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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 283207, 10 pages
http://dx.doi.org/10.1155/2013/283207
Research Article

Development of Degree-of-Priority Based Control Strategy for Emergency Vehicle Preemption Operation

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China

Received 22 August 2013; Accepted 5 November 2013

Academic Editor: Huimin Niu

Copyright © 2013 Jiawen Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.

1. Introduction

Providing safe and fast driving environment for emergency vehicles to reduce travel time and delay is a critical issue in traffic evacuation. Under effective preemption, ones can reach their destinations at the earliest possible time which is one of most critical factors in saving lives and reducing property loss. At the same time, reducing the adverse impacts of emergency vehicles on normal traffic, so that they can cause the least disturbance to network traffic flow, is the key to avoid the grid-lock caused by emergency accidents [1, 2]. While substantial progress has been made in the areas of vehicle detection and communication technologies to increase the efficiency of emergency vehicles, current state-of-the-art in signal preemption in China has not reached the point where signal clearance strategy, considering the adverse impact or normal traffic, can be automatically generated and implemented in real time.

To date, most preemption systems developed operate on a single-intersection basis and require local detection of an emergency vehicle to activate a signal preemption sequence at each intersection [3, 4]. Existing signal preemption methods can be classified into several categories such as optical, infrared light, acoustic, special types of loop detection, and GPS-based systems [5, 6]. The optical systems that are developed in the 1960’s use a strobe-lamp on the vehicle and an optical sensor per approach to an intersection requiring a clear line-of-sight path between the vehicle and the intersection [7]. The sound-based systems use the directional microphones installed at an intersection to detect the siren of vehicles approaching a given intersection; therefore, no special equipment is required for the emergency vehicles [8]. In a GPS-based system being operated in Taicang, China, both an emergency vehicle and an intersection are equipped with a GPS receiver and a radio transceiver for two-way communication. In recent studies a route-based dynamic strategy was developed for efficient preemption of traffic signals for emergency vehicles in real time by Kwon et al. [9] And Louisell and Collura [10] proposed a simple algorithm to estimate emergency vehicle travel time savings based on a field operational. Mussa and Selekwa [11] reported on the development of a transition procedure based on the quadratic optimization method that aimed at reducing disutility measures to motorists during the transition period. Haghani et al. [12] concentrated on developing an optimization model for developing flexible dispatching strategies that take the advantage of available real-time travel time information. In recent years, Yun et al. [13, 14] have optimized the exit phase controls for emergency vehicle preemption and compared the emergency vehicle preemption methods with a hardware-in-the-loop simulation. He et al. [15] presented a heuristic algorithm for traffic signal control with simultaneous multiple priority requests at isolated intersections in the context of vehicle-to-infrastructure communications being available on priority vehicles, and the method could reduce average bus delay in congested conditions by about 50%. Savolainen et al. [16] developed a dynamically activated emergency vehicle alert system to provide an additional visual cue to motorists of an impending emergency vehicle’s approach. Route selection is one of the fundamental problems in emergency logistics management [1720]. In order to send the commodities as quickly as possible, the path which costs least should be selected. A vast amount of the literature [2125] has been produced up until now.

As indicated above, while there has been substantial progress in developing local preemption technologies, and much research has focused on the impact of transit priority [2628], the impact of EV preemption has not been well studied; a few research studies on decreasing the adverse impacts of preemption on normal traffic have been found in the literature. Qin and Khan [29] report two new control strategies for emergency vehicle signal preemption which reduce the response time and minimize the impact of EV operation on general traffic. Developing degree-of-priority based control strategy that can provide an efficient and safe traveling environment for emergency vehicles with minimum disruption on network traffic is of critical importance in managing urban traffic.

Based on the above achievements, this paper presents a dynamic preemption approach that combines a degree of control priority classification procedure and a route-based preemption method to provide the most appropriate route and control strategy for an emergency vehicle under a given network, traffic conditions, and emergency issue conditions. The proposed method was evaluated in part of the roads network in Beijing, China, using a microscopic network simulation model, and its effectiveness was compared with the existing local-detection based method.

