Traffic Emergencies Research Articles

Scalable Pythagorean Mean based Incident Detection in Smart Transportation Systems

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this paper, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee, compare with the state-of-the-art ML methods, to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.

Safety Tunnel-Based Model Predictive Path-Planning Controller With Potential Functions for Emergency Navigation

The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.

Design of vehicle positioning system based on artificial intelligence cloud computing technology

AbstractWith the continuous progress of society, people's living consumption needs are also constantly changing. The traditional vehicle positioning technology can no longer meet the new positioning needs of travelers, so a more accurate positioning system is urgently needed. This paper aims to study a more accurate positioning system and proposes a vehicle positioning system based on radio frequency identification (RFID). After comparing several common positioning methods, the use of LANDMARC positioning algorithm makes the vehicle positioning more accurate. This paper has effectively integrated advanced computer and artificial intelligence technology and applies it to control and management and finally established a comprehensive traffic positioning system that can effectively alleviate traffic congestion, reduce traffic accidents and quickly deal with traffic emergencies. The experimental results have shown that when the vehicle runs at a speed of 40 km/h and the communication rate is 860 kpbs, the average positioning accuracy of the improved algorithm is doubled compared with the traditional algorithm, and the error is reduced by nearly one. Therefore, compared with the traditional positioning system and artificial intelligence, the RFID vehicle positioning system under cloud computing has more accurate positioning information and more timely and convenient services.

Cooperative Decision-Making of Connected and Autonomous Vehicles in an Emergency

Safety is one of the major concerns in autonomous driving tasks, and enhancing the collision avoidance ability of connected and autonomous vehicles (CAVs) is an effective way to improve road safety. Most current autonomous driving algorithms make braking and stopping decisions in a traffic emergency. However, such decisions may not be optimal. In this paper, we study the optimal collision avoidance decision-making method for emergencies. To address this challenge, we propose a cooperative decision-making scheme for CAVs in an emergency. Unlike previous decision-making methods, our scheme enables vehicles to avoid collisions by indicating the optimal emergency destinations, which leads to a new task: the prediction of the optimal collision avoidance destination. In the proposed scheme, the potential collision avoidance destinations are evaluated based on a deep reinforcement learning (DRL) model, and we propose a safety evaluation map (SEM) to describe the evaluation results. The cooperative ability of CAVs is considered in the proposed scheme, and a well-designed reward function is applied to train the DRL model. Extensive experiments demonstrate that the proposed model can accurately evaluate potential collision avoidance destinations and is effective in reducing traffic accident rates and accident damage in various traffic emergencies compared to state-of-the-art baseline methods.

The use of mixed reality scenarios for training crisis managers and emergency responders in an ADR tanker accident

The article focuses on the potential use of HoloLens 2 devices in educational and training processes in schools, as well as in the professional practice of training crisis managers and firefighters. The article is based on the Erasmus+ FightARs international project, which aims to develop specific educational and training scenarios for the needs of the participating partners involved, allowing to convey to students the possibility of situational understanding and situational awareness. This is made possible by four secondary scenarios focusing on road traffic emergencies, in which the student learns the methodological procedures for specific cases, and one primary scenario in which a road traffic accident involving an ADR tanker and an electric car with an injured driver is on the scene, where the student should apply the knowledge acquired from the four secondary scenarios.

A novel intelligent traffic recovery model for emergency vehicles based on context-aware reinforcement learning

Management of traffic emergencies has become very popular in recent years. However, timely response to emergencies and recovering from an emergency is an important problem in itself. The strategies in the current studies merely suggest that after an emergency vehicle passes, the state should iterate to the next phase. Therefore, this paper proposes a novel approach for recovering from an emergency situation at an intersection based on real scenarios. The proposed method is a combination of context-aware and Reinforcement Learning (RL) models that predicts better alternatives for different states rather than just iterating to the next phase. In this regard, a new algorithm, named Interrupt Algorithm, is proposed to predict proper actions for recovering the emergency situation. This algorithm uses a Q-learning-based model that learns from traffic context for an emergency situation and chooses viable action from an action set. The recovery actions are categorized as max, min, and avg, respectively. Test results show that our proposed model outperforms traffic flow over than standard single choice recovering action-based approach by approximately 80%. Based on this, it may be more beneficial to choose different actions and therefore, proposed algorithm with the help of RL presents a more dynamic emergency recovery model.

