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Sustainable transportation systems performance evaluation using fuzzy logic
Ecological Indicators 71 (2016) 503–513
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Sustainable transportation systems performance evaluation using fuzzy logic Sonu Rajak a , P. Parthiban a,∗ , R. Dhanalakshmi b a b
Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620015, India Department of Computer Science and Engineering, National Institute of Technology Nagaland, Dimapur 797103, India
a r t i c l e
i n f o
Article history: Received 29 March 2016 Received in revised form 18 July 2016 Accepted 20 July 2016 Keywords: Sustainability Transport sustainability Performance evaluation Decision making Fuzzy logic Linguistic variables
a b s t r a c t Sustainability has become an overarching concern for transportation policy and planning around the world. This article presents an approach for urban transport sustainability performance evaluation using fuzzy logic. This article presents a model for transport sustainability performance evaluation. Appropriate transport sustainability indicators were identified based on literature. The model addresses all major dimensions of transport sustainability such as Economic Sustainability, Social Sustainability, Environmental Sustainability and Transportation System Effectiveness. Transport sustainability index has been computed as (5.05, 6.62, 8.12) and weaker transport sustainability attributes were found. Transport sustainability index highlights the question how far toward becoming transport sustainable is an enterprise or region? While, weaker transport sustainability attributes reveals that how can an enterprise or region improve its transport sustainability effectively? Appropriate actions were initiated to improve urban transport sustainability performance. The results indicate that the model is capable of effectively assessing transport sustainability and has practical relevance. An example is also used to illustrate the approach developed. The results obtained using fuzzy approach has been validated with conventional crisp approach. 20 transport sustainability attributes out of 60 are found to be weaker and appropriate actions were derived to improve the weaker attributes. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Sustainable development is that meets the needs of the present without compromising the ability of future generations; it balances economic, social and environmental objectives (Bartlett, 2012). Black (1996) define sustainable transportation as “satisfying current transport and mobility needs without compromising the ability of future generations to meet these needs”. Sustainable transport planning refers to transport policy analysis and planning practices that support sustainable development. Some externalities of transportation systems significantly impact on vast aspects, including energy, land use, traffic safety, accessibility and economic development. It is widely accepted that sustainable transportation systems imply balancing current and future economic development, transport qualities and environmental preservation (Shiftan et al., 2003; Steg and Gifford, 2005). The aim of sustainable trans-
∗ Corresponding author. E-mail addresses: [email protected] (S. Rajak), parthee [email protected] (P. Parthiban), r [email protected] (R. Dhanalakshmi). http://dx.doi.org/10.1016/j.ecolind.2016.07.031 1470-160X/© 2016 Elsevier Ltd. All rights reserved.
portation is to control pollution, energy consumption, accidents and improving livability and economic well-being of the city. The growing economy nurtured the industrial, business, and other activities in the cities; offered more job opportunities; higher income, and generated wealth & general welfare (Malayath and Verma, 2013). A critical component of sustainable transport planning is the development of a comprehensive evaluation program that evaluates transport system performance based on an appropriate set of environmental, social and economic indicators (Bongardt et al., 2011). Due to vague and ambiguous indicators which exist within transport sustainability assessment, measures are describe in terms of linguistic variable which are characterized by ambiguity and multi-possibility, and the conventional assessment approaches cannot suitably nor effectively handle such measurement (Lin et al., 2006). However, fuzzy logic provides a useful tool which eliminates the drawbacks like vagueness, uncertainties, ambiguity, and impreciseness (Vinodh and Devadasan, 2011). This article presents a comprehensive model for evaluation of transport sustainable performance. The architecture has four enablers, 20 criteria and 60 attributes. Appropriate performance indicators are being developed based on exhaustive literature review as well as in discussion with practitioners and experts. After gathering appropriate inputs
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S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
Literature review on transport sustainability evaluation based on structural, regional and urban dimensions and fuzzy logic.
Development of conceptual model for urban transport sustainability evaluation
Assessment of performance ratings and weights of transport sustainability attributes using linguistic terms
Approximation of linguistic terms by fuzzy numbers
Determination of transport sustainability level by using Fuzzy Transport Sustainability Index (FTSI)
Identification and analysis of the principal obstacles using Fuzzy Performance Important Index (FPII) Fig. 1. Research Methods used in this study.
from experts and decision makers in terms of linguistic variables, inputs are fuzzified and Fuzzy Transport Sustainability Index (FTSI) has been computed. The computed FTSI is matched with standard transport sustainability labels to validate transport sustainability level. Then, Fuzzy Performance Importance Index (FPII) of various transport sustainability attributes was computed to identify weaker areas so as to identify proposals for improvement of transport sustainability performance. 2. Literature review The issue related to sustainable development principles; definition, evaluation and implementation of sustainable transportation have been studied by several authors. (Gudmundsson and Höjer, 1996; Jeon and Amekudzi, 2005; Litman and Burwell, 2006). Assessment of the Barriers to achieve sustainable transportation has been investigated by many authors (Banister, 1996; Tricker and Hull, 2005; May, 2008; Browne et al., 2011). A framework for transport sustainability, the interaction of factors that influence indicators; tradeoffs among the indicators has been presented by Richardson (2005). Kennedy (2005) presented four pillars for sustainable transportation namely: effective governance of land use and transportation; fair, efficient, stable funding; strategic infrastructure investments; and attention to neighborhood design. Black and Sato (2007) summarized climate change and its impact on transport over the past sixteen years from 1989 to 2006. They purely concerned for global warming into a general concern for sustainable transportation and the other factors that make transport non-sustainable: air quality problems, injuries and fatalities from vehicle incidents, petroleum resource depletion and congestion. Twenty-Two Quality-of-Life aspects have been given by Poortinga et al. (2004); Steg and Gifford (2005). Bongardt et al. (2011) reviewed the existing set of sustainable transportation indicators and Key challenges in the transport sector to determine which are most appropriate for sustainable transport planning and policy purposes on an international level. Sustainable trans-
portation: a US perspective has been analysed by Black (1996). Malayath and Verma (2013) reviewed the ability of travel demand models applied in India in analyzing the sustainable transport policies. A framework which includes set of sustainability indicators, principles, measures, and along with data sources to measure sustainable regional development for the twin cities region over the long term have been proposed by Kirk et al. (2010). Boschmann and Kwan (2008) reviewed research on Socially Sustainable Urban Transportation (SSUT) and argued that how urban transportation influences the achievement of social sustainability in urban regions including social equity, social exclusion, and quality of life. Singal (2010) described about the steps being taken by the Indian government to promote sustainable urban transport, while the author suggests the need to make cities pedestrian-friendly for quick and ongoing relief, and proposed four essential ingredients for sustainable urban transport in the long term. Tanguay et al. (2010) developed the urban Sustainable Development Indicators (SDI) to measure the sustainability of cities and Haghshenas and Vaziri (2012) ranked the various world cities in terms of urban sustainable transport composite index. A framework for identifying and selecting a small subset of sustainable transport indicators has been developed by Castillo and Pitfield (2010). Salling and Pryn (2015) developed a sustainable planning and decision support framework for transport infrastructure assessment. Transportation related tools and strategy in four sectors namely Technology & Infrastructure; Business & Finance; Policies and Institutions; Social and Community Groups, discussed by Shay and Khattak (2010). Nijkamp et al. (2007) proposed a methodological/operational contribution to sustainable mobility policy in the Naples metropolitan area. Scenario analysis has been used to design combined landuse/transportation plans accompanied sustainability perspective. Song et al. (2013) developed a Simulation-Based Optimization (SBO) approach for sustainable transportation systems evaluation and optimization. They attempted to find the optimal combination of transportation planning and operations strategies which should minimize costs of multimodal traveling. A literature review on application of fuzzy logic in performance management has been conducted by Gurrea et al. (2014). Fuzzy logic has been used by many authors for example Lin et al. (2006) and Vinodh and Devadasan (2011) evaluated agility index of an enterprise using fuzzy logic; Vinodh and Vimal (2012) used fuzzy logic for leanness assessment. Yang and Li (2002) presented multigrade fuzzy approach to evaluate agility. Convertino and Valverde (2013) presented a Portfolio Decision Analytic (PDA) framework to promote the efficient allocation of scarce resources in coastal ecosystems and claim that PDA framework allows decision makers to achieve higher environmental benefits, with equal or lower costs, than those achievable by Multi Criteria Decision Analysis (MCDA) model. Jeon et al. (2010) demonstrated an application of Multiple Criteria Decision Making (MCDM) approach for evaluating the selection of transportation and land use plans in the Atlanta region using multiple sustainability parameters. They used four indicators namely; Transportation Effectiveness, Environmental Sustainability, Economic Sustainability and Social Sustainability Indicators. They introduced composite sustainability index as decision support tool for transportation planning along with identifying the most sustainable plan for predetermined objectives. Wellar (2009) presented 42 techniques that could be used in making decisions to identify, adopt, or implement sustainable transport practices and focused on 20 of them including, Benefit-Cost Analysis, CrossImpact Analysis, Delphi Techniques, Impact Assessment, Life-cycle Analysis, MCDM, Open House, surveys, and indexing. A hybrid approach based on the Analytical Hierarchy Process (AHP) and Dempster −Shafer theory is proposed by Awasthi and Chauhan (2011) for evaluating the impact of environment-friendly transport measures like multi-modal transport solutions, mode sharing,
Table 1 Conceptual model for Transport Sustainability evaluation. Transport Sustainability enablers
Transport Sustainability criteria
Transport Sustainability attributes
Economic Sustainability (TS1 )
Economic productivity (TS11 ) (Litman, 2005)
• Income, economic activity- Per capita GDP (TS111 ) • Operational efficiency (TS112 ) • Transport budget and road taxes (Tax revenues) (TS113 ) (Gilbert and Tanguay, 2000)
Economic development (TS12 ) (Deakin, 2001; Jeon et al., 2010)
• Access to education and employment opportunities (TS121 ) (Litman, 2008) • Support for local industries (TS122 ) (Litman, 2005) • Infrastructure costs (TS123 ) (Litman, 2007., (Dobranskyte-Niskota et al., 2007)
Affordability (TS13 ) (Salling and Pryn, 2015)
• Availability and quality of affordable various modes and types of travel (TS131 ) (Litman, 2005) • Reduce the Impact of low-income households that spend more than 20% of budgets on transport (TS132 ) (Litman, 2005) • Transport diversity (TS133 ) • Parking facilities (TS141 ) • Minimum taxation on fuel (TS142 ) (Bongardt, 2011) • Bottlenecks (TS143 )
Resource efficiency (TS15 ) (Litman and Burwell, 2006)
• Per capita transport energy consumption (TS151 ) (Jeon and Amekudzi, 2005,) • Responses to Consumer Demand (TS152 ) (Gudmundsson, 2001) • Availability of transportation (TS153 )
Social Sustainability (TS2 )
Equity/Fairness (TS21 ) (Litman, 2007)
• Civil and human rights (TS211 ) • Reduce Portion of destinations accessible by people with disabilities and low incomes (TS212 ) (Litman, 2005) • Transport system diversity (TS213 ) (Litman, 2005)
Human safety, security and health (TS22 )
• Human safety-security on public transport (TS221 ) (Gilbert, 2005)
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
Traffic congestion (TS14 ) (Litman and Burwell, 2006; Litman, 2007)
• Traveler assault (crime) prevention (TS222 ) (Litman, 2005) • Reduce the Per capita traffic casualty (injury and death) rates (TS223 ) (Litman, 2005) Community livability (TS23 ) (Litman, 2007; Litman and Burwell, 2006)
• Accessibility to employment (TS231 ) • Human health impacts (TS232 ) • Accessibility to public services (TS233 )
Community cohesion (TS24 ) (Litman, 2007; Jakimaviˇcius, 2008)
• Land use mix (TS241 ) (Dobranskyte-Niskota et al., 2007) • Employment stability (TS242 ) • Interconnectivity of transport modes (TS243 )
Government efficiency (TS25 )
• Integrated, comprehensive and inclusive planning (TS251 ) • Efficient pricing (TS252 )
505
• Indigenous rights (TS253 )
506
Table 1 (Continued) Transport Sustainability enablers Transport Sustainability criteria Stakeholder involvement (TS26 )
Transport Sustainability attributes • Human capital (TS261 ) • Workforce Development (TS262 ) • Community capital (TS263 )
Cultural preservation (TS27)
• Preservation of cultural resources and traditions (TS271 ) (Litman, 2005) • Responsiveness to traditional communities. (TS272 ) (Litman, 2005) • Prevention to cultural Barriers (TS273 ) (Aotearoa, 2009)
Environmental Sustainability (TS3 )
Climate change prevention and mitigation (TS31 ) (Litman, 2005)
• Per capita emissions of global air pollutants (CO2, CFCs, CH4, etc.) (TS311 ) (Litman, 2005) • Transport infrastructure and operations affected by climate change (TS312 ) • Consumption of natural resources (TS313 ) (Zegras, 2006)
Pollution prevention (TS32 ) (Dobranskyte-Niskota et al., 2007)
• Control on Air pollution (TS321 ) (Haghshenas and Vaziri, 2012)
• Control on Water pollution (TS323 ) Non-Renewable resource conservation (TS33 ) (Litman, 2005; Gudmundsson et al., 2016)
• Percentage recyclable materials in production process of vehicles (TS331 ) • Percentage recyclable materials in infrastructure (TS332 ) • Per vehicle/mode/object non-recyclable/recyclable materials ratio (TS333 )
Open space preservation and biodiversity protection (TS34 )
• Portion of land paved for transport facilities (TS341 ) • Policies to protect high value farmlands and ecological habitat (TS342 ) • Support for smart growth development (TS343 )
Hydrologic impacts (TS35 ) (Litman, 2007; Litman and Burwell, 2006)
• Reduce in Per capita fuel consumption (TS351 ) (Litman, 2005) • Management of used oil and leaks (TS352 ) • Per capita impervious surface area (TS353 )
Transportation system effectiveness (TS4 ) (Jeon et al., 2010)
Improve mobility (TS41 ) (Dobranskyte-Niskota et al., 2007)
• Access to transit (TS411 )
• Travel rate (TS412 ) (Jeon et al., 2010) • Efficient Transport operations (TS413 ) Improve system performance (TS42 )
• Total vehicle-miles traveled (TS421 ) (Jeon et al., 2010) • Transit passenger miles traveled (TS422 ) (Jeon et al., 2010) • Freight ton-miles (TS423 ) (Jeon et al., 2010)
Transportation reliability (TS43 )
• per capita Congestion cost (TS431 ) (Litman, 2008; Kirk et al., 2010) • Travel time index (TS432 ) (Kirk et al., 2010) • Percentage of daily traffic in congested conditions (TS433 ) (Kirk et al., 2010)
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
• Control on Noise pollution (TS322 ) (Haghshenas and Vaziri, 2012)
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Table 2 Fuzzy numbers for approximating linguistic variables. Performance rating
Importance weighting
Linguistic variable
Fuzzy number
Linguistic variable
Fuzzy number
Worst (W) Very poor (VP) Poor (P) Fair (F) Good (G) Very Good (VG) Excellent (E)
(0, 0.5, 1.5) (1, 2, 3) (2, 3.5, 5) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (8.5, 9.5, 10)
Very low (VL) Low (L) Fairly low (FL) Medium (M) Fairly high (FH) High (H) Very high (VH)
(0, 0.05, 0.15) (0.1, 0.2, 0.3) (0.2, 0.35, 0.5) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.85, 0.95, 1.0)
intelligent transport solutions, etc. on city sustainability. A multiplicative AHP decision support model has been presented by Pryn et al. (2015) to assessment of sustainable transport infrastructure. Based on the literature review, to the best knowledge of authors it has been found that no concrete research has been carried out in the context of transport sustainability evaluation with performance indicators. Also, the deployment of fuzzy approach for evaluation of transport sustainability performance has not been attempted to the best knowledge of authors. In this context, this article presents an approach for evaluation of transport sustainable performance.
