001.docx
DOI: 10.3303/CET2189021
Paper Received: 26 May 2021; Revised: 6 September 2021; Accepted: 18 November 2021
Please cite this article as: Ku D., Kim J., Yu Y., Kim S., Lee S., Lee S., 2021, Assessment of Eco-Friendly Effects on Green Transportation
Demand Management, Chemical Engineering Transactions, 89, 121-126 DOI:10.3303/CET2189021
CHEMICAL ENGINEERING TRANSACTIONS
VOL. 89, 2021
A publication of
The Italian Association
of Chemical Engineering
Online at www.cetjournal.it
Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš
Copyright © 2021, AIDIC Servizi S.r.l.
ISBN 978-88-95608-87-7; ISSN 2283-9216
Assessment of Eco-Friendly Effects on Green Transportation
Demand Management
Donggyun Kua, Jooyoung Kimb, Yeonseung Yuc, Sion Kimd, Shinhae Leee,
Seungjae Leea,*
a Department of Transportation Engineering, University of Seoul, Korea
b Department of Transportation Policy, Korea National University of Transportation, Korea
c Korea Research Institute of Transportation Industries, Korea
d Department of Transportation Engineering, Department of Smart Cities, University of Seoul, Korea/
e Department of Transportation Systems Research, The Seoul Institute, Korea
sjlee@uos.ac.kr
Transportation demand management has been implemented in Seoul to realize the transit-oriented
development for its evolution into an eco-friendly city. The policies include reorganizing road space, restricting
the operation of fifth-grade vehicles, increasing parking fees, and expanding shared bicycles. The Seoul
Metropolitan Government monitors traffic changes by installing cameras at the point of entry into the city
center for policy assessment. Furthermore, it acquires data about the number of public parking lots and
shared bicycles. In this study, a quantitative evaluation of green traffic promotion area designation and
implementation policies is conducted using various data. The analysis is conducted by classifying the effects
of increasing and reducing eco-friendly transportation, and the benefits of reducing public bicycle use, air
pollution, and parking costs are considered. The benefits of reducing air pollution by reducing traffic volume
and the benefits of reducing parking costs by decreasing the number of parking units are calculated and
estimated. The analysis shows that the cost-benefit of reducing air pollution by reducing traffic volume is
approximately 42.5 B KRW/y. This qualitatively translates to approximately 1.3 M trees. The cost benefit of
reducing the number of parked cars is estimated to be 18.2 B KRW. In addition, the average daily use of
public bicycles, an eco-friendly mode of transportation, increases by approximately 390 units, as proven
statistically. The results of this study confirm that transportation demand management enables eco-friendly
goals to be achieved. Finally, It is expected to be used as an indicator for the Seoul Metropolitan
Government's public transportation-oriented development policy.
1. Introduction
Rapid economic growth and urbanization have resulted in various traffic problems, such as air environment
problems, traffic accidents, and traffic congestion (Ku et al., 2020). As this traffic problem intensifies, the
paradigm of transportation policy is changing worldwide from vehicle- to human-oriented to eco-friendly. This
trend has resulted in the implementation of a transit-oriented development policy through expanding the use of
public transportation and curbing the use of passenger cars in developed countries (Ibraeva et al., 2015). In
London, the Low Employment Zone was designated in 2008 to reduce air pollutant emissions. The number of
vehicles in the district decreased, the proportion of low-pollution vehicles increased (Ellison et al., 2013).
Inspired by this, the Seoul Metropolitan Government implemented a transportation demand management
policy to transform Seoul into an eco-friendly city and promote changes in the urban structure. The Seoul
Metropolitan Government's Green Transport Promotion Area Policy, which was implemented at the end of
2019, was benchmarked with low-pollution areas (Ku et al., 2020) in large cities worldwide, including Milan,
Rome, and Paris. The target area included Jongno-gu and 15 dongs in the Seoul metropolitan area described
in Figure 1b. In the area, vehicles will be classified into 5 grades based on the degree of hazardous gas
emissions; Grade 5 vehicles with the highest hazardous gas emissions will be restricted from entering the
district and fined for violations. The reorganization of road space, and improvements in parking ceiling are also
implemented. The ultimate aim is to circumvent and reduce traffic in the area, reducing congestion and air
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pollution. Various traffic big data are required to quantitatively evaluate the policy. The Seoul Metropolitan
Government acquired data by installing cameras at 45 locations. A car traffic management system based on a
control camera can acquire various transportation data such as traffic examination, traffic changes, and toll
charges collection. Real-time monitoring and systematic post-evaluation can be performed using these data.
