Content uploaded by Prakash Ranjitkar
Author content
All content in this area was uploaded by Prakash Ranjitkar on Sep 27, 2019
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=ttrv20
Transport Reviews
ISSN: 0144-1647 (Print) 1464-5327 (Online) Journal homepage: https://www.tandfonline.com/loi/ttrv20
A systematic review of route optimisation and pre-
emption methods for emergency vehicles
Subash Humagain, Roopak Sinha, Edmund Lai & Prakash Ranjitkar
To cite this article: Subash Humagain, Roopak Sinha, Edmund Lai & Prakash Ranjitkar (2019):
A systematic review of route optimisation and pre-emption methods for emergency vehicles,
Transport Reviews, DOI: 10.1080/01441647.2019.1649319
To link to this article: https://doi.org/10.1080/01441647.2019.1649319
Published online: 30 Jul 2019.
Submit your article to this journal
Article views: 85
View related articles
View Crossmark data
A systematic review of route optimisation and pre-emption
methods for emergency vehicles
Subash Humagain
a
, Roopak Sinha
a
, Edmund Lai
a
and Prakash Ranjitkar
b
a
Information Technology and Software Engineering, Auckland University of Technology, Auckland, New
Zealand;
b
Department of Civil and Environmental Engineering, University of Auckland, Auckland, New
Zealand
ABSTRACT
Reducing the travel time of emergency vehicles (EVs) is an effective
way to improve critical services such as ambulance, fire, and police.
Route optimisation and pre-emption are powerful techniques used
to reduce EV travel time. This paper presents a systematic literature
review of optimisation and pre-emption techniques for routing EVs.
A detailed classification of existing techniques is presented along
with critical analysis and discussion. The study observes the
limitations of existing routing systems and lack of real-world
applications of the proposed pre-emption systems, leading to
several interesting and important knowledge and implementation
gaps that require further investigation. These gaps include
optimisations using real-time dynamic traffic data, considering
time to travel as a critical parameter within dynamic route
planning algorithms, considering advanced algorithms, assessing
and minimising the effects of EV routing on other traffic, and
addressing safety concerns in traffic networks containing multiple
EVs at the same time.
ARTICLE HISTORY
Received 14 November 2018
Accepted 15 July 2019
KEYWORDS
Route optimisation; traffic
signal pre-emption;
emergency vehicle routing;
path planning; traffic signal
priority
1. Introduction
The response time of an emergency vehicle (EV) is the time interval from receiving an emer-
gency call to the arrival of an EV to the emergency location. The response time depends
primarily on the time it takes for an EV to travel to the location of an emergency. This travel
time of an EV depends on several static parameters such as distance and number of inter-
sections on the route (signalised/unsignalised), as well as dynamic parameters like flow,
average speed, and number of stops. The presence of numerous such parameters
makes reducing EV travel times complex and challenging.
Increased response time of EVs can have an irreparable loss of life and property. In
medical emergencies such as cardiac arrest, every one-minute delay in response time
causes mortality rate to increase by 1% and imposes additional USD 1542 in hospital
costs, leading to 7 billion dollars increase in healthcare expenditure per year only
in USA (RapidSOS, 2015). Other emergencies like building fires typically grow by
20% per minute causing an average USD 4000 of additional damages (RapidSOS,
© 2019 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Subash Humagain subash.humagain@aut.ac.nz Information Technology and Software Engineering,
Auckland University of Technology, AUT Tower, Level 7, 2-14 Wakefield Street, Auckland, New Zealand.
TRANSPORT REVIEWS
https://doi.org/10.1080/01441647.2019.1649319
2015). These factors have led to extensive research on reducing EV travel time (Fitch,
2005).
Several studies have explored this problem, proposing several solutions, which can be
broadly classified into route optimisation and pre-emption (Zhu, Chen, & Bing, 2014).
Optimisation is the process of attaining the highest achievable performance under a
given set of constraints by maximising desired factors and minimising undesired ones.
Route optimisation tries to choose the best route to achieve the minimum EV travel
times. Route pre-emption, sometimes referred to as “traffic signal prioritisation”or
“transit signal priority”, changes or alters traffic control in order to grant priority to
special vehicles like EVs. The most commonly used tactic is manipulating traffic signals
in the route of an EV, halting lower-priority traffic and providing right-of-way to the EV
(Gedawy, 2010; Paniati & Amoni, 2006).
Reducing the response times of emergency services requires identifying the most
effective optimisation and pre-emption techniques, and factors for reducing EV travel
times. This article presents a Systematic Literature Review (SLR) (Kitchenham, 2004)of
the existing techniques proposed for route optimisation and pre-emption for EVs as
explained in Sections 3 and 4. We have reviewed 72 research articles out of which 85%
are from 2006 to 2018, ensuring the relevance of this study to current research. For
each study, we extract sufficient contextual and methodological details for individual
analysis and comparison with others. This study can significantly help researchers pursu-
ing research to improve EV travel times to understand the current state-of-the-art, and
industry and government stakeholders looking to adopt better techniques for routing EVs.
After describing the SLR method in Section 2, we survey route optimisation and pre-
emption techniques in Sections 3 and 4. Section 5 reviews methods employing both
optimisation and pre-emption. Section 6 provides knowledge gaps and Section 7 provides
concluding remarks.
2. Systematic literature review method
A systematic literature review (SLR) allows answering a clearly formulated question by
appraising related research (Kitchenham, 2004). The primary research question in this
study was “What optimization and pre-emption techniques from academic literature
and industry can be used for effective EV routing?”
Different keywords related to route optimisation, transit signal priority and road pre-
emption for EVs were used for searching existing works. We experimented with several
search strings and the following is the trial search string used in this survey: (((“route optim-
ization”)OR((“Preemption”OR “pre-emption”)OR(“priority”)) AND “Emergency Vehicles”)).
The number of results found was recorded, and screening was undertaken on sections
of search results checking relevance at the title, abstract and full-text levels. To make the
literature search as inclusive as possible, we used relevant bibliographic databases like ITS
Network and Transportation Research Board, web-based search engines like Google
Scholar, Scopus, IEEE, Science Direct, ACM Digital Library and ISI Web of Science, as well
as bibliographies of related reviews and directed calls for evidence using professional
social networks like research gate and LinkedIn (Kitchenham, 2004).
We excluded studies concentrating on optimisation models in emergency logistics and
optimisation for multiple service stations during emergencies as this paper is primarily
2S. HUMAGAIN ET AL.
concentrated on reducing the response time of EVs focusing on travel time. We also
excluded short papers (less than 4 pages), articles published in non-recognised journals,
and those not written in English.
Our initial search retrieved 1069 studies, which were pruned to exclude papers meeting
any of the exclusion criteria or those that were duplicated. In the end, 72 papers were
chosen for a detailed review. Out of these, 25 articles propose route optimisation, 34
report pre-emption based techniques, and the remaining 13 propose systems employing
both. Sections 3, 4 and 5 explore optimisation, pre-emption and both techniques that aid
in reducing the response time of EVs identified from the literature search.
3. Route optimisation methods
A majority of Emergency Management Systems use dedicated software to locate, dispatch
and route EVs. EVs contain installed software like Sygic and Infoware to guide the driver
towards the emergency location using customised traffic information. Such software
employs route optimisation to ensure that EVs reach their destination in time (Togneri
& Deriaz, 2013).
Route optimisation treats traffic networks as graphs. Different cost functions like dis-
tances over available travel paths, travel times along sections of the network, and fuel
efficiency are assigned as weights to the edges of the graph. Optimisation algorithms
then maximise or minimise the cost function (Winter, 2002). According to Eksioglu,
Vural, and Reisman (2009) route optimisation is mainly distance (path)-dependent or
travel time-dependent. Knowing the position of an EV is critical in suggesting optimised
routes. A majority of studies employ global positioning system (GPS) technology. We
use the vehicle routing problem (VRP) taxonomy defined by (Eksioglu et al., 2009) for com-
puting transportation cost and to categorise the reviewed studies into path-based optim-
isation and time-based optimisation, and then critically analyse these studies.