Актуальные вопросы оказания медицинской помощи пострадавшим в дорожно-транспортных чрезвычайных ситуациях в Алтайском крае

The aim of the research was: – to elaborate organizational and methodical bases of medical evacuation for the population of the Altai region in cases of traffic accidents including emergency situations, in conditions of healthcare optimization, namely, under the process of merging of the territorial center for disaster medicine with the emergency medical aid station; – to determine starting points for further development of the Emergency Medicine Service of the Altai Krai. Materials and research methods. The statistical data of registration and reporting form no.55 “Information on the Activity of Healthcare Institution (Medical Formation) which took part in liquidation of medical and sanitary consequences of emergencies” of Altai Krai Disaster Medicine Service approved by the Order of the Ministry of Healthcare and Social Development of Russia dated February 3, 2005, no.112, and official statistical data of Altai Krai State Road Safety Department were analyzed. The sample included statistical data for 2019-2021. The study used a set of analytical and statistical methods, which made it possible to process and to analyze the data obtained, to assess the system of medical care and to develop a set of measures to improve the medical evacuation system in Altai Krai using the data about victims of traffic accidents, to predict the duration of their treatment and rehabilitation. The statistical data were processed by means of Statistica 10.0 program complex with the calculation of intensive indices, mean representativeness error (±m) and significance of differences by Fisher’s method. Results of the study and their analysis. The results of the analysis of organizational and methodological bases of medical evacuation of the population of Altai Krai in road traffic accidents in the conditions of optimization of healthcare in 2019-2021 are presented. The prospects for further development of the regional Disaster Medicine Service, directions of interdepartmental planning and conducting of special tactical exercises, which are one of the basic tools ensuring readiness of managers and personnel of territorial medical institutions for actions on liquidation of consequences of traffic accidents and emergencies, were determined. The necessity of training the population and interested persons in rendering first aid, which is carried out within the framework of the work of the training center of disaster medicine, has been substantiated.

Model Predictive Path-Planning Controller With Potential Function for Emergency Collision Avoidance on Highway Driving

Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.

Анализ причин транспортных происшествий на объектах топливно-энергетического комплекса

Have been presented the results of the analysis of industrial accidents related to traffic emergencies at the Fuel and Energy Complex’s facilities. Using the risk analysis methods have been identified root causes of an incident, and their level has been established. The frequently used phrase "the driver has lost operating the vehicle" does not contain information to identify the root causes of the accident and requires explanations for further development of measures to eliminate and prevent these causes.

Defining Highway Node Acceptance Capacity (HNAC): Theoretical Analysis and Data Simulation

A new concept of Highway Node Acceptance Capacity (HNAC) is proposed in this paper inspired by a field data observation. To understand HNAC in microscopic view, boundary condition of successful merging is found using car-following behaviours and lane-changing rules, which could also explain traffic oscillations. In macroscopic view, linear positive relationship between HNAC and background traffic volume is obtained based on moving bottleneck. To determine the explicit form of the relationship, data simulation considering car-following behaviours and traffic flow theory is used. In the results, the synchronization phenomenon of oscillation in on-ramp (with respect to main road) and intersected road is found. The explicit equation of HNAC is determined based on standard deviation and correlation coefficient analysis, and also proved to be accurate with model validation, which is helpful in studies related to propagation mechanism of traffic emergencies on highway network.