In this study, the effectiveness of implementing policies in green transportation promotion areas shall be
classified as an increase in eco-friendly transportation and a decrease in non-eco-friendly transportation through
such collected data which is verified quantitatively.
2. Methodology
2.1 Discharging pollutant
Hanpatanakit et al. (2018) conducted a survey using the bottom-up approach to estimate carbon dioxide
emissions from tourist traffic on the island of Kok Mak in Thailand, where transport energy consumption was
calculated using the method presented by the Intergovernmental Panel on Climate. Zhang et al. (2016)
presented a mixed-integer programming model for the energy consumption optimization of intercity high-
speed transport systems. In this study, they optimized the connections between large population centers and
the transportation methods, as well as calculated costs while considering the lifespan of transport
infrastructure and energy consumption. Li et al. (2015) developed a cost-optimization superstructure model to
derive an optimal oil-saving route for road passenger transport by 2030. Seven vehicle categories, alternative
fuels, and eight powertrain options were considered to minimize fuel and vehicle costs. Chen et al. (2018)
developed an optimization model for safe and efficient logistics route exploration for hazardous chemicals.
They derived improved path navigation capabilities by solving a dual-purpose optimization model that
minimizes transport risk and distribution distance based on the type and weight of hazardous chemicals.
2.2 Evaluation of environmentally friendly transportation policy
Ku et al. (2021) investigated a methodology for evaluating the economics of rapid transit. The demand for
bicycle use was derived by considering bicycle rental stations as transportation zones. Regarding the benefit
analysis, it was deemed inappropriate to evaluate eco-friendly transportation projects such as Cycle Rapid
Transit (CRT) based on only the benefits of reducing travel time and vehicle operation costs and adding them
as a benefit analysis item of existing transportation projects. Sellitto et al. (2012) presented a model for
evaluating user perceptions of the environmental effects of public transport operations. A survey of 300 public
transportation users was conducted and analyzed via structural equation modeling. Although vehicle safety
and pollutant emissions affected user perception, it was reasonable to conclude that user satisfaction
increased significantly. Awasthi et al. (2011) introduced fuzzy TOPSIS to derive a comprehensive score for
sustainability assessment and to select the best alternative for eco-friendly transportation systems. Decisions
were made via a multicriteria decision-making approach by weighting the corresponding scores.
2.3 Summary
Research has been conducted on estimating pollutant emissions or solving optimization problems, and
evaluating eco-friendly transportation policies, but it focuses on the development of general and theoretical
models and methodologies. When evaluating a specific policy, the focus was on the environment before the
implementation of the policy, failing to accurately derive the effect from the implementation of the policy.
In the case of this study, data were collected and compared before and after the implementation to analyze
the implementation effects of the Seoul Metropolitan Government's green transportation promotion regional
policy, and external variables were also verified through statistical significance.
3. Results
The framework of this study is presented in Figure 1.
Figure 1: Framework of this study
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In this study, separate analyses were conducted by reducing non-environmental transportation modes and
increasing eco-friendly ones through implementing green transportation promotion areas. It was estimated that
reducing traffic volume would reduce air pollution and parking costs. The benefits of air pollution reduction
were presented in terms of the number of trees for estimating its scale intuitively. The effect of increasing the
number of public bicycles was analysed by eliminating seasonal characteristics and related infrastructure ones.
3.1 Traffic reduction effect
According to the analysis of the effect of reducing traffic volume owing to the designation of green
transportation promotion areas and the implementation of eco-friendly transportation policies, the daily traffic
volume decreased by approximately 13 % from 778,000 units/d (July 2019) to 679,000 units/d (September
2020) described in Figure 2a. Based on the analysis, the amount of change in grade 5 vehicles decreased by
47 % from 15,000/d to 8,000/d described in Figure 2b, indicating a significant reduction in traffic volume. The
traffic volume of vehicles without pollutant reduction devices among grade 5 vehicles decreased by
approximately 87 % from 9,000 units/d to 1,000 units/d, which prevented a significant number of pollution
vehicles from entering green traffic promotion areas.