3.1. Path-based optimisation
Intuitively, taking the shortest path between an EV’s source and the intended destination
can minimise travel time. In computer science, Dijkstra’s shortest path algorithm is well-
known for such optimisation and several of the surveyed works extend this algorithm in
some way. Kula, Tozanli, and Tarakcio (2012) developed a stochastic shortest path strategy
by adding path-specific information such as hourly traffic behaviour and pedestrian invol-
vement to a K-shortest path model which produce multiple paths with different travel
times. Nordin, Zaharudin, Maasar, and Nordin (2012) developed a deterministic shortest
path calculator using an informed search algorithm called A* algorithm to find the shortest
path among multiple points. Kai, Yao-ting, and Yue-peng (2014) and Winn (2014) used the
geographical information system (GIS) network to determine the starting position of EVs
and found the shortest path to the destination by calculating shortest distance from a
starting node to every following node afterwards using Dijkstra’s algorithm. Brady and
Park (2016) also described a similar shortest path algorithm for routing EVs with additional
road features like lane count, intersection control devices, and construction works.
Panahi and Delavar (2009) use Dijkstra’s algorithm but their approach can intelligently
update the proposed path during driving by integrating data from GIS and real-time traffic
TRANSPORT REVIEWS 3
conditions. Smitha et al. (2012) and Elmandili, Toulni, and Nsiri (2013) described the Vehi-
cular Ad Hoc Network (VANET) based navigation, which also used Dijkstra’s algorithm for
routing. Real-time traffic congestion was determined by GPS evaluating a larger cluster of
slow-moving nodes and suggesting a new route if needed. Nicoara and Haidu (2014),
Winn (2014) and Sun, Yue, and Yao (2014) used GIS-based networks to find the shortest
route access for EVs using real-time traffic data. Similarly, Fleischman et al. (2010) used
GIS to estimate transport times for ambulances creating a linear regression model that
increases the accuracy of these road network estimates using patient characteristics,
use of lights and sirens, daylight, and rush-hour intervals.
The speed of calculation of the shortest path in Dijkstra’s algorithm is dependent on the
number of nodes available. Bu and Fang (2010) used improved Dijkstra’s algorithm for
solving the shortest path problem by focusing dynamically in a smaller area including
the accident location and current EV location. As the EV approaches the accident location,
the number of nodes involved in the calculation of the shortest path gradually decreases,
subsequently reducing the computation time.
3.2. Time-based optimisation
For EV routing, the actual time taken to reach the emergency location is more important
than distance, cost, fuel consumption, etc. Cooke and Halsey (1966) were the first to
provide theoretical insights on shortest path algorithms that can have variable travel
times between vertices depending upon physical parameters. Hadas and Ceder (1996)
implemented the shortest path algorithm to produce multiple time-based paths for EVs.
Zhu et al. (2014) developed an optimisation model, which was based on both the
shortest rescue time and the lowest rescue cost using a simulated annealing algorithm
to find the global optimum of the objective functions to minimise rescue time and cost.
Wang and Liu (2011) proposed an Internet of Things application using RFID tags in
ambulances and wireless sensor nodes on the roads to collect real-time traffic data
and forecasted path to provide the fastest route. Choosumrong, Bozon, and Raghavan
(2012) used the routing algorithm to calculate minimum travel time from the accident
point to the nearest hospital using parameters like availability of beds and the patient’s
state. Apart from the above, Elalouf (2012) used exact pseudo-polynomial algorithm to
find the optimal time-dependent route using real-time data for uncertain traffic con-
ditions. The algorithm uses dynamic programming to simplify a complicated problem
into simple sub-problems by breaking them. Finally, they improved the solution
using an e-approximation algorithm by limiting results within allowable lower and
higher bound of cost function.
Derekenaris et al. (2001) developed a solution to deploy an ambulance requiring the
least time to reach the site of an incident. Like most other approaches, they used GPS
to locate available ambulances and then employed Dijkstra’s algorithm to calculate the
shortest travel time between available ambulances and incident site. Vlad et al. (2008)
described a model that finds the fastest path for an EV to reach the site of an emergency
by controlling traffic signals. It used a learning routing algorithm which reaches a decision
with the help of a neural network that calculates expected time of arrival of every feasible
route that EV may follow. Real-time traffic data collected from GPS equipment installed on
EVs was used to train the neural network.
4S. HUMAGAIN ET AL.
3.3. Other optimisation methods
Some studies propose alternative approaches for routing EVs. Bura and Boryczka (2010)
presented an ant-colony based vehicle navigation system that supports dynamic traffic
conditions like traffic load and temporarily closed roads. Musolino, Polimeni, Rindone,
and Vitetta (2013) dynamically designed routes taking into account travel time variations
within a day for the same network. Barrachina et al. (2014) compared emergency services
arrival time between density-based and non-density-based road networks. Similarly, Chen,
Shen, Chen, and Yang (2014) determined the optimised route using Dijkstra’s algorithm for
different traffic conditions like morning peak, evening peak, and daytime. Azmi and
Mustafa (2015) used Vehicle to Infrastructure (V2I) based communication that assisted
in reducing EV travel times by increasing the average speed achieved from avoiding con-
gested areas. Road side units sent congestion information to network simulator that
instructs EVs to follow the route.
3.4. Discussion
Optimisation techniques provide either the shortest or the fastest path for an EV to travel.
Some of these studies are theoretical, some use simulation to validate an underlying
theory, whilst a few are practically implemented. Table 1 provides a comparison of optim-
isation techniques used in reducing travelling time of EVs. Column 1 lists the studies and
column 2 states the types of optimisation model they have used.
In terms of algorithms, Dijkstra’s algorithm is the most popular choice but different
algorithms like ant colony, linear regression, annealing, pseudo-polynomial, etc. have
also been implemented. Column 3 describes if a technique is deterministic (D) such
that it provides the same output for the same inputs every time, or if the technique is sto-
chastic (S) and involves some level of randomness or unpredictability. Optimisation tech-
niques from this aspect seem almost equally divided into deterministic and stochastic
category. Column 4 provides details of other parameters and techniques used to
achieve specific optimisation. Studies listed in this section suggest the use of multiple
optimisation techniques with different level of implementation maturity. In conditions
where studies suggest diverse approaches and solutions, it is difficult to reach a conclusion
in finding the best optimisation technique. Another problem with these studies is that
they usually report simulation results obtained for a certain geographical location. Each
location has distinct traffic parameters. These parameters play a vital role in the perform-
ance of past studies. Hence, a direct comparison in performance is not possible. So, in
addition to the qualitative comparison based on columns 2-4, we have also further com-
pared these works using the Technology Readiness Level (TRL) in column 5 that describes
the progression of technologies (Mankins, 1995). It classifies technology maturation into
nine different levels. Lower levels like TRL1 relate to new but untested technologies
where only basic principles have been observed. Each subsequent level indicates a
more mature technology, with TRL9 indicating a commercially produced technology.
The annealing algorithm by Zhu et al. (2014) for finding shortest rescue time and rescue
cost for EVs was implemented using MATLAB. No traffic domain simulation and verification
for these technologies have been performed. As these techniques have undergone only
low-fidelity simulation testing, they are categorised as TRL4 (validated in a laboratory
environment).
TRANSPORT REVIEWS 5
Most studies used well-known mathematical algorithmic implementations. More com-
prehensive verification and validation were performed on these using simulation models,
resulting in a slightly higher level of credibility. When such validation is done in a relevant
simulation environment, they belong to TRL5. Hence, works like K-shortest path by Kula
et al. (2012), pseudo-polynomial algorithm by Elalouf (2012), Dijkstra’s algorithm by
Brady and Park (2016), etc. are classified as TRL5.
Works that involved prototype development, analysis using real-world test data and
comprehensive system validation are categorised as TRL6 because they use well-
known calibrated traffic simulation models. For example, path selection model by
Nicoara and Haidu (2014), an optimisation model using internet of things by Wang
and Liu (2011), Dijkstra’s algorithm by Derekenaris et al. (2001) etc. are included in
this category.
When developed system prototypes are demonstrated in real environment, they
belong to TRL7. Very few works involve system prototypes tested in the real world.
