Traffic congestion relief associated with public transport: state-of-the-art

Abstract

Public transport (PT) influences the urban road system in many ways, including traffic congestion, environment, society, safety and land use impacts. While there are many studies focusing on the benefits of PT, research on congestion impacts, a fundamental component of any analysis of transport performance, associated with PT has received little attention. This paper aims to review the traffic congestion impacts of PT and how they are assessed. Traffic congestion is most commonly related to vehicle travel; yet, the real measure of congestion in transport systems is people travel. This paper looks at the appropriateness of existing traffic congestion measures and how suitable they are for measuring the impact of an existing PT system in the short-term. The literature review indicates that most studies relating to the congestion impacts of PT have used vehicle-based congestion measures. People-based measures may be more appropriate in assessing PT impacts. The paper also proposes a new framework for looking at the short-term effects of an existing PT system on traffic congestion. It suggests a few areas where further work can be undertaken to improve our understanding of traffic congestion incorporating PT such as exploring the mode shift from PT to car, estimating network-wide PT congestion creation impacts and determining the net congestion impact of PT.

Full text

Vol.:(0123456789)
Public Transport
https://doi.org/10.1007/s12469-020-00231-3
1 3
ORIGINAL RESEARCH
Trac congestion relief associated withpublic transport:
state‑of‑the‑art
DuyQ.Nguyen‑Phuoc1 · WilliamYoung2· GrahamCurrie3· ChrisDeGruyter4
Accepted: 8 February 2020
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract
Public transport (PT) influences the urban road system in many ways, including traf-
fic congestion, environment, society, safety and land use impacts. While there are
many studies focusing on the benefits of PT, research on congestion impacts, a fun-
damental component of any analysis of transport performance, associated with PT
has received little attention. This paper aims to review the traffic congestion impacts
of PT and how they are assessed. Traffic congestion is most commonly related to
vehicle travel; yet, the real measure of congestion in transport systems is people
travel. This paper looks at the appropriateness of existing traffic congestion meas-
ures and how suitable they are for measuring the impact of an existing PT system in
the short-term. The literature review indicates that most studies relating to the con-
gestion impacts of PT have used vehicle-based congestion measures. People-based
measures may be more appropriate in assessing PT impacts. The paper also pro-
poses a new framework for looking at the short-term effects of an existing PT sys-
tem on traffic congestion. It suggests a few areas where further work can be under-
taken to improve our understanding of traffic congestion incorporating PT such as
exploring the mode shift from PT to car, estimating network-wide PT congestion
creation impacts and determining the net congestion impact of PT.
Keywords Public transport· Traffic congestion· Mode shift· Network-wide·
People-based
1 Introduction
Traffic congestion is a major urban transportation issue as it can be a barrier to
economic growth (Douglas 1993). Some authors have suggested that high qual-
ity, grade-separated PT would reduce traffic congestion and that improvement in
urban PT can be a cost-effective investment when considering all economic effects
* Duy Q. Nguyen-Phuoc
npqduy@dut.udn.vn
Extended author information available on the last page of the article

D.Q.Nguyen-Phuoc et al.
1 3
(Aftabuzzaman et al. 2010a; Bollinger and Ihlanfeldt 1997; Pavkova etal. 2015).
However, other researchers have argued that current transportation evaluation prac-
tices tend to overlook and undervalue the benefits of PT (Litman 2015; Rubin and
Mansour 2013) due to the negative impacts that PT can have on creating congestion
such as the operation of at-grade rail crossings (Okitsu and Lo 2010; Taggart etal.
1987), tram priority, bus stop operations (Chandler and Hoel 2004; Rymer etal.
1989). In addition, it has been suggested that investments on PT are ineffective at
reducing traffic congestion and financially wasteful (O’Toole 2004; Stopher 2004;
Taylor 2004). They believed that when a vehicle driver shifts mode to PT, another
driver uses this open road space. In order to compare these arguments, understand-
ing the net traffic congestion impact associated with PT is important.
Once congestion of traffic systems with PT components is understood, the routes
or corridors facing congestion can be targeted for attention to seek a desired level
of congestion relief. Further, appropriate PT policies that can encourage desired
development in designated locations providing congestion relief can be explored.
Recently, there has been a limited number of studies assessing the impacts of PT on
traffic congestion. These will be explored in this paper.
There is a growing interest in understanding the impact of PT on traffic conges-
tion in urban areas. PT is often seen to be increasing congestion when in fact there
are considerable overall benefits to the system-wide level of congestion by its pres-
ence. This paper aims to look at the appropriateness of existing traffic congestion
measures and how suitable they are for measuring the impact of a PT system in the
short-term. The paper also proposes a new framework for looking at the short-term
effects of an existing PT system on traffic congestion. Investigating traffic congestion
relief impact associated with PT requires a good understanding of two major areas:
• The measurement of traffic congestion, particularly measurement of congestion
levels for mixed traffic flow with existing PT systems; and
• The adaption of congestion measures into the estimation of congestion impact
associated with PT.
In order to meet the research aim, a detailed literature review of published aca-
demic research papers and industry reports relating to the assessment of traffic con-
gestion and PT transport congestion impact was undertaken. Google Scholar (https ://
schol ar.googl e.com/) and the ISI Web of Knowledge (http://www.isiwe bofkn owled
ge.com) were two general databases searched. A number of different search terms
were used to source relevant studies such as traffic congestion, congestion measure,
PT impacts, PT congestion relief, PT congestion reduction, PT congestion creation,
PT congestion impact. After reviewing the title and abstract of each searched pub-
lication, a total of 93 studies were found to be relevant. However, only 51 of these
focused specifically on the assessment of PT congestion impacts and, therefore, pro-
vided the main basis for the literature review. A number of the remaining 42 publi-
cations were used to provide context as needed.
This paper is presented as follows. The detailed review of the impacts of PT on
traffic congestion is first presented. There are three sub-sections: a review of vari-
ous definitions of traffic congestion, the measurement of traffic congestion and the

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Traffic congestion relief associated withpublic transport:…
assessment of traffic congestion impacts associated with PT. The paper concludes
with the identification of gaps in knowledge and a discussion on opportunities avail-
able to advance knowledge in the identified areas.
2 The impacts ofPT ontrac congestion
The common way to determine the impact of PT on traffic congestion is to contrast
congestion measures in a scenario ‘with PT’ and ‘without PT’. Hence, it is neces-
sary to understand how traffic congestion is defined and how to measure traffic con-
gestion, particularly in mixed traffic conditions (PT vehicles operate with private
vehicles). This section firstly provides the definition and measurement of traffic con-
gestion. A review of traffic congestion impacts resulting from PT, including methods
used for assessing the impacts and their associated results, is then presented.
2.1 Denitions oftrac congestion
In order to measure the level of traffic congestion, an understanding of definitions
of traffic congestion is important. There are a variety of congestion definitions pro-
posed by scholars; however, none of them are accepted as a universal definition
(Downs 2004). These definitions of traffic congestion can be categorised into:
• demand related;
• delay related; and
• cost related.
Table1 summarises the definitions of traffic congestion presented in the litera-
ture. There is no definition that presents the whole picture of traffic congestion. In
terms of cause and effect, definitions are related:
• to demand (Rosenbloom 1978; Pucher et al. 1979; Rothenberg 1985; Vaziri
2002), which can be considered the cause of congestion (demand exceeds capac-
ity);
• while delay-related definitions (Meyer 1997; Lomax etal. 1997; Weisbrod etal.
2001; Downs 2004; Lee and Vuchic 2005; Falcocchio and Levinson 2015) and
• cost-related definitions (Litman 2000; Vuchic etal. 1998; Verhoef 2000; Kockel-
man and Kalmanje 2005; Naudé and Tsolakis 2005) can represent the effect of
congestion.
According to Calderdale Council (2015), traffic congestion is an inherently dif-
ficult concept to define as it has both physical and relative dimensions. In physical
terms, congestion can be explained as the way in which vehicles interact to impede
other vehicles. These interactions and their influence on individual journeys usually
increase since travel demand approaches the capacity of a road or when capacity
itself is reduced through road works or PT operations (such as trams). However, the