Figure 2: Effect of reducing traffic volume by car type (a) whole vehicles; (b) Type 5 vehicles
Street traffic reduced from 2,202,044 (1,000 veh-km) to 1,494,257 (1,000 veh-km), and the average distance
per vehicle reduced from 8.13 to 7.32 km (Table 1). This is primarily due to the transition of medium-and long-
distance traffic using other transportation modes and routes.
Table 1: Effect of reducing traffic volume and average travel distance (Units: t/y)
The traffic volume decreased owing to the designation of green transportation promotion areas and the
implementation of policies, which benefited the transportation and environmental sectors. The benefits of
reducing air pollution in the transportation sector were calculated by comparing the traffic volume, distance,
and time of passage in the analysis area before and after the implementation of the policy. A pre-investigated
methodology for calculating the benefits of reducing air pollution in Korea’s transportation sector was applied
(Nam et al., 2008).
This methodology defines CO, CO2, SO2, hydrocarbons (HC), nitrogen oxides (NOx), and fine dust (PM) as air
pollution contributors. Pollutant emissions are significantly affected by the type and performance of the vehicle,
the driving conditions, the traffic, and the road conditions. The original units for each vehicle type and speed
were calculated and applied differently. The emission coefficient for each pollutant generated by driving a car
was calculated, and the air pollution cost unit for each type and speed was estimated by applying the air
pollution cost unit for each pollutant. The emission coefficient for each pollutant generated via motor vehicle
operation was applied to the total traffic distance (veh-km) in the analysis area and compared before and after
the implementation of the policy. By calculating the change in air pollutants following the implementation of the
policy, it was discovered that CO decreased by 42.40 %, CO2 by 38.69 %, and NOx by 36.60 % (Table 2).
Table 2: Reduction in air pollution (Units: t/y)
Classification CO NOx VOC PM2.5 CO2
Before 1,717 1,686 202 49 581,125
After 989 1,069 127 32 356,309
Difference (%) -728(-42.4%) -617(-36.6%) -74(-36.6%) -18(-36.7%) -224,817(-38.7%)
Classification Before After Difference Difference (%)
Distance-Volume (1,000 veh-km) 2,202,044 1,494,257 -707,787 -32.14
Traffic Volume (1,000 veh/y) 270,965 204,165 -66,801 -24.65
Average Travel Distance(km) 8.13 7.32 -0.8 -9.84
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The total air pollution reduction benefits, i.e., calculated using Eq(1), multiplying the cost units to handle air
pollutant to the amount of air pollutant reduction as in Eq(2), was 42.5 B KRW/y. If the previous methodology
pertaining to the absorption volume of CO2 by trees is applied to this result, then it may be converted to the effect
of planting 1.3 M trees,
𝑉𝑂𝑃𝐶𝑆 = 𝑉𝑂𝑃𝐶𝐵𝑒𝑓𝑜𝑟𝑒 − 𝑉𝑂𝑃𝐶𝐴𝑓𝑡𝑒𝑟 (1)
𝑉𝑂𝑃𝐶 = ∑ ∑ 𝐷𝑙𝑘 × 𝑉𝑇𝑘
3
𝑘=1 × 365𝑙 (2)
where VOPC is Valuation of Pollution Cost Saving, Dlk is total travel distance(veh·km) by link(l), by vehicle type(k),
VTk is air pollution cost (KRW/km) of the link speed by vehicle type(k), k is vehicle type (1: auto, 2: bus, 3: freight).
3.2 Parking changes
Among the policies to curb demand for passenger cars, parking conditions control policies such as “parking
fee surcharge” and “restricting the number of parking spaces” are known to exhibit excellent effects (Zoeter et
al., 2014). The Seoul Metropolitan Government implemented a parking fee surcharge policy for fifth-grade
vehicles in public parking lots located in Seoul to encourage passengers to switch to public transportation.
According to a survey of the number of parking used at 22 public parking lots in the analysis area, an average
of 7,679 cars used parking lots daily before the implementation of the premium policy. After the
implementation of the policy, an average of 7,062 cars used parking lots daily, indicating an 8.0 % decrease in
users (Table 3).
