Content uploaded by Md. Habibur Rahman
All content in this area was uploaded by Md. Habibur Rahman
Content may be subject to copyright.
Planning Studies and Practice, Vol. 3, No. 1, pp. 32-44
An Impact Evaluation of Traffic Congestion on
CHIN Hoong Chor i and RAHMAN Md Habibur ii,*
i,ii Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576
*Corresponding author: email: email@example.com
Traffic congestion is a pressing concern in the urban transportation system. In addition to
imposing significant economic losses, congestion effects downgrade and endanger the
ecological and environmental situation of the urban society. A number of studies have
been conducted on the economic assessment of traffic congestion. However, inspite of
being one of the leading ecological and environmental threats, traffic congestion has not
received adequate attention from the ecological and environmental aspects. A healthy
transportation system must be ecologically sustainable as well as economically and
socially sustainable. Based on energy ecological footprint concept, this study develops a
methodology to evaluate the impact of traffic congestion on the ecology of an area and
models the extent of regional greenness required to mitigate the adverse environmental
impacts of traffic congestion. Traffic congestion effects are measured in terms of the
consumption of ecological resources. The methodology is illustrated using congestion
data from the urban areas of Baltimore in Maryland. In this case study, the trend in the
ecological impact due to traffic congestion is traced through the period 1993-2007.
Keywords: traffic congestion, ecology, effect evaluation, energy footprint, environmental
An environmentally sustainable transportation has been defined by the Organization for
Economic Cooperation and Development (OECD 2004) as “one where transportation
does not endanger public health or ecosystems and meets needs for access consistent
with use of renewable resources below their rates of regeneration and use of non-
renewable resources below the rates of developments of renewable substitutes”.
Spaethling (1996) defines sustainable transportation as “infrastructure and travel policies
that serve multiple goals of economic development, environmental stewardship and
social equity, have the objective to optimize the use of transportation systems to achieve
economic and related social and economic goals, without sacrificing the ability of future
generations to achieve the same goals”.
Citation for this article:
Chin, H. C. and Rahman, M. H. (2011). An Impact Evaluation of Traffic Congestion on
Ecology. Planning Studies & Practice, 3(1), 32-44.
An Impact Evaluation of Traffic Congestion on Ecology 33
In recent years, the established leading concern towards achieving sustainable
transportation is the environmental effects of emissions that are generated from traffic
congestion. Traffic congestion occurs due to increased or over-use of the limited road
network facilities by vehicles at a certain time dimension and characterized by increased
queuing, longer travel times, slower and stop-and-go vehicular condition and increased
passenger discomfort, environmental degradation and economic loss. In the modern
urban society, traffic congestion is the prime source of greenhouse gas (GHG)
emissions and air pollution. According to a global estimation, nature accounts for more
than 90 percent of carbon monoxide (CO) in the air (NRC 1970). Of the 10 percent of
CO that is man-made, about 70 percent is attributed to highway vehicles (NRC 1970).
Traffic-related pollution accounts for an average of 60 percent of the total pollutants in
the atmosphere. Of this, the private cars contribute from 90 to 95 percent of the air
Regarding the evaluation of the adverse effects of traffic congestion a number of
research work have been done in recent decades. Many researchers (e.g., Button et al.
1998, Verhoef and Rouwendal 2004) have proposed economic models which estimate
the economic loss due to traffic congestion in monetary units. Although it may be easier
to translate the effect of extra travel time loss and increased fuel consumption into
economic terms, the conversion of these effects into ecological damage, global warming
and associated climate change may not be straightforward. Therefore, not much
research work has been found to focus on the estimation of adverse climatic impacts
resulting from traffic congestion. Without a proper ecological understanding, it may be
very difficult to measure the performances as well as to set policies for an
environmentally sustainable transportation system.
The objective of this study is to evaluate the adverse climatic impact of traffic congestion
in terms of ecological resource-based unit known as „energy ecological footprint‟. This
study also quantifies the ecological resources required to deal with the carbon emissions
from the existing level of traffic congestion. While traffic congestion may have some
indirect impacts on human health and biodiversity, this study only focuses on the direct
impacts of traffic congestion to earth‟s ecology. In particular, this study only considers
emissions of green house gases from the traffic congestion.