Table 3 Aggregated performance rating and weight of Transport Sustainability attributes. TSi
TSij
TSijk
Wi
Wij
Wijk
Rijk
TS1
TS11
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
H
H
FH H FH H H FH H FH FH H FH FH FH H H H FH FH H H FH FH FH FH H H FH FH FH H FH H FH FH FH FH FH FH H H H H FH M FH FH FH H H H M FH FH H H FH FH FH H FH
F G F G G F G G F G G F G VG VG G F G VG VG G F G G F G F F G G G G F G F F F F F F F G G F F F F G G F F G G G G G F F G G
TS12
TS13
TS14
3. Methods
TS15
The scope of this study is to know how transport sustainable an enterprise or a region is and how an enterprise/region can improve its transport sustainability effectively. The method for Fuzzy Transport Sustainability Evaluation (FTSE) framework is shown in Fig. 1. Based on the literature survey, a conceptual model for urban transport sustainability evaluation has been developed, but the conduct of the study provided guidelines for practitioners and experts to systematically quantify and analyze transport sustainability performance to any type of enterprise/region and should be valid for national, local or urban dimensions. The model is divided into three levels namely: enabler, criteria, and attributes. Then, the linguistic ratings and weights have been gathered from experts and decision makers. Fuzzy logic is the tool for transforming human knowledge and its decision-making ability into a mathematical formula, which is difficult to obtain with conventional analytical techniques. It provides a meaningful representation of uncertainties and also representation of vague concepts expressed in natural language (Klir and Yuan, 1995). These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Triangular fuzzy number is used here to approximate the linguistic variables because; it is widely used in performance assessment studies (Lin et al., 2006; Vinodh and Devadasan, 2011). Additional discussion regarding the fuzzy logic can be found in book by Klir and Yuan (1995). Using fuzzy operation three levels of calculation has been done and the Fuzzy Transport Sustainability Index (FTSI) of the enterprise has been determined. FTSI has been matched with the natural expression linguistic terms using Euclidean distance method to find the transport sustainability level of the enterprise. Finally, the principal obstacles have to be identified and analysed for improvement. Transport sustainability index is given by:
TS2
TS22
TS23
TS24
TS25
TS26
TS27
TS3
N
TSij
(1)
TS31
TS32
TS33
TS34
TS35
TS4
(Transport Sustainability Index )i =
TS21
TS41
TS42
j=1
TS43
where, TSij is the transport sustainability levels of capability j of enterprise i.
H
FH
FH
H
H
H
H
FH
FH
FH
H
FH
H
H
H
FH
FH
FH
FH
FH
FH
H
508
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
Table 4 Linguistic variables approximated by fuzzy numbers. TSi
TSij
TSijk
Wi
Wij
Wijk
Rijk
TS1
TS11
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8)
(3, 5, 7) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8)
TS12
TS13
TS14
TS15
TS2
TS21
TS22
TS23
TS24
TS25
TS26
TS27
TS3
TS31
TS32
TS33
TS34
TS35
TS4
TS41
TS42
TS43
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
N
(Ri × Wi )
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
i=1
Wi = 1
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
(2)
i=1 N
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
If Ri and Wi denote the transport sustainability index and the weight of each transport sustainability capability, respectively, the transport sustainability index can be defined as:
(Transport Sustainability Index ) =
(0.7, 0.8, 0.9)
where, The fuzzy index of level two transport sustainability criteria TSij , level one transport sustainability enabler TSi and FTSI can be calculated using the formula:
n
TSIij = nk=1 Wijk × TSijk /
Wijk
(4)
k=1
(3)
where TSijk represent performance rating and Wijk represent importance weight of transport sustainability attributes.
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513 Table 5 Fuzzy index of each grade of Transport Sustainability attributes. TSi
TSij
Ri
Rij
TS1
TS11 TS12 TS13 TS14 TS15 TS21 TS22 TS23 TS24 TS25 TS26 TS27 TS31 TS32 TS33 TS34 TS35 TS41 TS42 TS43
(4.76, 6.27, 7.83)
(3.82, 5.57, 7.36) (4.47, 6.07, 7.69) (4.41, 6.04, 7.68) (4.41, 6.04, 7.68) (6.47, 7.57, 8.69) (4.41, 6.04, 7.68) (6.47, 7.57, 8.69) (4.33, 6. 7.67) (3.74, 5.53, 7.35) (4.41, 6.04, 7.68) (4.41, 6.04, 7.68) (3.67, 5.50, 7.33) (3, 5, 7) (3.67, 5.50, 7.33) (3.77, 5.54, 7.35) (3.82, 5.57, 7.36) (3.82, 5.57, 7.36) (5, 6.5, 8) (4.41, 6.04, 7.68) (4.41, 6.04, 7.68)
TS2
TS3
TS4
(4.58, 6.14, 7.74)
(3.58, 5.42, 7.27)
(4.63, 6.20, 7.79)
4. Development of conceptual model for transport sustainability evaluation The sustainability assessment conceptual model is developed and is shown in Table 1.The model is divided into three levels. The first level consists of four transport sustainability enablers; the second level consists of 20 transport sustainability criteria; and the third level consists of 60 transport sustainability attributes. The model is comprehensive, and it has been developed from
TS11 =
509
to assess the importance weights of the transport sustainability attributes, the linguistic variables very high (VH), high (H), fairly high (FH), medium (M), fairly low (FL), low (L), very low (VL) are used as shown in Table 2. 5.2. Measurement of performance ratings and importance weights of transport sustainability attributes using linguistic terms Once the linguistic variables for evaluating the performance ratings and the importance weights of the transport sustainability capabilities are defined, aggregated performance rating and importance weight of transport sustainability attributes is provided by the expert team and is shown in Table 3. 5.3. Approximation of the linguistic terms by fuzzy numbers Using the concept of fuzzy theory, the linguistic variables can be approximated by a fuzzy number (Lin et al., 2006; Vinodh and Devadasan, 2011). In the application of the relation between linguistic terms and fuzzy numbers, the linguistic terms shown in Table 2 are transferred into fuzzy numbers as shown in Table 4. 5.4. Determination of fuzzy transport sustainability index (FTSI) FTSI represents the overall enterprise level transport sustainability. The fuzzy index has been calculated at the criterion level and then extended to enabler level. Fuzzy index at the criterion level encompasses several transport sustainability attributes. The fuzzy index of level two transport sustainability criteria can be calculated using the formula (4). As a sample, the fuzzy index for Economic productivity (TS11 ) criterion can be calculated as follows:
[(0.5, 0.65, 0.8) x (3, 5, 7) + (0.7, 0.8, 0.9) x (5, 6.5, 8) + (0.5, 0.65, 0.8) x (3, 5, 7)] [(0.5, 0.65, 0.8) + (0.7, 0.8, 0.9) + (0.5, 0.65, 0.8)] TS11 = (3.82, 5.57, 7.36)
literature analysis and in discussion with and practitioners working in transportation domain. The model considers four major dimensions of transport sustainability namely; Economic Sustainability, Social Sustainability, Environmental Sustainability and Transportation System Effectiveness (Jeon et al., 2010). Four transport sustainability enablers and 20 transport sustainability criteria are comprehensive enough to measure transport sustainability. As the sample, Economic productivity criterion consist of attributes namely Income, economic activity- Per capita GDP, Operational efficiency, Transport budget and road taxes (Tax revenues). 5. An illustrative example The detailed steps of Transport Sustainability evaluation are being presented in the sections below. 5.1. Determination of the appropriate linguistic scale for assessing the performance ratings and importance weights of transport sustainability attributes The linguistic terms are used to assess the performance ratings and importance weights of transport sustainability attributes since it is difficult for experts to determine the score of a vague attribute such as interchange ability of personnel. The linguistic scale used by Lin et al. (2006) has been adopted in this research study. In order to assess the performance rating of the transport sustainability capabilities, the linguistic variables excellent (E), very good (VG), good (G), fair (F), poor (P), very poor (VP), worst (W) are used. In order