D.Q.Nguyen-Phuoc et al.
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Table 1 Definitions of traffic congestion Source: author’s synthesis
Author Definition
Demand-related Rosenbloom (1978) Traffic congestion occurs when travel demand exceeds the existing road system’s capacity
Pucher etal. (1979) Congestion denotes any condition in which demand for a facility exceeds free-flow capacity at maximum
design speed
Rothenberg (1985) Congestion is a condition in which the number of vehicles attempting to use a roadway at any time
exceeds the ability of the roadways to carry the load at generally acceptable service levels
Vaziri (2002) Congestion occurs when traffic demand approaches and exceeds highway capacity
Delay-related Meyer (1997) Congestion means there are more people trying to use a given transportation facility during a specific
period of time than the facility can handle with what are considered acceptable levels of delay or
inconvenience
Lomax (1997) Traffic congestion is travel time or delay in excess of that normally incurred under light or free-flow
travel conditions
Weisbrod etal. (2001) Traffic congestion is a condition of traffic delay (when the flow of traffic is slowed below reasonable
speeds) because the number of vehicles trying to use the road exceeds the traffic network capacity to
handle those
Downs (2004) Traffic congestion occurs when traffic is moving at speeds below the designed capacity of a roadway
Lee and Vuchic (2005) Congestion is the phenomenon of increased auto travel time due to increased travel demand
Falcocchio and Levinson (2015) Congestion in transportation occurs when the occupancy of spaces (roadways, sidewalks, transit lines and
terminals) by vehicles or people reaches unacceptable levels of discomfort and delay
Cost-related Litman (2000) Traffic congestion represents the incremental costs resulting from interference among road users
Vuchic etal. (1998), Verhoef (2000),
Kockelman and Kalmanje (2005)
Congestion can be viewed as the result from under-pricing of the road network and marginal cost pricing
can be used to internalise the congestion externality
Naudé and Tsolakis (2005) Congestion may be regarded as the point at which an additional road user joins the traffic flow and affects
marginal cost in such a way that marginal social cost of road use exceeds the marginal private cost of
road use at the ‘optimal’ level of congestion

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Traffic congestion relief associated withpublic transport:…
physical definition ignores the fact that congestion can mean very different things to
different people. In relative perspective, congestion can, therefore, also be defined
in terms of the difference between the expectations of road users about the road net-
work and how it actually performs.
Figure1 shows that the majority of traffic congestion definitions relate to a homo-
geneous unit measure of vehicles. These could be vehicles or passenger car units.
However, vehicles have been used more commonly than passengers. The base road
capacity or vehicle free speed is determined for this homogeneous vehicle measure.
Only few definitions relate to road network.
For mixed traffic conditions, that is those including cars, trucks, bicycles, PT
means and private vehicles, the different behaviours of the vehicles must be taken
into account. In particular, PT stop operations, acceleration and deceleration from
stops and lower speeds influence capacity as well as free speed. The methods to rec-
ognise this diversity will be discussed later.
Further, the average occupancies of PT are generally much higher than those of
private vehicles, so the definition of congestion for mixed traffic could take into
account variations in the occupancy of the vehicles. The definitions of congestion
that relate to people may be more suitable for congestion where PT and vehicles are
occupying the road. This will be also discussed in this paper.
2.2 Measures oftrac congestion
The measurement of traffic congestion was initially related to homogeneous vehi-
cle types on a road carriageway. For instance, car flow and delay were the major
units of the measurement in the period of 1987–2005 (Lindley 1987; Lomax etal.
1997; Hall and Vyas 2000; Lomax and Schrank 2005). These values were esti-
mated by comparing the real traffic flow on the width of road to free-flow travel
People
Lomax (1997)
Falcocchio and
Levinson (2015)
Rosenbloom (1978)
Pucher et al. (1979)
Rothenberg (1985)
Weisbrod et al. (2001)
Vaziri (2002)
Downs (2004)
Lee and Vuchic (2005)
Litman (2000)
Naudé and Tsolakis
(2005)
Vuchic et al. (1998)
Verhoef (2000)
Kockelman and
Kalmanje (2005)
Vehicle
Road network
Fig. 1 Object focused in congestion measurement

D.Q.Nguyen-Phuoc et al.
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or the acceptable travel time. A number of thresholds were used to identify the
beginning of delay.
As traffic flows became more complex with a mix of traffic flows on the same
carriageway, multi-modal performance indicators have been developed (Holian
and McLaughlin 2016; Dowling 2009) and the equation measures of their impacts
on the total vehicle flow were required. For instance, a multimodal level of ser-
vice (MMLOS) for urban streets that takes into account interactions among trans-
port modes (autos, buses, bicycles, and pedestrians) in the urban street environ-
ment was proposed by Dowling (2009). The MMLOS method estimates the level
of service for each mode using a combination of readily available data and data
normally gathered by an agency to assess auto and transit level of service. The
concept of passenger car unit (PCU) defined as “the number of passenger cars
displaced in the traffic flow by a truck or a bus, under the prevailing road and
traffic conditions (HCM 1965)” was another appropriate way to account for the
impact of big-size vehicles. A number of methods have been proposed to deter-
mine PCU in mixed traffic conditions (HCM 2000; Tiwari etal. 2000; Chandra
and Kumar 2003). For example, guidelines developed by TfL (2010) in London
suggested that buses have a PCU of 2.0 while the PCU of cars is 1.0. Bus occu-
pancy is around 17 passengers in London (Transport Committee 2013) while car
occupancy (all trip purposes) from UK National Travel Survey (Department for
Transport 2017) is about 1.5 passengers. Hence, the occupants per PCU for cars
is around 1.5 and for buses is about 8.5 in London.
The introduction of road space allocation and priority lanes moved the meas-
ures of congestion from a carriageway to a lane. Capacity for the lanes could
be treated separately and congestion levels in the lanes considered separately. In
general, the behaviour of vehicles in the above measures was similar. They both
moved between an origin and a destination. If they stopped on route they would
park, dismount and undertake the desired activity. The introduction of on-road PT
into mixed traffic behaviour introduces a change in general behaviour. This main
change being the stop on-route to allow passengers to board and alight. Similarly,
the existence of PT at-grade level crossings influences the road congestion levels
(Nguyen-Phuoc etal. 2017a). The consideration of these groups was needed, par-
ticularly in some countries where PT systems have been developing rapidly.
The measure of congestion can be categorised into three major groups:
1. basic congestion indicators;
2. level of service and;
3. indices.
The level of service (LOS) which represents a range of operating conditions
is one of the most popular measures of traffic congestion (Aftabuzzaman 2007).
There are six classes of LOS ranging from A to F, A being the highest level of
service and F being fully congested. For the context of estimating the PT con-
gestion impact, this is not suitable as it cannot provide a continuous range of
congestion values. Congestion indices, such as travel rate index (TRI), Texas

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Traffic congestion relief associated withpublic transport:…
transportation institute (TTI) or roadway congestion index (RCI) were normally
developed by aggregating several congestion-related elements into an equation to
measure the congestion level for a road segment or a particular route, but not for
a road network. Thus, most previous studies on the assessment of PT conges-
tion impacts have used basic congestion indicators which commonly related to
delay or capacity. Congestion indicators and metrics and congestion thresholds
will now be reviewed to determine which indicators are suitable to apply for PT
measurement.
2.2.1 Congestion indicators andmetrics
Congestion has been categorised by four aspects of its occurrence: intensity, dura-
tion, extent and variability (Lomax 1997; Systematics 2008; Schuman 2011).
• Intensity measures the amount of congestion delay experienced at an intersec-
tion approach, sections of route, several routes or an entire urban area (Falcoc-
chio and Levinson 2015). Its metrics are expressed as a rate (e.g. min/km). The
units of measurement used are travel delay, vehicle-hours of delay, person-hours
of delay, a travel time index or a travel rate index. This congestion indicator is
appropriate for PT measurement since delay per person is considered. Otherwise,
in heterogeneous traffic conditions, there is a difference between free speeds of
PT and private transport. Thus, the delay per person of delay per vehicle can be a
metric to measure the level of traffic congestion of heterogeneous traffic flow.
• Duration reflects the amount of time that a road or system is congested. The
duration of congestion depends upon the types of congestion (recurring or non-
recurring). City size and the type of roadways also impact congestion duration.
Congestion is generally of long duration on major roadways in large urban areas
due to high traffic volume. In contrast, duration is less frequent in small urban
areas. The amount of congested time (e.g. hours or minutes) is one of the key
metrics used to measure this perspective of traffic congestion.
• Extent measures how far congestion spreads (the length of roads, the number of
roads, the percentage of roads that are congested), and how many system users
or components (vehicles, roads etc.) are influenced by congestion. The extent of
congestion varies by the size of urban areas and the type of roadways. Freeways
generally experience more delay than other types of road as they usually account
for about half of all urban travel in the US (Schrank etal. 2012).
• Variability accesses the variation in the amount, duration and extent of conges-
tion over time.
In assessing PT impact, there are other dimensions that need to be considered
such as vehicle composition, person/vehicle delay, etc. They might have an influence
on congestion for PT, PT and vehicles together and different vehicles.
Table2 summarises congestion indicators and their metrics for measuring traf-
fic congestion. There are a variety of congestion metrics which represent different
perspectives and assumptions. Some metrics are used on route-based or whole
area-based analysis. Some metrics reflect the per-capita or per-vehicle impact