Table 3: Changes in the number uses of parking lots at public parking spaces (Units: veh/d, %)
Classification Before After Difference
Green traffic
promotion areas
(22 spaces)
Dec, 2019 7,679
Jan, 2020 7,367 -312 -4.1 %
Feb, 2020 6,918 -761 -9.9 %
Mar, 2020 6,930 -749 -9.8 %
Average 7,062 -617 -8.0 %
A reduction in passenger traffic due to eco-friendly transportation policies may reduce parking demand in non-
residential areas. Resources such as land and facility operating costs incurred in operating parking lots can be
used for other purposes, resulting in social benefits (Jang et al., 2007). In this study, the methodology proposed
by the Korea Development Institute for reducing parking costs was applied. In this methodology, it is assumed
that parking fees reflect the cost of constructing and operating parking lots. This is because limitations exist that
hinder the opportunity cost of parking to be calculated accurately. The benefits of reducing parking costs were
calculated from the savings in parking fees. To calculate the benefits of reducing parking costs, the average
parking fee (KRW 2,067) in the analytical area and the percentages of traffic to work and shopping were applied.
It can be expressed like Eq(3) and(4). The result indicates an annual benefit of approximately 18.2 B KRW.
𝑉𝑂𝑃𝐶𝑆 = 𝑉𝑂𝑃𝐶𝐵𝑒𝑓𝑜𝑟𝑒 − 𝑉𝑂𝑃𝐶𝐴𝑓𝑡𝑒𝑟 (3)
𝑉𝑂𝑃𝐶 =
1
2
∑ ∑ (𝐷𝑖𝑗
𝑦
× 𝑝 × 𝛼0
𝑘𝑦
)𝑖𝑗𝑖𝑗
(4)
Where VOPCS is Valuation of Parking Cost Saving, i is the origin of trips, j is the destination of trips, 𝐷𝑖𝑗
𝑦
is
auto traffic volume from I to j in y year(veh/y), p is the proportion of purpose trips, y is years, 𝛼0
𝑘𝑦
is the parking
fee per vehicle in y years (KRW/(veh·y))
3.3 Changes in public bicycle use
A public bicycle rental system is essential for reducing passenger car traffic and increasing the number of
public transport users. The Seoul Metropolitan Government operates “Ttareungi,” a public bicycle rental
service, and is expanding related infrastructure such as rental stations and stands to promote its use (Shin et
al., 2007). In Table 4, as of June 2020, 167 rental stations were installed in the Green Transportation
Promotion Area (Jongro-gu, Jung-gu), which is an increase of approximately 56 % compared with the number
in the previous year. The number of bicycle racks increased by approximately 64 % annually to 1,930 (as of
June 2020). The designation and implementation of the Green Transportation Promotion Area began in July
2019. To reduce confusion among citizens, a guidance period of six months was provided before the policy was
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legally implemented. To analyse the policy effects, data regarding the use of public bicycles were acquired and
analysed for six months before and after the implementation of the policy, excluding the guidance period.
Table 4: Trends in infrastructure and use of public bicycles before and after implementation of green
transportation promotion areas
Classification Jen Feb Mar Apr May Jun
Before
(2019)
Rental offices 107 107 107 107 107 107
Rests 1,180 1,080 1,180 1,180 1,180 1,180
Number of uses 1,470 1,414 2,279 3,407 4,308 4,498
After
(2020)
Rental offices 111 129 111 111 111 167
Rests 1,277 1,554 1,277 1,277 1,277 1,930
Number of uses 1,946 1,949 2,804 4,063 4,083 4,870
The abovementioned procedure was performed to measure the effect of implementing green transportation
promotion areas that included the effects of the variables by constructing models including other factors such as
the effects of increasing public bicycle infrastructure or seasonal factors. For seasonal factors, the effects from
each month were regarded as different, and each effect was treated as a dummy variable. It is described in Eq(5).
𝑈𝑠𝑒𝑠𝑏𝑖𝑐𝑦𝑐𝑙𝑒 = 𝛽0 + 𝛽1 ∗ (𝑅𝑒𝑛𝑡𝑂𝑓𝑓𝑖𝑐𝑒𝑠) + 𝛽2 ∗ 𝑅𝑒𝑠𝑡𝑠 + ∑ 𝛽𝑖+2 ∗ 𝑀𝑜𝑛𝑡ℎ𝑑𝑢𝑚𝑚𝑦𝑖
6
𝑖=1 + 𝛽9 ∗
𝑖𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑒𝑑 + 𝜇
(5)
Where 𝑈𝑠𝑒𝑠𝑏𝑖𝑐𝑦𝑐𝑙𝑒 is the daily usages of public bicycles, 𝛽0 is intercepts, 𝑅𝑒𝑛𝑡𝑂𝑓𝑓𝑖𝑐𝑒𝑠 is the number of rented offices
in green traffic promotion areas, Rests is the number of rests in green traffic promotion areas, 𝑀𝑜𝑛𝑡ℎ𝑑𝑢𝑚𝑚𝑦𝑖 is
dummy variables describing month 𝑖(e.g. Jan≡(𝛽3=1, Others=0), Apr≡(𝛽6=1, Others=0)), 𝐼𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑒𝑑 is dummy
variables whether the policy is implemented (𝑏𝑒𝑓𝑜𝑟𝑒=0, 𝐴𝑓𝑡𝑒𝑟=1), 𝜇 is residuals.