By using the same equation, fuzzy indexes of level two transport sustainability criteria and level one transport sustainability enabler have been obtained and shown in Table 5. Finally, the FTSI has been calculated as
FTSI =
[(4.76, 6.27, 7.83) × (0.7, 0.8, 0.9) + (4.58, 6.14, 7.74) × (0.7, 0.8, 0.9) + (3.58, 5.42, 7.27) × (0.7, 0.8, 0.9) + (4.63, 6.20, 7.89) × (0.5, 0.65, 0.8)] [(0.7, 0.8, 0.9) + (0.7, 0.8, 0.9) + (0.7, 0.8, 0.9) + (0.5, 0.65, 0.8)]
FTSI = (5.05, 6.62, 8.12) The obtained fuzzy transport sustainability index has been validated using conventional crisp technique and transport sustainability index is found 6.05 as shown in Table 6. This shows the corroboration of results of fuzzy and conventional approaches. 5.5. Determination of Euclidean distance to match FTSI with approximate transport sustainability level Once the FTSI has been obtained, the FTSI can be further matched with the linguistic label. Euclidean distance method is the most commonly chosen type of distance method. The main advantage of Euclidean method over other methods is the distance between any two objects is not affected by the addition of new objects to the analysis (Lin et al., 2006). In Euclidean distance method, the naturallanguage expression set TS = {Extremely Transport Sustainable [ETS (7, 8.5, 10)], Very Transport Sustainable [VTS (5.5, 7, 8.5)], Transport Sustainable [TS (3.5, 5, 6.5)], Fairly Transport Sustainable [FTS (1.5, 3, 4.5)], Slowly Transport Sustainable [STS (0, 1.5, 3)]} is selected for labeling. By using the Euclidean distance method Eq. 5, the
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Table 6 Computation of index of each grade of Transport Sustainability attributes using crisp approach. TSi
TSij
TSijk
Wi
Wij
Wijk
Rijk
Rij
Ri
TSI
TS1
TS11
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
0.8
0.8
0.65 0.8 0.65 0.8 0.8 0.65 0.8 0.65 0.65 0.8 0.65 0.65 0.65 0.8 0.8 0.8 0.65 0.65 0.8 0.8 0.65 0.65 0.65 0.65 0.8 0.8 0.65 0.65 0.65 0.8 0.65 0.8 0.65 0.65 0.65 0.65 0.65 0.65 0.8 0.8 0.8 0.8 0.65 0.5 0.65 0.65 0.65 0.8 0.8 0.8 0.5 0.65 0.65 0.8 0.8 0.65 0.65 0.65 0.8 0.65
5 6.5 5 6.5 6.5 5 6.5 6.5 5 6.5 6.5 5 6.5 8 8 6.5 5 6.5 8 8 6.5 5 6.5 6.5 5 6.5 5 5 6.5 6.5 6.5 6.5 5 6.5 5 5 5 5 5 5 5 6.5 6.5 5 5 5 5 6.5 6.5 8 5 6.5 6.5 6.5 6.5 6.5 5 5 6.5 6.5
5.57
6.27
6.05
TS12
TS13
TS14
TS15
TS2
TS21
TS22
TS23
TS24
TS25
TS26
TS27
TS3
TS31
TS32
TS33
TS34
TS35
TS4
TS41
TS42
TS43
0.8
0.65
0.65
0.8
0.8
0.8
0.8
0.65
0.65
0.65
0.8
0.65
0.8
0.8
0.8
0.65
0.65
0.65
0.65
0.65
0.65
0.8
Table 7 Comparison of fuzzy logic approach and crisp approach. Approach
Transport sustainability index
Range
Linguistic labeling
Fuzzy logic Crisp approach
(5.05, 6.62, 8.12) 6.05
3.07
Very Transport Sustainable Very Transport Sustainable
6.07
D (TSI, VTSi ) = 0.70
6.04
D (TSI, TSi ) = 2.77 6.04
D (TSI, FTSi ) = 6.23 7.57
D (TSI, STSi ) = 8.83 6.04
6.14
6. Results and discussion 7.57
Thus by matching a linguistic label with the minimum D, the transport sustainability index has been identified as ‘Very Transport Sustainable’. The linguistics and corresponding membership functions are shown in Fig. 2. With regard to the efficiency of the method to measure transport sustainability index, the result generated by both fuzzy and conventional approaches leads to same conclusions as shown in Table 7. The above method is not only used to identify the transport sustainability level, but also used to identify the principal obstacles for improvement. Fuzzy Performance Importance Index (FPII) is used to identify the obstacles (Lin et. al., 2006). It combines the performance rating and importance weight of transport sustainability attributes. The higher the FPII of a factor, the higher is the contribution. The FPII is calculated as follows:
6.00
5.53
6.04
6.04
5.50
5.00
5.63
FPIIijk = Wijk × TCijk where,
5.50
Wijk = (1, 1, 1) − Wijk 5.54
(6)
5.57
Wijk is the fuzzy importance weight of the transport sustainability element capability ijk. For example, the calculation of FPII of Income, economic activity- Per capita GDP (TS111 ) is shown below:
6.71
FPII111 = [(1, 1, 1) − (0.5, 0.65, 0.8)] × (3, 5, 7)
6.50
FPII111 = (0.6, 1.75, 3.5)
6.18
FPII for the remaining attributes are calculated using above principle and is shown in Table 8. Since fuzzy numbers do not always yield a totally ordered set as real numbers do, all the FPIIs must be ranked (Vinodh and Vimal, 2012). Here, the ranking of the fuzzy number is based on centroid method for membership function (a, b, c) is given in Eq. 8, where a, b, and c are the lower, middle, and upper numbers of triangular fuzzy number.
6.04
6.04
Ranking Score = (a + 4b + c) /6 Euclidean distance D from the TSI to each member in set TS is calculated as follows. D (TSI, TSi ) =
x ∈ p , [f TSI (x) − fTS (x)]
2
1/2
(5)
The model calculation for D (TSI, ETS) is shown as follows D (TSI, ETSi ) =
(5.05 − 7)2 + (6.62 − 8.5)2 + (8.12 − 10)
D (TSI, ETSi ) = 3.30
(7)
2 1/2
(8)
The model calculation for the FPII111 attribute is shown below Ranking Score =
(0.6 + 4 × 1.75 + 3.5) 6
Ranking Score = 1.85 The ranking score for the reaming attributes are calculated using above principle and it is shown in Table 8. To identify the obstacles, value 1.35 has been set as management threshold to distinguish the critical obstacles. As shown in Table 8, 20 attributes have a
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
511
F(x)
FTS
STS
0
1
2
3
TS
4
5
ETS
VTS
6
7
8
9 -
10 x FTSI
Fig. 2. Linguistic levels to match Fuzzy Transport Sustainability Index [STS (0, 1.5, 3); FTS (1.5, 3, 4.5); TS (3.5, 5, 6.5); VTS (5.5, 7, 8.5); ETS (7, 8.5, 10)]. Table 8 Fuzzy performance importance indexes of Transport Sustainable attributes.
Transport Sustainable attribute
Aggregated fuzzy performance rating
W ijk = [(1, 1, 1) − Wijk ]
FPII
Ranking score
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
(3, 5, 7) (5, 6.5, 8) (3, 5, 7) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (7, 8, 9) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8)
(0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.3, 0.50, 0.7) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.3, 0.50, 0.7) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5)
(0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.7, 1.60, 2.7) (0.7, 1.60, 2.7) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.7, 1.60, 2.7) (0.7, 1.60, 2.7) (1, 2.28, 4) (0.6, 1.75, 3.5) (1, 2.28, 4) (1, 2.28, 4) (0.3, 1.00, 2.1) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.3, 1.00, 2.1) (0.3, 1.00, 2.1) (0.3, 1.00, 2.1) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.9, 2.50, 4.9) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (0.5, 1.30, 2.4) (0.3, 1.00, 2.1) (0.9, 2.50, 4.9) (1, 2.28, 4) (1, 2.28, 4) (0.5, 1.30, 2.4) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (1, 2.28, 4)
1.85 1.35a 1.85 1.35a 1.35a 1.85 1.35a 2.35 1.85 1.35a 2.35 1.85 2.35 1.63 1.63 1.35a 1.85 2.35 1.63 1.63 2.35 1.85 2.35 2.35 1.07a 1.35a 1.85 1.85 2.35 1.35a 2.35 1.35a 1.85 2.35 1.85 1.85 1.85 1.85 1.07a 1.07a 1.07a 1.35a 2.35 2.63 1.85 1.85 1.85 1.35a 1.35a 1.07a 2.63 2.35 2.35 1.35a 1.35a 2.35 1.85 1.85 1.35a 2.35
a
Weaker attributes.