D.Q.Nguyen-Phuoc et al.
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Table 2 Overview of congestion indicators and their metrics Source: NCHRP 398, vol 1, Table S-5, p 7 (Lomax etal. 1997)
VMT vehicle-miles of travel, PMT person-miles of travel
Congestion aspect System type Can be used for heterogeneous
traffic with PT
Single roadway Corridor Area wide network
Intensity (e.g., level or total
amount of congestion)
Travel rate; delay rate; relative
delay rate; minute-miles; lane-
mile hours
Average speed or travel rate;
delay per PMT; delay ratio
Accessibility; total delay in
vehicle-hours; delay per
vehicle; total delay in person-
hours; delay per person; delay
per PMT
Yes (if indicators concern people)
Duration (e.g., amount of time
system is congested)
Hours facility operates below
acceptable speed
Hours facility operates below
acceptable speed
Set of travel time contour maps;
‘bandwidth’ maps showing
amount of congested time for
system sections
No
Extent (e.g., number of people
affected or geographic distri-
bution)
% or amount of congested VMT
or PMT; % or lane-miles of
congested road
% of VMT or PMT in conges-
tion; % or miles of congested
road
% of trips in congestion:
person-miles or person-hours
of congestion; % or lane miles
of congested road
Yes (if indicators concern people)
Reliability (e.g., variation in the
amount of congestion)
Average travel rate or
speed ± standard deviation;
delay ± standard deviation
Average travel rate or
speed ± standard deviation;
delay ± standard deviation
Travel time contour maps with
variation lines; average travel/
time ± standard deviation;
delay ± standard deviation
No

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Traffic congestion relief associated withpublic transport:…
and others reflect the gross impact. Hence, based on the objective of measuring
congestion and the availability of the required data, appropriate measures will be
used. For instance, TfL (2013) used average vehicle delay (minutes per vehicle-
km) as a relative measure of congestion to compare the performance of the road
network in London, UK across years.
The level of congestion has been measured by contrasting the condition of traf-
fic in the scenario of ‘no congestion’ (free speed, no delay) and ‘with congestion’.
However, in mixed traffic flow with PT operations such as buses or trams, how
should the speed/capacity of mixed traffic flow be determined since PT normally
has lower speed compared to private vehicles as well as stop operations at stations.
This area needs to be explored. For PT congestion impact measurement, the inten-
sity and extent of congestion are more appropriate to assess the level of congestion
than the duration and reliability which only focus on vehicles. Indeed, total delay in
person-hours, delay per person or delay per PMT can be used to show the intensity
of traffic congestion of roads with PT operations as people are concerned rather than
vehicles. In terms of determining the extent of congestion, person-miles or person-
hours affected by congestion might be appropriate indicators to measure the conges-
tion impact associated with PT. For example, DfT (2014) used delay per person as
a congestion measure and suggested to use a higher weighted average value of time
for car users than bus users when identifying the impacts of bus priority lanes on
traffic congestion. This reflects the higher proportion of ‘in work’ travel time by car
users than bus users. Similarly, Bayle (2012) used mean weighted journey time for
bus passengers when assessing the performance of a Bus Rapid Transit system on
the Sydney road network. This is considered to be an application of ‘person delay’
in the estimation of PT congestion relief.
2.2.2 Congestion thresholds
Traffic congestion reflects the difference between road traffic conditions (such as
travel time, volume/capacity) during busy traffic periods and when the road is
lightly travelled. In order to identify the level of traffic congestion of a roadway
or an area, threshold values have been introduced. According to Falcocchio and
Levinson (2015) traffic congestion thresholds can be defined as follows:
1. Using free-flow speed as a congestion threshold or,
2. Establishing an acceptable minimum speed for various types of facilities and
operating environments and vehicle types.
Using free-flow speed as a threshold for congestion might be suitable for homo-
geneous traffic, traffic in rural areas and off-peak periods. In large urban areas where
traffic congestion occurs frequently and traffic is mixed, particularly in peak hours,
the toleration of congestion can be higher than the one in rural areas, so it might not
be appropriate to use free-low speed as a congestion threshold. The thresholds for

D.Q.Nguyen-Phuoc et al.
1 3
‘tolerable’ congestion levels can be set by traffic authorities for each type of roadway
(Falcocchio and Levinson 2015). Fox example:
• Lindley (1987) used a volume to capacity (V/C) ratio of 0.77 as a threshold for
congestion (or the speed of 55 mph corresponding to V/C ratio of 0.77).
• Lomax etal. (1999) used the 85th percentile speed in the off-peak period as the
free-flow speed.
• Hall and Vyas (2000) considered the posted speed limit as the nominal free-flow
speed for comparing with congested speed.
• Lomax and Schrank (2005) used 60mph for freeways and 35 mph for arterial
roads as free-flow speed.
• According to WSDT (2011), congestion thresholds were established as 75% of
posted speed limits.
The review shows that all of the congestion thresholds relate to vehicles, none
of these thresholds relate to traffic with PT present. Thus, they can not be used
effectively for PT congestion measurement when people should be considered. It is
needed to develop a threshold for determining the level of congestion of roadways/
networks with PT operations.
The congestion vehicle-based measures are usually used to quantify congestion
intensity (the number of vehicles suffering from traffic congestion) but it does not
reflect congestion exposure (the amount of people suffering traffic congestion). Peo-
ple-based measures may be more suitable for reflecting congestion exposure as well
as considering the congestion relief impacts caused by mode shift from private car
to PT. Hence, congestion exposure indicators which measure people (such as peo-
ple delay per hour, people delay per kilometre) are useful for planning purposes as
they can measure congestion costs. However, people-based measures require more
detailed data on many factors such as travel demand, the occupation of PT means
and PT travel conditions.
2.3 Assessing trac congestion impact associated withPT
The impact of PT on traffic congestion is often demonstrated by contrasting the level
of vehicle congestion in two scenarios: ‘with PT’ and ‘without PT’. In the scenario
of ‘without PT’, it can be seen that the PT withdrawal would result in mode shift
from PT to private car which increases the level of vehicle congestion. This increase
in congestion is considered to be the benefit of PT in acting to reduce traffic conges-
tion. Hence, mode shift to car when PT is removed is recognised a key parameter
used to estimate PT congestion relief impact. This mode shift has also been used to
determine congestion level increase in case of a PT disruption. On the other hand,
PT also contributes to increase the level of congestion on the road network due to
the operation of at-grade rail crossings, the slow moving of PT vehicles or the take
up of road space for priority PT lanes. Thus, there is a need to understand the net
traffic congestion impact associated with PT.

