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In this paper, a novel emergency vehicle path planning approach tailored for university campus traffic is introduced, leveraging reinforcement learning combined with the cuckoo search algorithm. Firstly, an evaluation index system for campus traffic conditions is established, employing the analytic hierarchy process and expert evaluations to assess the prevailing traffic scenarios. Based on these assessments, an objective function is formulated specifically for emergency vehicle path planning within university campuses. Subsequently, the reinforcement learning cuckoo search algorithm is applied to solve this objective function, yielding an optimal path planning strategy. Experimental results demonstrate the efficacy of the proposed method. It achieves a vehicle detour coefficient ranging between 0.01 and 0.13, with an average vehicle travel distance of 5.34 kilometers and an average path planning time of 1.38 seconds. These findings underscore the method's capacity to significantly improve path efficiency and reduce planning time for emergency vehicles navigating university campuses. [ABSTRACT FROM AUTHOR]

Traffic congestion is a growing concern in urban centers worldwide, leading to significant delays, particularly for emergency vehicles such as fire trucks and ambulances. This not only increases emergency response time but also the risk of life and property loss. To address this issue, our research introduces a traffic control system that prioritizes emergency vehicle egress and mitigates intersection congestion. Other than validation through simulation, the system's efficacy is further substantiated by real-world hardware implementation. The system employs an access point (AP) at intersections to receive location and direction data from approaching vehicles. Emergency vehicles are given precedence, while non- emergency vehicle data is used to adjust traffic light durations, thereby optimizing the traffic flow. The simulation results demonstrate the system's reduced lane opening times and average waiting periods for emergency vehicles. Advancing from simulation to application, we have executed a real-world hardware validation at a high-traffic intersection. This phase entailed the precise installation and calibration of the necessary hardware components, transitioning from theoretical models to practical, operational technology. The hardware setup confirms the system's practical viability and offers a more comprehensive assessment of its impact on traffic efficiency and emergency response times. This dual approach of simulation and hardware validation provides a thorough evaluation of the system's capabilities, establishing a foundation for future traffic management solutions. Additionally, the implementation of the system leads to a notable reduction in CO2 emissions at intersections, contributing to environmental sustainability efforts. [ABSTRACT FROM AUTHOR]

The rise in autonomous vehicles further impacts road networks and driving conditions over the road networks. Cameras and sensors allow these vehicles to gather the characteristics of their surrounding traffic. One crucial factor in this environment is the appearance of emergency vehicles, which require special rules and priorities. Machine learning and deep learning techniques are used to develop intelligent models for detecting emergency vehicles from images. Vehicles use this model to analyze regularly captured road environment photos, requiring swift actions for safety on road networks. In this work, we mainly developed a Generative Adversarial Network (GAN) model that generates new emergency vehicles. This is to introduce a comprehensive expanded dataset that assists emergency vehicles detection and classification processes. Then, using Convolutional Neural Networks (CNNs), we constructed a vehicle detection model demonstrating satisfactory performance in identifying emergency vehicles. The detection model yielded an accuracy of 90.9% using the newly generated dataset. To ensure the reliability of the dataset, we employed 10-fold cross-validation, achieving accuracy exceeding 87%. Our work highlights the significance of accurate datasets in developing intelligent models for emergency vehicle detection. Finally, we validated the accuracy of our model using an external dataset. We compared our proposed model's performance against four other online models, all evaluated using the same external dataset. Our proposed model achieved an accuracy of 85% on the external dataset. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the major modern-day crisis in the world. There are many reasons behind this problem, among which the common reasons are poor traffic management, cars changing lanes, unplanned stoppage, dysfunctional traffic lights, drivers not following rules, emergency vehicle priorities not met etc. To overcome such situations traffic police is placed and the traffic congestion is handled by them manually. But in congested cities, it is very tough to handle huge traffic by a traffic police manually. As more and more vehicles are being commissioned in an already congested traffic system, there is an urgent need for a whole new traffic control system using advanced technologies to utilize the already existent infrastructures to its fullest extent. In this work, we create a fully automated system for traffic control based on traffic density with the help of a machine learning algorithm. We used foreground background subtraction to identify the vehicles in each lane. Using K-nearest neighbour algorithm we computed the density of each lane. Using KNN algorithm we found the accuracy as 99.04% and recall as 73.18%. We then create a database with the density values of each lane using phpmyadmin. The density values are fetched by NodeMCU from the cloud and traffic signals are activated based on the largest density in a round robin fashion. We further improvise the system for prioritizing emergency vehicles in the congestion. We use the Yolo object detection algorithm to detect emergency vehicles like ambulances so that traffic can be cleared up for them. [ABSTRACT FROM AUTHOR]