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lower performance than the threshold. These 20 attributes represent significant contributions and it could be enhanced to achieve transport sustainability. For example, Regular check-up and maintenance could reduce the oil leaks and enhance the operational efficiency; Availability and quality of affordable various modes and types of travel could be enhanced by proper planning; Parking facilities could be enhanced by proper parking management; Compact development and mixed use development could enhance Land use mix; Workforce development and Employment stability could be ensured by effective manpower planning; Control on Air pollution could be enhanced by proper guidelines and government policy (e.g., Government of India has developed “Auto Fuel Policy (AFP) for India”); Control on Noise pollution could be enhanced by design modification (Help of experts in design) and also by using personal protective equipments Reduce in dissemination transport waste such as asbestos, rubber, gasoline, lubricants could enhance Control on Water pollution. 7. Conclusions This article presented the fuzzy based approach to evaluation of urban transport sustainability performance. The purpose of the study is the contribution of a conceptual model for transport sustainability performance evaluation, computation of FTSI and identification of weaker attributes. The study begins with the gathering of inputs ratings and weights from experts and decision makers. Since conventional transport sustainability performance measurement is associated with vagueness and uncertainties, fuzzy logic approach has been used in the study. Triangular fuzzy numbers are used in performance evaluation. The inputs are fuzzified and The FTSI has been computed as (5.05, 6.62, 8.12). Based on Euclidean distance computation, the enterprise is found to be ‘Very Transport Sustainable’. The computed result has been validated using the conventional crisp approach. Besides computing transport sustainability index, the weaker areas for improvement also have been identified. FPII of transport sustainability attributes were computed. 20 attributes out of 60 attributes were found to be weaker; proposals have been developed for the transport sustainability improvement. The conduct of the study provided guidelines for practitioners to systematically quantify and analyze transport sustainability performance. The approach could be applied to any type of enterprise/region. 7.1. Limitations and future scope The conceptual model for transport sustainability evaluation could also be extended beyond 20 criteria. Trapezoidal fuzzy functions also could be used to improve the effectiveness of results. Also, dedicated knowledge based expert system and Global Sensitivity and Uncertainty Analyses (GSUA) could be developed to enhance the effectiveness of computation and analysis. References Aotearoa, T.A.M., 2009. Cultural Indicators for New Zealand. Ministry for Culture and Heritage, Te Manatu¯ Taonga, Wellington New Zealand, ISBN 978-0-478-18466-2. Awasthi, A., Chauhan, S.S., 2011. Using AHP and DempstereShafer theory for evaluating sustainable transport solutions. Environ. Modell. Software 26, 787–796. Banister, D., 1996. Barriers to Implementation of Unban Sustainability. European regional science association 36th European congress, ETH Zurich, Switzerland, pp. 26–30, August 1996. Bartlett, A.A., 2012. The Meaning of Sustainability, teachers clearinghouse for science and society education newsletter. vol. 31, no. 1, winter 2012, pp. 1–17. [email protected]. Black, W.R., 1996. Sustainable transportation: a US perspective. J. Transp. Geogr. 4 (3), 151–159.
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Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Sustainable transportation systems performance evaluation using fuzzy logic Sonu Rajak a , P. Parthiban a,∗ , R. Dhanalakshmi b a b
Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620015, India Department of Computer Science and Engineering, National Institute of Technology Nagaland, Dimapur 797103, India
a r t i c l e
i n f o
Article history: Received 29 March 2016 Received in revised form 18 July 2016 Accepted 20 July 2016 Keywords: Sustainability Transport sustainability Performance evaluation Decision making Fuzzy logic Linguistic variables
a b s t r a c t Sustainability has become an overarching concern for transportation policy and planning around the world. This article presents an approach for urban transport sustainability performance evaluation using fuzzy logic. This article presents a model for transport sustainability performance evaluation. Appropriate transport sustainability indicators were identified based on literature. The model addresses all major dimensions of transport sustainability such as Economic Sustainability, Social Sustainability, Environmental Sustainability and Transportation System Effectiveness. Transport sustainability index has been computed as (5.05, 6.62, 8.12) and weaker transport sustainability attributes were found. Transport sustainability index highlights the question how far toward becoming transport sustainable is an enterprise or region? While, weaker transport sustainability attributes reveals that how can an enterprise or region improve its transport sustainability effectively? Appropriate actions were initiated to improve urban transport sustainability performance. The results indicate that the model is capable of effectively assessing transport sustainability and has practical relevance. An example is also used to illustrate the approach developed. The results obtained using fuzzy approach has been validated with conventional crisp approach. 20 transport sustainability attributes out of 60 are found to be weaker and appropriate actions were derived to improve the weaker attributes. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction Sustainable development is that meets the needs of the present without compromising the ability of future generations; it balances economic, social and environmental objectives (Bartlett, 2012). Black (1996) define sustainable transportation as “satisfying current transport and mobility needs without compromising the ability of future generations to meet these needs”. Sustainable transport planning refers to transport policy analysis and planning practices that support sustainable development. Some externalities of transportation systems significantly impact on vast aspects, including energy, land use, traffic safety, accessibility and economic development. It is widely accepted that sustainable transportation systems imply balancing current and future economic development, transport qualities and environmental preservation (Shiftan et al., 2003; Steg and Gifford, 2005). The aim of sustainable trans-
∗ Corresponding author. E-mail addresses: [email protected] (S. Rajak), parthee [email protected] (P. Parthiban), r [email protected] (R. Dhanalakshmi). http://dx.doi.org/10.1016/j.ecolind.2016.07.031 1470-160X/© 2016 Elsevier Ltd. All rights reserved.
portation is to control pollution, energy consumption, accidents and improving livability and economic well-being of the city. The growing economy nurtured the industrial, business, and other activities in the cities; offered more job opportunities; higher income, and generated wealth & general welfare (Malayath and Verma, 2013). A critical component of sustainable transport planning is the development of a comprehensive evaluation program that evaluates transport system performance based on an appropriate set of environmental, social and economic indicators (Bongardt et al., 2011). Due to vague and ambiguous indicators which exist within transport sustainability assessment, measures are describe in terms of linguistic variable which are characterized by ambiguity and multi-possibility, and the conventional assessment approaches cannot suitably nor effectively handle such measurement (Lin et al., 2006). However, fuzzy logic provides a useful tool which eliminates the drawbacks like vagueness, uncertainties, ambiguity, and impreciseness (Vinodh and Devadasan, 2011). This article presents a comprehensive model for evaluation of transport sustainable performance. The architecture has four enablers, 20 criteria and 60 attributes. Appropriate performance indicators are being developed based on exhaustive literature review as well as in discussion with practitioners and experts. After gathering appropriate inputs
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Literature review on transport sustainability evaluation based on structural, regional and urban dimensions and fuzzy logic.
Development of conceptual model for urban transport sustainability evaluation
Assessment of performance ratings and weights of transport sustainability attributes using linguistic terms
Approximation of linguistic terms by fuzzy numbers
Determination of transport sustainability level by using Fuzzy Transport Sustainability Index (FTSI)
Identification and analysis of the principal obstacles using Fuzzy Performance Important Index (FPII) Fig. 1. Research Methods used in this study.