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Traffic congestion relief associated withpublic transport:…
A comprehensive review of studies investigating the mode shift, congestion gen-
eration impacts of PT operations and their adaption into assessing the network-wide
impacts of PT on traffic congestion are presented in the following sub-sections.
2.3.1 Mode shift whenPT isunavailable
Existing studies have tended to focus on the congestion impacts of transferring PT
trips onto the road network and the increase in congestion resulting from this move.
However, only a few published studies focus on the travel mode shift of PT users
to alternative transport modes when PT withdrawal occurs. Mode shift is often
explored in the event of PT strikes.
Exel and Rietveld (2001) reviewed 13 studies of PT strikes in Europe and the
United States to explore the behavioural response of PT users. They found that the
impact of PT strikes varied depending on the type of strike, travel patterns and pol-
icy responses. In 2003, the Washington State Department of Transportation (WDOT
2003) developed a methodology to estimate the economic value of PT trips by com-
paring the difference between this value in two situations, ‘with PT’ and ‘without
PT’. A field survey was conducted in Wisconsin, America to examine the choices
that PT riders might make if all PT was unavailable. The findings showed that
3.7–14.6% of PT users would shift to car as a driver, while 9–14.8% would switch
to car as a passenger in the absence of PT. These figures varied depending on the
purpose of trips.
Table 3 summarises the literature-identified behavioural response of users for
a number of PT strikes around the world. It shows that there is a wide range in
the mode shift to car as a driver (5–50%), which would directly contribute to the
increase in traffic congestion. This can be due to the difference in demographic and
trip characteristics of PT users in a particular area. For example, in the event of an
urban PT strike in Leeds (UK) in 1978, only 5% of the users shifted to a car as a
driver (Exel and Rietveld 2001). This was due to the low rate of household car own-
ership in the UK at the time (55%) and a majority of PT users who had no car in
their households (Exel and Rietveld 2001).
Recently, only two studies have explored factors affecting mode shift from PT
in the event of PT cancellations (Table4). Exel and Rietveld (2009) investigated
the actual behavioural reactions of train travellers to the rail strike in the Nether-
lands and explored the characteristic of travellers and trips that may affect chosen
alternatives. They found that 24% of the train travellers shifted to a car as a driver
and 14% shifted to another mode (as a passenger). A multinomial logit regression
also showed that age, gender, trip distance, frequency of train use and trip purpose
had an impact on the behavioural response of users when train operations ceased.
However, the analysis in this study focused only on a limited number of variables
that were available through secondary data and did not include important variables
such as driver license holding, car ownership, or accessibility. More recently, Pnev-
matikou etal. (2015) explored the changes in travel patterns of metro users during
and following a metro disruption. Data was collected from two surveys (revealed
preference and stated preference) carried out in Athens, Greece in 2011. A multino-
mial logit model and a nested logit model were developed to analyse the travellers’

D.Q.Nguyen-Phuoc et al.
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Table 3 Evidence of mode shift when PT was unavailable Source: author’s synthesis
a Urban traffic
b Interurban traffic
c Average value
Source Year Location PT mode removed Mode shift to car Cancel trip (%)
As a driver (%) As a passenger (%)
Exel and Rietveld (2001) 1966 New York, USA All 50 17 10
1974 Los Angeles, USA Bus 50 25 –
1978 Leeds, UK All 5 60 15
1981 The Hague, Netherlands All 10 25 5
1995 Ile-de-France, France All 28 21 11
1995 The Netherlands Bus 30 10
1998 Norway Bus 20%a, 40–60%b–
WDOT (2003) 2001 Wisconsin, USA All 8%c (3.7–14.6%) 12%c (9–14.8%) 56%c (52–67.3%)
Exel and Rietveld (2009) 2004 The Netherlands Train 24 14 44

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Traffic congestion relief associated withpublic transport:…
behaviour and mode choice during metro disruptions. They found that gender,
income, trip purpose, travel cost and transfer inconvenience were important fac-
tors impacting mode decisions. It can be seen that mode shift from PT to car can
be impacted by other factors such as park-and-ride (PNR) schemes. PNR schemes
have been found to reduce car use to CBDs as PNR services are often subsidized to
attract car users to use PT (Parkhurst 2000; Meek etal. 2008). When PT becomes
unavailable, PT users who are using PNR services are likely to switch to using a car
since they have already used a car for a part of their trip. Hence, it is necessary to do
more research to explore factors influencing this mode shift.
It is clear that the mode shift of passengers from PT to private vehicles during
strikes is considerable which can lead to a significant impact on congestion. To bet-
ter understand this mode shift is very important for the assessment of traffic conges-
tion relief impact associated with PT.
2.3.2 PT creating trac congestion
Although PT is often considered to be an effective measure to mitigate traffic con-
gestion, the operation of PT also has some negative effects on traffic flow. In this
sub-section, a detailed literature review of academic research papers and industry
reports relating to the negative impact of train operations (at-grade rail crossings),
tram operations and bus operations is undertaken.
2.3.2.1 Negative trac impact oftrain operations The direct impact of train opera-
tions, particularly at-grade rail crossings, on road travel is a major concern for traffic
authorities in cities with a large number of level crossings. Congestion comparisons
of the removal of level crossing should, therefore, include their impact on delay in the
‘with PT’ option. Studies about level crossing traffic congestion impact assessment
is very limited. In NCHRP Report 288, Taggart etal. (1987) explored some formulas
for calculating the travel delay experienced by each vehicle at an at-grade crossing.
These equations are based on the average annual train, vehicular traffic and the clo-
sure time that is calculated from average train length and the average train speed at
the crossing. Hakkert and Gitelman (1997) developed a simplified tool for evaluating
level crossings in Israel. From the field data collected at the 31 most problematic
Table 4 Factors affecting mode shift when PT was unavailable Source: author’s synthesis
Source Location PT mode
removed
Method Survey
data
Factors affecting mode shift
Exel and Rietveld
(2009)
The Nether-
lands
Train Quanti-
tative
Second-
ary
Age, gender, trip distance,
frequency of train use and
trip purpose
Pnevmatikou etal.
(2015)
Athens,
Greece
Train Quanti-
tative
Primary Gender, income, trip pur-
pose, travel cost, transfer
inconvenience

D.Q.Nguyen-Phuoc et al.
1 3
locations, they calculated the cost of safety problems and travel delay and used them
for comparing level crossings. Schrader and Hoffpauer (2001) created a methodology
for considering the prioritization of potential highway–railway grade separation loca-
tions in Central Arkansas. In this method, delay at at-grade rail crossings is one of
seven factors and estimated by a formula developed by Taggart etal. (1987). Micro-
simulation is recognised to be a popular tool for assessing the travel delay of road
vehicles associated with at-grade rail crossings (Chandler and Hoel 2004; Powell
1982; Rymer etal. 1989). Other research focusing on the delay at at-grade rail cross-
ings was undertaken by Okitsu and Lo (2010). First, they undertook a 24-h video
recording at 33 level crossings in Los Angeles County’s San Gabriel Valley. From the
recording, they determined several parameters, such as upstream traffic signal phas-
ing and downstream signal green-to-cycle ratios and applied them to Webster’s inter-
section delay model. Thus, delay caused by blockages at at-grade crossings in every
individual event throughout the day could be identified. VicRoads (2010) undertook
a field survey to measure travel times before and after the grade separation of a rail-
road crossing in Melbourne, Australia. The results showed that travel times decreased
up to 22% in peak periods following the grade separation.
2.3.2.2 Negative trac impact oftram operations In terms of exploring the nega-
tive effects of tram operations on congestion, Chandler and Hoel (2004) investigated
the effects of light-rail crossings on average delays experienced by vehicles using
microsimulation. This topic was also explored by Rymer etal. (1989). Currie and his
colleagues estimated the impact of curbside stops on the efficient use of road space
(Currie, G., M. Sarvi and W. Young, unpublished data on VicRoads R&D Project
799, 2004). They compared tram operations on roads ‘with’ and ‘without’ curbside
stops using traffic simulation. They found that curbside stops reduce average tram and
traffic speeds by between 8 to 12%.
The provision of segregated tram lanes has been identified as an efficient means
of improving transit reliability and running times when transit vehicles share road
space with congested urban traffic (Vuchic 2007). However, the reallocation of a
proportion of the road space to PT lanes reduces road capacity and can increase
the level of traffic congestion (Kittelson etal. 2003). Cairns etal. (1998) examined
around sixty locations where road space was allocated to tram lanes or bus lanes.
They found that on average the traffic volume on routes affected by the reallocation
of road space decreased by between 14 to 25%. In 2003, Currie and his colleagues
used traffic microsimulation to investigate the on-road operational implications of
alternative transit priority measures. From the findings of simulation modelling,
they developed a framework to estimate the benefits and costs of priority measures
to transit and traffic (Currie etal. 2007).
2.3.2.3 Negative trac impact ofbus operations The effect of bus operations on cre-
ating traffic congestion includes the effects of bus stop operations and the impacts of a
priority bus system such as exclusive bus lanes and priority signals for buses.
The effect of bus stops on traffic flow has received a great deal of research atten-
tion. In the literature, there is a wide range of parameters explored to assess traffic

