Search

In urban areas, emergency vehicles (EVs) need efficient traffic signal preemption to ensure timely responses during peak hours. While emergency vehicle traffic signal preemption (EVTSP) has garnered significant attention in the literature, the issues of queue spillbacks and negative impacts on non‐priority traffic have been relatively underreported. These issues are particularly critical during peak hours, notably in densely populated urban areas. This study presents an EVTSP system that considers queue spillbacks on approaches along the routes of EVs and the negative impacts of signal preemption on non‐priority traffic. The proposed control system has four key components: (1) a queue length management algorithm to ensure that an EV will not be impeded by excessive queues, particularly on its initial approaches along its route; (2) a signal preemption algorithm to guarantee uninterrupted passage of an EV even for approaches experiencing queue spillbacks; (3) a traffic status recovery algorithm to alleviate the extra waiting time for non‐priority vehicles after an EV crosses each intersection; and (4) a signal plan recovery algorithm to smoothly transit traffic signals to normal operation. The experimental results confirm that the proposed system considerably improves the travel time of an EV and mitigates the negative impacts on non‐priority traffic. [ABSTRACT FROM AUTHOR]

Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans. [ABSTRACT FROM AUTHOR]

As a result of the rapid growth of the world population, traffic signaling systems for monitoring and controlling the roads have turned to be an important issue facing humanity. To effectively overcome this problem, an accurate method for congestion reduction on the roads should be used which has a direct relation between the population and the cars’ usage. Various approaches derived from deep and transfer learning have been investigated in this context. This research implemented an optimized transfer learning approach for densely connected convolutional neural network (DenseNet201) models for multiple classifications (non-emergency cars, ambulance, police, and firefighter). Due to the non-availability of public datasets, a customized dataset has been created. This paper aims to improve the performance accuracy of vehicle classification using certain preprocessing algorithms on the input images and testing various optimization methods. The performance accuracy of the proposed model is evaluated using k-folds cross-validation 20:80 for the test and training, respectively. The metrics which are used for comparison with other deep learning techniques are based on exactness, recall, accuracy and F1-score. Test outcomes specify that the proposed transfer model-based optimization outperforms alternative deep learning algorithms regarding vehicle accuracy in classifying and reaches 98.6%. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) has demonstrated its great potential for Adaptive Traffic Signal Control (ATSC) tasks at single‐intersection. In the transportation network multi‐agent environment, cooperative learning among multi‐agents has become a hot research topic. Based on the distributed control model, this paper presents a hybrid reward function model for the dynamic density method of intersections, which emphasizes the priority of emergency vehicles (EMV) while maximizing the traffic efficiency of social vehicles, and solves the problem of sparse reward due to the ambiguous guidance relationship between the multi‐agent Deep Reinforcement Learning (MDRL) state and reward function of the urban road network scenario. On the other hand, based on multi‐agent A2C (MA2C) algorithm, this paper presents Shared Experience MA2C (SEMA2C) between agents. In the transportation network, each intersection represented by an agent has similar task objectives. SMEA2C algorithm takes the current agent as the main body of self‐learning, and utilizes the principle of importance sampling to learn from the experience data of the agents located at adjacent intersections. The experimental results show that the proposed SEMA2C performs well in multi‐agent traffic signal control tasks, and has greater advantages than the similar algorithms. [ABSTRACT FROM AUTHOR]

Traffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established. Measurement(s) Ambulance Siren and Road Noises. (Recordings) Technology Type(s) Microphone Sample Characteristic - Environment Roads Sample Characteristic - Location Pakistan [ABSTRACT FROM AUTHOR]

In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents. [ABSTRACT FROM AUTHOR]

Traffic lights are an important controlling factor in traffic flows, and good policies will facilitate traffic congestion. A car's waiting time is highly related to the period in which the traffic lights are green or red; thus, a proper controlling policy reduces the waiting time and speeds up the car movement. Emergency vehicles always have a higher priority than other cars and need to reach their destination faster. In the last decade, a lot of research has been done to solve the traffic light scheduling issue using artificial intelligence techniques. Reinforcement learning with simulating human learning has the outstanding performance to solve complex problems, such as traffic lights control. In this study, a deep reinforcement learning traffic light control was implemented at a crossroad using real-time traffic information with an emphasis on emergency vehicles. The agent learns to adjust its policies to prioritize the emergency cars over the other cars. The results show less waiting time and speed up the cars' movement. It also indicates the emergency vehicles will cross the intersection with the least delay. With 200 to 600 cars and one emergency vehicle, the average delay decreases 2–16% in total using reinforcement learning. Considering more emergency vehicles, there is a 27–40% decrease in average delay for emergency vehicles. [ABSTRACT FROM AUTHOR]

Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi'an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents. [ABSTRACT FROM AUTHOR]

Traffic incidents can create major non-recurring congestion and have the potential to be fatal. Traffic Engineers and researchers have worked vigorously to reduce and prevent traffic crashes and make roadways safer. Secondary crashes are collisions that have taken place inside of incident scene or within the queue, influenced by the already occurred primary incident. Secondary crash occurrences are less frequent than primary incidents, however, the incident management without considering a potential secondary crash can cause a worst-case scenario to both emergency vehicles and travelers. Although statistical models have been developed in the past to estimate the probability of secondary crashes, they do not consider time-series changes of the probability. A Markov chain, a stochastic model is used in this study to model randomly occurring incidents in a sequence. It is different from previously developed semi-Markov models by considering incident duration as a first-order to estimate the secondary crash parameters in the second order. Based on author's previous models on incident duration prediction, this study develops a multivariate second-order Markov model to estimate the probability of a secondary crash based on various primary incidents. This analysis will determine and identify if the probability of a secondary crash is higher at a specific location or higher due to a specific type of primary incident. Findings from our analysis can aid in developing countermeasures such as allowing emergency operators to allocate more resources to clear primary incidents quicker, or better prepare for secondary crashes based on the predicted probability of additional incidents. [ABSTRACT FROM AUTHOR]

In view of the disturbance of the emergency signal preemption to the normal traffic flow, a scientific and reasonable signal transition optimization model was proposed in this paper, in which fair and efficiency are both considered. Since the emergency signal preemption may lead to longer vehicle queuing and greater vehicle delay at intersections, the difference of queue length and the vehicle delay were selected as the objectives taking into account the fair benefit, and a multi-objective signal recovery transition optimization model for emergency rescue was established based on the power function method. The queue length was calculated by the 95% queue length model of the SYNCHRO, and the average vehicle delay was calculated with HCM2000 delay model; furthermore, parameters’ weight in the model was calibrated with the variation coefficient method, and model was solved by genetic algorithm. Moreover, power function method was used to score the signal recovery transition schemes of emergency traffic. Finally, based on the survey data from the typical route network in the Lion Mountain area of Suzhou City, the model proposed in this study was compared with three classical smooth transition schemes (immediate transition scheme, two-cycle transition scheme and three-cycle transition scheme) at the scenario of evening peak. Simulation results showed that the model in this paper can reduce the vehicle delay and queue length compared with the above three smooth transition schemes; the average reduction ratio of queue length delay was 13.82%, and this number of queue length was 13.65%, from which we can conclude that the model proposed in this has better performance than other three classical transition schemes in emergency signal transition applicability. [ABSTRACT FROM AUTHOR]

In this paper, we propose STCM, a context-aware secure traffic control model to manage competing traffic flows at a given intersection by using secure messages with real-time traffic information. The vehicle is modeled as a virtual sensor which reports the traffic state, such as its speed and location, to a traffic light controller through a secure and computationally lightweight protocol. During the reporting process, a vehicle’s identity and location are kept anonymous to any other vehicle in the system. At an intersection, the traffic light controller receives the messages with traffic information, verifies the identities of the vehicles, and dynamically implements and optimizes the traffic light phases in real-time. Moreover, the system is able to detect the presence of emergency vehicles (such as ambulances and fire fighting trucks) in the communication range and prioritize the intersection crossing of such vehicles to in order to minimize their waiting times. The simulation results demonstrate that the system significantly reduces the waiting time of the vehicles in both light and heavy traffic flows compared to the pretimed signal control and the adaptive Webster’s method. Simulation results also yield effective robustness against impersonating attacks from malicious vehicles. [ABSTRACT FROM AUTHOR]