from experts and decision makers in terms of linguistic variables, inputs are fuzzified and Fuzzy Transport Sustainability Index (FTSI) has been computed. The computed FTSI is matched with standard transport sustainability labels to validate transport sustainability level. Then, Fuzzy Performance Importance Index (FPII) of various transport sustainability attributes was computed to identify weaker areas so as to identify proposals for improvement of transport sustainability performance. 2. Literature review The issue related to sustainable development principles; definition, evaluation and implementation of sustainable transportation have been studied by several authors. (Gudmundsson and Höjer, 1996; Jeon and Amekudzi, 2005; Litman and Burwell, 2006). Assessment of the Barriers to achieve sustainable transportation has been investigated by many authors (Banister, 1996; Tricker and Hull, 2005; May, 2008; Browne et al., 2011). A framework for transport sustainability, the interaction of factors that influence indicators; tradeoffs among the indicators has been presented by Richardson (2005). Kennedy (2005) presented four pillars for sustainable transportation namely: effective governance of land use and transportation; fair, efficient, stable funding; strategic infrastructure investments; and attention to neighborhood design. Black and Sato (2007) summarized climate change and its impact on transport over the past sixteen years from 1989 to 2006. They purely concerned for global warming into a general concern for sustainable transportation and the other factors that make transport non-sustainable: air quality problems, injuries and fatalities from vehicle incidents, petroleum resource depletion and congestion. Twenty-Two Quality-of-Life aspects have been given by Poortinga et al. (2004); Steg and Gifford (2005). Bongardt et al. (2011) reviewed the existing set of sustainable transportation indicators and Key challenges in the transport sector to determine which are most appropriate for sustainable transport planning and policy purposes on an international level. Sustainable trans-
portation: a US perspective has been analysed by Black (1996). Malayath and Verma (2013) reviewed the ability of travel demand models applied in India in analyzing the sustainable transport policies. A framework which includes set of sustainability indicators, principles, measures, and along with data sources to measure sustainable regional development for the twin cities region over the long term have been proposed by Kirk et al. (2010). Boschmann and Kwan (2008) reviewed research on Socially Sustainable Urban Transportation (SSUT) and argued that how urban transportation influences the achievement of social sustainability in urban regions including social equity, social exclusion, and quality of life. Singal (2010) described about the steps being taken by the Indian government to promote sustainable urban transport, while the author suggests the need to make cities pedestrian-friendly for quick and ongoing relief, and proposed four essential ingredients for sustainable urban transport in the long term. Tanguay et al. (2010) developed the urban Sustainable Development Indicators (SDI) to measure the sustainability of cities and Haghshenas and Vaziri (2012) ranked the various world cities in terms of urban sustainable transport composite index. A framework for identifying and selecting a small subset of sustainable transport indicators has been developed by Castillo and Pitfield (2010). Salling and Pryn (2015) developed a sustainable planning and decision support framework for transport infrastructure assessment. Transportation related tools and strategy in four sectors namely Technology & Infrastructure; Business & Finance; Policies and Institutions; Social and Community Groups, discussed by Shay and Khattak (2010). Nijkamp et al. (2007) proposed a methodological/operational contribution to sustainable mobility policy in the Naples metropolitan area. Scenario analysis has been used to design combined landuse/transportation plans accompanied sustainability perspective. Song et al. (2013) developed a Simulation-Based Optimization (SBO) approach for sustainable transportation systems evaluation and optimization. They attempted to find the optimal combination of transportation planning and operations strategies which should minimize costs of multimodal traveling. A literature review on application of fuzzy logic in performance management has been conducted by Gurrea et al. (2014). Fuzzy logic has been used by many authors for example Lin et al. (2006) and Vinodh and Devadasan (2011) evaluated agility index of an enterprise using fuzzy logic; Vinodh and Vimal (2012) used fuzzy logic for leanness assessment. Yang and Li (2002) presented multigrade fuzzy approach to evaluate agility. Convertino and Valverde (2013) presented a Portfolio Decision Analytic (PDA) framework to promote the efficient allocation of scarce resources in coastal ecosystems and claim that PDA framework allows decision makers to achieve higher environmental benefits, with equal or lower costs, than those achievable by Multi Criteria Decision Analysis (MCDA) model. Jeon et al. (2010) demonstrated an application of Multiple Criteria Decision Making (MCDM) approach for evaluating the selection of transportation and land use plans in the Atlanta region using multiple sustainability parameters. They used four indicators namely; Transportation Effectiveness, Environmental Sustainability, Economic Sustainability and Social Sustainability Indicators. They introduced composite sustainability index as decision support tool for transportation planning along with identifying the most sustainable plan for predetermined objectives. Wellar (2009) presented 42 techniques that could be used in making decisions to identify, adopt, or implement sustainable transport practices and focused on 20 of them including, Benefit-Cost Analysis, CrossImpact Analysis, Delphi Techniques, Impact Assessment, Life-cycle Analysis, MCDM, Open House, surveys, and indexing. A hybrid approach based on the Analytical Hierarchy Process (AHP) and Dempster −Shafer theory is proposed by Awasthi and Chauhan (2011) for evaluating the impact of environment-friendly transport measures like multi-modal transport solutions, mode sharing,
Table 1 Conceptual model for Transport Sustainability evaluation. Transport Sustainability enablers
Transport Sustainability criteria
Transport Sustainability attributes
Economic Sustainability (TS1 )
Economic productivity (TS11 ) (Litman, 2005)
• Income, economic activity- Per capita GDP (TS111 ) • Operational efficiency (TS112 ) • Transport budget and road taxes (Tax revenues) (TS113 ) (Gilbert and Tanguay, 2000)
Economic development (TS12 ) (Deakin, 2001; Jeon et al., 2010)
• Access to education and employment opportunities (TS121 ) (Litman, 2008) • Support for local industries (TS122 ) (Litman, 2005) • Infrastructure costs (TS123 ) (Litman, 2007., (Dobranskyte-Niskota et al., 2007)
Affordability (TS13 ) (Salling and Pryn, 2015)
• Availability and quality of affordable various modes and types of travel (TS131 ) (Litman, 2005) • Reduce the Impact of low-income households that spend more than 20% of budgets on transport (TS132 ) (Litman, 2005) • Transport diversity (TS133 ) • Parking facilities (TS141 ) • Minimum taxation on fuel (TS142 ) (Bongardt, 2011) • Bottlenecks (TS143 )
Resource efficiency (TS15 ) (Litman and Burwell, 2006)
• Per capita transport energy consumption (TS151 ) (Jeon and Amekudzi, 2005,) • Responses to Consumer Demand (TS152 ) (Gudmundsson, 2001) • Availability of transportation (TS153 )
Social Sustainability (TS2 )
Equity/Fairness (TS21 ) (Litman, 2007)
• Civil and human rights (TS211 ) • Reduce Portion of destinations accessible by people with disabilities and low incomes (TS212 ) (Litman, 2005) • Transport system diversity (TS213 ) (Litman, 2005)
Human safety, security and health (TS22 )
• Human safety-security on public transport (TS221 ) (Gilbert, 2005)
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
Traffic congestion (TS14 ) (Litman and Burwell, 2006; Litman, 2007)
• Traveler assault (crime) prevention (TS222 ) (Litman, 2005) • Reduce the Per capita traffic casualty (injury and death) rates (TS223 ) (Litman, 2005) Community livability (TS23 ) (Litman, 2007; Litman and Burwell, 2006)
• Accessibility to employment (TS231 ) • Human health impacts (TS232 ) • Accessibility to public services (TS233 )
Community cohesion (TS24 ) (Litman, 2007; Jakimaviˇcius, 2008)
• Land use mix (TS241 ) (Dobranskyte-Niskota et al., 2007) • Employment stability (TS242 ) • Interconnectivity of transport modes (TS243 )
Government efficiency (TS25 )
• Integrated, comprehensive and inclusive planning (TS251 ) • Efficient pricing (TS252 )
505
• Indigenous rights (TS253 )
506
Table 1 (Continued) Transport Sustainability enablers Transport Sustainability criteria Stakeholder involvement (TS26 )
Transport Sustainability attributes • Human capital (TS261 ) • Workforce Development (TS262 ) • Community capital (TS263 )
Cultural preservation (TS27)
• Preservation of cultural resources and traditions (TS271 ) (Litman, 2005) • Responsiveness to traditional communities. (TS272 ) (Litman, 2005) • Prevention to cultural Barriers (TS273 ) (Aotearoa, 2009)
Environmental Sustainability (TS3 )
Climate change prevention and mitigation (TS31 ) (Litman, 2005)
• Per capita emissions of global air pollutants (CO2, CFCs, CH4, etc.) (TS311 ) (Litman, 2005) • Transport infrastructure and operations affected by climate change (TS312 ) • Consumption of natural resources (TS313 ) (Zegras, 2006)
Pollution prevention (TS32 ) (Dobranskyte-Niskota et al., 2007)
• Control on Air pollution (TS321 ) (Haghshenas and Vaziri, 2012)
• Control on Water pollution (TS323 ) Non-Renewable resource conservation (TS33 ) (Litman, 2005; Gudmundsson et al., 2016)
• Percentage recyclable materials in production process of vehicles (TS331 ) • Percentage recyclable materials in infrastructure (TS332 ) • Per vehicle/mode/object non-recyclable/recyclable materials ratio (TS333 )
Open space preservation and biodiversity protection (TS34 )
• Portion of land paved for transport facilities (TS341 ) • Policies to protect high value farmlands and ecological habitat (TS342 ) • Support for smart growth development (TS343 )
Hydrologic impacts (TS35 ) (Litman, 2007; Litman and Burwell, 2006)
• Reduce in Per capita fuel consumption (TS351 ) (Litman, 2005) • Management of used oil and leaks (TS352 ) • Per capita impervious surface area (TS353 )
Transportation system effectiveness (TS4 ) (Jeon et al., 2010)
Improve mobility (TS41 ) (Dobranskyte-Niskota et al., 2007)
• Access to transit (TS411 )
• Travel rate (TS412 ) (Jeon et al., 2010) • Efficient Transport operations (TS413 ) Improve system performance (TS42 )
• Total vehicle-miles traveled (TS421 ) (Jeon et al., 2010) • Transit passenger miles traveled (TS422 ) (Jeon et al., 2010) • Freight ton-miles (TS423 ) (Jeon et al., 2010)
Transportation reliability (TS43 )
• per capita Congestion cost (TS431 ) (Litman, 2008; Kirk et al., 2010) • Travel time index (TS432 ) (Kirk et al., 2010) • Percentage of daily traffic in congested conditions (TS433 ) (Kirk et al., 2010)
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
• Control on Noise pollution (TS322 ) (Haghshenas and Vaziri, 2012)
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Table 2 Fuzzy numbers for approximating linguistic variables. Performance rating
Importance weighting
Linguistic variable
Fuzzy number
Linguistic variable
Fuzzy number
Worst (W) Very poor (VP) Poor (P) Fair (F) Good (G) Very Good (VG) Excellent (E)
(0, 0.5, 1.5) (1, 2, 3) (2, 3.5, 5) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (8.5, 9.5, 10)
Very low (VL) Low (L) Fairly low (FL) Medium (M) Fairly high (FH) High (H) Very high (VH)
(0, 0.05, 0.15) (0.1, 0.2, 0.3) (0.2, 0.35, 0.5) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.85, 0.95, 1.0)
intelligent transport solutions, etc. on city sustainability. A multiplicative AHP decision support model has been presented by Pryn et al. (2015) to assessment of sustainable transport infrastructure. Based on the literature review, to the best knowledge of authors it has been found that no concrete research has been carried out in the context of transport sustainability evaluation with performance indicators. Also, the deployment of fuzzy approach for evaluation of transport sustainability performance has not been attempted to the best knowledge of authors. In this context, this article presents an approach for evaluation of transport sustainable performance.