1 3
Traffic congestion relief associated withpublic transport:…
delay caused by bus stop operations such as dwell time, bus frequency, the loca-
tion of bus stops, the type of bus stops, the number of lanes and the components of
the heterogeneous traffic flow. However, most studies have only considered selected
parameters in their research. Theoretical models such as Cellular Automata (CA)
models have been frequently used to simulate the impact of bus operations at bus
stops on traffic flow (Zhao etal. 2007; Yuan etal. 2007; Tang etal. 2009; Xia and
Xue 2010). Other researchers investigated bus stop impact on vehicle traffic by col-
lecting field data and using statistical models to find the relationship between the
impact and bus parameters (such as bus frequency, bus dwell time) (Kwami etal.
2009; Ben-Edigbe and Mashros 2011). However, the wide range of data related to
bus stops is very difficult to collect in the field. Traffic simulation is, therefore, rec-
ognised as an effective method to analyse the effect of a wide range of parameters on
traffic flow near bus stops (Fitzpatrick and Nowlin 1997; Koshy and Arasan 2005).
From the literature review, bus stops have been recognised to have impacts on the
traffic flow and the impacts are different regarding bus stop design (such as curb-
side bus stop or bus bay), traffic conditions or bus parameters. Most studies have
considered dwell time as one of the key parameters to estimate the impact of bus
stops. The effect of bus arrival frequency, bus speed, traffic volume, stream speed or
even legal constraints, bus driver behaviours have not received much consideration.
Therefore, a model that can considers the impact of a wide range of parameters on
the assessment of traffic delay associated with bus stops is needed.
Transit priority lanes as well as dedicated or intermittent bus lanes are one of
many measures to improve the speed and reliability of PT services (Chen et al.
2010; Chiabaut and Barcet 2019; Ben-Dor etal. 2018). However, some applications
are controversial as they may cause a reduction of road capacity for general traffic
and increase the level of traffic congestion. The effect of bus lanes on traffic was
evaluated in a number of studies using field surveys or simulation (Chen etal. 2010;
Cherry etal. 2005; Shalaby 1999; Patankar etal. 2007; Eichler and Daganzo 2006;
Chiabaut and Barcet 2019; Levin and Khani 2018; Ben-Dor etal. 2018; Chiabaut
etal. 2018).
As shown in the above studies, the level of traffic congestion can increase due
to the operation of PT such as the operation of at-grade rail crossings and tram/bus
operations in traffic. While there have been attempts to explore these impacts on
adjacent road links or corridors, little is known about the network-wide impacts of
PT in generating congestion. Indeed, the operation of PT can result in traffic volume
changes in the surrounding area because of the traffic diversion and reassignment.
Assessing the negative impacts of PT operations on the road network is important
since it can be used to aggregate with the positive impacts to more accurately evalu-
ate the performance of PT services on traffic congestion.
2.3.3 Network‑wide impact ofPT ontrac congestion
A summary of research on assessing the PT congestion impact is presented in
Table5.
Many of the studies on the reduction in traffic congestion due to PT used mixed
traffic flow conditions on a carriageway. However, they did not investigate mixed

D.Q.Nguyen-Phuoc et al.
1 3
Table 5 Traffic congestion relief associated with PT Source: author’s synthesis
Source Location Method Mode
shift to
car (%)
Other results
Crain and Flynn (1975) Los Angeles, USA Observing the traffic condition during a PT
strike
– On one important freeway, the additional travel
time was 10–15min in the morning peak
Lo and Hall (2006) Los Angeles, USA Observing the traffic condition during a PT
strike
– Average traffic speeds on highways decrease
by 20% during the strike. The traffic speed is
estimated by the freeway performance measure-
ment system (PeMS) algorithm for real-time
speed estimates from single-loop detectors
installed on the highways
Aftabuzzaman etal. (2010b) Melbourne, Australia Comparing the level of congestion in two
scenarios: ‘with’ and ‘without’ PT using a
regional transport network model
32 Removing PT is estimated to increase the number
of congested links by about 1400 or 30%. Aver-
age travel speeds decrease by 15.5%. Actual
travel time per kilometer increased by about
18%. These congestion measures were calcu-
lated from the outcome of the model
Schrank etal. (2012) 498 urban areas in the
USA
Using an analytical model and an assumption of
all rail commuters shift to private cars travel-
ling on freeways in the event of a PT service
shutdown
100 The total delay on the road network increases by
15% (an additional 865 million hours of delay)
Anderson (2013) Los Angeles, USA Using a choice model and data from a sudden
strike
– The average highway delay would increase 47%
(0.194min per mile) during peak periods when
PT ceases
Ewing etal. (2014) Salt Lake, USA Observing the traffic condition before and after
the operation of a LRT
– Daily vehicle traffic (vehicles per day) on the
study corridor is reduced by about 50%
Adler and Van Ommeren (2015) Rotterdam, Netherlands Observing the traffic condition during multiple
PT strikes
– Average car speed on highway ring road is
decreased by 3%, is reduced on inner city roads
by 10%. Car speed was measured by independ-
ent speed
measurements

1 3
Traffic congestion relief associated withpublic transport:…
Table 5 (continued)
Source Location Method Mode
shift to
car (%)
Other results
Moylan etal. (2016) San Francisco, USA Comparing travel time before and during the
PT strike using data from detectors and a non-
parametric modelling technique
100 Morning peak conditions on a parallel road
were at the 80th percentile of annual volume-
weighted travel times. Using volume-weighted
travel time as a performance metric gives a bet-
ter picture of the strike’s impact since the rail
system preferentially serves busy corridors
Nguyen-Phuoc etal. (2017b) Melbourne, Australia Comparing the level of congestion in two sce-
narios: ‘with’ and ‘without’ tram operations
using a regional transport network model
23 Total network delay and vehicle time travelled
increase by 1.2%. These congestion measures
were calculated from the outcome of the model

D.Q.Nguyen-Phuoc et al.
1 3
PT/private transport flow nor did they consider the change in vehicle occupancy
or person delay. A popular method that has been used to investigate the benefit of
PT systems is to explore the impact of a single transit strike on traffic flow (Crain
and Flynn 1975; Lo and Hall 2006; Adler and Van Ommeren 2015; Moylan etal.
2016). Traffic conditions during the strike were measured to understand how transit
actually affects congestion experienced by drivers. They measured the traffic speed,
travel delay on freeways before and during a strike by using various sensors installed
on the roads. The impact of PT was estimated by considering the increase in vehi-
cles caused by the mode shift from PT operating in other roads to private vehicles,
they did not consider the change in people flow. Thus, the results of these studies
overestimated or underestimated the congestion relief benefit of PT depending on
the validity of its base assumptions. The benefit of a PT system on reducing traffic
congestion can also be estimated by observing traffic conditions before and after the
operation of a PT system. Ewing etal. (2014) investigated the effects that Salt Lake
City’s University TRAX light rail transit (LRT) system has on vehicle traffic on par-
allel roadways. The study found significant declines in roadway traffic after the LRT
line was completed, despite a significant development in the area.
There was also another approach for estimating PT congestion relief which
adopted analytical models of the transportation system and field data (Parry and
Small 2009; Schrank etal. 2012; Anderson 2013; Moylan etal. 2016). Parry and
Small (2009) estimated the optimal transit operating subsidy by developing an ana-
lytical model of a transportation system and using a costing measure of congestion.
They assumed that each passenger mile travelled on PT diverts nearly 0.9 passenger
miles from roadways. Anderson (2013) used a choice model and data from a strike
in 2003 by Los Angeles transit workers for calibrating his model. He explored that
the transit generated a much larger congestion relief impact than earlier estimates.
The third approach to explore the PT congestion impacts is using transport mod-
elling, taking separate PT travels and transferring them onto the existing road sys-
tem (Aftabuzzaman etal. 2010b; Nguyen-Phuoc etal. 2017b, 2018a, b). The differ-
ence between the level of congestion in two scenarios ‘with PT’ and ‘without PT’ is
recognised to be the PT congestion effects. The assumption of mode shift from PT
to vehicle when PT ceases was used in the study of Aftabuzzaman etal. (2010b). In
the scenario ‘with PT’, they assumed that PT means sharing a vehicle traffic lane
which has no impact on vehicle traffic so they defined congestion in relation to the
travel delay of vehicles. On the other hand, in the scenario ‘without PT’, the allo-
cation of priority PT lanes to vehicle traffic lanes was ignored. Hence, the results
of these studies showed only the positive impact of PT rather than the net impact
as the potential congestion generation impact of PT operations was not taken into
account. Recently, Nguyen-Phuoc etal. (2017b) conducted a study to assess the net
impacts of a light-rail system on traffic congestion in Melbourne. They considered
both positive and negative effects of trams on traffic. They used microsimulation to
model the impacts of trams on increasing traffic congestion on a road link. However,
their microsimulation models were not calibrated and relatively simple, since a lim-
ited number of factors was considered. For the positive effects, a fixed mode shift
from PT to car in the event of a strike, obtained from a field survey, was adopted.
The microsimulation results and the mode shift were incorporated into a transport