These handful of works were deployed in real cities and were validated through verifiable
results showing that optimisation enhanced with carefully chosen recent techniques could
significantly reduce travel time. Hence, works like linear regression by Fleischman et al.
(2010), Dijkstra’s algorithm by Elmandili et al. (2013), etc. are classified under TRL7.
Table 1. Analysis of EV route optimisation method.
References Models Type Other parameters/Techniques TRL
Hadas and Ceder (1996); Kula
et al. (2012)
K-Shortest Path S Accident place TRL5
Nordin et al. (2012) A* Algorithm D Uses C # programing TRL6
Kai et al. (2014); Winn (2014) Dijkstra’s Algorithm D Arc GIS network TRL6
Brady and Park (2016) Dijkstra’s Algorithm (using
GIS for position)
D lane count, intersection control devices and
median count
TRL5
Panahi and Delavar (2009) Dijkstra’s Algorithm (using
GIS)
S Dynamic, real-time traffic TRL7
Elmandili et al. (2013);
Smitha et al. (2012)
Dijkstra’s Algorithm S VANAT for real-time traffic, GPS for
calculating position and congestion
TRL5
Bu and Fang (2010) Improved Dijkstra’s S Dynamic in terms of searching TRL5
Nicoara and Haidu (2014);
Sun et al. (2014)
Path Selection D Shortest route, real-time traffic data TRL6
Fleischman et al. (2010) Linear regression D Transport time TRL7
Zhu et al. (2014) Annealing Algorithms D Cost, Road resistance, distance TRL4
Wang, Wu, et al. (2013) Internet of Things S RFID & Road side units collect real-time data TRL6
Choosumrong et al. (2012) PgRouting Algorithm D Beds available, patient status TRL5
Elalouf (2012) Exact Pseudo-polynomial
Algorithm
S Dynamic/real-time data TRL5
Derekenaris et al. (2001) Dijkstra’s Algorithm D Locating ambulance location/ calculating
travel time
TRL6
Vlad, Morel, Morel, and Vlad
(2008)
Learning routing algorithm D Determine real-time traffic volume TRL5
Bura and Boryczka (2010) Ant colony static and
dynamic optimisation
S Comparison of 3 version of Ant-based
navigation
TRL6
Musolino et al. (2013)Taking 3 different level of
congestion
SDifferent route model TRL6
Barrachina et al. (2014) Evolution Strategy D Comparison of density and non-density-
based network
TRL6
Chen et al. (2014) Dijkstra’s D Optimal route taking morning and evening
peak
TRL5
Azmi and Mustafa (2015) Network layer model D V2I based communication for avoiding
congestion
TRL7
Note:*D represents deterministic and S represents stochastic.
6S. HUMAGAIN ET AL.
4. Pre-emption techniques
Pre-emption involves allowing EVs to take priority at intersections. The most traditional
and commonly used pre-emption system is the humble siren accompanied with
flashing lights which are manually operated. Manual prioritisations such as police
officers controlling intersections and use of sirens, sound, and flashing lights have draw-
backs in determining the presence and direction of EV (Eltayeb, Almubarak, & Attia,
2013). However, technology has evolved to provide automated and more effective pre-
emption to allow EVs to travel even faster. Current pre-emption techniques carry out
three major categories of tasks: defining when to activate pre-emption, determining the
position of an EV using sensing techniques to trigger pre-emption, and using communi-
cation techniques between an EV and the infrastructure/other vehicles to execute pre-
emption. We have categorised pre-emption works based on these tasks.
4.1. Activation
Defining when to activate pre-emption is needed for the efficient execution of pre-
emption. It usually depends on either pre-emption is carried out for a particular traffic
signal or for an entire route. Jones, Judge, Beck, and Keegan (1999), Jordan and Cetin
(2015), Kodire, Bhaskaran, and Vishwas (2017), Barthwal and Menghani (2017), and
Siddiqa and Shakeel (2014) decompose road network into zones. The presence of an EV
in a particular zone was identified via installed GPS and then pre-emption was done
making related traffic signal go green. Similarly Huang, Shiue, and Luo (2015), Kotani &
Yamazaki,(2011) and Wang, Wu, Yang, and Huang (2013) also used GPS to locate EV but
pre-emption was activated when the EV was approaching a traffic signal.
A few studies like Hegde, Sali, and Indira (2013), Iyyappan and Nandagopal (2013),
Nellore and Hancke (2016), Noori, Fu, and Shiravi (2016), Unibaso et al. (2010), Van Gulik
and Vlacic (2002), Weng, Huang, Su, and Yu (2011), and Xie et al. (2017) set a fixed distance
between a traffic signal and an EV to trigger pre-emption. The distance was measured
using technologies like GPS, Infrared, VANET and RFID.
Another approach of activating pre-emption is to create a green wave for the entire
route of EV. Initially, direction, lane, and route of EV responding to emergencies are
defined and all the traffic lights located in that route are allowed to go green. Agarwal
and Paruchuri (2016), Bachelder (2011), Bycraft (2000), Chowdhury (2016), Idris, Sivalin-
gam, Tamil, Razak, and Noor (2013), Kamalanathsharma and Hancock (2012), Kang et al.
(2014), Moroi and Takami (2015), Pighin & Fierens(2016), and Yoo, Kim, and Park (2010)
generate a green wave for the entire lane on which EV is travelling.
Unlike above-mentioned approaches, Bhavani, Vishwasri, and Chandrakala (2016) and
Jayaraj and Hemanth (2015) focused on implemented pre-emption techniques for a
single intersection. They used RFID to count the vehicle queue approaching an intersec-
tion to estimate the time to activate pre-emption. Similarly, Qin and Khan (2012) devel-
oped control strategies for a traffic light when an EV is passing through an intersection.
4.2. Sensing
Once pre-emption approach is fixed, it is fundamental to identify the location of EV. Mul-
tiple sensors, both vehicle-mounted or spread over the traffic network, support in
TRANSPORT REVIEWS 7
identifying the exact position of EV. An analysis of studies reveals the use of GPS as the
technology of choice in determining position of EV to activate pre-emption. Barthwal
and Menghani (2017), Idris et al. (2013), Iyyappan and Nandagopal (2013), Jones et al.
(1999), CA Jordan & Cetin(2015), Kodire et al. (2017), Unibaso et al. (2010), and Wang,
Wu, et al. (2013) all use GPS for sensing EV locations. In addition to employing GPS,
Hegde et al. (2013) and Van Gulik and Vlacic (2002) used RFID tags for the determination
of the emergency case.
Vehicular ad hoc networks (VANETs) are the connected environments which support
vehicle-to-X (V2X: vehicle, road, human, infrastructure, internet) communication through
multiple communication protocols. In a connected environment, it is easier to locate
the position of an EV as they are sharing location information among themselves too
often. Agarwal and Paruchuri (2016), Jayaraj and Hemanth (2015), Kamalanathsharma
and Hancock (2012) and Noori et al. (2016) use VANETs to determine the position of EV
which considers EVs and roadside units as nodes of a vehicular network. Nellore and
Hancke (2016) used a visual sensing technique using cameras to determine the position
of EV in VANET.
Some studies like Bhavani et al. (2016) and Siddiqa and Shakeel (2014) employed
ZigBee technology to ascertain the progression direction of EV. As they created a green
wave for the entire corridor knowing direction was sufficient to activate pre-emption.
4.3. Communication approach
Pre-emption may be actuated through vehicle-mounted devices or remotely via emer-
gency control centres (Kamalanathsharma & Hancock, 2012). In remote actuation, emer-
gency dispatchers determine factors like the level of emergency and monitor the EVs
location to activate pre-emption over the upcoming intersection. Vehicle-mounted pre-
emption devices are usually integrated with the vehicle’s warning lights and can be
switched on and offwhen needed. Current technologies use acoustic sensors, localised
radio sensors, GPS or line-of-sight sensors to activate pre-emption. An additional light
near the traffic lights known as a confirmation beacon notifies other traffic that the
traffic light is under the influence of pre-emption and warns other drivers about the
EV’s approach (Huang et al., 2015). In some countries, a flashing confirmation beacon indi-
cates to a vehicle that an EV is approaching from an opposing direction (front or side)
while solid light indicates that the EV is behind the vehicle (Huang et al., 2015).