Advances in autonomous vehicular technology facilitate the development of intelligent traffic regulation systems. Such a system aims to configure and regulate vehicular mobility patterns to enhance in-road transportation safety and efficiency. To effectively function, the autonomous system must enable high-rate communications and rapid dissemination of vehicle-to-vehicle messaging flows. Instead of applying classical mobility models to capture human driver behaviors, the use of autonomously controlled driverless vehicles brings up another design dimensionality: the regulation and shaping of vehicular flows. The induced joint impact of the vehicular flow process on the message communications networking system, on the vehicular throughput rate, and on on-ramp waiting times for highway systems, has not been addressed by the existing studies. In this paper, we investigate the integrated design of these aspects. We synthesize and study methods that are used to optimally group autonomously controlled vehicles to travel along a highway in platoons. Vehicular formations are structured to yield effective autonomous mobility operation and to realize high-performance multihop dissemination of multiclass messaging flows. We then investigate an on-ramp traffic flow control mechanism that serves to regulate the admission of vehicles into the highway. We characterize the tradeoffs available to the system's designer in attaining high message communication throughput rates, accounting for time delays experienced by on-ramp waiting vehicles, while also striving to enhance the highway's capacity for accommodating high vehicular flow rate levels. [ABSTRACT FROM PUBLISHER]

Traveling vehicles participate in emphasizing the global warming problem due to the gases produced by them. The exponential increase in the number of daily traveling vehicles has exaggerated the world pollution problem threatening the life on the planet. This encourages several environmental organizations to look for designing green vehicles. Moreover, several countries have forced green driving rules and technologies. Road intersections are considered high fuel consumption areas over the road network. This is due to the required power to stop moving vehicles and restart stopped vehicles at these intersections. This work introduces an efficient traffic light scheduling algorithm (SmartLight) that controls the competing traffic flows at the road intersections. It is designed to reduce the total consumed fuel of vehicles and thus it reduces their produced gases. The topology of the road intersection, the context of the competing traffic flows, and the real-time traffic characteristics of each flow are mainly considered to schedule the phases of each located traffic light. The phases of the traffic light cycle are primly set to allow emergency vehicles to pass through the intersection without stopping. Then, the traffic flow that contains heavy vehicles or has the highest weight among the competing traffic flows is assigned the highest priority to pass through the signalized intersection. Finally, the average waiting delay time of each flow on the signalized intersection should not exceed a pre-determined threshold to guarantee fair sharing of the signalized intersection. The scheduling time of each phase is set based on the lengths of platoons of vehicles that are scheduled during that phase from different un-conflicted traffic flows. An experimental study has evaluated the performance of the proposed algorithm (SmartLight) compared to previous traffic light scheduling algorithms in terms of total fuel consumption, gas emission, the average queuing delay time of vehicles, and the throughput of the road intersection. [ABSTRACT FROM AUTHOR]

To improve emergency response efficiency under uncertainty, an urban emergency vehicle scheduling model is optimized by mathematical planning. The existing scheduling models are only built in accordance with current incidents while ignoring future incidents. To deal efficiently with future incidents, we adopt a different and novel approach in this paper by establishing a mathematical model which minimizes the overall response time cost with weights as the objective function. In terms of time and space constraints, the travel time calculation of the emergency vehicle is considered under different traffic flow conditions. Case results show that if the potential incidents really occur, the overall response time cost will be minimized by the scheduling policy. Furthermore, the selection of the confidence level determines the degree of system's emphasis on future events. Dealing with future serious incidents would be focused on prevention as the confidence level tends to one, while current incidents would be responded to with a priority as confidence level tends to zero. [ABSTRACT FROM AUTHOR]

