Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction

Zhao, Jiandong; Guo, Yujie; Duan, Xiaohong

doi:10.1155/2017/9184891

Cited by 31 publications

(15 citation statements)

References 29 publications

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“…They analyzed route comparison with the proposed model which can support EV route choice and eventually improve EV service and operation. According to Zhao et al (2017), EV passage is usually constraint by the factors of time-efficiency, resource requirement and reliability of the road network. The researchers built a model of dynamic path planning to provide the shortest travel time for the EV with the minimum degree of traffic congestion.…”

Section: Fire and Rescue Responsementioning

confidence: 99%

Fire! Fire! Emergency Vehicle Response Time during Pandemic Covid-19

Omar¹,

Ghapar²,

Jusoh³

et al. 2021

IJARBSS

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Emergency Vehicles (EV) consists of fire and rescue engines is expected able to perform its function efficiently especially in the case of fire incident. Even though the fire incident rarely to happen, but the arrival on time of the fire engine at the incident location is very crucial as it can save human lives and loss of valuables. This paper is intended to measure the Key Performance Index (KPI) by measuring the response time of the fire fighter to respond to an emergency case. This study attempts to quantify the duration of the time taken of an EV begins when the emergency call is answered until it reached to the incident location. Emergency Vehicles (EV) consists of fire and rescue engines is expected able to perform its function efficiently especially in the case of fire incident. Even though the fire incident rarely to happen, but the arrival on time of the fire engine at the incident location is very crucial as it can save human lives and loss of valuables. This paper is intended to measure the Key Performance Index (KPI) by measuring the response time of the fire fighter to respond to an emergency case. This study attempts to quantify the duration of the time taken of an EV begins when the emergency call is answered until it reached to the incident location. A qualitative approach was adopted, and data were obtained through in-depth interviews from 10 respondents who are mainly the officers in the fire station. The findings found that external factors such as road factors, parking, weather, inaccurate address, and population density were the main contributing factors that made the stations' response times did not meet its key performance indicator (KPI). Additionally, the internal factors are the main factors that contribute to the success of the key performance indicator response time. Therefore, this study recommends the authorities involved take action to rectify the problems. The result of this study hopefully can be extended to include the elements of traffic management and intersection in measuring EV response time in the future.

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Section: Fire and Rescue Responsementioning

confidence: 99%

Fire! Fire! Emergency Vehicle Response Time during Pandemic Covid-19

Omar¹,

Ghapar²,

Jusoh³

et al. 2021

IJARBSS

View full text Add to dashboard Cite

show abstract

“…IoV can bring some fundamental changes to urban emergency response and accident management. In the IoV, when an emergency occurs, for example, car accidents, dangerous road conditions, the connected cars, or roadside units can automatically send real-time data via IoV about the emergency along with the location information to the emergency management system [20,21] . The emergency management system can automatically respond to this (for example, send a warning to nearby vehicles) or make a request for human emergency team involvement (for example, human emergency team will ask police and ambulances to handle the case).…”

Section: Emergency Responsementioning

confidence: 99%

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Luo

et al. 2020

Intell. and Converged Netw.

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Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure, wireless communication networks, and central cloud platforms. Wireless recommendations play an important role in the IoV network, for example, recommending appropriate routes, recommending driving strategies, and recommending content. In this paper, we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.

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“…To make the research results more realistic and valuable, there are two assumptions about data acquisition and traffic rules for the rescue vehicle. On one hand, accessibility and the strong real-time property of traffic data are the most important basis of dynamic path planning research, which must be considered in this research [37]. With the continuous improvement of urban road infrastructure, the data collection technology of traffic condition is gradually developed to be perfect in many countries, which means the traffic data collection become accessible and real-time.…”

Section: Mathematical Descriptionmentioning

confidence: 99%

A Methodology of Timing Co-Evolutionary Path Optimization for Accident Emergency Rescue Considering Future Environmental Uncertainty

Wen

Duan

et al. 2019

IEEE Access

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The severe impact of traffic accidents, along with a large number of deaths and disabilities, necessitates further improvements in rescue path optimization. To make the emergency rescue more efficient and furthermore ensure health care in life-saving and mitigating traffic congestion as soon as possible, a methodology for rescue vehicle path optimization, timing co-evolutionary path optimization (TCEPO), is proposed to optimize the rescue path. Distinguishing from conventional online re-optimization (OLRO) and co-evolutionary path optimization (CEPO), in TCEPO, each optimization process co-evolves with future traffic environment that keeps changing over time, and the best path will be modified timely based on the predicted routing environmental dynamics (PRED) and recent traffic data. Besides, for better computation efficiency, this research reports an improved ripple spreading algorithm (RSA) as a realization of TCEPO to resolve the optimality problem. The modeling and solutions of TCEPO are discussed in detail to illustrate the applications in emergency rescue path optimization. In order to compare the performance of three methods (OLRO, CEPO and TCEPO), the same optimization tasks and scenarios are presented, and numerical simulation is carried out 100 times. Experimental results clearly prove that the proposed TCEPO possesses stronger robustness and is about 17.65% to 40.02% shorter than CEPO, as well as about 26.34% to 38.47% shorter than OLRO in terms of the travelling time under the PRED with various uncertainties. These advantages have a great impact on raising efficiency and reliability of emergency rescue, which can help rescue vehicles reach the destination as quickly as possible and save more lives.

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Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction

Cited by 31 publications

References 29 publications

Fire! Fire! Emergency Vehicle Response Time during Pandemic Covid-19

Fire! Fire! Emergency Vehicle Response Time during Pandemic Covid-19

Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

A Methodology of Timing Co-Evolutionary Path Optimization for Accident Emergency Rescue Considering Future Environmental Uncertainty

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