OPTICOM, EMTRAC, and Transmax are EV pre-emption products that are currently used
in different cities of USA, UK, Canada and Australia (Global Traffic Technologies, 2016). All
these commercial systems use infrared and GPS technology. Once approaching to traffic
signals, EVs automatically send requests for pre-emption. The traffic signals wirelessly
receive and authenticate the requests to provide green lights to the EVs (Viriyasitavat &
Tonguz, 2012). Japan uses the FAST vehicle pre-emption system (Kotani & Yamazaki,
2011). It consists of a device mounted on EVs and overhead infrared beacons installed
along roads. When EVs travel past these beacons, the traffic control system activates
pre-emption at an upcoming intersection. When EVs are located near to upcoming
traffic lights the green time is extended whereas red time is reduced when EVs are far.
VANETs treat moving vehicles and roadside units as nodes of a mobile network, within a
range of 300 metres (Pighin & Fierens, 2016). This technology has been prominently used
8S. HUMAGAIN ET AL.
for communication between different entities involved in route pre-emption. Agarwal and
Paruchuri (2016), Jayaraj and Hemanth (2015), Jordan and Cetin (2015), Moroi and Takami
(2015), Nellore and Hancke (2016), Noori et al.(2016), Pighin and Fierens (2016), and
Unibaso et al. (2010) all implemented VANET as communication tool to execute pre-
emption effectively. Wang, Ma, and Yang (2013) and Xie et al. (2017) used dedicated
short-range communication broadcast to activate pre-emption.
Vehicle-mounted transceivers send and receive signals to communicate with entities
responsible to execute pre-emption. Jones et al. (1999) and Van Gulik and Vlacic (2002)
used a radio antenna to communicate with the traffic control system. Hegde et al.
(2013), Kodire et al. (2017) and Siddiqa and Shakeel (2014) used ZigBee transceivers to
communicate and accomplish pre-emption. Iyyappan and Nandagopal (2013), Kotani
and Yamazaki (2011), and Weng et al. (2011) all used some form of transceivers to com-
municate with traffic signals to actuate pre-emption.
In the case where emergency control centre activates pre-emption EVs send messages
using GSM cellular technology to activate pre-emption. Bachelder (2011), Barthwal and
Menghani (2017), and Bhavani et al. (2016) used GSM technology for sending messages
to control centre and request pre-emption.
Apart from above-mentioned studies, few other algorithms can help EVs to get priority
at intersections. Asaduzzaman and Vidyasankar (2018) proposed an algorithm to control
traffic signal that can adjust time space of traffic phases to assist high priority vehicles.
Qin and Khan (2012) developed control strategies for traffic signals to expedite the move-
ment of EVs and avoid accidents. Viriyasitavat and Tonguz (2012) developed virtual traffic
lights that can prioritise the movement of EVs.
4.4. Discussion
Table 2 presents a comparison of pre-emption techniques. Column 2 describes types of
pre-emption as active (A) or passive (P). In active pre-emption, a pre-emption signal is
adjusted as EV approaches an intersection. An active system can be a combination of
real or fixed-time control strategies, and scheduled or headway-based strategies (Chada
& Newland, 2002). For each work, it provides details on the pre-emption technique,
how pre-emption is initiated, and how pre-emption works. Column 3 describes each
work on basis of the control strategy (C.S.) used, like fixed time (FT), real-time (RT), sche-
dule-based (SB) and headway-based (HB). Column 4 describes the concept and equipment
used for sensing the presence of EVs. Column 5 “Initiating Pre-emption”notes the process
of initiating pre-emption and column 6 “Methods”lists how pre-emption is implemented.
Most pre-emption techniques are real-time control models that rely on constantly
updated information regarding route and traffic network to make decisions. A real-time
control model is flexible to changing conditions. Some studies also apply a signal
control plan for a fixed time based on the conditions of a particular area like its congestion
and vehicular flow. Fixed time control does not receive constantly updated road infor-
mation and the best control scheme is applied regardless of actual traffic conditions.
A few studies use pre-emption control based on the schedule of an EV’s arrival. In such
cases, the proper location of the EV is not known and most of the time this requires less
communication equipment making this a more cost-effective (Bachelder, 2011; Idris et al.,
2013; Iyyappan & Nandagopal, 2013; Kamalanathsharma & Hancock, 2012; Kang et al.,
TRANSPORT REVIEWS 9
2014). In a headway-based control scheme used by one of the studies we reviewed, pre-
emption is activated so that EVs can lead other vehicles heading in the same direction as it
is effective in reducing waiting times (Pighin & Fierens, 2016). In such techniques, some-
times there is a possibility of EVs colliding with other vehicles. Some other studies
invoke pre-emption for an EV’s entire route passively. Our literature search indicates
that passive priority systems that use fixed-time control strategies are rarely used
though they have the benefit of being lower in cost.
5. Techniques employing both optimisation and pre-emption
Studies combining both optimisation and pre-emption first find an optimal route calcu-
lated using distance or time as the critical parameter and then use pre-emption on the
selected route. Huang, Yang, and Ma (2011) and Eltayeb et al. (2013) suggest the shortest
path and clear the path in advance from other vehicles and pedestrians by identifying the
Table 2. Analysis of EV pre-emption technique.
References P C.S Concept/Equipment Initiating pre-emption Method
Jones et al. (1999) A FT GPS for position EV in defined area Green Light
Idris et al. (2013) A SB GPS for Position All networks within
route
Green Light
Kodire et al. (2017) A RT GPS for position Sends signal using
ZigBee
Green Light
Hegde et al. (2013); Van Gulik and Vlacic
(2002); Yoo et al. (2010)
A RT GPS for position and
RFID congestion
Congestion level Green Light
Unibaso et al. (2010) A RT GPS &VANET CAM message Green Light
Bycraft (2000) A FT Finalising route first Communicative
Sensors
Green Light
Kamalanathsharma and Hancock (2012) A SB Real-time trafficOffset value Green Light
Kotani and Yamazaki (2011); Weng et al.
(2011)
A RT Infrared Crossing of IR Green Light
Iyyappan and Nandagopal (2013) A SB Sensors and GPS for
accident location
All networks within
route
Green Light
Siddiqa and Shakeel (2014) A FT ZigBee &GPS Must reach a defined
area
Green Light
Bhavani et al. (2016) A FT GPS Detects EV using RFID
tag
Green Light
Wang et al. (2013) A RT VANET Congestion level Green Light
Jordan and Cetin (2015); Moroi and Takami
(2015)
P–VANET Alerting another
vehicle
lane
allocation
Pighin and Fierens (2016) A HB VANET EV Arrival Green Light
Noori (2013); Noori et al. (2016) A RT VANET Queue length Green Light
Jordan and Cetin (2014) A RT VANET Congestion level Change lane
Jayaraj and Hemanth (2015) A FT VANET Lane reservation Green Light
Agarwal and Paruchuri (2016) P - VANET Fixed lane lane
allocation
Barthwal and Menghani (2017) A FT M2M communication Trafficflow controlling Informs
other
Bachelder (2011) A SB GSM Inter network
communication
Green Light
Huang et al. (2015) A RT Traffic Control Time Petri nets Green Light
Kang et al. (2014) A SB Traffic Signal Control Green wave for EV Green Light
Nellore and Hancke (2016); Qin and Khan
(2012); Viriyasitavat and Tonguz (2012); Xie
et al. (2017)
A RT Distance between EV
and network
Sensing, distance and
presence of EV
Green Light
Chowdhury (2016); Wang et al. (2013) A FT IOT Type of incident Green Light
Asaduzzaman and Vidyasankar (2018) A FT Algorithmic control for
traffic signal
Request from EV Green Light
10 S. HUMAGAIN ET AL.
position of the EV using GPS. Chakraborty, Tiwari, and Sinha (2015) assign a green signal
when an EV is present near an intersection, measuring the queue length of traffic from a
network. Similarly, Kwon and Kim (2003) and Djahel, Smith, Wang, and Murphy (2015)
propose an optimised route based on congestion level, then assign priority operations
such as change in traffic signal, change in speed limit, lane clearance, using the reverse
lane, re-routing to another route, etc. Mirchandani and Lucas (2010) use existing transpon-
ders in EVs to pre-empt signals towards a destination. Shirani, Hendessi, Montazeri, and
Zefreh (2008) use packet signal with velocity information sent by one vehicle to
another in VANET for finding the shortest path and pre-empt the particular path.