1. A Brief Overview of Traffic Congestion and Its Relation to Ecological
Rothenberg (1985) has defined road traffic congestion as “a condition in which the
number of vehicles attempting to use a roadway at any given time exceeds the ability of
the roadway to carry the load at generally acceptable service levels”. There are both
recurring and non-recurring types of congestion. The recurring congestion typically
occurs during the morning and afternoon rush periods as a result of commuting travel to
and from work, whereas, the non-recurring congestion is caused by random incidents,
most often due to breakdowns, weather, work zones and accidents.
34 Chin Hoong Chor and Rahman Md Habibur
The recurring types of congestion which are identified as the characteristics of rush hour
traffic are well documented. However, non-recurring congestion due to random events
and its specific characteristics and patterns are not well identified. Therefore, estimates
of non-recurring congestion are quite difficult to obtain compared to estimates of
recurring congestion. However, much progress has been observed in recent
technological innovations and advancements in estimating both recurring and non-
recurring traffic congestion more accurately using real time traffic information data.
Pattara-aticom et al. (2006) proposed an estimation procedure of road traffic congestion
based on vehicle velocities using routine GPS measurements from main roads in urban
areas of Bangkok, Thailand. Another study by Pattara-aticom et al. (2007) developed
estimates of road traffic congestion in the metropolitan area of Bangkok, Thailand based
on the measurements of Cell Dwell Time (CDT) with simple threshold and fuzzy logic. In
USA, the statewide database of traffic characteristics on the highways of all urban areas
is the Highway Performance Monitoring System (HPMS). This is a sampling program of
functionally classifying highways and is operated and guided by the Federal Highway
Administration (FHWA). The computer program developed by Lindley (1986) is used to
analyze the HPMS database for the estimation of interstate highway congestion. The
methodology was significantly revised in 2006 and 2007 (Schrank et al. 2007) to take
advantage of new studies and detailed data sources that have not available in previous
studies. This program estimates traffic congestion on the assumption that congestion
arises when the average speed falls below the desired speed, i.e. when the Level of
Service (LOS) downgrades from C to D. These are circumstances when the traffic may
approach unstable flow, stop-and-go situations, and at its extreme, end in zero velocity
(McShane et al. 1990).
Traffic congestion seriously degrades the natural ecological environment as emission
rate increases with reduced speed or congestion. A report from the California Air
Resources Board (1989) shows that the exhaust hydrocarbon (HC) emission from
vehicles at a speed of 55 mile per hour (mph) is 1 gram increasing to 7 grams at a stop-
and-go (congested) speed of 20 mph. Figure 1 illustrates how different gas emissions
increases with reduced vehicular speed. The situation is most severe at the intersections
when the vehicles are occasionally stopped. As vehicles approach the intersections the
speed significantly reduces and in the immediate vicinity of the intersection, the vehicular
average speed can be less than 5 mph (KDAA 2008). These are the situations when
vehicles emit the most of their gas emissions. These figures imply that congestion may
exert a greater damage on the ecological environment than the mere presence of
vehicles on the roads. At increased levels of congestion, the vehicular emission rate
increases significantly and this results in many ecological, health and environmental
adverse effects. Therefore from a practical standpoint, an environmentally sustainable
transportation system can be achieved when traffic congestion and its adverse
environmental effects can be properly combated or mitigated.
An Impact Evaluation of Traffic Congestion on Ecology 35
Figure 1: Change of Exhaust Emission Rates with Vehicular Speed
Source: Mobile 6.2
2. A Review of Models for Impact Evaluation of Traffic Congestion
In past two decades, a number of studies (e.g., Verhoef and Rouwendal 2004,
Johansson-Stenman 2006) have focused on the evaluation of adverse impacts resulting
from traffic congestion. These studies often applied economic models to estimate the
economic loss due to the traffic congestion. For example, it is estimated that congestion
costs the United States economy $78 billion annually in the form of 4.2 billion lost hours
and 2.9 billion gallons of wasted fuel (TTI 2007). Another study shows that the economic
loss from traffic congestion problems in UK is approximately US$33 billion per year
In combating traffic congestion, these economic models propose congestion pricing
(determined in terms of monetary or economic loss created by traffic congestion) as a
demand-side management strategy. Every road user has an influence on the other road
users and drivers know their own expected costs, but neglect their effects on others.
