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This paper focuses on the signal preemption control of

Once an unexpected event has happened, an evacuation or a rescue must be carried out to help people in hazardous situations, and emergency vehicles (EV) play an important role in this process. To reduce the negative impact of unexpected events, EV is used to transferr people from dangerous areas to emergency shelters or medical assistance organizations as rapidly as possible. As is well-known to us all, the quality of the emergency service relies on the travel time that EV spends in the evacuation route [

To ensure that EV can pass through intersections safely and rapidly, some scholars proposed the signal preemption strategies. Paniati and Amoni [

Most of the signal preemption methods adopted currently are based on isolated intersection, namely, taking single intersection as a basis, and signal preemption of this intersection will be activated after local detector has detected EV and thus form a signal priority sequence from intersection to intersection [

Studies have shown that implementing signal preemption from the perspective of entire route can reduce the response time of EV [

So far, most of the developed preemption systems operate on a single intersection basis and require local detection of EV at each intersection to activate signal preemption sequence. Owing to the fact that these kinds of strategies depend on local detection and clear intersection one by one, and signal preemption procedure can not start until an EV is detected, inherent delays at intersections are unavoidable. The basic method of route-based signal preemption control of EV is that, when unexpected events occur, in consideration of dynamically changing traffic situations, the route that EV can use to reach the scene of the accident in the shortest time is calculated and recommended as the evacuation route. Once an evacuation route is selected, particular signal preemption control strategy is presented to determine the activation time for preemption at each intersection on the emergency route and realize the signal preemption control of EV ultimately.

Evacuation route planning is an important component of emergency management that seeks to minimize the loss of life or harm to the public during unexpected events [

In this paper, we will study the problem of dynamic signal preemption control of EV based on entire route. We limit the scope of this paper to signal preemption control of EVs under the assumption that the evacuation route has been determined, and the problem of evacuation route calculation will not be discussed further. The objectives of research reported here were to

First, on the premise of having selected a concrete evacuation route, taking isolated intersection as objective and considering the distance between EV detector and each intersection, the operating speed of EV and the number of queued vehicles at each intersection, the earliest-possible start time, and the latest-possible start time of green light at each intersection in the route are given. And then, in order to establish a real-time signal control strategy to ensure that EV can pass through intersection with operating speed or without stopping and minimize the impact of EV on social vehicles, a multiobjective programming model is presented and a particle swarm algorithm is designed to find the Pareto optimal solution set of this model. Finally, a simulation analysis is carried out.

After the optimal evacuation route of EV has been given, to ensure that the delay of EV is much smaller and the social vehicles passing through the whole system are much larger, we need to determine the most suitable time of opening the green light for EV of each intersection in the given route. Suppose that there are

Diagram of intersection.

Phase diagram of traffic signal.

The time parameters mentioned in this paper include the earliest-possible start time and the latest-possible start time of green light at each intersection.

The earliest-possible start time is defined as the earliest time that traffic light of EV direction can be changed to green from the time the EV is detected and on the premise of ensuring the efficiency of the whole system.

The latest-possible start time is defined as the latest time that traffic light of EV direction must be changed to green so as to ensure that EV can pass through the intersection without reducing speed or without stopping.

We employ

Provided that

Let

It can be seen from (

If

Assume that

If the distance between EV detector and intersection 1 is far enough and queued vehicles are not too much,

Let

In Case 1, there is no need to perform signal preemption at intersection 2, and both

In Case 2, after both phase 1 and phase 2 have executed the minimum green time and then green indication goes back to phase 1, we should check whether the following equation is satisfied:

In Case 3, we need to consider the restriction of the maximum green time. Let both phase 1 and phase 2 execute the maximum green time, and then let green indication go back to phase 1. But we should check whether

If (

Case 1 corresponds to the situation that the time needed by green light of phase 1 turning green after the normal green duration of phase 2 and clearing the queue length of phase 1 is no more than the time spent by EV for arriving at intersection 2; then there is no need to perform signal preemption at intersection 2 and

The time parameters of the other intersections on the line are similar to those of intersection 2, which is no longer introduced here.

Let

At present, the evolutionary algorithm is the main algorithm for solving multiobjective programming problems. Owing to the advantages of fast convergence speed and easy implementation, the particle swarm algorithm (PSO) has been successfully applied in many optimization problems [

Generate particle swarm

Since the density of nondominated solutions is inversely proportional to their diversity [

In standard multiobjective PSO algorithm, location updating requires the inertia factor

In order to prevent the algorithm from falling into local optimum, mutation operator is introduced. Let

Assume that the evacuation route is composed of 4 intersections, as shown in Figure

Intersections on evacutation route.