1 3
Traffic congestion relief associated withpublic transport:…
network model to explore the net network-wide effect of PT. In these above studies,
the intensity and extent of congestion were used to assess the PT congestion relief
effects.
The review shows that there are three major approaches to assess the traffic con-
gestion impacts of PT: (1) comparing the traffic flow conditions before and after
PT strikes using field data, (2) using analytical models and field data and (3) using
simulation models. Collecting field data can be resource-intensive, with PT strikes
occurring infrequently. A traffic simulation approach may, therefore, be a more eco-
nomical and faster tool to investigate this phenomenon. Microsimulation can model
the interactions of PT means with surrounding vehicles and traffic conditions, so the
impact of PT on creating traffic congestion on a road segment can be investigated. In
contrast, simply doing direct observation of traffic movements may not show more
complex vehicle interaction, capacity and network effects. However, it is still neces-
sary to have empirical data to correctly model those interactions, capacity and net-
work effects. Macroscopic models can then be used to incorporate microsimulation
results into an entire road network to assess the network-wide impacts of PT on traf-
fic congestion.
For the simulation approach, mode shift from PT to car is recognised to be a
main parameter used for estimating PT congestion relief impacts (Aftabuzzaman
etal. 2010a). Most previous studies assessing PT congestion relief impacts used a
simplistic assumption, a fixed share of mode shift to car if PT was not available.
However, the mode shift varies for cities around the world and is influenced by
demographic and trip characteristics of the PT users (Nguyen-Phuoc etal. 2018c, d).
Thus, identifying factors affecting mode shift is needed. A better understanding can
help to vary the share of mode shift to car when PT is unavailable for different areas
(e.g. inner, middle, and outer city). Hence, a more precise methodology for estimat-
ing the impacts of PT on traffic congestion can be undertaken.
All approaches assessing PT congestion impact used vehicles as a key object. No
study looks at the congestion measure of mixed traffic flow with PT for the ‘with
PT’ scenario. The impact of PT could be underestimated because in the scenario
‘with PT’, PT which is usually occupied by a number of people is considered a nor-
mal vehicle when determining the level of congestion. However, in a scenario ‘with-
out PT’, PT users can shift to the car as a driver which leads to the increase in the
number of vehicles or shift to the car as a passenger which leads to the increase in
the car occupancy rate. Thus, assessing the benefit of PT by comparing the delay of
vehicles between two scenarios is unsuitable. In this case, the average delay per per-
son or total delay of persons should be more appropriate.
Most previous studies have just looked at vehicle congestion as a result of remov-
ing PT. They neglect the capacity impact of the PT vehicles in the scenario ‘with
PT’. The lack of comprehensive and balanced impact assessments on PT congestion
impact is identified as a key research gap. Further the influence of vehicle occu-
pancy before and after the study needs to be included in the analysis to determine
the change in person travel or congestion. Hence, in conclusion it appears most stud-
ies have not looked comprehensively at the capacity of heterogeneous traffic.

D.Q.Nguyen-Phuoc et al.
1 3
3 Discussion andconclusion
The paper has reviewed the literature on traffic congestion assessment focusing on
recent studies of the estimation of a traffic congestion impact associated with exist-
ing PT systems. The review shows that there has been a variety of definitions of traf-
fic congestion but most of them are concerned on vehicle congestion. Few studies
have attempted to look at the congestion levels where PT and other modes on a car-
riageway are mixed. In the context of measuring PT congestion impacts, definitions
related to people delay are considered to be more appropriate as PT is different to
other types of vehicles in terms of occupancy. Some congestion indicators concern-
ing people such as total delay in person-hours, delay per person or delay per PMT
are suggested to determine the intensity of traffic congestion of roads with PT opera-
tions. In terms of estimating the extent of congestion, person-miles or person-hours
affected by congestion should be appropriate indicators to assess the congestion
impact caused by PT. In addition, the use of people-based indicators can also help
to evaluate a part of traffic congestion cost using the time loss (value of time) and
money wasted of road users because of travel delays. However, this cost not only
comes from travel delays but also from an increased impact on the environment,
increased vehicle costs from travel delays or increased chance of vehicle collisions.
The impact of PT on reducing congestion cost is worthy to investigate in further
research.
The review shows that a key benefit of PT is relieving traffic congestion;
however, there are limited studies focusing on traffic congestion impact assess-
ments concerning PT. Whilst most of the research assesses the congestion impact
of PT on a road segment of a corridor, a limited group of studies explores the
network-wide impact of PT with simple approaches. In transport networks, traf-
fic flows always form self-adjusting relationships among different routes. The
ground transport system’s equilibrium can result from the operation of PT since
it directly impacts the existing traffic flow on roads with PT. Thus, there is a need
to assess the network-wide impacts of PT by considering the movement of traffic.
For instance, if an effective PT vehicle operates on a congested road, a number of
people would switch from car to PT and reduce the level of congestion. But once
traffic moves faster, other people from other routes, other modes could shift onto
the improved road. The traffic congestion relief caused by PT on that road could
not be significant but the level of congestion of other surrounding roads would
decrease.
There is a difference among the impacts on congestion of different types of
PT modes. For instance, some PT systems such as subways have only positive
effects on reducing congestion by attracting people from car to PT. For other PT
systems that operate on shared roads with vehicles such as trams or buses, beside
their congestion relief impacts they have negative effects on creating congestion
caused by slow-moving PT vehicles or the occupation of priority PT lanes.
Some key research gaps in the study of the congestion impacts of PT are
detailed in Table6. A better understanding of mode shift when PT is unavailable
will contribute to develop a more precise model which aims to assess the impact

1 3
Traffic congestion relief associated withpublic transport:…
Table 6 Summary of research opportunities based on research gaps
Research topic Gaps in knowledge Need for more research Research approach
Measure traffic congestion The level of congestion of mixed traffic
flow with PT operations is not meas-
ured accurately
To measure the speed/capacity of mixed
traffic flow with the consideration of
PT stop operation impact as well as
the lower speed of PT than private
vehicles. People should be focused in
these measures
Using microsimulation to model the
impact of PT operations on traffic flow
There is no congestion threshold related
to people
To create congestion threshold regarding
to people
Impact of PT on reducing traffic conges-
tion
The factors impacting mode shift from
PT to car when PT is unavailable are
not clearly understood
To have better understanding of factors
affecting mode shift from PT to car
when PT is unavailable
Conducting qualitative interviews of PT
users to identify these factors
Conducting a survey of PT users’ actual
behaviour to investigate which factors
have a significant impact on the mode
shift
The share of mode shift from PT to car
is assumed to be constant for all areas
in the PT system resulting in errors in
the assessment of PT congestion relief
To vary the share of mode shift to car for
different areas
Developing a method for estimating mode
shift to car when PT is removed that
varies for different areas based on traffic
characteristics
Most research on the assessment of PT
congestion relief impact adopted the
fixed share of mode shift from PT to
car which might lead to inaccurate
results
To assess the positive impact of PT on
reducing traffic congestion with the
consideration of the various mode shift
for different areas
Modelling the traffic flow on the network
in a scenario ‘with PT’ and ‘without
PT’ (using the mode shift data) to esti-
mate traffic congestion relief associated
with PT
Impact of PT on creating traffic conges-
tion
No studies exploring the network-wide
impact of PT (at-grade rail crossings,
tram operations and bus operations) on
generating traffic congestion to date
To assess the negative impact of PT
operations on generating traffic
congestion
Modelling traffic flow on the network in
a scenario ‘with PT’ and ‘without PT’
to estimate the impact of PT on creating
traffic congestion

D.Q.Nguyen-Phuoc et al.
1 3
Table 6 (continued)
Research topic Gaps in knowledge Need for more research Research approach
Net impact of PT on traffic congestion No research assessing the net impact
of PT on the ability to mitigate traffic
congestion
Previous studies on PT congestion
impact focused only on the positive
impact of PT on relieving traffic con-
gestion and did not consider the impact
of PT on generating traffic congestion
To assess the net impact of PT on traffic
congestion
Modelling traffic flow on the network in a
scenario ‘with PT’ and ‘without PT’ to
estimate the net impact of PT on traffic
congestion using the mode shift data
and the simulated negative impact data
as inputs