These effects comprise time delay, fuel consumption at very slow speed, environmental
pollution, and traffic accidents. Consequently, the principal objectives of congestion
pricing are to shift some trips to the off-peak periods or to uncongested areas or to
encourage trips using high-occupancy vehicles (Small, 1992).
Although it may be easier to translate the effect of extra travel time loss and increased
fuel consumption into economic terms, the conversion of these effects into ecological
damage, global warming and associated climate change may not be as straightforward.
Therefore, not much research has been found to focus on the estimation of adverse
climatic impacts resulting from traffic congestion. The extent of harmful ecological impact
resulting from traffic congestion needs to be properly understood. Without an ecological
understanding, it may be very difficult to measure the performances as well as to set
policies for an environmentally sustainable transportation system.
510 15 20 25 30 35 40 45 50 55 60 65
CO2 (gm/ mile) VOC (mili-gm/ mile)
36 Chin Hoong Chor and Rahman Md Habibur
3. The ‘Energy Ecological Footprint’ Concept
The „Energy Ecological Footprint‟ (EEF) concept was first proposed by Wackernagel and
Rees (1996) in their ecological footprint discussion. It is defined as the footprint of the
impact which is caused by the emission of green house gas emissions and measured by
the amount of forest land area which is required to absorb the carbon dioxide (CO2)
equivalent GHG emissions generated from activities, processes, consumptions or
products. Many organizations also use the term as „Carbon Footprint‟ (e.g., BP 2008,
Wiedmann and Minx 2008).
The energy ecological footprint concept gained increased popularity in various areas
especially in the industrial sectors as a model for measuring and tracking ecological
responsibility and conducting fair ecological justice. The overwhelmed popularity behind
the energy ecological footprint concept lies in the common agreement to prevent GHG
emissions from accumulating in the atmosphere and presenting the demand for green
forest areas to sequester the emission, resulting in greater visibility and understanding of
impact on earth due to human activities. The greatest significance of this concept is that,
it puts the magnitude of emissions into a meaningful physical and visual context. It is
especially intended for those unfamiliar with climate science to readily visualize and
understand the impact of their activities in terms of an area-based unit (Kitzes and
4. Ecological Model for Traffic Congestion
4.1. Proposed Model
Traffic congestion is the scenario which occurs when vehicles on the road are
characterized by the reduced speeds, stop-and-go situations and possibly zero speed. It
can be easily measured by the difference of the existing speed from the free-flow speed.
Therefore, for a particular vehicle j in congestion the amount of CO2-equivalent
emissions can be modeled as:
where, is the extra travel time of the congested vehicle j, s is the distance travelled
by the vehicle, is the free flow vehicle speed, is the vehicle speed at the congested
condition, is the global warming potential factor of the gas type i and is the emission
rate of gas type i in terms of tonnes per unit of time. The total yearly CO2 equivalent
emissions can be found by accumulating all lost vehicle-hours due to congestion in a
particular year. Using the forest yearly CO2 sequestration rate (), the amount of energy
ecological footprint or forest area required to cope with the adverse environmental and
ecological effects of traffic congestion can be found:
An Impact Evaluation of Traffic Congestion on Ecology 37
where, is the energy ecological footprint from traffic congestion which denotes the
total forest area required to combat with the emission impacts of traffic congestion, CO2-e
is the CO2-equivalent emissions measured in tons of CO2 and is the CO2 sequestration
rate by forest land (tons of CO2 that is absorbed by a hectare of forest land).
4.2. Determination of Parameters
4.2.1. Carbon Equivalence of GHGs (ηi)
The principal GHG gases are carbon dioxide (CO2), methane (CH4), oxides of nitrogen
(NOx) and other few hydro-fluro carbons (HFCs) (EPA 2005). From passenger vehicles
the dominating GHG is CO2 accounting for an average of 95 percent by weight, while
CH4, NOx and other HFC emissions represent roughly 5 percent of the GHG emissions
(EPA 2005; also see Figure 2, Panorama 2009).
