures the vehicle can navigate intersections without hindrance. By combining the server, mobile apps, and ESP8266 microcontroller, this project offers a comprehensive solution to improve emergency vehicle navigation in urban areas. It optimizes traffic flow, enhances public safety, and provides additional control for emergency vehicle drivers. The goal is to provide an efficient and accessible solution that can be implemented in various urban settings.

본 논문은 신호등 신호에 따른 자율 주행 (또는 반-자율 주행) 차량의 주행 제어를 위한 인공전위 함수(artificial potential function)를 제안한다. 신호등은 보행자의 안전과 직결된 일반적이면서도 가장 중요한 교통 신호 중 하나로서, 도로 위 자율 주행을 위해서는 차량이 교통 신호를 준수하게 해주는 주행 알고리즘의 설계가 중요하다. 본 논문은 세 가지 신호 색(녹색, 노랑, 빨강)으로 구성된 차량 신호등의 신호에 따라 차량의 움직임을 제어할 수 있는 인공 전위 함수를 제안한다. 제안된 인공 전위 함수는 세 가지 신호 색 각각에 대한 인공 전위 함수들로 구성되며, 신호에 따라 전위 함수가 전환되며 인공 전위 장(artificial potential field)을 변화시킨다. 제안된 신호등 전위 함수는 차량 주행을 위한 기존의 인공전위 함수들과 결합되어 최종 인공 전위 장을 생성한다. 제안하는 방법은 시뮬레이션을 통해 다양한 신호등 상황에 대해 실험되었으며, 신호등의 신호에 따라 차량이 자연스럽게 반응하는 모습을 보여준다.

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.

Accurate state recognition of traffic lights is important for a safe urban drive of autonomous vehicles. A traffic light with lower luminance than its ambient is difficult to recognize particularly in a distant. This paper proposes a strategy that improves accuracy of signal state recognition of distant traffic lights with low luminance. Digital map is used to get candidate regions of traffic lights on a captured image. Each candidate is cropped and resized to equalize the size of lamp area on the image. On the resized images, brightness channel image is modified by enhancing pixels that have hue value in the range of traffic signal color. Then, high value pixels on the SV m image, which is the muitilpied image of the satulation channel and the modified brightness channel, is regarded as candidates of lamp area. This strategy attempts to emphasize traffic signal lights darker than its ambient. Experiments in real driving data showed the performance of the proposed method.

Unsignalized intersection Jl. Raya Cangkiran - Jl Cangkiran Gunung Pati, Tambangan kec.Mijen, Semarang is a three-way intersection that has access to the Cupan Terminal. Many people pass through this intersection. In conditions at these intersections, traffic jams occur during rush hours, and there is no traffic light which makes traffic conditions even more irregular, the safety of motorists is also threatened. Therefore, this study was carried out to analyze unsignalized intersections, planning the use of traffic lights, and knowing the traffic characteristics of Jl. Raya Cangkiran - Jl. Gunungpati Cup, Semarang. Data collection was carried out for 3 days, namely Saturday 25 February 2023, Monday 27 February 2023, and Wednesday, 1 March 2023 by taking peak hours at 06.00 WIB - 08.00 WIB, 12.00 WIB - 13.00 WIB, and 16.00 WIB - 18.00 WIB. Data collection methods used are capacity, degree of saturation, delays, queue opportunities, and Traffic Signaling Devices (APILL). The performance analysis of this unsignalized intersection uses the 1997 Indonesian Highway Capacity Manual (MKJI) to calculate the degree of saturation. As for the level of service using the Minister of Transportation Regulation No. 96 PM 2015. The results of the research are that in the existing conditions, alternative B is chosen as the best alternative for planning traffic lights with 2 phases of the 1-hour peak scenario on Saturday morning with a degree of saturation of 0.823 pcu/hour, a queue length of 103,045 m on the West approach, and a value delay of 14,784 veh/sec. From the results of the analysis, it can be concluded that the results of calculating the traffic evaluation for the current conditions obtained that the highest vehicle volume occurred on Monday, 27 February 2023. Then Jl. Raya Cangkiran – Boja, Semarang, fulfills the criteria for installing traffic lights and needs to install traffic lights or traffic control lights. As well as from the 3 planned alternatives, alternative B was chosen to install a 2-phase traffic signal light.