1 3
Traffic congestion relief associated withpublic transport:…
of PT on reducing traffic congestion. In addition, the network-wide impacts of PT
on creating traffic congestion is not understood clearly. The research in this area
is needed to develop comprehensive and balanced impact assessments of PT.
The literature regarding the assessment of traffic congestion impacts associated
with PT demonstrates clear gaps in the knowledge. They are:
• The appropriate congestion measures of mixed traffic flow with PT operations
have not been clearly defined.
• The nature and scale of the mode shift from PT to car when PT is not speci-
fied.
• The network-wide impact of PT operations in relieving traffic congestion has not
been assessed accurately.
• The network-wide impact of PT operations in creating traffic congestion is not
well understood.
• The net impact of PT on traffic congestion is not known.
These research gaps are diverse and offer much opportunity for advancing knowl-
edge, particularly in terms of understanding the net network-wide congestion impact
of PT. It can help traffic authorities to identify the effectiveness of PT on reliev-
ing congestion on congested routes or corridors. From that, policies or improvement
projects related to PT can be proposed to reduce traffic congestion. However, con-
gestion relief can have adverse impacts such as increasing delay for modal shifters
from car to PT due to overcrowding, increasing PT congestion costs or increasing
public finance costs. Hence, these potential issues need to be considered alongside
attempts that aim to seek congestion relief.
References
Adler M, Van Ommeren JN (2015) Does public transit reduce car travel externalities? Tinbergen Institute
Discussion Paper 15-011/VIII
Aftabuzzaman M (2007) Measuring traffic congestion-a critical review. Australasian Transport Research
Forum, Melbourne
Aftabuzzaman M, Currie G, Sarvi M (2010a) Evaluating the congestion relief impacts of public transport
in monetary terms. J Public Transp 13:1–23
Aftabuzzaman M, Currie G, Sarvi M (2010b) Modeling the spatial impacts of public transport on traffic
congestion relief in Melbourne, Australia. Transp Res Rec 2144:1–10
Anderson ML (2013) Subways, strikes, and slowdowns: the impacts of public transit on traffic conges-
tion. NBER Working paper series. University of California, Berkeley
Bayle R (2012) Identifying the performance parameters of importance in the design of Bus Rapid Transit:
an experimental framework using microscopic simulation. Institute of Transport and Logistic Stud-
ies (ITLS), University of Sydney, Sydney
Ben-Dor G, Ben-Elia E, Benenson I (2018) Assessing the impacts of dedicated bus lanes on urban traffic
congestion and modal split with an agent-based model. Procedia Comput Sci 130:824–829
Ben-Edigbe J, Mashros N (2011) Determining impact of bus-stops on roadway capacity. In: Proceedings
of the Irish transport research network, Aug 1–7
Bollinger CR, Ihlanfeldt KR (1997) The impact of rapid rail transit on economic development: the case of
Atlanta’s MARTA. J Urban Econ 42:179–204

D.Q.Nguyen-Phuoc et al.
1 3
Cairns S, Hass-Klau C, Goodwin P (1998) Traffic impact of highway capacity reductions: assessment of
the evidence. Landor Publishing, London
Calderdale Council (2015) Tackling traffic congestion and pollution. Calderdale Council, West Yorkshire
Chandler C, Hoel LA (2004) Effects of light rail transit on traffic congestion. Center for Transportation
Studies, University of Virginia, Virginia
Chandra S, Kumar U (2003) Effect of lane width on capacity under mixed traffic conditions in India. J
Transp Eng 129:155–160
Chen X, Yu L, Zhu L, Guo J, Sun M (2010) Microscopic traffic simulation approach to the capacity
impact analysis of weaving sections for the exclusive bus lanes on an urban expressway. J Transp
Eng 136:895–902
Cherry C, Tang E, Deakin E, Skabardonis A (2005) High-occupancy vehicle systems: analysis of freeway
improvements for express bus service. Transp Res Rec 1925:256–264
Chiabaut N, Barcet A (2019) Demonstration and evaluation of an intermittent bus lane strategy. Public
Transp 11:443–456
Chiabaut N, Küng M, Menendez M, Leclercq L (2018) Perimeter control as an alternative to dedicated
bus lanes: a case study. Transp Res Rec 2672:110–120
Crain JL, Flynn SD (1975) Southern California rapid transit district 1974 strike impact study. No. DMT-
004 Final Rpt. California Department of Transportation, United States
Currie G, Sarvi M, Young B (2007) A new approach to evaluating on-road public transport priority pro-
jects: balancing the demand for limited road-space. Transportation 34:413–428
Department for Transport (2017) National Travel Survey: England 2016
DfT (2014) Transport analysis guidance (TAG), Unit A1.3 user and provider impacts. Department for
Transport (DfT), London
Douglas N (1993) Travel demand, economic growth and transport infrastructure in rapidly growing econ-
omies. The Australian Transport Research Forum (ATRF), Queensland
Dowling R (2009) Multimodal level of service analysis for urban streets: users guide. NCHRP, Oakland
Downs A (2004) Still stuck in traffic: coping with peak-hour congestion. The Brookings Institution,
Washington, DC
Eichler M, Daganzo CF (2006) Bus lanes with intermittent priority: strategy formulae and an evaluation.
Transp Res Part B Methodol 40:731–744
Ewing R, Tian G, Spain A (2014) Effect of light-rail transit on traffic in a travel corridor. Transportation
Research and Education Center (TREC), Portland
Exel NJAV, Rietveld P (2001) Public transport strikes and traveller behaviour. Transp Policy 8:237–246
Exel NJAV, Rietveld P (2009) When strike comes to town… anticipated and actual behavioural reactions
to a one-day, pre-announced, complete rail strike in the Netherlands. Transp Res A Policy Pract
43:526–535
Falcocchio JC, Levinson HS (2015) Road traffic congestion: a concise guide, Roger P. Roess. New York
University Polytechnic School of Engineering, New York
Fitzpatrick K, Nowlin R (1997) Effects of bus stop design on suburban arterial operations. Transp Res
Rec 1571:31–41
Hakkert AS, Gitelman V (1997) Development of evaluation tools for road-rail crossing consideration for
grade separation. Transp Res Rec 1605:96–105
Hall R, Vyas N (2000) Buses as a traffic probe: demonstration project. Transp Res Rec 1731:96–103
HCM (1965) Highway Capacity Manual. The Board, Washington, DC
HCM (2000) Highway Capacity Manual. The Board, Washington, DC
Holian M, McLaughlin R (2016) Benefit-cost analysis for transportation planning and public policy:
towards multimodal demand modeling. Mineta Transportation Institute Publications, San Jose
Kittelson P, Quade K, Hunter-Zaworski K (2003) Transit capacity and quality of service manual. Trans-
portation Research Board, National Academy Press, Washington, DC
Kockelman KM, Kalmanje S (2005) Credit-based congestion pricing: a policy proposal and the public’s
response. Transp Res A Policy Pract 39:671–690
Koshy RZ, Arasan VT (2005) Influence of bus stops on flow characteristics of mixed traffic. J Transp Eng
13:640–643
Kwami AV, Kuan YX, Zhi X (2009) Effect of bus bays on capacity of curb lanes. J Am Sci 5:107–118
Lee YJ, Vuchic VR (2005) Transit network design with variable demand. J Transp Eng 131:1–10
Levin MW, Khani A (2018) Dynamic transit lanes for connected and autonomous vehicles. Public Transp
10:399–426