The most common suggested method for including these non-CO2 GHG gases in carbon
footprint accounts is through the use of global warming potentials (GWPs) (Barrett et al.
2002, Dias et al. 2005, Holden and Hoyer 2005), which compares the amount of heat
trapped by a certain mass of the GHG in question to that by the same mass of CO2
(IPCC 2001). The global warming potential factors () of commonly recognized GHGs
are obtained from the International Panel on Climate Change (IPCC 2001) and these
values are 1, 23 and 5 for CO2, CH4 and NOx, respectively.
Figure 2: Greenhouse Gas Emissions From Road Traffic, France, 2006
Source: Panorama 2009
4.2.2. Emission Rates of GHGs (βi)
The choice of an appropriate inventory model is important in determining the emission
rates from road vehicles. Most inventory models are appropriate for specific region(s)
because the emission factors that are incorporated are often based on local empirical
measurements of vehicle emission and weather characteristics. Hence they may not be
transferable to other regions. For example, the COPERT (EMISIA 2006) is a more
suitable emission inventory model in the European region while EMFAC (CARB 2002) is
GHG emissions due to traffic
38 Chin Hoong Chor and Rahman Md Habibur
more fitted for California. For all USA regions, the most fitted and widely used emission
inventory model is MOBILE6 (EPA 2003a). In this study, US EPA‟s MOBILE6.2 emission
inventory model was used to calculate the fleet-average CO2, CH4 and NOx emission
rates for cars. The MOBILE6.2 model calculates emission factors which are based on
empirical measurements conducted for vehicles operated during prescribed drive cycles
to simulate typical trips. MOBILE6.2 is based on numerous facility driving cycles (EPA
1999) to simulate vehicle travel on freeway and arterial roadway types for different levels
of service (i.e., congestion categories), plus local streets and freeway ramps at fixed
In this study, the MOBILE6 emission inventory model and the historic local weather
parametric values are used and applied in the case of Baltimore, Maryland. When
computing annual average emission factors, EPA recommends that the yearly average
emission rates be determined by averaging winter (January) and summer (July)
emission rates (EPA 2004). The historic minimum and maximum temperatures and
relative humidity for months January and July for each year throughout 1993-2007 were
obtained from Weather underground (2010). The absolute humidity values were
computed from temperature and relative humidity of these months and years (see Table
1). The fuel RVP values used for winter and summer seasons are 13 psi and 8.5 psi,
respectively (EPA 2003b).
The emission rates were determined for the vehicles miles travelled (VMT)-average
congested speed of Baltimore, Maryland, which were estimated using:
in which is the VMT-average congested speed, and are congested speeds at
freeways and arterials respectively and and are the percentages of VMT
on the freeways and arterials respectively. The historic congested VMT data for
Baltimore were obtained from TTI (2009) and average VMT percentages of freeways
and urban arterial roads for a particular year were computed using following equations:
denotes the annual total VMT on freeways and
is the annual total
VMT on arterial roads. In addition, the congested speeds for freeways and arterials were
obtained from TTI (2010). Using these values, the VMT-average congested speed of a
specific year was computed using equation (3). Based on the yearly average congested
speeds, the emission rates () of various GHGs were determined and tabulated in Table
An Impact Evaluation of Traffic Congestion on Ecology 39
Table 1: Historic Weather Parameters Used in MOBILE6.2 Modeling
Source: Weather Underground (2010)
Table 2: Vehicular Emission Rates of GHGs for 1993-2007
Annual average emission rates (ton/veh-hr)
Source: Mobile 6.2
40 Chin Hoong Chor and Rahman Md Habibur
4.2.3. Carbon Assimilation Rate (γ)
The commonly used figure for the assimilation rate of CO2 in forests () is 6.6 ton CO2
per hectare (Wada 1994, Simpson et al. 2000). However, the CO2 assimilation rate of
forests can vary from region to region as it depends on local climatic and forestry factors.
Therefore a regional assimilation rate should be used instead of using a global value.