The interaction between emergency vehicle drivers and surrounding road users is associated with risks. This study explored the application of geofencing to improve interactions between drivers and emergency vehicles to reduce the risk of collisions in high-risk scenarios. Two high-risk scenarios, an off-ramp collision, and an intersection scenario, were used in two driving simulator experiments with 64 participants in total. Half of the drivers received a geofence-based warning about the upcoming traffic situation. The results indicate that geofencing, when applied to provide warnings in specific locations, improves driver behavior. In the off-ramp experiment, all drivers who received a warning avoided the off-ramp and thereby avoided the collision site, whereas all other drivers took the off-ramp. In the intersection experiment, the warning led to earlier deceleration, allowing the emergency vehicle to pass safely and with minimal delay; whereas nearly half of those who did not get a warning failed to yield to the emergency vehicle. The drivers acted based on the warning they received, even when they had not yet seen the emergency vehicle. The findings suggest that geofencing can improve driver behavior by detecting emergency vehicles early and reliably, thereby improving traffic safety and minimizing delay for emergency vehicles on call.

Traffic congestion is a growing concern in urban centers worldwide, leading to significant delays, particularly for emergency vehicles such as fire trucks and ambulances. This not only increases emergency response time but also the risk of life and property loss. To address this issue, our research introduces a traffic control system that prioritizes emergency vehicle egress and mitigates intersection congestion. Other than validation through simulation, the system’s efficacy is further substantiated by real-world hardware implementation. The system employs an access point (AP) at intersections to receive location and direction data from approaching vehicles. Emergency vehicles are given precedence, while non- emergency vehicle data is used to adjust traffic light durations, thereby optimizing the traffic flow. The simulation results demonstrate the system’s reduced lane opening times and average waiting periods for emergency vehicles. Advancing from simulation to application, we have executed a real-world hardware validation at a high-traffic intersection. This phase entailed the precise installation and calibration of the necessary hardware components, transitioning from theoretical models to practical, operational technology. The hardware setup confirms the system’s practical viability and offers a more comprehensive assessment of its impact on traffic efficiency and emergency response times. This dual approach of simulation and hardware validation provides a thorough evaluation of the system’s capabilities, establishing a foundation for future traffic management solutions. Additionally, the implementation of the system leads to a notable reduction in CO2 emissions at intersections, contributing to environmental sustainability efforts.

The rise in autonomous vehicles further impacts road networks and driving conditions over the road networks. Cameras and sensors allow these vehicles to gather the characteristics of their surrounding traffic. One crucial factor in this environment is the appearance of emergency vehicles, which require special rules and priorities. Machine learning and deep learning techniques are used to develop intelligent models for detecting emergency vehicles from images. Vehicles use this model to analyze regularly captured road environment photos, requiring swift actions for safety on road networks. In this work, we mainly developed a Generative Adversarial Network (GAN) model that generates new emergency vehicles. This is to introduce a comprehensive expanded dataset that assists emergency vehicles detection and classification processes. Then, using Convolutional Neural Networks (CNNs), we constructed a vehicle detection model demonstrating satisfactory performance in identifying emergency vehicles. The detection model yielded an accuracy of 90.9% using the newly generated dataset. To ensure the reliability of the dataset, we employed 10-fold cross-validation, achieving accuracy exceeding 87%. Our work highlights the significance of accurate datasets in developing intelligent models for emergency vehicle detection. Finally, we validated the accuracy of our model using an external dataset. We compared our proposed model’s performance against four other online models, all evaluated using the same external dataset. Our proposed model achieved an accuracy of 85% on the external dataset.