Traffic lights signalling equipment at crossroads are very important. To regulate the procedure for placing traffic lights signalling equipment in Indonesia, the Minister of Transportation of the Republic of Indonesia Regulation Number PM 49 of 2014 regarding Traffic signal system is issued. The regulation above does not regulate how far the minimum visibility from the driver to the traffic light is. The limited space in the queue became a problem related to the views of drivers. Researchers conducted a visibility analysis using Digital Human Modelling (DHM) Tecnomatix Jack. Input variable is the type of vehicle, namely motorcycles, sedans and jeeps and the measured output is the distance from traffic light to the vehicle. A traffic light survey was conducted in Denpasar City, Bali, Indonesia. The anthropometric data of the drivers used Indonesian men, around 50% percentiles with ages ranging from 17 to 47 years from the anthropometric data of Indonesians. The testing of waiting distance is using motorcycles, jeeps, and sedans. The traffic lights used are curved type with the visibility of side lights and top lights. The results obtained that the optimal distance for the left side of the traffic light for motorcycles, jeeps, and sedans are 6.57m, 6.77m, and 6.92m, respectively and side lights up to 11.9 m, 7.64 m and 8.74 m. From the survey results, several traffic lights in Denpasar City still do not meet the minimum required distance and some queues require additional lights that are placed in front of the driver so that they can tackle the limited queuing space.

The paper describes the design of an intelligent auto traffic signal control system. Traffic congestion is the major issues that would be considered. At the junctions of the road, vehicular traffic intersects and it can be controlled by the traffic signals. One of the main need of the traffic signal is that good coordination and control among the vehicles. The traffic on the road is very high during the peak hours. This leads to the emergency vehicles to stick in the traffic jam. Dynamic control of traffic is needed during peak hours. Hence we propose an automatic traffic light control for emergency vehicles. The traffic light in the path of any emergency vehicles is controlled. It does not depend on the way of monitoring people to be manual which results in time delay. So, Human life is not affected due to the time delay in the arrival emergency vehicles. To control the traffic, the man power is not required in this method. The emergency vehicles find the shortest path to the location of the accident. If the train crosses the junction, the emergency vehicle gets alerted before five km. In front of the zebra line crossing, a divider can be created for safety crossing of people. The system uses AT mega 8micro controller, IR sensors. This system is used to control the traffic lights. And also save the time in emergency periods.

In this paper, we wish to construct a optimal traffic cycle using wire remote control. if police vehicle or ambulance suddenly enter the traffic Intersection, it will increase the traffic accident. In this paper, wireless traffic light use the radio traffic control signal and research about the hardware manufacture to check special detectors on urgency vehicles may safety and rapidly enter traffic intersection. Also, this paper present a traffic signal control conditions that analyzes different traffic intersection flows in cases of saturated flows, where the real traffic volume demand is large and the capacity constraints of bottlenecks have significant effects on the flow patterns. Through computer simulation this wireless traffic light has been proven to be much more safety and efficient than fixed traffic signal light which does not consider emergency vehicles for safety escort.

Use of autonomous vehicles aims to eventually reduce the number of motor vehicle fatalities caused by humans. Deep learning plays an important role in making this possible because it can leverage the huge amount of training data that comes from autonomous car sensors. Automatic recognition of traffic light and vehicle signal is a perception module critical to autonomous vehicles because a deadly car accident could happen if a vehicle fails to follow traffic lights or vehicle signals. A practical Traffic Light Recognition (TLR) or Vehicle Signal Recognition (VSR) faces some challenges, including varying illumination conditions, false positives and long computation time. In this chapter, we propose a novel approach to recognize Traffic Light (TL) and Vehicle Signal (VS) with high dynamic range imaging and deep learning in real-time. Different from existing approaches which use only bright images, we use both high exposure/bright and low exposure/dark images provided by a high dynamic range camera. TL candidates can be detected robustly from low exposure/dark frames because they have a clean dark background. The TL candidates on the consecutive high exposure/bright frames are then classified accurately using a convolutional neural network. The dual-channel mechanism can achieve promising results because it uses undistorted color and shape information of low exposure/dark frames as well as rich texture of high exposure/bright frames. Furthermore, the TLR performance is boosted by incorporating a temporal trajectory tracking method. To speed up the process, a region of interest is generated to reduce the search regions for the TL candidates. The experimental results on a large dual-channel database have shown that our dual-channel approach outperforms the state of the art which uses only bright images. Encouraged by the promising performance of the TLR, we extend the dual-channel approach to vehicle signal recognition. The algorithm reported in this chapter has been integrated into our autonomous vehicle via Data Distribute Service (DDS) and works robustly in real roads.