Based on a regional study, Kurt Pregitzer of Michigan Technological University estimated
a suggestive figure of 7.3 tons of CO2 sequestration per hectare for a typical USA urban
county (Guangqing and Brian 2005), which have been used in this study.
5. Demonstration of the Ecological Model
5.1. Dataset for Analysis
To demonstrate the application of the ecological impact model for traffic congestion in
this study, the traffic congestion data (TTI 2009) reported by the Federal Highway
Administration (FHWA) are used for urban areas of Baltimore County, Maryland (MD).
Baltimore was considered as an ideal region for this study because of the availability of
the information and also being one of the US regions, which have a moderate to high
level of traffic congestion (TTI 2009). Over time, the increased demand for urbanization
has led to the aggressive encroachment of rural areas which have been gradually
converted into urban and suburban zones. The study area holds an urban size of 770
square miles (199,500 hectares) with a large population of 2,320,000 as of 2007 (TTI
The urban area and the congestion statistics of the Baltimore area, Maryland for 1993-
2007 are presented in Table 3. The detailed methodology used in the estimation of the
total annual passengers-hours of delay due to traffic congestion is elaborated in the
Urban Mobility Methodology Report of Texas Transportation Institute (TTI 2010). In USA,
private cars constitute a major portion of the annual vehicle-miles of travel (VMT). For
example, in year 2007, the total vehicle-mile travelled by private automobiles is
2,795,883 million whereas this figure for buses is 6,980 million, which is only 0.25
percent of the previous. Similarly, the total passenger-mile in 2007 is 4,584,054 million
for private automobiles whereas this figure is 147,985 million for buses, which
constitutes only 3.2 percent of the previous (BTS 2010). Hence, estimating the
congestion impact of the cars may not underestimate the total impact. Therefore, in this
study the congestion statistics of the cars has been used and impact has been estimated
5.2. Results and Discussion
Using the average vehicle occupancy of 1.25 (TTI 2009) for a typical US region, the total
annual vehicle-hours of delay has been obtained and presented in Table 4. Using all the
parametric figures described in section 4.2, and traffic congestion data of the urban
areas of Baltimore, Maryland as referred in section 5.1, the total forest area required to
absorb the CO2 equivalent emissions due to congestion in the Baltimore area has been
computed using equation (2) and presented in Table 4. In Table 4, the final column
presents the computed demand for forest area due to congestion effect as a percentage
of total urban area. The gradual increase in this demand as percentage of total urban
area is clearly noticeable which has increased by 45 percent over a 15 year period from
1993 to 2007. In year 2007, the emissions effects from traffic congestion demanded for
an amount of forest land, which is 48 percent of the total available area of Baltimore.
An Impact Evaluation of Traffic Congestion on Ecology 41
Table 3: Traffic Congestion Statistics of Baltimore for 1993-2007
Urban area size
Daily number of
Percent of delay
due to incidents
Total annual delay due to
Source: Texas Transportation Institute (TTI), (2009)
Figure 3 presents the computed trends in forest demand to absorb the emissions from
traffic congestion and the increase in urban area size of Baltimore, which is a result of
gradual encroachment of rural areas by increased urbanization. It is clear from Figure 3
that the forest demand due to congestion effect is increasing with a higher rate than the
urban area. It shows that the effect of traffic congestion on demand for forest area is not
Figure 3: Trends of Forest Demand to Combat With the Emissions From Congestion
*Source: Texas Transportation Institute (TTI), (2009)
R² = 0.9305
Slope = 1008 hec/year
R² = 0.8213
Slope = 3249 hec/year
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Computed forest land demand due to congestion effect
42 Chin Hoong Chor and Rahman Md Habibur
Table 4: Demand on Forest Land due to Congestion Effect of Baltimore for 1993-2007
Total annual delay
due to congestion*
Computed forest land demand due
to congestion effect
total urban area
*Source: Texas Transportation Institute (TTI), (2009)
In this study, an ecological model has been developed which evaluates the impact of
traffic congestion on ecology in the form of energy ecological footprint. The ecological
model in this study seeks to provide a clear explication of the increasing demand on
ecological resources due to traffic congestion effects. The model is illustrated for the
urban areas of Baltimore, Maryland for the period of 1993-2007. In this case, the energy
ecological footprint of Baltimore from the traffic congestion effect in 2007 is nearly half of
its total urban land area. Forest demand due to congestion effect is increasing at a
higher rate than the growth in urban area. Therefore to ensure a more sustainable
transportation system, mitigation measures are needed to reduce the amount of urban
This study estimates energy ecological footprint from the direct impact of traffic
congestion, i.e., only operational emissions of GHGs from congestion are considered in
this study. However congestion may have some indirect impacts, i.e., additional
emissions may also occur due to the loss in man-hours, wasteful usage of vehicle-hours
and fuels etc. Therefore a whole life cycle approach of estimating energy ecological
footprint from traffic congestion effect can be a potential future area of research.