Table 3 Aggregated performance rating and weight of Transport Sustainability attributes. TSi
TSij
TSijk
Wi
Wij
Wijk
Rijk
TS1
TS11
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
H
H
FH H FH H H FH H FH FH H FH FH FH H H H FH FH H H FH FH FH FH H H FH FH FH H FH H FH FH FH FH FH FH H H H H FH M FH FH FH H H H M FH FH H H FH FH FH H FH
F G F G G F G G F G G F G VG VG G F G VG VG G F G G F G F F G G G G F G F F F F F F F G G F F F F G G F F G G G G G F F G G
TS12
TS13
TS14
3. Methods
TS15
The scope of this study is to know how transport sustainable an enterprise or a region is and how an enterprise/region can improve its transport sustainability effectively. The method for Fuzzy Transport Sustainability Evaluation (FTSE) framework is shown in Fig. 1. Based on the literature survey, a conceptual model for urban transport sustainability evaluation has been developed, but the conduct of the study provided guidelines for practitioners and experts to systematically quantify and analyze transport sustainability performance to any type of enterprise/region and should be valid for national, local or urban dimensions. The model is divided into three levels namely: enabler, criteria, and attributes. Then, the linguistic ratings and weights have been gathered from experts and decision makers. Fuzzy logic is the tool for transforming human knowledge and its decision-making ability into a mathematical formula, which is difficult to obtain with conventional analytical techniques. It provides a meaningful representation of uncertainties and also representation of vague concepts expressed in natural language (Klir and Yuan, 1995). These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Triangular fuzzy number is used here to approximate the linguistic variables because; it is widely used in performance assessment studies (Lin et al., 2006; Vinodh and Devadasan, 2011). Additional discussion regarding the fuzzy logic can be found in book by Klir and Yuan (1995). Using fuzzy operation three levels of calculation has been done and the Fuzzy Transport Sustainability Index (FTSI) of the enterprise has been determined. FTSI has been matched with the natural expression linguistic terms using Euclidean distance method to find the transport sustainability level of the enterprise. Finally, the principal obstacles have to be identified and analysed for improvement. Transport sustainability index is given by:
TS2
TS22
TS23
TS24
TS25
TS26
TS27
TS3
N
TSij
(1)
TS31
TS32
TS33
TS34
TS35
TS4
(Transport Sustainability Index )i =
TS21
TS41
TS42
j=1
TS43
where, TSij is the transport sustainability levels of capability j of enterprise i.
H
FH
FH
H
H
H
H
FH
FH
FH
H
FH
H
H
H
FH
FH
FH
FH
FH
FH
H
508
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
Table 4 Linguistic variables approximated by fuzzy numbers. TSi
TSij
TSijk
Wi
Wij
Wijk
Rijk
TS1
TS11
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.3, 0.5, 0.7) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.5, 0.65, 0.8) (0.7, 0.8, 0.9) (0.5, 0.65, 0.8)
(3, 5, 7) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8)
TS12
TS13
TS14
TS15
TS2
TS21
TS22
TS23
TS24
TS25
TS26
TS27
TS3
TS31
TS32
TS33
TS34
TS35
TS4
TS41
TS42
TS43
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
N
(Ri × Wi )
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
i=1
Wi = 1
(0.5, 0.65, 0.8)
(0.5, 0.65, 0.8)
(0.7, 0.8, 0.9)
(2)
i=1 N
(0.7, 0.8, 0.9)
(0.7, 0.8, 0.9)
If Ri and Wi denote the transport sustainability index and the weight of each transport sustainability capability, respectively, the transport sustainability index can be defined as:
(Transport Sustainability Index ) =
(0.7, 0.8, 0.9)
where, The fuzzy index of level two transport sustainability criteria TSij , level one transport sustainability enabler TSi and FTSI can be calculated using the formula:
n
TSIij = nk=1 Wijk × TSijk /
Wijk
(4)
k=1
(3)
where TSijk represent performance rating and Wijk represent importance weight of transport sustainability attributes.