1 3
Traffic congestion relief associated withpublic transport:…
Lindley JA (1987) Urban freeway congestion: quantification of the problem and effectiveness of potential
solutions. ITE J 57:27–32
Litman T (2000) Transportation market reforms for sustainability. Transp Res Rec 1702:11–20
Litman T (2015) Evaluating public transit benefits and costs. Victoria Transport Policy Institute, Victoria.
http://www.vtpi.org
Lo SC, Hall RW (2006) Effects of the Los Angeles transit strike on highway congestion. Transp Res A
Policy Pract 40:903–917
Lomax TJ (1997) Quantifying congestion. Transportation Research Board, Washington, DC
Lomax TJ, Schrank DL (2005) The 2005 urban mobility report. Texas Transportation Institute, Texas
Lomax T, Turner S, Shunk G, Levinson H, Pratt R, Bay P, Douglas G (1997) NCHRP Report 398: quanti-
fying congestion. Volume 1: Final report. National Research Council, Washington DC
Lomax T, Schrank D, Eisele W (1999) The keys to estimating mobility in urban areas, applying defini-
tions and measures that everyone understands. Texas Transportation Institute, The Texas A&M Uni-
versity System, College Station
Meek S, Ison S, Enoch M (2008) Role of bus-based park and ride in the UK: a temporal and evaluative
review. Transp Rev 28:781–803
Meyer MD (1997) A toolbox for alleviating traffic congestion and enhancing mobility. Institute of Trans-
portation Engineers, Washington, DC
Moylan E, Foti F, Skabardonis A (2016) Observed and simulated traffic impacts from the 2013 Bay Area
Rapid Transit strike. Transp Plan Technol 39:162–179
Naudé C, Tsolakis D (2005) Defining transport congestion. ARRB contract report no. VC71854.
Melbourne
Nguyen-Phuoc DQ, Currie G, de Gruyter C, Young W (2017a) Local and system-wide traffic effects of
urban road-rail level crossings: a new estimation technique. J Transp Geogr 60:89–97
Nguyen-Phuoc DQ, Currie G, de Gruyter C, Young W (2017b) Net impacts of streetcar operations on
traffic congestion in Melbourne, Australia. Transp Res Rec 2648:1–9
Nguyen-Phuoc DQ, Currie G, de Gruyter C, Kim I, Young W (2018a) Modelling the net traffic conges-
tion impact of bus operations in Melbourne. Transp Res A Policy Pract 117:1–12
Nguyen-Phuoc DQ, Currie G, de Gruyter C, Young W (2018b) Congestion relief and public transport:
an enhanced method using disaggregate mode shift evidence. Case Stud Transp Policy 6:518–528
Nguyen-Phuoc DQ, Currie G, de Gruyter C, Young W (2018c) How do public transport users adjust their
travel behaviour if public transport ceases? A qualitative study. Transp Res F Traffic Psychol Behav
54:1–14
Nguyen-Phuoc DQ, Currie G, de Gruyter C, Young W (2018d) Transit user reactions to major service
withdrawal—a behavioural study. Transp Policy 64:29–37
Okitsu W, Lo K (2010) Simulation-free railroad grade crossing delay calculations. Western ITE Annual
Meeting, San Francisco
O’Toole R (2004) Great rail disasters: The impact of rail transit on urban livability. Independence Insti-
tute, Colorado, United States
Parkhurst G (2000) Influence of bus-based park and ride facilities on users’ car traffic. Transp Policy
7:159–172
Parry IWH, Small KA (2009) Should urban transit subsidies be reduced? Am Econ Rev 3:700–724
Patankar VM, Kumar R, Tiwari G (2007) Impacts of bus rapid transit lanes on traffic and commuter
mobility. J Urban Plan Dev 133:99–106
Pavkova K, Currie G, Sarvi M, Delbosc A (2015) New methods exploring urban public transport con-
gestion using Lorenz curves. In: 33rd Conference of Australian Institutes of Transport Research
(CAITR), Melbourne
Pnevmatikou AM, Karlaftis MG, Kepaptsoglou K (2015) Metro service disruptions: how do people
choose to travel? Transportation 42:933–949
Powell JL (1982) Effects of rail-highway grade crossings on highway users. In: 61st Annual meeting of
the Transportation Research Board, Washington DC
Pucher J, Altshuler A, Womack J (1979) The urban transportation system: politics and policy innovation.
MIT Press, Cambridge
Rosenbloom S (1978) Peak-period traffic congestion: a state-of-the-art analysis and evaluation of effec-
tive solutions. Transportation 7:167–191
Rothenberg MJ (1985) Urban congestion in the United States: what does the future hold?. Institute of
Transportation Engineers, Washington, DC

D.Q.Nguyen-Phuoc et al.
1 3
Rubin TA, Mansour F (2013) Transit utilization and traffic congestion: Is there a connection? Policy
Study 427. Reason Foundation, Los Angeles
Rymer B, Urbanik T II, Cline JC Jr (1989) Delay at light rail transit grade crossings. Transp Res Rec
221:621–634
Schrader MH, Hoffpauer JR (2001) Methodology for evaluating highway–Railway grade separations.
Transp Res Rec 1754:77–80
Schrank D, Eisele B, Lomax T (2012) TTI’s 2012 urban mobility report. Texas A&M Transportation
Institute, Texas
Schuman R (2011) INRIX national traffic scorecard: 2010 annual report. Technical report, INRIX,
Kirkland
Shalaby AS (1999) Simulating performance impacts of bus lanes and supporting measures. J Transp Eng
125:390–397
Stopher PR (2004) Reducing road congestion: a reality check. Transp Policy 11(2):117–131
Systematics C (2008) NCHRP Report 168: Cost-effective performance measures for travel time delay,
variations, and reliability. Dowling Associates, System Metrics Group, and Texas Transportation
Institute, Texas
Taggart RC, Lauria P, Groat G, Rees C, Brick-Turin A (1987) NCHRP Report 288: Evaluating grade-
separated rail and highway crossing alternatives. Transportation Research Board, NRC, Washington,
DC
Tang TQ, Li Y, Huang HJ (2009) The effects of bus stop on traffic flow. Int J Mod Phys C 20:941–952
Taylor BD (2004) The politics of congestion mitigation. Transp Policy 11(3):299–302
TFL (2010) Traffic modelling guidelines. Transport for London (TfL), London
TFL (2013) Travel in London, Report 6. Transport for London (TfL), London
Tiwari G, Fazio J, Pavitravas S (2000) Passenger car units for heterogeneous traffic using a modified
density method. In: Proceedings of fourth international symposium on highway capacity, Hawaii,
pp 246–257
Transport Committee (2013) Bus services in London, UK. https ://www.londo n.gov.uk/sites /defau lt/files /
Bus%20Ser vices %20in%20Lon don%20Rep ort%2C%20Tra nspor t%20Com mitte e_0.pdf. Accessed 8
Feb 2020
Vaziri M (2002) Development of highway congestion index with fuzzy set models. Transp Res Rec
1802:16–22
Verhoef ET (2000) The implementation of marginal external cost pricing in road transport. Pap Reg Sci
79:307–332
VICROADS (2010) Nunawading Stringvale road rail separation impact study. VicRoads, Melbourne
Vuchic VR (2007) Urban transit systems and technology. Wiley, New York
Vuchic V, Lee YJ, Shin Y (1998) Travel costs and intermodal distribution in urban transportation. Transp
Res Rec 1649:105–112
WDOT (2003) The socio-economic benefits of transit in Wisconsin. Wisconsin Department of Transpor-
tation Research, Development and Technology Transfer, Wisconsin
Weisbrod G, Vary D, Treyz G (2001) Economic implications of congestion. Transportation Research
Board, National Research Council, Washington DC
WSDT (2011) The 2011 Congestion Report. Washington State Department of Transportation, Washing-
ton, DC
Xia YX, Xue Y (2010) Analysis of the effect of bay-bus-stop on traffic flow in one-direction two road by
the continuum model. In: 2010 International conference on artificial intelligence and computational
Intelligence (AICI), IEEE, pp 19–22
Yuan YM, Jiang R, Wu QS, Wang R (2007) Traffic behavior in a two-lane system consisting of a mixture
of buses and cars. Int J Mod Phys C 18:1925–1938
Zhao XM, Gao ZY, Jia B (2007) The capacity drop caused by the combined effect of the intersection and
the bus stop in a CA model. Phys A 385:645–658
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Aliations
DuyQ.Nguyen‑Phuoc1 · WilliamYoung2· GrahamCurrie3· ChrisDeGruyter4
William Young
bill.young@monash.edu
Graham Currie
graham.currie@monash.edu
Chris De Gruyter
chris.degruyter@rmit.edu.au
1 University ofScience andTechnology, The University ofDanang, 54 Nguyen Luong Bang
Street, Lien Chieu District, Danang, Vietnam
2 Department ofCivil Engineering, Monash University, Wellington Road and Blackburn Road,
Clayton, VIC3800, Australia
3 Public Transport Research Group, Institute ofTransport Studies, Monash University, Clayton,
VIC3800, Australia
4 Centre forUrban Research, School ofGlobal, Urban andSocial Studies, RMIT University, City
Campus, 124 La Trobe Street, Melbourne, VIC3000, Australia