The computations in this study are based on published forest sequestration rate as
referred in the text. There was no attempt to validate these rates.
An Impact Evaluation of Traffic Congestion on Ecology 43
Air Resources Board. 1989. The Air Pollution Transportation Linkage, State of California.
Barrett, J., Vallack, H., Jones, A. and Haq, G. 2002. A Material Flow Analysis and Ecological
Footprint of York. Technical Report, Stockholm Environment Institute, Stockholm, Sweden.
BP. 2008. Carbon footprint calculator, http://www.bp.com, accessed 22 August 2008.
BTS (Bureau of Transportation Statistics). 2010. National Transportation Statistics,
http://www.bts.gov/publications/national_transportation_statistics/, accessed 02 May, 2010.
Button, K. J., Verhoef, E. 1998. Road Pricing, Traffic Congestion and the Environment: Issues of
Efficiency and Social Feasibility. Edward Elgar, Aldershot.
CARB (California Air Resources Board). 2002. EMFAC2007 Emission Modeling. Accessed 02
January 2009 at http://www.arb.ca.gov/msei/onroad/latest_version.htm
Coder, R. D. 1996. Identified Benefits of Community Trees and Forests. University of Georgia,
Dias, M. E., Vaughan, B. E. and Rykiel, E. J. 2005. Ethanol as Fuel: Energy, Carbon Dioxide
Balances, and Ecological Footprint. BioScience, 55: 593-602.
EMISIA. 2006. COPERT 4 Emission Modeling. Accessed 02 January 2009 at
EPA (Environmental Protection Agency). 1999. Facility-Specific Speed Correction Factors, Draft.
Report Number M6.SPD.002. Office of Mobile Sources.
EPA (Environmental Protection Agency). 2003a. MOBILE6 Vehicle Emission Modeling, Accessed
15 February 2010 at http://www.epa.gov/oms/m6.htm
EPA (Environmental Protection Agency). 2003b. User‟s Guide to MOBILE6.1 and MOBILE6.2.
Accessed 28 July 2010 at http://www.epa.gov/oms/models/mobile6/420r03010.pdf
EPA (Environmental Protection Agency). 2004. Technical Guidance on the Use of MOBILE6.2 for
Emission Inventory Preparation. Publication EPA420-R-04-013. Office of Transportation and
EPA (Environmental Protection Agency). 2005. Emission facts: Greenhouse gas emissions from
a typical passenger vehicle, http://www.epa.gov/OMS/climate/420f05004.pdf, accessed 10
Goodwin, P. 2004. The economic costs of road traffic congestion. University College London, The
Rail Freight Group: London, UK.
Guangqing, C. and Brian, S. 2005. Sustainable Transport Planning: Estimating the Ecological
Footprint of Vehicle Travel in Future Years. Journal of Urban Planning and Development.
Herva, M., Franco, A., Ferreiro, S., Lvarez, A. A. and Rocaa, E. 2008. An Approach for the
Application of the Ecological Footprint as Environmental Indicator in the Textile Sector.
Journal of Hazardous Materials, 156, 478–487.
Holden, E, Hoyer, K. G. 2005. The Ecological Footprints of Fuels. Transportation Research Part
D. 10: 395–403.
IEA (International Energy Agency), 2007, http://www.iea.org/, accessed 14 August, 2009.