This paper is organized as follows: the first part is the introduction of emergency vehicle preemption methods. The second part is the model of degree of priority classification and travel time prediction. The third part is development of emergency vehicle preemption strategy and performance evaluation. Last part is conclusion.

2. Degree-of-Priority Based Signal Preemption

Figure 1 shows the structure of the degree-of-priority based signal preemption strategy developed in this study. Input data include emergency demand parameters, network traffic parameters, and network data. Emergency demand parameters are obtained when emergency issue happens. Network traffic parameters are continuously collected by field detectors. Network geometry data are static data provided by government. Control strategy for emergency vehicle preemption will be started as soon as emergency issue happens, and the first step is classifying degree-of-priority of emergency vehicles in each road section, which corresponds to intensity needs of emergency vehicle preemption. Then travel time of emergency flow in each road section is predicted based on the input data and degree of priority. Next, two-objective optimization model is established to calculate route selection parameters and select the optimal route for emergency flow. In this research, the well-known Dijkstra algorithm [30] is adopted to find the optimal route that has the minimum route selection parameter for a given origin-destination pair. At last, signal control method is developed to optimize the emergency vehicle preemption plan, and affected area restore control plan is proposed.

283207.fig.001
Figure 1: Structure of degree-of-priority based signal preemption strategies.

3. Degree of Priority Classification Model

Degree of priority is an important parameter of emergency vehicle in this research, which corresponds to the important level and degree of influence of emergency vehicle in each road section. Because both importance level of emergency vehicle and degree of influence to normal network traffic flow are difficult to measure accurately, degree of priority could be calculated by the genetic algorithm or fuzzy algorithm. In this study, researchers developed a three-step, multilayer fuzzy algorithm to calculate the degree of priority in three steps.

To calculate preemption demand intensity, the seriousness and urgency of the emergency issue should be estimated based on desired travel time of emergency vehicle and estimated loss of the emergency issue. In the fuzzy reasoning of preemption demand intensity calculation, fuzzy inputs are the estimated loss parameter of emergency issue and the urgency parameter . Fuzzy output is preemption demand intensity . For the convenience of computing, in this research the ranges of estimated loss parameter and the urgency parameter are defined as 0 to 1 (see Figure 2).

283207.fig.002
Figure 2: Emergency vehicle preemption signal control priority classification process.

The estimated loss parameter can be calculated as follows:

In (1), is the estimated loss parameter of emergency issue . is the estimated economic loss of emergency issue with 10,000 CNY as its unit. is the conversion coefficient of casualties. is the estimated number of casualties. According to this formula, emergency events anticipated to have a higher estimated loss would result in a higher intensity of response.

The urgency parameter can be calculated as follows:

In (2), is the urgency parameter of emergency issue . is the desired travel time of emergency vehicle appointed by decision makers. is the length of road section in the shortest route . is the top speed of the emergency vehicle in road section . From the formula we can find with the decrease of the urgency parameter , the emergency vehicle is more urgent. It makes these two parameters fuzzy by membership function, as shown in Figures 3(a) and 3(b), to calculate the preemption demand intensity.

fig3
Figure 3: Membership function in degree of priority classification model.

Fuzzy rules of preemption demand intensity classification are shown in Table 1(a). In Table 1, {VH, H, M, L, VL} is short for {Very High, High, Medium, Low, Very Low}.

tab1
Table 1: Fuzzy rules in degree of priority classification model.

The membership function of preemption demand intensity is triangular, and the range is [0, 1]. Then, the exact value function of preemption demand intensity is obtained through the center-of-gravity defuzzification.

To consider the impact of emergency vehicle preemption on normal network traffic flow, preemption impact intensity is proposed to reflect the degree of this impact. Fuzzy inputs of preemption impact intensity fuzzy calculation are city road grade of road section and saturation of road section . The city road grade is divided into expressway, arterial road, secondary trunk road, and branch road, which are indicated by value of . {80, 60, 40, 30} indicates {Expressway, Arterial Road, Secondary Trunk Road, Branch Road}, respectively. Saturation of road section is calculated by volumes and capacity of the road section. It makes these two parameters fuzzy by membership function that is shown in Figures 3(c) and 3(d) to calculate preemption impact intensity. Fuzzy rules of preemption impact intensity calculation are shown in Table 1(b).

