Discrete Dynamics in Nature and Society

Volume 2018, Article ID 7095485, 12 pages

https://doi.org/10.1155/2018/7095485

## Signal Preemption Control of Emergency Vehicles Based on Timed Colored Petri Nets

School of Traffic and Transportation, LanZhou Jiaotong University, Lanzhou 730070, Gansu, China

Correspondence should be addressed to Haibo Mu; nc.utjzl.liam@mmmbhm

Received 14 December 2017; Accepted 4 July 2018; Published 1 August 2018

Academic Editor: Paolo Renna

Copyright © 2018 Haibo Mu 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 focuses on the use of timed colored Petri nets (TCPN) to study emergency vehicle (EV) preemption control problem. TCPN is adopted to establish an urban traffic network model composed of three submodels, namely, traffic flow model, traffic signal display and phase switch model, and traffic signal switch control model. An EV preemption optimization control system, consisting of monitoring subsystem, phase time determination subsystem, and phase switching control subsystem, is designed. The calculation method of the travelling speed of EV on road sections is presented, and the methods of determining the actual green time of current phase and the other phase are given. Some computational comparisons are performed to verify the signal preemption control strategies, and simulation results indicate that the proposed approach can provide efficient and safe running environments for emergency vehicles and minimize EV’s interference to social vehicles simultaneously.

#### 1. Introduction

With the continuous increase of traffic volume, traffic congestion and transportation delay on urban roads become more and more serious. Improving transportation safety and efficiency becomes a problem to be solved urgently [1]. Traffic signal control is an effective way to solve this problem. Signal timing and signal coordination control can save energy, reduce vehicle delay, improve transportation efficiency, and ensure transportation safety [2]. In addition, as an important step to cope with urban emergent events, emergency evacuation is viewed as an important measure to decrease the loss of emergent events. The people in impacted areas may suffer from some further injures, even death, if they stay for too long at the accident area. Therefore, one of the core problems of emergency evacuation is to transfer people in impacted areas to emergency shelters or medical assistance organizations as rapidly as possible [3], and emergency vehicles (EV) have larger priority than social vehicles in the case of emergencies. To ensure that EV can pass through intersections safely and rapidly, some scholars proposed signal preemption control strategy, and many methods and technologies of EV preemption have been employed today.

It was in [4] that bus preemption and EV preemption were integrated to study the signal preemption control strategy based on dynamic programming technology, and traffic flow in the network was controlled by the signal phase. To shorten the response time of EV, two kinds of signal preemption control strategies, namely, the real-time control strategy from general signal to EV preemption and the control strategy from EV preemption to general signal, were presented by Qin and Khan [5]. Wang et al. [6] proposed a priority level based EV preemption control strategy, and a travel time estimation model and an optimal route determination model were established for different levels of EV to minimize evacuation time and reduce the adverse effects on normal traffic. Ma and Cui [7] proposed a multiagent based EV preemption control system to study the coordinated control problem of multiple EVs in different import directions that pass through the same signalized intersection during the same period. In addition, the mechanism of centralized server and national transportation communications for ITS Protocol have also been used in EV preemption control field [8, 9].

Although there are many methods based on different traffic control strategies, all of them are mainly based on advanced communication, information, and electronic technologies to solve the EV preemption problem. While in practice, the collection of information needed for wireless communication is very difficult.

Petri net (PN) is a powerful modeling and analysis tool, and it has been proved to be a powerful modeling tool for various kinds of discrete event systems [10]. Its formal expression can clearly reflect traffic signal control logic. Urban traffic system is highly concurrent and asynchronous. At the same time, since vehicles have to share traffic facilities and compete for lanes, green light phases, and other resources, resource sharing and conflict are also characteristics of urban traffic system. Since PNs have incomparable advantages in describing concurrency, asynchronization, parallelism, and conflict, they can be adopted to describe the characteristics of traffic signal control and reflect the characteristics of road traffic flow clearly. Therefore, PNs are suitable for describing urban traffic system and performing simulation. Urban traffic system models based on PNs have been proved to be effective tools for analyzing system performance, assisting intelligence, and optimizing traffic control.

It was in 1994 that PN was first applied to solve traffic signal control problem [11]. Afterwards, some experts and scholars began to use various types of PN models, such as timed Petri nets [12–16], continuous Petri nets [17, 18], hybrid Petri nets [19, 20], and colored Petri nets (CPN) [21–23] to establish simulation models of traffic in smart cities and to study urban traffic signal control problems. These papers mentioned above are aimed at general urban traffic network without EV. Some scholars adopted PNs to study traffic signal control problem under unexpected events when EVs were used for emergency evacuation or rescue. Huang et al. [24, 25] adopted timed Petri net to model the preemption of emergency vehicle systems and proposed a new EV preemption policy to ensure the safety and speed of EVs that passing through intersections. Huang and Weng [26] applied synchronized timed Petri net to design and analyze an urban emergency vehicle preemption control system. Qi et al. [27] employed timed Petri nets and synchronized Petri nets to design a real-time traffic control system for intersections facing accidents so as to provide secondary accident prevention and prevent additional accidents. Zhong et al. [28] studied the performance of China typical Urban Emergency Response System (UERS) and established its PN model, and the performance of UERS was analyzed through the Markov chain of the established model.