In urban and city environments, road transportation contributes significantly to the generation of substantial traffic. However, this surge in vehicles leads to complex issues, including hindered emergency vehicle movement due to high density and congestion. Scarcity of human personnel amplifies these challenges. As traffic conditions worsen, the need for automated solutions to manage emergency situations becomes more evident. Intelligent traffic monitoring can identify and prioritize emergency vehicles, potentially saving lives. However, categorizing emergency vehicles through visual analysis faces difficulties such as clutter, occlusions, and traffic variations. Visual-based techniques for vehicle detection rely on clear rear views, but this is problematic in dense traffic. In contrast, audio-based methods are resilient to the Doppler Effect from moving vehicles, but handling diverse background noises remains unexplored. Using acoustics for emergency vehicle localization presents challenges related to sensor range and real-world noise. Addressing these issues, this study introduces a novel solution: combining visual and audio data for enhanced detection and localization of emergency vehicles in road networks. Leveraging this multi-modal approach aims to bolster accuracy and robustness in emergency vehicle management.The proposed methodology consists of several key steps. The presence of an emergency vehicle is initially detected through the preprocessing of visual images, involving the removal of clutter and occlusions via an adaptive background model. Subsequently, a cell-wise classification strategy utilizing a customized Visual Geometry Group Network (VGGNet) deep learning model is employed to determine the presence of emergency vehicles within individual cells. To further reinforce the accuracy of emergency vehicle presence detection, the outcomes from the audio data analysis are integrated. This involves the extraction of spectral features from audio streams, followed by classification utilizing a support vector machine (SVM) model. The fusion of information derived from both visual and audio sources is utilized in the construction of a more comprehensive and refined traffic state map. This augmented map facilitates the effective management of emergency vehicle transit. In empirical evaluations, the proposed solution demonstrates its capability to mitigate challenges like visual clutter, occlusions, and variations in traffic density common issues encountered in traditional visual analysis methods. Notably, the proposed approach achieves an impressive accuracy rate of approximately 98.15% in the localization of emergency vehicles. [ABSTRACT FROM AUTHOR]

Emergency vehicles getting stuck in traffic jams have always been a great concern in the cities. This would be more concern when the car is autonomous. So, evaluating the vehicle's maneuver in the presence of an emergency vehicle is required. This problem has been questioned in different ways. This paper attempts to deal with the performance of the vehicle in front of the emergency vehicle by considering the emergency vehicle's right-of-way. To evaluate the behavior of such a vehicle, we propose an algorithm that comprises three parts. In the first part, using a decision tree model, all feasible maneuvers that can be done by the front vehicle in the presence of an emergency vehicle are predicted. In the next part, the performed maneuver is detected using Hidden Markov Model. Finally, the best possible maneuver is compared with the performed maneuver. The proposed algorithm is implemented on a simulator, also developed in this research. The simulator generates different driving behaviors to train the models and evaluate the proposed algorithm.

Considerable research has been conducted in over a decade on traffic management systems that utilize Wireless Sensor Networks (WSNs) to mitigate congestion and prioritize emergency vehicles. The traffic management is becoming a wide interesting area for both academic and industrial researchers. The real-time traffic management is a dynamic scheme and is very challenging to provide an accurate signalling time and priority for a specific vehicle. This paper introduces a novel emergency vehicle information passing system that utilizes Radio Frequency (RF) sensors. This research study presents an innovative system for transmitting emergency vehicle information, which makes use of Radio Frequency (RF) sensors. The system effectively transmits crucial data, such as vehicle ID, approaching direction, mileage driven, and destination time. By doing so, it enables other vehicles to allocate appropriate space and facilitate smoother passage for emergency vehicles. The main intention of this approach is to improve the communication speed among the nodes and to reduce the response time. The communication among the nodes is done with different frequencies to enhance the method's effectiveness. We also propose a priority-based MAC (PMAC), which guarantees a slot allocation for emergency message transmission in the network. The effectiveness of the proposed approach is assessed through simulation using NS-2. The findings emphasize the effectiveness of the RF sensor when it comes to its ability to respond quickly and showcasing the PMAC's capability to reduce end-to-end delay. [ABSTRACT FROM AUTHOR]