The membership function of preemption impact intensity is triangular, and the range is [0, 1]. Then, the exact value function of preemption impact intensity is obtained through the center-of-gravity defuzzification.

To calculate degree of priority of emergency vehicle on each road section, fuzzy rules of emergency vehicle preemption priority classification are used. The fuzzy rule is show in Table 1(c).

In Table 1(c), when preemption impact intensity is very high, the degree of priority is classified as a low value to avoid the impact on normal network traffic flow. And when preemption impact intensity is very low, normal network traffic flow volume is low. So the road impedance to emergency vehicle is low enough to classify degree of priority as a low value. Therefore, when Preemption Impact Intensity is medium, a high value is given according to these rules.

The membership function of emergency vehicle degree of priority is triangular, and the range is [0, 1]. Then, the exact value function of emergency vehicle preemption priority is obtained through the center-of-gravity defuzzification. The emergency vehicle degree of priority is an important input in selecting control method.

4. Travel Time Prediction Model for Emergency Vehicle

Road section clearance time, , is an important parameter that affects total travel time and delay of emergency vehicles. It stands for the time difference between the moment when the sections are changed into emergency state and the moment when the emergency vehicles enter the sections. The road section clearance time of different control methods is not the same, and it is related to emergency vehicle degree of priority of the road section. The higher degree of priority, the longer road section clearance time. In the urban road system, the difference of traffic characteristics vehicles will become discrete. The traffic flow setup from the upper intersection will become discrete, and the length of platoon will become long before the platoon arrives at the lower intersection. This phenomenon is called platoon dispersion. Previous studies have demonstrated that the speeds of vehicles follow Gaussian distributions.

In the multilane sections, lane-clear speed varies directly with mean time-headway. Lane-clear speed has the dimensions of pcu/kms.

The lane-change behavior is very fast then the distance of platoon moving in the lane-change period is short, so the distance is negligible. Total quantity of vehicles on road section can be calculated as follows:

In (3), is total quantity of vehicles on road section . Parameter is the current volume of section . Parameter is length of section . Parameter is the number of lanes in section . Parameter is mean travel speed of normal vehicles in section . And quantity of vehicles that leave the lane in unit time is calculated as follows:

In (4), Parameter is lane clear influence coefficient that represents the influence of different lane-clear methods on lane clear speed. Its value is defined as 1 when the lane-clear method is changing the lane from inside lane to outside lane in the two-lane sections. The value is demarcated by following simulation experience. And parameter is the ratio of lane-clear speed to mean time-headway. The value is demarcated by following simulation experience.

Then we can find quantity of vehicles on the emergency lane after normal vehicle avoiding emergency vehicle is calculated as follows:

In (5), is road section clearance time. It is a function of emergency vehicle degree of priority . The formula is shown in (4). This formula should be calibrated based on field data in application. Consider the following:

The applications and studies of road section travel function are the most popular methods to estimate the emergency vehicle travel time by free flow speed, traffic volume, or capacity of the section. This study proposed an emergency vehicle travel time estimation model for the emergency vehicle control method based on bureau of public roads function (BPR function) [31]. BPR function was proposed by FHWA in 1964 in the US [32]. It is the most widely used impedance function in the traffic-planning field. In this function, travel time is the nonlinear function using the ratio of traffic volume to capacity as the parameter.

The definition of the public roads function is defined as follows:

In (7), is emergency vehicle estimated travel time in road section at . Parameter is the travel time when the volume of section is zero. Parameter is the volume of section at . Parameter is the capacity of section at . Parameters and are coefficients whose advised values are and .

Based on above BPR function, this study built the emergency vehicle travel time estimation model for the sections that are applied to different kinds of control methods as follows:

In (8), can be calculated by (5), then, one can get the following:

In the formulation, (8) and (9), is emergency vehicle estimated travel time in section . Parameter is current volume of section . Parameter is length of section . Parameter is the number of lanes in section . Parameter is mean travel speed of normal vehicles in section . Parameter is mean travel speed of emergency vehicles. Parameter is lane clear influence coefficient. Parameter is the ratio of lane-clear speed to mean time-headway. Parameter is road section clearance time of section . Parameters and are coefficients demarcated by following simulation experience.