A Framework for Enhancing the Operational Phase of Traffic Management Plans

Road traffic emergencies are dangerous and unexpected situations that require immediate actions by the authorities. These actions involve to attend to the people who have been affected by the emergency and to minimize its consequences. A Traffic Management Plan (TMP) is a set of pre-defined measures and actions designed to produce an effective and efficient use of available resources in order to deal with a specific road incident. The operational phase of a TMP involves the coordination of several independent agencies (road managers, traffic police, firemen, etc.). These agencies must provide the resources required by the TMP in the deployment of the measures and actions. In this paper, a new framework to support the TMP operational phase is presented. This framework models each agency as an intelligent agent and it uses a reverse combinatorial distributed auction as the core component of a negotiation process. The goal of this negotiation process is to obtain a common agreement on the best possible allocation of resources taking into account the role, competencies and interest of the involved agencies. The framework has been implemented in a real scenario with real data. The tests developed have demonstrated that the system is able to manage the resources in terms of the execution time and the quality of the provided solutions.

Intelligent design and implementation of blockchain and Internet of things–based traffic system

With the continuous development of Internet of things, all kinds of smart systems are quickly evolving to make our day-to-day life smoother and safer. Like many other sectors, transportation has entered a period of rapid change. Intelligent Traffic System is one of the fastest-growing fields within the smart systems, which is expected to increase road safety, mitigate traffic congestion, and enable fuel efficiency. The main functionalities of Intelligent Traffic System are as follows: (1) monitoring real-time traffic conditions in specific areas, (2) locating traffic emergencies (i.e. traffic accidents) in specific areas, and (3) dynamic monitoring and managing the continuous use in public transit services (i.e. change in car lanes) that may lead to changes in macro traffic conditions. This article will use the above-mentioned functionalities of the Intelligent Traffic System as underlying simulative scenarios, to design and to implement a smart transportation system based on Internet of things and blockchain—both share inherent distributed technology characteristics—combining both Internet of things sensor nodes and distributed ledger technology, to (1) record the changes in intelligent transportation systems and (2) set up a credit-token mechanism for paying the use and misuses in public transit services accordingly. The Intelligent Traffic System described in this article is intended to be used as experimental project only, given the terms and conditions as depicted in the simulated scenario. In real-life traffic scenarios, it may generate more complex system and data security issues, which will be elaborated and analyzed at the end of this article. Intelligent Traffic System is a comprehensive smart system; it can significantly change and reinvent the wheel for traffic conditions. Based on the system development as discussed in this article, there are still a lot of demands and challenges that need to be addressed in the future. Such topic scope will be explored in depth in our subsequent research.

Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?

With the continuous expansion of the network scale and increasing of passengers, metro emergencies such as operational equipment failure are happening more frequently. Due to the narrow space and crowds of people, metro emergencies always have more of an impact than road traffic emergencies. In order to adopt appropriate measures to ensure passenger safety and avoid risks, we need to get a better understanding of passengers’ travel choice behaviors under emergencies. Most of the existing research studies related to travel choice behaviors took the random utility maximization (RUM) principle for granted, but failed to realize the potential of different decision-making processes and changes to the decision-making environment. In this research, we aim to analyze metro passengers’ travel choice behaviors under metro network emergency contexts. Based on the data collected from a survey about travel choices under metro emergencies in the Guangzhou Metro, we compared the performances of models that follow the RUM and random regret minimization (RRM) principles, and established a hybrid RUM-RRM model as well as a nested logit model following RRM (NL-RRM) to estimate the effects of various factors on passengers’ travel choice behaviors. Comparisons illustrate that the hybrid model and NL-RRM model can improve model fit, and the combination of RUM and RRM outperforms either of them respectively.

Research on traffic decision making method based on image analysis case based reasoning

As the deteriorating urban traffic incurs multiple traffic safety problems, this article designed a system framework for the traffic monitoring and management system according to its characteristics and provided a traffic emergency aid decision making method by integrating the case-based reasoning specific to the traffic emergencies. Meanwhile, the article built a traffic aid decision making case base, designed overall-local characteristic attribute similarity calculating measures and provided a case retrieval strategy. Finally the traffic aid decision making method was verified by examples.

Space-Time Hybrid Model for Short-Time Travel Speed Prediction

Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction.

