A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Abstract

Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines nonintrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min's flexible signal preemption method(FSPM), and Qin's intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

“…In recent decades, numerous studies have focused on developing EMV-TPC systems at intersections [77][78][79]. Similar to freight and transit signal priority, the process of EMV-TPC strategies at intersections can be divided into four stages:…”

“…Consequently, network-based optimization methods have been proposed that focus more on coordinating multiple intersections at the network scale to additionally mitigate the adverse effects on the road network resulting from prioritizing EMVs [71,79,86,91]. Petrică et al [91] proposed a traffic light priority control system for EMVs and public transportation that reduced average emergency response times and optimized traffic flow at intersections.…”

Smart System for Emergency Traffic Recommendations : Urban Ambulance Mobility

“…The approach presented in [13] provides green indicates to help EV to pass through intersections quickly without stopping, and restores the road network to the normal situation as soon as possible by using linear programming to find the shortest green time in each phase after an EV passes the intersection. However, the proposed contribution does not take into account the dynamics the global traffic state and the impact of a scheduling strategy on the ordinary vehicles.…”

Emergency Vehicle Aware Lane Change Decision Model for Autonomous Vehicles Using Deep Reinforcement Learning

“…In the incident an EMV gets blocked by any vehicle on the road, the consequence can be damaging. Several countries have targeted reducing the travel times for EMVs, as it helps significantly in rescuing lives and protecting people's possessions [3]. In the UK, health authorities have set a target of 8 minutes for the rescue time [4].…”