If road section clearance time takes zero, that is to say, the emergency vehicle control method is disabled, can be calculated by the following equation:

5. Development of Emergency Vehicle Preemption Strategies

5.1. Route Selection Parameter Calculation and Optimal Route Selection

In order to select the optimal route for emergency vehicle, route selection parameter should be calculated. The route selection method should provide an efficient and safe traveling route for emergency vehicle with minimum disruption on network traffic. And the disruption on network traffic of emergency vehicle declines with declining degree of priority. Therefore, the model has two optimization objectives including reducing route travel time to minimum and controlling the sum of degree of priority in the route to minimum. In this study, route selection parameter is defined as follows:

In (9), is emergency vehicle estimated travel time in road section . is emergency vehicle degree of priority in road section . Parameter is degree of priority adjusting coefficients which will be determined in specific case.

Once the location of an emergency issue and emergency facilities (police stations, aid stations, etc.) is determined, the route-selection algorithm determines the best route that has the minimum route selection parameter for a given origin/destination pair. In the proposed strategy, a network is represented as a set of links/nodes and the well-known shortest-path algorithm developed by Dijkstra [30] is adopted to find the optimal route. Dijkstra’s algorithm has been proven to result in the shortest-path from a single source on a weighted directed graph, where all edge weights have nonnegative values [33]. In the proposed strategy, a given network is modeled as a set of directional links with nonnegative route selection parameter , and Dijkstra’s algorithm is applied to find the minimum route.

5.2. Control Method Selection and Optimization

As soon as the emergency route is determined, this study proposes a strategy to select the control method based on degree of priority. Experience-based control method choice suggestion is shown in Table 2.

tab2
Table 2: Emergency vehicle control method choice suggestion.

When degree of priority of the emergency vehicle is within 0.0 to 0.4, no control method will be implemented to minimize negative effects of emergency vehicle on normal network traffic flow. And if degree of priority is within 0.4 to 0.7, green-wave signal control method will be implemented to reduce travel time of emergency vehicle. Further, when degree of priority is within 0.7 to 0.9, the degree of priority is high enough to implement lane clearance control method additionally based on green-wave signal control method. At last, if degree of priority is within 0.9 to 1.0, related road sections will be temporarily closed to reduce travel time of emergency vehicle.

Determining the right offset times to activate the green signal for the intersections along the emergency route is of critical importance in reducing travel time of emergency vehicles and minimizing negative effects of signal preemption on normal traffic flow. As soon as the road sections that implement green-wave signal control method are determined for a given emergency vehicle, the signal offset time of related intersections is adjusted to ensure unidirectional green wave preemption. We record the target offset between downstream intersection and upstream intersection of road section as . Then offset can be calculated as follows:

Record the original offset between downstream intersection and upstream intersection as . So the offset adjustment recorded as is calculated as follows:

If , offset should be adjusted per cycle; is calculated as follows:

Else, offset should be adjusted per cycle .

When the emergency vehicle departs, change the cycle of each downstream intersection to until the offset is equal to target offset.

Lane clearance control method is an important method to reduce travel time of emergency vehicle further. Emergency lane is enabled by traffic police or variable lane control system when the emergency vehicle reache last road section. Startup time of emergency lane is determined by road section travel time of emergency vehicle.

6. Performance Evaluation

Finally the proposed degree-of-priority based control strategy is evaluated in a part of road network in Xicheng district, Beijing, China. Figure 4 is the diagram of the road network.

283207.fig.004
Figure 4: The simulation model of road network in beijing.

This area adopted SCOOT signalized intersections control system. Network traffic parameters and network data are shown in Table 3.

tab3
Table 3: Network parameters and data.