The results of the model are presented in the left part of Table 5. It shows that the infrastructure variable
parameters demonstrated negligible statistical significance, and that the number of public bicycles uses, and
the degree of infrastructure construction were almost not correlated.
Table 5: A model describing the number of uses of public bicycles, including infrastructure variables
Classification Before removing infrastructure variables After removing infrastructure variables
Variables Estimated Std error T statistics P value Estimated Std error T statistics P value
Intercepts 1,572.13 876.82 1.79 1.71E-01 1,499.83 117.54 12.76 1.42E-05
Rental offices -11.79 32.44 -0.36 7.40E-01 - - - -
Rests 1.03 2.62 0.39 7.20E-01 - - - -
Month dummy1 -11.78 293.40 -0.04 7.05E-02 - - - -
Month dummy2 833.5 280.03 2.98 5.88E-02 846.75 176.31 4.80 2.99E-03
Month dummy3 2,027 280.03 7.24 5.44E-03 2040.25 176.31 11.57 2.51E-05
Month dummy4 2,487.5 280.03 8.88 3.01E-03 2500.75 176.31 14.18 7.67E-06
Month dummy5 2,969 356.98 8.32 3.64E-03 2989.25 176.31 16.95 2.69E-06
Implemented 305.02 279.60 1.09 3.55E-01 389.83 117.54 3.37 1.61E-02
The corresponding variables are removed, and the model is rebuilt. After rebuilding the model, in the right part of
Table 5, the statistical significance of the parameters of all variables was verified to confirm that their effects were
statistically significant. The analysis showed that the value indicating whether the green transportation
promotion area was implemented was estimated to be 389.83, which was statistically significant.
4. Conclusions
In this study, the quantitative effects of the green transportation area policy were analysed. To analyse the
effectiveness of this policy, The changes in eco-friendly and non-environmental transportation modes was
analysed. For the former, the number of uses of public bicycles was analysed. For the latter, the benefits of
reducing air pollution and parking costs due to changes in traffic in the focus area were analysed. After
implementing the policy, 707,787,000 km of street traffic was reduced annually, and the average travel
distance was reduced by 0.8 km. The cost-benefit of reducing air pollution by reducing traffic volume was
calculated to be approximately 42.5 B KRW/y. In terms of the number of parking lots, a decrease of 617
parking lots per day on average was recorded in 22 public parking lots in the green transportation promotion
area, and the cost benefit of reducing parking costs was 18.2 B KRW. To analyse the changes in the number
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of public bicycle use, a model including infrastructure and seasonal factors was established to analyse the
actual effect of implementing policies in green transportation promotion areas. The effect of increasing the
number of public bicycles used by an average of 390 units/d was observed after implementing the green
transportation promotion area. It was confirmed that eco-friendly transportation policies achieved goals that
promoted eco-friendliness. In addition to the effects estimated in this study when implementing eco-friendly
transportation policies, various effects such as health benefits and noise reduction benefits should be
considered. Although they are not yet included in the domestic market as quantitative effects of implementing
the policy, other countries are determined to include them. Further studies reflecting these effects should be
carried out later. In addition, in the case of green transportation promotion areas analyzed in this study, it was
only a short-term effect analysis not long after the policy was implemented. Research should be conducted
later to identify trends in policy effects through mid-to-long-term data analysis. We hope that the results of this
study will be applicable as an evaluation indicator of eco-friendly transportation policies for developing a
completely eco-friendly Seoul.
Acknowledgments
This study was supported by the Ministry of Education of the Republic of Korea and the National Research
Foundation of Korea (NRF-2017R1D1A1B06032857).
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