However, we can see from the methods adopted by existing research on EV preemption based on PN that traffic signal will change immediately from the current phase to the phase of EV as soon as EV is detected to ensure the smooth passage of EV. Since traffic signal stays in the designated phase for a long time, the right of way of traffic flow in other directions is derived, the delay of vehicles is increased, and traffic jams might be caused. It is very important to establish dynamic preemption strategy in urban traffic management to provide efficient and safe operation environment for emergency vehicles and minimize interference to social vehicles.

In this paper, an urban traffic network model based on timed colored Petri nets (TCPN) is presented, and an EV preemption optimization control system consisting of monitoring, phase time determination, and phase switching control is designed. For convenience and without loss of generality, four-phase lights are modeled with a fixed number of discrete time intervals by TCPN. The TCPN based urban traffic network model consists of three parts. The first part is traffic flow model for road section and intersection. In the traffic flow model of road section, space discretization method is adopted to divide the area between two intersections into three parts, namely, subsection 1, subsection 2, and detection area. Vehicles coming from gas stations, maintenance stations, and other places on the roadside and entering the road sections can be described by this model. The second part is signal display and phase switch model. Under normal circumstances, traffic signal at each intersection is switched orderly. When an EV is detected, in the premise of minimizing the interference to social vehicles, traffic signal can be switched to the phase of EV as soon as possible. The third part is traffic signal switch control model. It ensures that traffic signal can change from EV phase to the next phase after EV has passed through the intersection. The traffic signal optimization system can provide EVs with no or less delay and minimize their interference to social vehicles.

The remainder of the paper is organized as follows. Section 2 provides the definitions of TCPN in a compact way. Section 3 explains the TCPN representation and its signal control logic. Analysis of the EV preemption optimization control system is explained in Section 4. Simulation is carried out in Section 5 and conclusions are presented in Section 6.

#### 2. Basic Concepts of TCPN

CPN is a high-level modeling formalism which has been widely used to model and verify systems. TCPN is obtained by extending the concept of time on the basis of CPN. This extension is made by introducing a global clock for the model and time stamps for the entities [29]. The global clock represents the model time while the time stamps describe the earliest model time at which the entities of the model can be used for the transition evaluation process [30, 31]. If the time stamp of a token is not larger than the current model time, then the token can be used. Otherwise the token is not ready and cannot be used in the transition enabling procedure.

TCPN is a tuple TCPN= (*∑*,* P*,* T*,* A*,* N*,* C*,* G*,* E*,* I*,* R*, ), where(i)* ∑ *is a finite set of non-empty color sets;(ii)* P* is a finite set of places;(iii)* T* is a finite set of transitions;(iv)* A *is a finite set of directed arcs such that ;(v)* N* is a node function that satisfies ;(vi)* C*: is a color function which assigns a set of color sets for each place;(vii)*G *is a guard function. It is defined from into expressions such that where is the type of ,* B *represents Boolean type, is variable set of function , and for all variables ;(viii)* E* is an arc expression function. It is defined from into timed or untimed expressions such that where is the multiset of ;(ix)* I *is an initialization function. It is defined from into timed or untimed closed expressions such that ;(x)* R* is a set of time values, also called time stamps. It is a subset of R closed under + and containing 0;(xi) is an element of* R*, called the start time.

Marking is represented by the number of tokens inside a place. For , is the initial marking of place , and the marking of under other state is denoted as . There are two kinds of markings in a TCPN model, namely, the timed marking and the untimed marking.

#### 3. Urban Traffic Network Model Based on TCPN

##### 3.1. Traffic Flow Models of Road Section and Intersection

Consider a signalized intersection shown in Figure 1(a). The traffic flow of each direction is composed of left-turn, straight, and right-turn vehicles. Two magnetic induction coils buried in the corresponding position of each lane are used to obtain the traffic flow information that enters or exits the intersection, and the range between the two magnetic induction coils is detection area [32]. Vehicles with labels 1, 2, 5, and 6 are combinations of straight and right-turn vehicles, while the ones with labels 3, 4, 7, and 8 are left-turn vehicles. The 4-phase signal control scheme is shown in Figure 1(b). In phase , vehicles with label and are permitted to pass through the intersection and right-turn vehicles are not restricted by traffic light.