Gedawy (2010) take real-time updates of congestion and other delays in travel time
to plan optimal paths using GPS and then proposed a traffic signal pre-emption
whereas Chen, Chen, and Chen (2013) use lane reservation strategy after suggesting
an optimised route suggested from historical data. Moraali (2011, April), Polineni, Ravi
Kumar, and Ravi Kumar (2015) and Salehinejad, Pouladi, and Talebi (2011) suggest
shortest path algorithms and activate pre-emption. They use GPS for locating a
vehicle and a control centre to activate green lights. Anand and Flora (2014) use tilt
and vibration sensors to detect accidents and the GPS gives the location. The server
sends stored shortest route to ambulances. The traffic signal is controlled to give
way to the ambulances using zombie protocol.
5.1. Discussion
Use of both optimisation and pre-emption is a more practical approach in reducing EV
travel times. Table 3 compares works in this category. Column 2 uses TRL to compare
and classify the optimisation technique implemented by these studies. Column 3 provides
details of other parameters to achieve these optimisations and column 4 characterise if
optimisation technique is deterministic (D) or stochastic (S). As an intuitive concept, EV
routing is more inclined towards prioritisation and most of the studies we have reviewed
in this section focus more on pre-emption than optimisation. Column 5 categorises pre-
emption as active (A) or passive (P). Column 6 describes each work in terms of control strat-
egy as discussed in section 4.4. Similarly, Column 7 explains the concept and equipment
used for sensing the presence of EV. Column 8 lists how pre-emption is initiated and
column 9 lists how pre-emption is implemented.
An ideal approach for achieving better optimisation and pre-emption could be combin-
ing the best of techniques, from each category, as discussed in Section 3 and Section
4. Implementing both techniques requires a lot of resources. In general thought, tech-
niques using both optimisation and pre-emption employ more mature models in terms
of implementation than techniques discussed earlier.
Huang et al. (2011) and Djahel et al. (2015) have developed a mathematical optimisation
model. Here verification and validation were simulation based so these studies are cate-
gorised as TRL5. Optimisation techniques used by Eltayeb et al. (2013), Gedawy (2010),
and Mirchandani and Lucas (2010) are summarised under TRL6 as they are able to adopt
a few mature models developed by other researchers to implement the system. These
models are simulation models with relevant environment verification. Some other works
like Anand and Flora (2014), Chakraborty et al. (2015), Chen et al. (2013), Kwon and Kim
(2003), Moraali (2011, April), Polineni, Ravi Kumar, and Ravi Kumar (2015), Salehinejad
TRANSPORT REVIEWS 11
et al. (2011), and Shirani et al. (2008) develop or borrow optimisation models that are used in
real traffic conditions, so we have grouped them under TRL7.
Huang et al. (2011) use pre-emption for the entire route and this is classified as a passive
technique, as it allows traffic signals to go green for a fixed time. All remaining studies
actively adjust the pre-emption signal once the EV approaches specific intersections.
Most studies use real-time updated traffic information to decide the duration of the
pre-emption signal. In contrast, Anand and Flora (2014) activate pre-emption signal as
EV arrives at the traffic network but operates the signal for a fixed duration. Only Chen
et al. (2013) employ active lane reservation for prioritising the EVs.
6. Gaps
This section describes the gaps in existing optimisation and pre-emption techniques. We
have categorised these gaps into implementation gaps and knowledge gaps. Implemen-
tation gaps focus on the practical difficulties in implementing advanced routing and pre-
emption techniques and knowledge gaps include the analysis of research gaps present in
these state-of-the-art technologies.
6.1. Implementation gaps
As most of the studies are research based, the feasibility of implementing them in the
real-world situation seems unclear. There are practical difficulties in the implementation
Table 3. Analysis of technique employing both optimisation and pre-emption.
References
Optimisation Pre-emption
TRL
Other
parameters/
Technique Type P C.S
Concept/
Equipment
Initiating
pre-emption Method
Eltayeb et al. (2013) TRL6 GPS D A RT GPS GSM Distance from
Network
Green light
Gedawy (2010) TRL6 GPS S A RT Heuristic
speed
Expected
Travel time
Green Light
Huang et al. (2011) TRL5 Historic Data S P FT Preset
Route
control
Entire Path Green Light
Chakraborty et al.
(2015)
TRL7 Real traffic S A RT Queue length Distance from
Network
Green Light
Kwon and Kim
(2003)
TRL7 Dijkstra’s D A RT GPS Location of EV Green light
Djahel et al. (2015) TRL5 Historic Data D A RT Choosing
response
Plan
Emergency,
Congestion
Level
Light change lane
clearance, use
reverse lane
Mirchandani and
Lucas (2010)
TRL6 Map D A RT Adaptive
signal
control
Real-time traffic
flow
Green Light
Shirani et al. (2008) TRL7 GPS D - RT VANET Speed -
Chen et al. (2013) TRL7 GPS D A _ VANET
GPS
Road
Condition
lane reservation
Moraali (2011,
April); Polineni
et al. (2015)
TRL7 A*algorithm D A RT GPS
GSM
Distance
Threshold
Green Light
Salehinejad et al.
(2011)
TRL7 Ant Colony S A RT GPS
Fuzzy value
Pheromone
level
Green light
Anand and Flora
(2014)
TRL7 GPS D A FT GPS GS Distance to
network
Green Light
12 S. HUMAGAIN ET AL.
of advanced routing and pre-emption techniques. A few of these gaps are discussed
below:
(1) Adoption of academic research: Researches develop efficient route optimisation and
pre-emption algorithms that can produce exceptional solutions but commercial EV
routing software does not use these state-of-the-art technologies, commercial
systems rather rely on simpler heuristics. This is because not all academic results
can be engineered into effective systems. For industries, it is more efficient to
develop simple optimisation systems that fit a variety of problems like courier,
logistic and trucking and give comparable results rather than to develop a
complex solution for a specific EV routing problem. So, it is practical to assume
that the implementation of advanced optimisation and pre-emption will be
gradual in nature (Pillac, 2012).
(2) Limited computation resources: Most of the literature suggest on real-time optimis-
ation that demand computationally expensive resources and large computational
time. In the case of EV routing, industry is not too much interested in investing
more on computing resources as it is a niche market with very few users (Winter,
2002). This eventually restricts number of real-time dynamic parameters to be con-
sidered during optimisation.
(3) Handling multiple vehicle: Current pre-emption systems are activated either by
vehicle-mounted devices or traffic control system. There exists a problem in assigning
priority when two vehicles request for pre-emption at the same time. As real-time
dynamic optimisation and pre-emption system will be completely automatic, there
will be ambiguity in providing preference to EVs for pre-emption.
(4) Lack of real-time validation: For the implementation of real-time dynamic optimisation
most of the required stochastic and real-time information will be available from
different connected sensors and IOT networking which do not exist now. Though
the relevance of dynamic real-time optimisation for EVs has been documented well,
there are always issues in comparing results from these approaches. Since these
studies use artificial data created by researchers themselves, based on real-world
applications, most of the results are predictive (Pillac, 2012).