Vehicular traffic is endlessly increasing everywhere in the world and can cause terrible traffic congestion at intersections. Most of the traffic lights today feature a fixed green light sequence, therefore the green light sequence is determined without taking the presence of the emergency vehicles into account. Therefore, emergency vehicles such as ambulances, police cars, fire engines, etc. stuck in a traffic jam and delayed in reaching their destination can lead to loss of property and valuable lives. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the measurement of the distance between the emergency vehicle and an intersection using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. The distance between the emergency vehicle and the intersection is calculated for comparison using Euclidean distance, Manhattan distance and Canberra distance techniques. The experimental results have shown that the Euclidean distance outperforms other distance measurement techniques. Along with visual sensing techniques to collect emergency vehicle information, it is very important to have a Medium Access Control (MAC) protocol to deliver the emergency vehicle information to the Traffic Management Center (TMC) with less delay. Then only the emergency vehicle is quickly served and can reach the destination in time. In this paper, we have also investigated the MAC layer in WSNs to prioritize the emergency vehicle data and to reduce the transmission delay for emergency messages.We have modified the medium access procedure used in standard IEEE 802.11p with PE-MAC protocol, which is a new back off selection and contention window adjustment scheme to achieve low broadcast delay for emergency messages. A VANET model for the UTMS is developed and simulated in NS-2. The performance of the standard IEEE 802.11p and the proposed PE-MAC is analysed in detail. The NS-2 simulation results have shown that the PE-MAC outperforms the IEEE 802.11p in terms of average end-to-end delay, throughput and energy consumption. The performance evaluation results have proven that the proposed PE-MAC prioritizes the emergency vehicle data and delivers the emergency messages to the TMC with less delay compared to the IEEE 802.11p. The transmission delay of the proposed PE-MAC is also compared with the standard IEEE 802.15.4, and Enhanced Back-off Selection scheme for IEEE 802.15.4 protocol [EBSS, an existing protocol to ensure fast transmission of the detected events on the road towards the TMC] and the comparative results have proven the effectiveness of the PE-MAC over them. Furthermore, this research work will provide an insight into the design of an intelligent urban traffic management system for the effective management of emergency vehicles and will help to save lives and property. [ABSTRACT FROM AUTHOR]

As cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficient route planning algorithm to attain a globally optimal vehicle control is still a challenge that needs to be solved, especially when the unique preferences of drivers are considered. The aim of this paper is to establish an accident management system that makes use of vehicular ad hoc networks coupled with systems that employ cellular technology in public transport. This system ensures the possibility of real-time communication among vehicles, ambulances, hospitals, roadside units, and central servers. In addition, the accident management system is able to lessen the amount of time required to alert an ambulance that it is required at an accident scene by using a multihop optimal forwarding algorithm. Moreover, an optimal route planning algorithm (ORPA) is proposed in this system to improve the aggregate spatial use of a road network, at the same time bringing down the travel cost of operating a vehicle. This can reduce the incidence of vehicles being stuck on congested roads. Simulations are performed to evaluate ORPA, and the results are compared with existing algorithms. The evaluation results provided evidence that ORPA outperformed others in terms of average ambulance speed and travelling time. Finally, our system makes it easier for ambulance to quickly make their way through traffic congestion so that the chance of saving lives is increased., 1. Introduction The concept of a smart city is linked to a paradigm shift where interest is being directed towards proposals and technological initiatives, which seek to make cities smart [...]

The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections. [ABSTRACT FROM AUTHOR]

This paper proposes a degree-of-priority based control strategy for emergency vehicle preemption operation to decrease the impacts of emergency vehicles on normal traffic.The proposed model features its effectiveness to the following three aspects: (1) a multilayer fuzzy model was established to determine the degree-of-priority based on emergency vehicle preemption demand intensity and preemption influence intensity; (2) for emergency vehicles with proper classification, a travel time estimation model for emergency traffic was formulated, an optimal emergency route determines model based on the level of priority of emergency events, and the emergency vehicle travel time was developed to minimize evacuation time as well as minimize the adverse impacts of preemption on normal traffic; and (3) a conditional traffic signals priority control method at each intersection of the evacuation route was built, so that traffic queue at each intersection can be cleared before the arrival of emergency vehicles. A simulation model based on field data was developed, and the performance of the proposed strategy was compared with the conventional local detection based method under the microscopic simulation model. The results validated the efficiency of the proposed strategy in terms of minimizing the delay of emergency vehicles and reducing adverse impacts on normal traffic. [ABSTRACT FROM AUTHOR]

The frequent occurrence of the city emergency leads to rapid development of emergency traffic management which is an important part in the Emergency Rescue System. In the intersection with heavy traffic, the emergency rescue vehicles often with increased delay, reduced safety, and sometimes even with a collision, which restrict the efficiency of the rescue. This paper established the intersection signal control optimization model based on detail analysis of emergency rescue vehicles traffic characteristics and traffic signal control. The models, on one hand, were able to guarantee the emergency vehicles through the intersection quickly and without delay; on the other hand, could ensure the minimum impact to other vehicles in the process of the emergency vehicles through the intersection. Finally, the model's practicality was verified by real cases. [ABSTRACT FROM AUTHOR]