In south-northward direction and west-eastward direction, the ratio of straight, right turn, and left turn is 0.7, 0.2, and 0.1, respectively. And it is 0.6, 0.3, and 0.1 respectively, in north-southward direction. In the particle swarm algorithm,

Assume that the number of vehicles waiting to pass through each intersection at time

The Pareto solution set of of EV when

Number | The real start time of green signal(s) | | | |||
---|---|---|---|---|---|---|

Intersection 1 | Intersection 2 | Intersection 3 | Intersection 4 | |||

| 10 | 33 | 53 | 81 | 0 | 112.1 |

| 10 | 34 | 54 | 81 | 2.3 | 113.6 |

| 10 | 34 | 55 | 82 | 2.4 | 114.8 |

| 11 | 34 | 55 | 83 | 3.9 | 116.6 |

| 12 | 34 | 56 | 83 | 4.7 | 118.7 |

| 13 | 34 | 58 | 84 | 4.8 | 120.4 |

The Pareto solution set of EV when

Number | The real start time of green signal(s) | | | |||
---|---|---|---|---|---|---|

Intersection 1 | Intersection 2 | Intersection 3 | Intersection 4 | |||

| 10 | 31 | 56 | 82 | 0 | 119.7 |

| 11 | 33 | 58 | 83 | 2.9 | 119.9 |

| 11 | 34 | 57 | 83 | 3.2 | 122.4 |

| 12 | 34 | 58 | 83 | 4.9 | 122.7 |

| 13 | 34 | 57 | 83 | 5.5 | 123.5 |

Assume that the number of vehicles waiting to pass through each intersection at time

The Pareto solution set for an increasing arrival rate when

Number | The real start time of green signal(s) | | | |||
---|---|---|---|---|---|---|

Intersection 1 | Intersection 2 | Intersection 3 | Intersection 4 | |||

| 2 | 46 | 73 | 108 | 29.4 | 165.7 |

| 2 | 45 | 75 | 108 | 29.5 | 167.1 |

| 0 | 45 | 76 | 114 | 33.2 | 171.2 |

| 1 | 43 | 76 | 111 | 36.7 | 175.4 |

| 1 | 44 | 80 | 116 | 37.9 | 183.5 |

| 4 | 44 | 80 | 111 | 38.4 | 184.4 |

| 7 | 43 | 82 | 123 | 38.4 | 186.9 |

| 4 | 43 | 80 | 117 | 39.3 | 189.1 |

| 7 | 43 | 84 | 119 | 39.9 | 193.6 |

The Pareto solution set for an increasing arrival rate when

Number | The real start time of green signal(s) | | | |||
---|---|---|---|---|---|---|

Intersection 1 | Intersection 2 | Intersection 3 | Intersection 4 | |||

| 11 | 43 | 79 | 110 | 27.2 | 169.9 |

| 10 | 45 | 80 | 108 | 29.4 | 174.7 |

| 4 | 33 | 76 | 106 | 29.7 | 180.2 |

| 2 | 34 | 73 | 111 | 30.1 | 183.8 |

| 6 | 46 | 79 | 116 | 31.8 | 185.7 |

| 4 | 35 | 71 | 107 | 36.6 | 187.0 |

| 12 | 43 | 77 | 108 | 38.3 | 189.2 |

To verify the control effect of the signal preemption control method presented in this paper under the condition of much larger traffic volume, for example, in the moring peak or the evening peak period, we assume that

The Pareto optimal set.

We can see from the simulation results shown in Tables

When multiple EVs arrive at the same time, the signal priority control method proposed in this paper is also applicable. If the arrival interval of adjacent EV is relatively long, it should be regarded as two signal priority processes. Now, after the front EV has passed through the intersection, we must consider signal transiton from priority signal to normal signal firstly and then adopt the method mentioned above to control the following EV.

In order to overcome the disadvantages of signal preemption control method from intersection to intersection, we study the dynamic signal preemption control method based on route for EV in this paper. Firstly, a single intersection is taken as the research object to determine the earliest-possible start time and the latest-possible start time of each intersection in evacuation direction of EV on the selected route. Then, considering the delay of EV at intersections and the effect of EV to social vehicles comprehensively, to ensure that EV can pass through the intersections without reducing speed and without stopping as far as possible and the impact on social vehicles can be minimal, a multiobjective programming model is established and the solving algorithm is designed. Simulation calculations, considering EV is detected at different times and different traffic states, are carried out, and the corresponding Pareto sets are given. The result shows that, by adopting this method, we can get more reasonable start time of green light at each intersection, reduce the delay of EV at intersections, and improve the efficiency of evacuation. Determine how the signal should be converted to the normal signal when signal preemption has been finished according to the signal timing, the traffic flow characteristics, and other parameters; namely, the signal transition strategy is the next focus of the study.

The authors declare that they have no conflicts of interest.

This research is supported by National Nature Science Foundation of China (nos. 61563029, 71671079, 71361018, and 71571090).