6.2. Knowledge gaps
Our survey shows research in this domain has adopted the use of one or both of optim-
isation and pre-emption for routing EVs. None of the studies provide any comparative evi-
dence that a particular optimisation technique and pre-emption system is better suited to
solve the problem of reducing the travelling time of the EV. We can also conclude that
although much research has been conducted for reducing the response time of EVs,
there has not been a considerable decrease in response time (Kendall, 2017; Moemi,
Isong, & Jonathan, 2017; Sabey, 2017). This indicates a potential “dead-end”in the way
research has approached the issue of reducing EV travel times and signals a need to
explore newer methods to approach this problem. Out of the 72 papers we reviewed,
35 have provided future research directions. Detailed analysis of these current research
gaps can guide us towards solving the dead-end that research has produced in reducing
EV travel times. Future research should focus on the following directions listed below:
TRANSPORT REVIEWS 13
(1) Real-Time dynamic traffic Data: Research should focus on integrating real-time on-
road traffic data to calculate more dynamic, reliable and accurate routes to EVs
(Bhavani et al., 2016; Elmandili et al., 2013; Fleischman et al., 2010; Huang et al.,
2011; Kai et al., 2014; Musolino et al., 2013; Nicoara & Haidu, 2014; Winn, 2014).
(2) Time as a critical parameter: Finding the shortest path is not enough to improve emer-
gency response system in a complex road network as minimum travel time is a major
parameter to consider (Barrachina et al., 2014; Choosumrong et al., 2012; Mali, Rao, &
Mantha, 2012).
(3) Advanced algorithms: Basic graph theory method and mathematical programming
method cannot meet the calculation requirement of real-time traffic (Brady & Park,
2016; Chakraborty et al., 2015; Elalouf, 2012; Sun et al., 2014).
(4) Use of VANET: With the advancement of the wireless communication technologies like
Cooperative Vehicle-Infrastructure System (CVIS), there is an opportunity to provide
appropriate traffic signal pre-emption for emergency vehicle based on real-time emer-
gency vehicle data, traffic volume data, and traffic signal timings (Agarwal & Paruchuri,
2016; Anand & Flora, 2014; Djahel et al., 2015; Jayaraj & Hemanth, 2015; Moraali, 2011,
April; Smitha et al., 2012; Wang et al., 2013).
(5) Concerns with multiple EVs: Future studies can include considerations of more severe
scenarios, such as disasters where a large number of EVs are required (Chen et al.,
2013; Chowdhury, 2016; Moroi & Takami, 2015; Pighin & Fierens, 2016).
(6) Safety of EV travel: It is a challenge to ensure safe passage of an emergency vehicle
(EV) or multiple EVs and at the same time to maintain safe and smooth trafficflow
in the road network (Qin & Khan, 2012; Yoo et al., 2010).
(7) Intelligent pre-emption: Limited research has been done on the use of intelligent pre-
emption control, which has the ability to use real-time traffic information to minimise
emergency vehicle delays. At the same time, reducing the adverse impacts of emer-
gency vehicles on normal traffic, so that they can cause the least disturbance to
network trafficflow is a challenge (Kang et al., 2014; Nellore & Hancke, 2016;Kamala-
nathsharma & Hancock, 2012; Siddiqa & Shakeel, 2014; Djahel et al., 2015; Unibaso
et al., 2010; Wang, Ma, et al., 2013; Weng et al., 2011).
A critical analysis of optimisation and pre-emption suggests that there is difference
between actual travel time and theoretically calculated travel time. This difference arises
as dynamic parameters like increased congestion, halt on a road, pedestrian flow,
queued vehicles, real and adaptive speed are not being addressed within the theoretical
models. Similarly, pre-emption is also not effective, as oftentimes the timing of activation
in implemented systems is not precise and pre-emption techniques often do not consider
the effect of pre-emption over other vehicles.
An appropriate solution to improve existing techniques will require dynamic optimis-
ation and efficient and precise pre-emption so as to cause minimal disruption to other
vehicles. The success of such combined and dynamic optimisation and pre-emption
systems depends on the availability of real-time dynamic traffic data. This means that
sensors deployed at various infrastructures of road network must communicate in real-
time and support real-time decision-making. In general, traffic infrastructure requires a
deeper integration with software systems to ensure high availability of accurate real-
time data.
14 S. HUMAGAIN ET AL.
7. Concluding remarks
This review has described and compared techniques used in reducing response time of
EVs. The optimisation and pre-emption can provide a solution for reducing response
time however they need many further improvements. It has been suggested in this
paper that researchers on emergency management services must focus on making optim-
isation more dynamic by using real-time dynamic traffic data and taking time as a critical
optimisation parameter. They also need to work on making pre-emption intelligent and
use advanced technologies like VANET. Such pre-emptive solutions need to ensure it
creates the minimal effect on other traffic. Further research should bring the most
advanced optimisation and pre-emption together. This, in turn, will solve the challenging
job of reducing response time.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
Agarwal, A., & Paruchuri, P. (2016). V2v communication for analysis of lane level dynamics for better
EV traversal. IEEE intelligent vehicles symposium, proceedings (pp. 368–375), 2016–Augus(IV). doi:10.
1109/IVS.2016.7535412
Anand, J., & Flora, T. G. A. (2014). Emergency traffic management for ambulance using wireless com-
munication. International Journal of Electronics & Communication,2(7), 1–4.
Asaduzzaman, M., & Vidyasankar, K. (2018). A priority algorithm to control the traffic signal for emer-
gency vehicles. IEEE vehicular technology conference, 2017 September, 1–7. doi:10.1109/VTCFall.
2017.8288364
Azmi, A., & Mustafa, E. (2015). Enhanced route guidance and navigation for emergency vehicle using
V2I-based cooperative communication. IEEE International Electronics Symposium: Emerging
Technology in Electronic and Information,2015, 145–150. doi:10.1109/ELECSYM.2015.7380831
Bachelder, A. D. (2011). Cellular-based preemption system. US Patent, 7,868,783. Retrieved from
https://www.google.com/patents/US7868783
Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J. C., Calafate, C. T., & Manzoni, P. (2014).
Reducing emergency services arrival time by using vehicular communications and Evolution strat-
egies. Expert Systems with Applications,41(4 part 1), 1206–1217. doi:10.1016/j.eswa.2013.08.004
Barthwal, S., & Menghani, P. (2017). An advance system for emergency vehicles: Based on M2M com-
munication. 2017 11th International conference on intelligent systems and control (pp. 374–378).
doi:10.1109/ISCO.2017.7856020
Bhavani, B., Vishwasri, Y., & Chandrakala, B. (2016). Design and implementation of intelligent smart
traffic control system for emergency ambulance clearance and stolen vehicle detection.
International Journal of Research,5(4), 265–270.
Brady, D., & Park, B. B. (2016). Improving emergency vehicle routing with additional road features. In
IEEE, 2nd international conference on vehicle technology and intelligent transport systems, 2016.
Bu, F., & Fang, H. (2010). Shortest path algorithm within dynamic restricted searching area in city
emergency rescue. IEEE International Conference on Emergency Management and Management
Sciences,2010, 371–374. doi:10.1109/ICEMMS.2010.5563425
Bura, W., & Boryczka, M. (2010). Ant colony system in ambulance navigation. Journal of Medical
Informatics and Technologies,15, 115–124.
Bycraft, J. R. (2000). Green light pre-emption of traffic signals for emergency vehicles Richmond, British
Columbia’s approach.Manual, City of Richmond Traffic Signal Control System. Retrieved from http://
www.richmondbc.org/__shared/assets/Route_Preemption9281.pdf
TRANSPORT REVIEWS 15
Chada, S., & Newland, R. (2002). Effectiveness of bus signal priority. ITE 2001 Annual Meeting and
Exhibit, 40. Retrieved from https://trid.trb.org/view.aspx?id=709608
Chakraborty, P. S. P. S., Tiwari, A., & Sinha, P. R. P. R. (2015). Adaptive and optimized emergency
vehicle dispatching algorithm for intelligent traffic management system. Procedia Computer
Science,57, 1384–1393. doi:10.1016/j.procs.2015.07.454
Chen, C.-Y. C.-Y., Chen, P.-Y. P.-Y., & Chen, W.-T. W.-T. (2013). A novel emergency vehicle dispatching
system. 2013, IEEE vehicular technology conference.doi:10.1109/VTCSpring.2013.6691836
Chen, Y. Z., Shen, S. F., Chen, T., & Yang, R. (2014). Path optimization study for vehicles evacuation
based on Dijkstra algorithm. Procedia Engineering,71, 159–165. doi:10.1016/j.proeng.2014.04.023
Choosumrong, S., Bozon, N., & Raghavan, V. (2012). Multi-criteria emergency route planning based on
analytical hierarchy process and pgRouting. Geoinformatics,23(4). doi:10.6010/geoinformatics.23.