ISAT (International Society of Arboriculture Tree Care) Bulletin, 2009. Benefits of trees,
http://www.coloradotrees.org/benefits.htm, accessed 12 August, 2009.
IPCC (Intergovernmental Panel on Climate Change) 2001. Climate Change 2001: The Scientific
Basis. Cambridge University Press, Cambridge, UK.
Johansson-Stenman, O. 2006. Optimal Environmental Road Pricing. Economics Letters, 90(2):
K. D. Anderson and Associates, Inc. 2008. Greenhouse gas emissions, http://www.co.el-
dorado.ca.us/DOT/CEQA/PVPatterson_AppendixBAirQuality.pdf, accessed 13 September,
44 Chin Hoong Chor and Rahman Md Habibur
Kitzes, J. and Wackernagel, M. 2008. Answers to Common Questions in Ecological Footprint
Accounting. Ecological Indicators 9, 812-817.
Lindley, J. A. 1986, Quantification of Urban Freeway Congestion and Analysis of Remedial
Measures, Federal Highway Administration, FHWA/RD-87/052.
McShane, W. R. and Roess, R. P. 1990. Traffic Engineering. Prentice Hall, New Jersey, USA.
NRC (National Research Council). 1970. Report of the Committee on Pollution, National
Academy of Science. Washington, D.C.
OCED (Organization for Economic Cooperation and Development). 2004. Environmentally
Sustainable Transport. Paris, France.
OTAQ (Office of Transportation and Air Quality). 2003. Major Elements of Operating I/M
Programs. Accessed 28 January 2010 at http://www.epa.gov/oms/epg/420b03012.pdf
Panorama. 2009. Greenhouse Gas Emissions and the Transport Sector. Innovation, Energy,
Pattara-atikom, W., Pongpaibool, P. and Thajchayapong, S. 2006. Estimating road traffic
congestion using vehicle velocity, Proceedings of the 2006 6th International Conference on
ITS Telecommunications, pp. 1001-1004.
Pattara-atikom, W., Peachavanish, R., and Luckana, R. 2007. Estimating road traffic congestion
using cell dwell time with simple threshold and fuzzy logic techniques, Proceedings of the
2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA, USA, pp. 956-961.
Rothenberg, M. J. 1985. Urban Congestion in the United States: What Does the Future Hold?
ITE Journal, 55 (7), 22-39.
Schrank, D. and Lomax, T. 2007. The 2007 Urban Mobility Report, Texas Transportation Institute,
Texas A&M University System, pp. 2-3.
Simpson, R., Petroeschevsky, A., Lowe, I. 2000. An Ecological Footprint Analysis of Australia,
Australian Journal of Environmental Management, 7 (1), 11-18.
Small, K. A. 1992. Special Issue: Congestion Pricing. Transportation, 19 (4), 287-291.
Spaethling, D. 1996. Sustainable transportation: the American experience. Proceedings of
Seminar C, Planning for Sustainability of the 24th European Transport Forum, PTRC
Education and Research Services Limited, London.
TTI (Texas Transportation Institute). 2009. Congestion data for your city,
http://mobility.tamu.edu/ums/congestion_data/, accessed 02 September, 2009.
TTI (Texas Transportation Institute). 2010. Methodology for Urban Mobility Report,
http://mobility.tamu.edu/ums/report/methodology.stm, accessed 02 December, 2010.
Verhoef, E. T. and Rouwendal, J. A. 2004. Behavioral Model of Traffic Congestion: Indigenizing
Speed Choice, Traffic Safety and Time Losses. Journal of Urban Economics, 56 (3): 408–
Wackernagel, M. and Rees, W.E. 1996. Our Ecological Footprint: Reducing Human Impact on
the Earth. New Society Publishers, Gabriola Island, British Columbia.
Wada, Y. 1994. Biophysical Productivity Data For Ecological Footprint Analysis, Report to the
UBC Taskforce on Healthy and Sustainable Communities, Vancouver, Canada.
Weather Underground. 2010. Weather History and Data Archive. Accessed 02 January 2010 at
Wiedmann, T., Minx, J. 2008. A definition of „Carbon Footprint‟. ISA UK Research and Consulting,