EMERGENCY SERVICES (1122)

Emergency services were working all over the world to deal with different types ofemergencies and the frequency of emergencies were increased with the passage of time. Manydeveloping countries even don’t have such services and it was difficult to estimate the scaleof emergencies in those countries. Objectives: To know about the types of emergencies andto help to plan for their prevention in future. Place and duration of study: Data was collectedfrom emergency services department 1122 in four big cities of Punjab from Nov.2004-Feb.2014.Methods: Descriptive observational study. Results: The road traffic emergencies received were243481 in Lahore, 92367 in Faisal Abad, 52507 in Multan and33847 in Rawalpindi. Medicalemergencies were 247692 in Lahore, 82910 in Faisal Abad, 112531 in Multan and 31069 inRawalpindi. Fire incidences were 14594 in Lahore, 6369 in Faisal Abad, 3707 in Multan and3929 in Rawalpindi. Crime incidence related emergencies were 13301 in Lahore, 15051 in FaisalAbad, 5326 in Multan and 1705 in Rawalpindi. Conclusions: Road traffic related emergencieswere increased even some times more than medical emergencies. Fake calls were also inhigher proportion.

National Highway Video Surveillance Networking Platform

In order to guarantee the stable operation of the highway network, improve the ability of handling highway emergencies and the public service level, and give full play to the advantage of the video surveillance system in finding and confirming traffic emergencies in time, most provinces are gradually improving the construction of provincial networking platform for the highway video surveillance, and based on it, acceleration of the construction of the national highway video surveillance networking platform is needed to strengthen the coordination and management of road network in the region and the ability to command. In this paper, based on current highway video surveillance network system structure and integration technologies, and the problems that the construction of the national highway video surveillance networking platform is facing, through in-depth study of the solutions, the development ideas with strong feasibility are provided to guide the construction of national level platform, which will ensure that the platform is practical and scientific.

Incident and Emergency Medical Services Management from a Regional Perspective

Traffic crashes and other emergencies have impacts on traffic operations in transportation networks, often resulting in non-recurring congestion. Congestion, in turn, may impede the ability of Emergency Medical Services (EMS) to provide timely response to those in need of medical attention. The work in this paper investigated the impact of incidents of varying severity and duration on transportation network performance in the Birmingham (AL, USA) area. The intensity and extent of the impact over space and time were assessed on the basis of average speeds. The analysis of incident scenarios was performed using the Visual Interactive System for Transport Algorithms (VISTA) platform. Moreover, first responders’ travel times to the scene of the incident were collected to identify best units for responding, in an effort to improve current dispatching practices. Finally, a secondary incident on the EMS to the hospital was considered to further demonstrate the superiority of Dynamic Traffic Assignment (DTA) over traditional static assignment methods in capturing dynamically changing traffic conditions. The study findings are expected to benefit local transportation planners, traffic engineers, emergency responders, and policy makers by allowing them to assess various response strategies to major incidents and emergencies and select the ones that minimize their potential impacts.

SPUTERS: An Integrated Traffic Surveillance and Emergency Response Architecture

Secure Progressively Updatable Traffic Emergency Response System (SPUTERS) is a framework for collecting traffic-surveillance data in crash-prone areas of roadways. SPUTERS receives as input video, audio, and text-based data from an integrated collection of distributed cameras and sensors, both vehicle-borne and those embedded in the roadway infrastructure. Under nominal roadway operating conditions, the multimedia data are used to develop incident countermeasures. During degraded modes of roadway operation, SPUTERS generates progressive updates to create a shared situational awareness among the people who orchestrate responses to traffic incidents and emergencies. In addition to addressing performance-oriented quality-of-service requirements for interactive display of data on handheld devices, SPUTERS relies on role-based access control to safeguard the privacy of roadway users under surveillance, preventing privileged information collected and managed by the system from being leaked to unauthorized persons. In order to enhance security, SPUTERS uses symmetric key encryption to guard against the unauthorized alteration of the surveillance data.