159
Chowdhury, A. (2016). Priority based and secured traffic management system for emergency vehicle
using IoT. 2016 IEEE, international conference on engineering and MIS.doi:10.1109/ICEMIS.2016.
7745309
Cooke, K. L., & Halsey, E. (1966). The shortest route through a network with time-dependent interno-
dal transit times. Journal of Mathematical Analysis and Applications,14(3), 493–498. doi:10.1016/
0022-247X(66)90009-6
Derekenaris, G., Garofalakis, J., Makris, C., Prentzas, J., Sioutas, S., & Tsakalidis, A. (2001). Integrating
GIS, GPS and GSM technologies for the effective management of ambulances. Computers,
Environment and Urban Systems,25(3), 267–278. doi:10.1016/S0198-9715(00)00025-9
Djahel, S., Smith, N., Wang, S., & Murphy, J. (2015). Reducing emergency services response time in
smart cities: An advanced adaptive and fuzzy approach. In 2015 IEEE 1st international smart
cities conference, ISC2 2015.doi:10.1109/ISC2.2015.7366151
Eksioglu, B., Vural, A. V., & Reisman, A. (2009). The vehicle routing problem: A taxonomic review.
Computers and Industrial Engineering,57(4), 1472–1483. doi:10.1016/j.cie.2009.05.009
Elalouf, A. (2012). Efficient routing of emergency vehicles under uncertain urban traffic conditions.
Journal of Service Science and Management,5(3), 241–248. doi:10.4236/jssm.2012.53029
Elmandili, H., Toulni, H., & Nsiri, B. (2013). Optimizing road traffic of emergency vehicles. International
conference on advanced logistics and transport, 2013 (59–62).doi:10.1109/ICAdLT.2013.6568435
Eltayeb, A. S., Almubarak, H. O., & Attia, T. A. (2013). A GPS based traffic light pre-emption control
system for emergency vehicles. IEEE, international conference on computer, electrical and electronics
engineering: “research makes a difference”, 2013. doi:10.1109/ICCEEE.2013.6634030
Fitch, J. (2005). Response times: Myths, measurement & management. JEMS: A Journal of Emergency
Medical Services. Retrieved from http://europepmc.org/abstract/med/16381089
Fleischman, R. J., Lundquist, M., Newgard, C. D., Malveau, S., Jui, J., & Warden, C. (2010). Predicting
ambulance time of arrival to the emergency department using global positioning systems and
Google maps. Prehospital Emergency Care,17(4), 458–465. doi:10.3109/10903127.2013.811562
Gedawy, H. K. (2010). Dynamic path planning and traffic light coordination for emergency vehicle
routing. Carnegie Mellon University.
Global Traffic Technologies. (2016). Emergency vehicle preemption solutions (February).
Hadas, Y., & Ceder, A. (1996). Shortest path of emergency vehicle under uncertain urban traffic con-
dition. Transportation Research Record,1560,34–39. https://doi.org/10.1177/
0361198196156000106
Hegde, R., Sali, R. R., & Indira, M. S. (2013). RFID and GPS based automatic lane clearance system for
ambulance. International Journal of Advanced Electrical and Electronics Engineering,2(3), 102–107.
Huang, Y. S., Shiue, J. Y., & Luo, J. (2015). A traffic signal control policy for emergency vehicles pre-
emption using timed petri nets. IFAC-PapersOnLine,48(3), 2183–2188. doi:10.1016/j.ifacol.2015.
06.412
Huang, W., Yang, X., & Ma, W. (2011). Signal priority control for emergency vehicle Operation. In 2nd
international conference on models (pp. 22–24). Retrieved from http://www.academia.edu/
download/34600434/006__W._Huang_et_al.__Signal_Priority_Control_for_Emergency_Vehicle_
Operation.pdf
16 S. HUMAGAIN ET AL.
Idris, M. Y. I., Sivalingam, M., Tamil, E. M., Razak, Z., & Noor, N. (2013). Emergency vehicle preemption
system based on global positioning system (GPS), A-Star (A*) algorithm and FPGA. Computer
Systems Science and Engineering,28(3), 147–156.
Iyyappan, S., & Nandagopal, V. (2013). Automatic accident detection and ambulance rescue with
intelligent traffic light system. International Journal of Advanced Research in Electrical Electronics
and Instrumentation Engineering,2(4), 2320–3765.
Jayaraj, V., & Hemanth, C. (2015). Emergency vehicle signalling using VANETS. IEEE 17th international
conference on high performance computing and communications, IEEE 7th international symposium
on cyberspace safety and security and IEEE 12th international conference on embedded software and
systems, 2015.doi:10.1109/hpcc-css-icess.2015.217
Jones, G. V., Judge, K., Beck, J. C., & Keegan, R. (1999). Automatic determination of traffic signal pre-
emption using GPS, apparatus and method. United States Patent. Retrieved from https://www.
google.com/patents/US5986575
Jordan, C., & Cetin, M. (2014). Improving travel times for emergency response vehicles: Traffic control
strategies based on connected vehicles technologies. Idaho: University of Idaho.
Jordan, C., & Cetin, M. (2015). Signal preemption strategy for emergency vehicles using vehicle to
infrastructure communication. Transportation Research Board 94th Annual. Retrieved from
https://trid.trb.org/view.aspx?id=1339383
Kai, N., Yao-ting, Z., & Yue-peng, M. (2014). Shortest path analysis based on Dijkstra’s algorithm in
emergency response system. Indonesian Journal of Electrical Engineering,12(5), 3476–3482.
Kamalanathsharma, R., & Hancock, K. (2012). Intelligent preemption control for emergency vehicles
in urban corridors. Proceedings of the 91st Annual Meeting of the Transportation Research Board,12-
0683(571). Retrieved from https://trid.trb.org/view.aspx?id=1128819
Kang, W., Xiong, G., Lv, Y., Dong, X., Zhu, F., & Kong, Q. (2014). Traffic signal coordination for emer-
gency vehicles. 17th IEEE international conference on intelligent transportation systems, 2014.
doi:10.1109/ITSC.2014.6957683
Kendall, P. (2017). Thousands die from ambulance delays. Retrieved from http://www.dailymail.co.uk/
health/article-55521/Thousands-die-ambulance-delays.html
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University,33
(TR/SE-0401), 28. doi:10.1.1.122.3308
Kodire, V., Bhaskaran, S., & Vishwas, H. N. (2017). GPS and ZigBee based traffic signal preemption. In
International conference on inventive computation technologies, 2016 (Vol. 2). doi:10.1109/
INVENTIVE.2016.7824811.
Kotani, J., & Yamazaki, K. (2011). Expanding fast emergency vehicle preemtion in Tokyo. TRR, 18th ITS
World Congress.
Kula, U., Tozanli, O., & Tarakcio, S. (2012). Emergency vehicle routing in disaster response operations.
23rd annual conference on production and operation management society, 2012. Retrieved from
http://www.pomsmeetings.org/ConfProceedings/025/FullPapers/FullPaper_files/025-1259.pdf
Kwon, E., & Kim, S. (2003). Development of dynamic route clearance strategies for emergency vehicle
operations, Phase I. Minneapolis. Retrieved from http://conservancy.umn.edu/handle/11299/915
Mali, V., Rao, M., & Mantha, S. (2012). Enhanced routing in disaster management based on GIS.
International Journal of Computer Applications® (IJCA),14–18.
Mankins, J. (1995, April). Technology readiness levels. White Paper. Retrieved from http://fellowships.
teiemt.gr/wp-content/uploads/2016/01/trl.pdf
Mirchandani, P. B., & Lucas, D. E. (2010). Integrated transit priority and rail/emergency preemption in
real-time traffic adaptive signal control. Journal of Intelligent Transportation Systems,8(1), 101–115.
doi:10.1080/15472450490437799
Moemi, T. J., Isong, B., & Jonathan, B. (2017). HFinder: Anti-emergency medical service late response
time system, 15–20.
