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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]

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]

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor–critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a 42.6% reduction in EMV travel time as well as an 23.5% shorter average travel time compared with existing approaches. • Propose a multi-agent reinforcement learning framework for traffic signal control. • Simultaneously solve emergency vehicle (EMV) routing and congestion alleviation. • Formulate a model to capture the establishment of emergency lane for EMV passage. • Empower the actor-critic method with policy sharing and spatially adjusted reward. • Conduct comprehensive experiments on both synthetic and real-world maps. • Reduce 42.6% of EMV travel time and 23.5% average trip time. [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]

To estimate travel times through road networks, in this study, we assume a stochastic demand and formulate a stochastic network equilibrium model whose travel times, flows, and demands are stochastic. This model enables us to examine network reliability under stochastic circumstances and to evaluate the effect of providing traffic information on travel times. For traffic information, we focus on travel time information and propose methods to evaluate the effect of providing that information. To examine the feasibility and validity of the proposed model and methods, we apply them to a simple network and the real road network of Kanazawa, Japan. The results indicate that providing ambulance drivers in Kanazawa with travel time information leads to an average reduction in travel time of approximately three minutes. [ABSTRACT FROM AUTHOR]

Aiming at two-lane road, this paper establishes three models to analyze the opposite-overtaking rules of emergency vehicle based on cellular automaton (CCA) model. Based on the simulation of mixed traffic flow for multi-density conditions, the density-speed diagrams have been obtained consequently. According to the analysis, when the traffic density of the opposite lane is low, the opposite driving behavior of emergency vehicle can improve the average speed effectively. At the same time, if the cocurrent lane is in high-density traffic, the traffic in the opposite lane will be disturbed, but the vehicles in the cocurrent lane will not be affected. The paper has further discussed the influence of different emergency vehicle driving behaviors on traffic. The results reveal that as the traffic of the opposite lane is in a low-density range, if emergency vehicle operates overtaking behavior precisely, the greater the density of the cocurrent lane is, the more obviously the speed improve. Meanwhile large random fluctuation of overtaking times will occur. While the risky lane change behavior displays different traffic characteristics, that is when the same direction lane is in high density, the speed increases slightly and the lane change number is changed regularly. [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]

Using geospatial information system (GIS) in emergency vehicles offers a valuable tool for network analysis, visualization and management. It generally offers tools to find the shortest or fastest route through a network These capabilities of GIS for analyzing spatial networks enable it to be used as a decision support system for dispatching and routing of vehicles. The efficient management of ambulance routing to the incident sites and then to the closest hospitals is a vital aspect of the quality of health services offered to citizens. Accomplishing an effective ambulance routing minimizes its response time and thus improves the response performance. This paper describes the development of a new dynamic routing system that offers a solution to the problem of ambulance routing. In urban areas response time of emergency vehicles is influenced by traffic congestion. Dynamic vehicle routing considers the changes of traffic congestion in computing the shortest path. The proposed system is based on integration of GIS and real-time traffic conditions. In this system historical traffic data has been used to determine on initial ambulance path using Dijkstra algorithm. Then the real-time traffic data related to traffic congestion is applied to update the emergency route. A dynamic shortest path is used for emergency vehicle routing. Results of this study illustrate that dynamic routing of emergency vehicles is much more efficient than the static solution. This efficiency is highlighted when unforeseen traffic congestion due to an incident is accounted for. The major contribution of the paper is the way an integrated dynamic ambulance routing system has been developed which has improved emergency response time by about 20%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cellular automaton model for the evacuation of an emergency vehicle (EV) in highways. To facilitate the evacuation of the EV, some additional lane-changing rules (pull over of ordinary vehicles) are introduced in a two-lane cellular automata model for traffic flow in highway. We find that this pulling over of ordinary vehicles promotes a faster moving of the EV in traffic flow and minimize the EV's impact on the overall traffic. [ABSTRACT FROM AUTHOR]