The paper presents a computer-vision based approach for real-time detection of different types of emergency vehicles under heavy traffic conditions. This enables preferential path clearance for emergency vehicles by the traffic controller which has the potential of saving lives, properties and increasing the ability to prevent crimes and drastically reducing the total time required by an emergency vehicle to reach its target destination. The main challenge emergency vehicles faced in and around the cities is heavy traffic jams, which significantly hampers their operations resulting in a disastrous outcome. In most of the cities, emergency vehicles are equipped with unique colors and sound system which enable the traffic police to identify them. As our cities become smarter and transition into an era of artificial intelligence, the old system may not be sustainable due to that fact that it needs humans to constantly monitor emergency vehicle arrival at the intersections and also the sound produces by such vehicles may be nuisance and discomforting to the general public. This paper proposed a method of automatic detection of four different categories of emergency vehicle irrespective of the vehicle’s shapes, models or manufacturers’ using modified version of YOLOv5 object detection algorithm. YOLO is an acronym for (You Only Look Once) and it is an object detection algorithm that divides images into a grid system. Each cell within the grid is responsible for detecting objects within itself. YOLO is one of the most famous object detection algorithms due to its speed and accuracy. YOLO models are used for object detection with high performance which consists of 84 classes to detect and differentiate between 84 different objects. The proposed model developed here is based on 4 classes which are (Firetrucks, Ambulance, Police Car, and Normal Cars) classes. The top layers (fully connected layers) of the YOLO algorithm were re-designed and retrained to get new learned weights while freezing the bottom layers (convolutional layers) and retaining the pre-trained YOLOv5 weights. After retraining with the proposed modified YOLOv5, the model has shown promising results and quite impressive metrics in detecting and classifying emergency vehicles and normal vehicles. Using Mean Average Precision (mAP) metric, for police cars we achieved 98%, 96% for fire trucks, 89% for ambulances and 97% for normal cars.

When an emergency vehicle approaches form behind, the driver must change his/her lane to the left lane and pull over to gave way to the emergency vehicle. However, in most cases the abrupt lane change maneuver of the vehicles in front of the emergency vehicle will cause a traffic congestion, and the emergency vehicles must decelerate as a result. To avoid the above situation, we designed a lane change algorithm based on a look-ahead concept for the vehicle driving in front of the emergency vehicle (defined as the ego vehicle). To reduce calculation load, a rule-based control method is adopted to control the longitudinal motion and a cosine function is adopted to control the lateral motion of the ego vehicle. This lane change algorithm allows the ego vehicle to choose a gap in the left lane or the right lane to merge to as soon as possible, and to change its lane safely without affecting the surrounding vehicles. The objective of the control method is to let the emergency vehicle pass smoothly without deceleration in congested traffic condition. A computer simulation was conducted to validate the effectiveness of the proposed method. The simulation results for 1000 initial conditions show that, for 720 initial conditions the proposed method manages to merge the ego vehicle effectively without collision, and the emergency vehicle does not need to slow down. Compared with a lane change control method without look-ahead concept, the proposed method increases the number of successful lane change maneuver by 555 cases. [ABSTRACT FROM AUTHOR]

Numerous approaches have attempted to develop systems that more appropriately manage street crossings in cities in recent years. Solutions range from intelligent traffic lights to complex, centralized protocols that evaluate the policies that vehicles must comply with at intersections. Such works attempt to provide traffic-control strategies at intersections where the complexity of a dynamic environment, with vehicles crossing in different directions and multiple conflict points, pose a significant challenge for city traffic optimization. Traditionally, a traffic-control system at an intersection gives the green light to one lane while keeping the other lanes on red. But there may be situations in which there are different levels of vehicle priority; for example, emergency vehicles may have priority at intersections. Thus, this work proposes a distributed junction-management protocol that pays special attention to emergency vehicles. The proposed algorithm implements rules based on the distributed intersection management (DIM) protocol; such rules are used by vehicles while negotiating their crossing through the intersection. The proposal also seeks to affect the traffic flow of non-priority vehicles minimally. An evaluation and comparison of the proposed algorithm are presented in the paper. [ABSTRACT FROM AUTHOR]