In this case, fire occurs in a restaurant near intersection C6. Emergency vehicle that is composed of three fire trucks starts from fire station near intersection C1. To reduce loss, the desired travel time of emergency vehicle is set at 180 s. The estimated loss is 2,000,000 CNY, and two of the injured are waiting to be rescued. A microsimulation model based on VISSIM is set up to calibrate some parameters in the strategies. The main processes of simulation are shown in Figure 5.

283207.fig.005
Figure 5: Main processes of simulation based on VISSIM.

At a random time, the section in the simulator entered into emergency state which is shown in Figure 5(1). When the section was under emergency state, the vehicles on emergency lane changed to outside lane to clear a lane for the emergency vehicle. Then the emergency vehicle passed the intersection quickly under intersection signal control strategy. The former process is shown in Figures 5(2), 5(3), 5(4), and 5(5). According to the simulation, parameters of control strategy in this case are shown in Table 4.

tab4
Table 4: Parameters of control strategy in evaluation.

Based on the degree-of-priority based control strategy and the data in Table 4, degree of priority and emergency vehicle travel time in each road section are calculated by Beijing Special Control System, which is developed by J+ traffic Tech Co. The results are shown in Table 5.

tab5
Table 5: Calculation results of degree-of-priority and emergency vehicle travel time.

By using shortest-path algorithm developed by Dijkstra, the emergency routes based on different are shown in Figures 6(b) and 6(c).

fig6
Figure 6: Sample network and selected emergency routes based on different .

In Figures 6(b) and 6(c) the red line indicates the road sections that adopt none of the control method, and the green line indicates the road sections that adopt green-wave signal control method. In this study, these two plans are evaluated in simulation software based on cellular automata developed by authors. The degree-of-priority based control strategy is compared with the local-detection based method in the software based on the same set of vehicle inputs. In the microsimulation traffic network, emergency vehicle detectors are set at 100 m from the intersection stop line. For a fair comparison, a common set of 15 different random seeds was used for the simulation of each preemption route, and their results were averaged. The simulation results are shown in Table 6.

tab6
Table 6: Simulation results from different preemption strategies.

As shown in Table 6, the emergency vehicle travel time under degree-of-priority based control strategy exhibits an indistinctive degraded performance. However, a clear pattern of the degraded performance in terms of average delay is exhibited under the degree-of-priority based control strategy. Along with degree of priority adjusting coefficient increase, thus the selected route is different, the average delay is declining in evidence. This indicates the efficiency of the proposed strategy by reducing unnecessary preemption in a given network, thus minimizing the delay because of preemption without decreasing much efficiency.

7. Conclusions

The primary objective of this paper is developing a degree-of-priority based control strategy for emergency vehicle operation to decrease the adverse impacts of emergency vehicles on normal traffic as well as avoid the grid-lock caused by emergency issues. A uniform optimization structure is designed which is consisted by a fuzzy logic based model that was proposed to determine the degree-of-priority of an emergency event. Then, an optimal emergency route selection model was built based on degree-of-priority and the estimated travel time of emergency vehicle. Four types of signal control methods were proposed to minimize the delay of emergency vehicles as well as adverse impacts on normal traffic. Therefore, the traffic signals at the intersections along the emergency route can be adjusted effectively in advance, and traffic queue at each intersection can be cleared for the approaching emergency vehicles. This strategy is evaluated in simulation software based on the data of a part of road network in Xicheng district, Beijing, China. The results indicate that the proposed approach is a viable addition to local-detection based method in urban area and can minimize the delay of emergency vehicles without much adverse effects on normal traffic. Future research includes the development of an efficient calibration method for the degree of priority with parameters which can be measured and the enhancement of the simulation model to evaluate the performance of the strategy in large road network.

Conflict of Interests

The authors declare that they have no conflict of interests regarding the publication of this paper.

Acknowledgments

This research was made possible through funding from the Ministry of Science and Technology of China under Grant no. 2011AA110305. The authors wish to acknowledge the vast and helpful assistance from Intelligent Transportation System Research Center Transportation System Engineering and ITS Research Group, School of  Transportation Engineering, Tongji University. The Beijing Special Control System was established with the great help of J+ traffic Tech Co., Ltd.

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