Moroi, Y., & Takami, K. (2015). A method of securing priority-use routes for emergency vehicles using
inter-vehicle and vehicle-road communication. IEEE, 7th international conference on new technol-
ogies, mobility and security, 2015. doi:10.1109/NTMS.2015.7266466
TRANSPORT REVIEWS 17
Musolino, G., Polimeni, A., Rindone, C., & Vitetta, A. (2013). Travel time forecasting and dynamic routes
design for emergency vehicles. Procedia –Social and Behavioral Sciences,87, 193–202. doi:10.1016/
j.sbspro.2013.10.603
Nellore, K., & Hancke, G. P. (2016). Traffic management for emergency vehicle priority based on visual
sensing. Sensors (Switzerland),16(11), doi:10.3390/s16111892
Nicoara, P.-S., & Haidu, I. (2014). A GIS based network analysis for the identification of shortest route
access to emergency medical facilities. Geographica Technica,09(2), 60–67.
Noori, H. (2013). Modeling the impact of VANET-enabled traffic lights control on the response time of
emergency vehicles in realistic large-scale urban area. IEEE international conference on communi-
cations workshops, 2013. doi:10.1109/ICCW.2013.6649290
Noori, H., Fu, L., & Shiravi, S. (2016). A connected vehicle based traffic signal control strategy for emer-
gency vehicle preemption. TRB, 95th annual meeting of the transportation research board, (May).
Nordin, N. A. M. A., Zaharudin, Z. A., Maasar, M. A., & Nordin, N. A. M. A. (2012). Finding shortest path of
the ambulance routing: Interface of a algorithm using C# programming. IEEE Symposium on
Humanities, Science and Engineering Research,2012, 1569–1573. doi:10.1109/SHUSER.2012.
6268841
Panahi, S., & Delavar, M. (2009). Dynamic shortest path in ambulance routing based on GIS.
International Journal of Geoinformatics,5(1).
Paniati, J., & Amoni, M. (2006). Traffic signal preemption for emergency vehicle a cross –cutting
study. US Federal Highway Administration, (January).
Pighin, M., & Fierens, P. I. (2016). VANET for emergency vehicles: Preliminary results. IEEE, 16th work-
shop on information processing and control, 2015.doi:10.1109/RPIC.2015.7497082
Pillac, V. (2012). Dynamic vehicle routing : solution methods and computational tools.
Polineni, J. N., Ravi Kumar, K., & Ravi Kumar, P. (2015). Traffic Free route system using GSM and GPS for
ambulance. International Journal of Advanced Technology and Innovative Research,07(18), 3593–
3598.
Qin, X., & Khan, A. M. A. M. A. M. (2012). Control strategies of traffic signal timing transition for emer-
gency vehicle preemption. Transportation Research Part C: Emerging Technologies,25.doi:10.1016/
j.trc.2012.04.004
RapidSOS. Quantifying the impact of emergency response times. (2015). Retrieved from www.
RapidSOS.com
Sabey, R. (2017). BLUE LIGHT WAIT. Retrieved from https://www.thesun.co.uk/news/3070023/4000-
ambulances-arrive-late-to-emergency-call-outs-for-seriously-ill-patients-as-nhs-fails-to-meet-
targets/
Salehinejad, H., Pouladi, F., & Talebi, S. (2011). Intelligent navigation of emergency vehicles.IEEE, pro-
ceedings –4th international conference on developments in eSystems Engineering, DeSE 2011. doi:10.
1109/DeSE.2011.17
Shirani, R., Hendessi, F., Montazeri, M. A., & Zefreh, M. S. (2008). Absolute priority for a vehicle in
VANET. In Manual, Communications in computer and information science (Vol. 6 CCIS). doi:10.
1007/978-3-540-89985-3_142.
Siddiqa, H., & Shakeel, A. (2014). Intelligent traffic light system to prioritized emergency purpose
vehicles based on Zigbee. International Journal of Advanced Research and Innovation,7(1),
505–509.
Sivalingam, Moraali. (2011, April). Smart emergency preemption system. Kuala lumpur: University of
Malaya.
Smitha, S., Narendra Kumar, G., Usha Rani, H. V., Divyashree, C. K., George, G., & Aparajitha, M. (2012).
GPS based shortest path for ambulances using VANETs. In Proceedings of the international confer-
ence on wireless networks, 2012 (Vol. 49, pp. 190–196). doi:10.7763/IPCSIT.2012.V49.35.
Sun, H., Yue, L., & Yao, S. (2014). Study on path selection of emergency rescue based on GIS. Advanced
Materials Research,864–867, 2804–2807. doi:10.4028/www.scientific.net/AMR.864-867.2804
Togneri, M. C., & Deriaz, M. (2013). On-board navigation system for smartphones. International con-
ference on indoor positioning and indoor navigation, 28 (February), 31st.
Unibaso, G., Ser, J. a. D., Gil-Lopez, S., Molinete, B., Del Ser, J., Gil-Lopez, S., & Molinete, B. (2010). A
novel CAM-based traffic light preemption algorithm for efficient guidance of emergency vehicles.
18 S. HUMAGAIN ET AL.
Proceedings of the IEEE Conference on Intelligent Transportation Systems,2010,74–79. doi:10.1109/
ITSC.2010.5625210
Van Gulik, J., & Vlacic, L. (2002). Intersection priority system. IEEE conference on intelligent transpor-
tation systems, 2002 (pp. 572–575) (September). doi:10.1109/ITSC.2002.1041281
Viriyasitavat, W., & Tonguz, O. K. (2012). Priority management of emergency vehicles at intersections
using self-organized traffic control. IEEE Vehicular Technology Conference,1–4. doi:10.1109/VTCFall.
2012.6399201
Vlad, R. C., Morel, C., Morel, J. Y., & Vlad, S. (2008). A learning real-time routing system for emergency
vehicles. IEEE International Conference on Automation, Quality and Testing, Robotics,2008(3), 390–
395. doi:10.1109/AQTR.2008.4588950
Wang, X., & Liu, J. (2011). Design and implementation for ambulance route search based on the
Internet of Things. IEEE, Third International Conference on Communications and Mobile
Computing,2011, 523–526. doi:10.1109/CMC.2011.110
Wang, J., Ma, W., & Yang, X. (2013). Development of degree-of-priority based control strategy for
emergency vehicle preemption operation. Discrete Dynamics in Nature and Society,2013.doi:10.
1155/2013/283207
Wang, Y., Wu, Z., Yang, X., & Huang, L. (2013). Design and implementation of an emergency vehicle
signal preemption system based on cooperative vehicle-infrastructure technology. Advances in
Mechanical Engineering,2013.doi:10.1155/2013/834976
Weng, Y.-S. Y., Huang, Y.-S. Y., Su, S. S.-F., & Yu, C.-S. Y. (2011). Modelling of emergency vehicle pre-
emption systems using statecharts. IEEE international conference on systems, man, and cybernetics,
2011.doi:10.1109/ICSMC.2011.6083742
Winn, M. T. (2014). Improving emergency vehicle routing with additional road features. Missouri:
Northwest Missouri State University.
Winter, S. (2002). Modeling costs of turns in route planning. GeoInformatica,6(4), 345–361.
Xie, H., Karunasekera, S., Kulik, L., Tanin, E., Zhang, R., & Ramamohanarao, K. (2017). A simulation study
of emergency vehicle prioritization in intelligent transportation systems. doi:10.1109/VTCSpring.
2017.8108282
Yoo, J. B., Kim, J., & Park, C. Y. (2010). Road reservation for fast and safe emergency vehicle response
using ubiquitous sensor network. In IEEE international conference on sensor networks, ubiquitous,
and trustworthy computing, 2010 (pp. 353–358). doi:10.1109/SUTC.2010.67
Zhu, C., Chen, X., & Bing, Z. (2014). Research on vehicles routing under emergencies. Journal of
Information and Computational Science,11(16), doi:10.12733/jics20104735
TRANSPORT REVIEWS 19