Introduction: Detailed Emergency Department attendance data for migraine are needed for service redesign. Methods: A service evaluation was undertaken, classifying adult emergency department headache attendances using the International Classification of Headache Disorders migraine C-E criteria, evaluating attendance characteristics. Results: Migraine/Probable migraine diagnosis was documented in 58% but coded in 24% attendances by ED clinicians. 29% of patients used no analgesia before attending, 43% attended ≥4 days after onset and 19% arrived by ambulance. Conclusion: This evaluation highlights sub-optimal acute management and discrepancy between migraine coding and diagnosis contributing to underreporting. We recommend further evaluation of identified cohorts and headache proforma use. [ABSTRACT FROM AUTHOR]

Emergency vehicles (EVs) are significant in disseminating sensitive information across the road-side communication networks. This amalgamation of vehicles and communication networks improves the reachability and accessibility of sensitive data along the driving scenario. However, the communication network experiences data and traffic congestion due to wireless medium and varying vehicle densities. In order to address the problem of data and traffic congestion, this paper introduces message dissemination with re-route planning (MDRP) method. This method initiates a dissemination boundary for selecting neighbors and weight for selecting the re-routing path. The weight is based on the traffic conditions of the road segment along with consideration of the timeout of the EV message. The joint process of rerouting and data transmission is supported by dependent queue management for improving the message delivery and reducing the impact of delaying instances in the travelling path. [ABSTRACT FROM AUTHOR]

Nowadays, the improvement of the emergency vehicles' priority is required to improve their response time. Presently, the emergency vehicles alert the road users using the sirens and lights, which might cause disturbances in the traffic or lead to accidents due to the unpredictability of the road users' behaviors. This paper introduces the priority indicator, which calculates the priority of several emergency vehicles as they approach an intersection and sort them in a way that allows granting priority to each one without causing delays neither for the emergency nor for the normal vehicles. The priority indicator is then introduced in a predictive traffic actuation program (DIRECTOR) that adjusts the signal timing in a dynamic way. Thus, this research aims to create a generalized priority form that simplifies granting the priority for emergency vehicles in a harmonized way in all over the Netherlands, making sure that it takes into consideration the several conditions of the intersection. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) is the synergy of information technology, real-time control, and communication networks. The system is expected to perform more complex traffic arrangements, in particular, traffic management of Emergency Vehicles (EV) such as fire trucks, ambulances, and so forth. Implementation of traffic management using only Traffic Signal Pre-emption does not give enough space for an EV to cross an intersection safely, especially on streets where there is only one lane. This paper proposes a model of accelerated emptying of traffic in front of EVs. Accelerated emptying model uses historical approach, based on current characteristics of traffic. For example, if the normal vehicle speed is equal to the EV speed before accelerated emptying, the system indicator will be 0%, thereby indicating no need for accelerated emptying. Similarly, a negative system indicator result means an accelerated emptying process is not necessary. However, if the system indicator is close to 100%, this result indicates accelerated emptying is necessary.

Abstract Favouring the crossing of Emergency Vehicles (EVs) through intersections in urban cities is very critical for people lives. There have been several efforts toward developing Traffic Signal Control Systems (TSCS) dedicated to control efficiently the traffic flow, but few are the efforts toward developing Traffic Signal Priority Systems (TSPS) dedicated to favour the crossing of EVs (such as ambulances, firefighters, police cars, etc.). Multi-Agent Systems were considered to develop several distributed TSCS, while very few works have developed distributed TSPS. Such systems lack on dealing with the EVs crossing issues while maintaining a fluid state of the traffic. In the literature, the Longest Queue First – Maximal Weight Matching (LQF-MWM) is proved to guarantee a stable TSCS. Recently, the LQF-MWM technique is increasingly used to benchmarck and assess adaptive TSCS. Moreover, the preemption is one of the most effective techniques used to prioritise the crossing of EVs. This paper is the first to rely on LQF-MWM assumptions, preemption technique, and Multi-Agent Systems to design a distributed TSPS. The suggested system has two main purposes, which are the guidance of EVs and the control of traffic signals. Nine agents are implemented to govern a network of nine intersections, where each agent uses the Multi Agent System based Preemptive Longest Queue First – Maximal Weight Matching. We have referred to VISSIM traffic simulation software for benchmarking and analysis. To assess the suggested system, we have developed a distributed and preemptive version of VISSIM Optimized Stage-Based Fixed-Time algorithm. Python is considered to develop the suggested systems, and Spade platform is considered as agents’ platform. Several Key Performance Indicators are considered to assess the performance of all controllers including delay time, travel time, vehicles queue occupancy, number of stops, distance traversed, and speed. Experimental results show a competitive performance of the developed system to maintain a fluid traffic and guide efficiency EVs.

Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on VISSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition. [ABSTRACT FROM AUTHOR]

There is a problem of coordination in encounters with an emergency vehicle when an automated driving vehicle travels on public roads. Effectiveness of the pre-deceleration running based on the earlier recognition of emergency vehicle using vehicle to vehicle communication is studied from the viewpoint of safety and ride comfort. Experimental data of the driver's maneuver and vehicle's status are collected and analyzed using the driving simulator. Recognition by vehicle to vehicle communication (V2V for short) leads to pre-deceleration and improves deceleration and jerk as compared to siren sound and red light of emergency vehicles. [ABSTRACT FROM AUTHOR]

The protection of emergency medical service (EMS) workers from airborne disease transmission is important during routine transport of patients with infectious respiratory illnesses and would be critical during a pandemic of a disease such as influenza. However, few studies have examined the effectiveness of ambulance ventilation systems at reducing EMS worker exposure to airborne particles (aerosols). In our study, a cough aerosol simulator mimicking a coughing patient with an infectious respiratory illness was placed on a patient cot in an ambulance. The concentration and dispersion of cough aerosol particles were measured for 15 min at locations corresponding to likely positions of an EMS worker treating the patient. Experiments were performed with the patient cot at an angle of 0° (horizontal), 30°, and 60°, and with the ambulance ventilation system set to 0, 5, and 12 air changes/hour (ACH). Our results showed that increasing the air change rate significantly reduced the airborne particle concentration (p < 0.001). Increasing the air change rate from 0 to 5 ACH reduced the mean aerosol concentration by 34% (SD = 19%) overall, while increasing it from 0 to 12 ACH reduced the concentration by 68% (SD = 9%). Changing the cot angle also affected the concentration (p < 0.001), but the effect was more modest, especially at 5 and 12 ACH. Contrary to our expectations, the aerosol concentrations at the different worker positions were not significantly different (p < 0.556). Flow visualization experiments showed that the ventilation system created a recirculation pattern which helped disperse the aerosol particles throughout the compartment, reducing the effectiveness of the system. Our findings indicate that the ambulance ventilation system reduced but did not eliminate worker exposure to infectious aerosol particles. Aerosol exposures were not significantly different at different locations within the compartment, including locations behind and beside the patient. Improved ventilation system designs with smoother and more unidirectional airflows could provide better worker protection. [ABSTRACT FROM AUTHOR]

This case involved a fatal collision between a police vehicle (operated by a police officer) and a nonpolice vehicle (operated by a civilian). With lights and sirens activated, the officer in pursuit ran a red light and crashed into the civilian's vehicle in an intersection whose traffic light controller included an emergency vehicle preemption system. The civilian driver was mortally injured, and died the next day. The estate of the deceased driver sued the police officer, municipal police department, and manufacturer of the emergency vehicle preemption system. The author was retained by counsel for the estate of the deceased to assist in the case against the manufacturer of the emergency vehicle preemption system and municipality. The evidence showed that the preemption system was working properly, but that the system's confirmation lights had been improperly programmed. A maximum speed was calculated at which a preempted green light for emergency drivers would be assured. Event logs in the police vehicle showed that the police officer was driving too fast for the traffic light controller to cycle through its sequence before the officer reached the intersection. [ABSTRACT FROM AUTHOR]