The study assessed driver, vehicular and road-related factors associated with road crashes (RC) in the Kintampo North Municipality. Cross-sectional study. Kintampo North Municipality. Demographics, vehicular and road usage information on registered drivers at Ghana Private Road and Transport Union (GPRTU) and Progressive Transport Owners Association (PROTOA) in Kintampo North Municipality. involvement in road crashes and related factors. A total of 227 drivers were approached for this study. None of them declined participation. They were all males. Most were between 28-37 years (30%). The proportion of drivers that reported RC ever involvement in at least one RC was 55.5% (95% CI: 8.0%, 62.1%). In the bivariate analysis, drink and drive changed lane without signalling, ever bribed police officer, drove beyond the maximum speed limit, paid a bribe at DVLA for driving license, violation of traffic signals were found to be associated with RC involvement (p<0.05). Drivers who violated traffic signals had 2.84 odds of being involved in road crashes compared to those who did not [aOR; 2.84 (95%CI:1.06,7.63)]. The proportion of drivers ever involved in road crashes was high. The major factor that is associated with RC involvement was a violation of the traffic light signals. Continuous driver education and enforcement of road traffic regulations by the appropriate authorities could curb the road crash menace in the Municipality. The authors funded this work.

Anticipation of events such as, changes in traffic light signals and preparing to brake or accelerate are critical behaviors during driving. Smart vehicles, equipped with on-board Brain-Computer Interface (BCI), could decode the driver's intention to perform an action from his brain activity, thus enriching the interaction with its driver. To this end, the contribution of this thesis are three fold: (i) it presents 3 experiments to investigate anticipatory behavior from Electroencephalogram (EEG), while driving: count-down paradigm, traffic light changes in a virtual city and on a real road, (ii) it proposes methods for synchronous single-trial EEG classification of this behavior as well as asynchronous detection of movement intention, (iii) it explores new filtering techniques toward online application. In the first part, we present our first experiment, inspired from the classical Contingent Negative Variation (CNV) paradigm, where a count-down of numbers predicted the appearance of a cue that instructed to brake or accelerate accordingly. Through the EEG data (N=18), we show the presence of anticipatory potentials locked to the stimuli onset, which are similar to the well-known central negative Slow Cortical Potentials (SCPs). We further demonstrate the discrimination between cases requiring an action (brake/accelerate) upon an imperative subsequent stimulus (`Start'/`Stop' cues) vs. the events that do not require such action (count-down cues). We also show the possibility of detecting driver's movement intention through these potentials. In the second part, we extend the study to a more realistic scenarios using traffic lights through next two experiments. For the second experiment, we recorded 10 subjects over 3 days in a car simulator. During the second and third day, the subjects received online classification feedback together with reaction time after braking. Through this data, we confirm the presence of the anticipatory SCPs in response to the traffic lights as well as offline single trial performances with similar patterns to those of the first experiment. Interestingly, for the brake trials, we observed an improvement in the anticipatory behavior, which is likely due to the feedback provision. In the real car experiment on a closed road, we recorded EEG (N=8) over 2 days. Remarkably, we confirmed the existence of the anticipatory SCPs and demonstrated the possibility of detecting these potentials, despite large amounts of driving related visual distractions and movement artifacts. Thirdly, we report a post-hoc analysis on investigating the influence of filtering on the SCP detection performance. We present a new spectral filtering method, called Predictive Cascade Filter (PCF), which theoretically reduced the group delay associated with filtering of low frequency bands. The grand averages illustrate this reduction, whereas the classification performance did not improve by using the PCF filters. Indeed, the lowpass PCF as well as the usual lowpass filter appeared to be the best when applied causally (pertinent to online application), whereas the bandpass filter performed best, when applied non-causally (pertinent to offline analysis). We believe, the contributions presented in this thesis can impact the advancement of neuro-technology into smart vehicles as well as other applications such as neuro-rehabilitation.

Traffic congestion has become a major problem in the cities where the population and the number of vehicles are continuously increasing. To ensure the safety of people, traffic light systems are incorporated at all necessary intersections of roads. Most of the traffic control systems used are now working based on fixed time for vehicular movement. So, a smart traffic control system is built which changes the waiting time of the vehicles based on situations, conditions, and priorities. Smart traffic system considers the vehicular density and special conditions such as the presence of an emergency vehicle at any of the roads and increases the waiting or clearance time accordingly. The system works such that, more the density of traffic at any side of the road, the longer would be the clearance time of that road with an increase in the duration of green light so as to allow more vehicles to pass through and hence reduce the traffic density. This avoids traffic congestion, wastage of time, and makes the system more efficient. A priority is given to the passage of emergency vehicles to prevent delays due to traffic congestion. So, the system is built such that during the presence of an emergency vehicle, the traffic light remains green till the emergency vehicle passes the signal and then returns to normal working. The system also considers the prevention of accidents and safety of the pedestrians during the night, when the traffic signals have only Amber lights, by use of sensors to detect any presence. The traffic lights along with alarms are used to indicate such presence. The system is built using Proteus software as a simulation tool for single-way and four ways traffic signals. Thus, the proposed work aims for a better and efficient traffic control system.