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513 Table 5 Fuzzy index of each grade of Transport Sustainability attributes. TSi
TSij
Ri
Rij
TS1
TS11 TS12 TS13 TS14 TS15 TS21 TS22 TS23 TS24 TS25 TS26 TS27 TS31 TS32 TS33 TS34 TS35 TS41 TS42 TS43
(4.76, 6.27, 7.83)
(3.82, 5.57, 7.36) (4.47, 6.07, 7.69) (4.41, 6.04, 7.68) (4.41, 6.04, 7.68) (6.47, 7.57, 8.69) (4.41, 6.04, 7.68) (6.47, 7.57, 8.69) (4.33, 6. 7.67) (3.74, 5.53, 7.35) (4.41, 6.04, 7.68) (4.41, 6.04, 7.68) (3.67, 5.50, 7.33) (3, 5, 7) (3.67, 5.50, 7.33) (3.77, 5.54, 7.35) (3.82, 5.57, 7.36) (3.82, 5.57, 7.36) (5, 6.5, 8) (4.41, 6.04, 7.68) (4.41, 6.04, 7.68)
TS2
TS3
TS4
(4.58, 6.14, 7.74)
(3.58, 5.42, 7.27)
(4.63, 6.20, 7.79)
4. Development of conceptual model for transport sustainability evaluation The sustainability assessment conceptual model is developed and is shown in Table 1.The model is divided into three levels. The first level consists of four transport sustainability enablers; the second level consists of 20 transport sustainability criteria; and the third level consists of 60 transport sustainability attributes. The model is comprehensive, and it has been developed from
TS11 =
509
to assess the importance weights of the transport sustainability attributes, the linguistic variables very high (VH), high (H), fairly high (FH), medium (M), fairly low (FL), low (L), very low (VL) are used as shown in Table 2. 5.2. Measurement of performance ratings and importance weights of transport sustainability attributes using linguistic terms Once the linguistic variables for evaluating the performance ratings and the importance weights of the transport sustainability capabilities are defined, aggregated performance rating and importance weight of transport sustainability attributes is provided by the expert team and is shown in Table 3. 5.3. Approximation of the linguistic terms by fuzzy numbers Using the concept of fuzzy theory, the linguistic variables can be approximated by a fuzzy number (Lin et al., 2006; Vinodh and Devadasan, 2011). In the application of the relation between linguistic terms and fuzzy numbers, the linguistic terms shown in Table 2 are transferred into fuzzy numbers as shown in Table 4. 5.4. Determination of fuzzy transport sustainability index (FTSI) FTSI represents the overall enterprise level transport sustainability. The fuzzy index has been calculated at the criterion level and then extended to enabler level. Fuzzy index at the criterion level encompasses several transport sustainability attributes. The fuzzy index of level two transport sustainability criteria can be calculated using the formula (4). As a sample, the fuzzy index for Economic productivity (TS11 ) criterion can be calculated as follows:
[(0.5, 0.65, 0.8) x (3, 5, 7) + (0.7, 0.8, 0.9) x (5, 6.5, 8) + (0.5, 0.65, 0.8) x (3, 5, 7)] [(0.5, 0.65, 0.8) + (0.7, 0.8, 0.9) + (0.5, 0.65, 0.8)] TS11 = (3.82, 5.57, 7.36)
literature analysis and in discussion with and practitioners working in transportation domain. The model considers four major dimensions of transport sustainability namely; Economic Sustainability, Social Sustainability, Environmental Sustainability and Transportation System Effectiveness (Jeon et al., 2010). Four transport sustainability enablers and 20 transport sustainability criteria are comprehensive enough to measure transport sustainability. As the sample, Economic productivity criterion consist of attributes namely Income, economic activity- Per capita GDP, Operational efficiency, Transport budget and road taxes (Tax revenues). 5. An illustrative example The detailed steps of Transport Sustainability evaluation are being presented in the sections below. 5.1. Determination of the appropriate linguistic scale for assessing the performance ratings and importance weights of transport sustainability attributes The linguistic terms are used to assess the performance ratings and importance weights of transport sustainability attributes since it is difficult for experts to determine the score of a vague attribute such as interchange ability of personnel. The linguistic scale used by Lin et al. (2006) has been adopted in this research study. In order to assess the performance rating of the transport sustainability capabilities, the linguistic variables excellent (E), very good (VG), good (G), fair (F), poor (P), very poor (VP), worst (W) are used. In order
By using the same equation, fuzzy indexes of level two transport sustainability criteria and level one transport sustainability enabler have been obtained and shown in Table 5. Finally, the FTSI has been calculated as
FTSI =
[(4.76, 6.27, 7.83) × (0.7, 0.8, 0.9) + (4.58, 6.14, 7.74) × (0.7, 0.8, 0.9) + (3.58, 5.42, 7.27) × (0.7, 0.8, 0.9) + (4.63, 6.20, 7.89) × (0.5, 0.65, 0.8)] [(0.7, 0.8, 0.9) + (0.7, 0.8, 0.9) + (0.7, 0.8, 0.9) + (0.5, 0.65, 0.8)]
FTSI = (5.05, 6.62, 8.12) The obtained fuzzy transport sustainability index has been validated using conventional crisp technique and transport sustainability index is found 6.05 as shown in Table 6. This shows the corroboration of results of fuzzy and conventional approaches. 5.5. Determination of Euclidean distance to match FTSI with approximate transport sustainability level Once the FTSI has been obtained, the FTSI can be further matched with the linguistic label. Euclidean distance method is the most commonly chosen type of distance method. The main advantage of Euclidean method over other methods is the distance between any two objects is not affected by the addition of new objects to the analysis (Lin et al., 2006). In Euclidean distance method, the naturallanguage expression set TS = {Extremely Transport Sustainable [ETS (7, 8.5, 10)], Very Transport Sustainable [VTS (5.5, 7, 8.5)], Transport Sustainable [TS (3.5, 5, 6.5)], Fairly Transport Sustainable [FTS (1.5, 3, 4.5)], Slowly Transport Sustainable [STS (0, 1.5, 3)]} is selected for labeling. By using the Euclidean distance method Eq. 5, the
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Table 6 Computation of index of each grade of Transport Sustainability attributes using crisp approach. TSi
TSij
TSijk
Wi
Wij
Wijk
Rijk
Rij
Ri
TSI
TS1
TS11
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
0.8
0.8
0.65 0.8 0.65 0.8 0.8 0.65 0.8 0.65 0.65 0.8 0.65 0.65 0.65 0.8 0.8 0.8 0.65 0.65 0.8 0.8 0.65 0.65 0.65 0.65 0.8 0.8 0.65 0.65 0.65 0.8 0.65 0.8 0.65 0.65 0.65 0.65 0.65 0.65 0.8 0.8 0.8 0.8 0.65 0.5 0.65 0.65 0.65 0.8 0.8 0.8 0.5 0.65 0.65 0.8 0.8 0.65 0.65 0.65 0.8 0.65
5 6.5 5 6.5 6.5 5 6.5 6.5 5 6.5 6.5 5 6.5 8 8 6.5 5 6.5 8 8 6.5 5 6.5 6.5 5 6.5 5 5 6.5 6.5 6.5 6.5 5 6.5 5 5 5 5 5 5 5 6.5 6.5 5 5 5 5 6.5 6.5 8 5 6.5 6.5 6.5 6.5 6.5 5 5 6.5 6.5
5.57
6.27
6.05
TS12
TS13
TS14
TS15
TS2
TS21
TS22
TS23
TS24
TS25
TS26
TS27
TS3
TS31
TS32
TS33
TS34
TS35
TS4
TS41
TS42
TS43
0.8
0.65
0.65
0.8
0.8
0.8
0.8
0.65
0.65
0.65
0.8
0.65
0.8
0.8
0.8
0.65
0.65
0.65
0.65
0.65
0.65
0.8
Table 7 Comparison of fuzzy logic approach and crisp approach. Approach
Transport sustainability index
Range
Linguistic labeling
Fuzzy logic Crisp approach
(5.05, 6.62, 8.12) 6.05
3.07
Very Transport Sustainable Very Transport Sustainable
6.07
D (TSI, VTSi ) = 0.70
6.04
D (TSI, TSi ) = 2.77 6.04
D (TSI, FTSi ) = 6.23 7.57
D (TSI, STSi ) = 8.83 6.04
6.14
6. Results and discussion 7.57
Thus by matching a linguistic label with the minimum D, the transport sustainability index has been identified as ‘Very Transport Sustainable’. The linguistics and corresponding membership functions are shown in Fig. 2. With regard to the efficiency of the method to measure transport sustainability index, the result generated by both fuzzy and conventional approaches leads to same conclusions as shown in Table 7. The above method is not only used to identify the transport sustainability level, but also used to identify the principal obstacles for improvement. Fuzzy Performance Importance Index (FPII) is used to identify the obstacles (Lin et. al., 2006). It combines the performance rating and importance weight of transport sustainability attributes. The higher the FPII of a factor, the higher is the contribution. The FPII is calculated as follows:
6.00
5.53
6.04
6.04
5.50
5.00
5.63
FPIIijk = Wijk × TCijk where,
5.50
Wijk = (1, 1, 1) − Wijk 5.54
(6)
5.57
Wijk is the fuzzy importance weight of the transport sustainability element capability ijk. For example, the calculation of FPII of Income, economic activity- Per capita GDP (TS111 ) is shown below:
6.71
FPII111 = [(1, 1, 1) − (0.5, 0.65, 0.8)] × (3, 5, 7)
6.50
FPII111 = (0.6, 1.75, 3.5)
6.18
FPII for the remaining attributes are calculated using above principle and is shown in Table 8. Since fuzzy numbers do not always yield a totally ordered set as real numbers do, all the FPIIs must be ranked (Vinodh and Vimal, 2012). Here, the ranking of the fuzzy number is based on centroid method for membership function (a, b, c) is given in Eq. 8, where a, b, and c are the lower, middle, and upper numbers of triangular fuzzy number.
6.04
6.04
Ranking Score = (a + 4b + c) /6 Euclidean distance D from the TSI to each member in set TS is calculated as follows. D (TSI, TSi ) =
x ∈ p , [f TSI (x) − fTS (x)]
2
1/2
(5)
The model calculation for D (TSI, ETS) is shown as follows D (TSI, ETSi ) =
(5.05 − 7)2 + (6.62 − 8.5)2 + (8.12 − 10)
D (TSI, ETSi ) = 3.30
(7)
2 1/2
(8)
The model calculation for the FPII111 attribute is shown below Ranking Score =
(0.6 + 4 × 1.75 + 3.5) 6
Ranking Score = 1.85 The ranking score for the reaming attributes are calculated using above principle and it is shown in Table 8. To identify the obstacles, value 1.35 has been set as management threshold to distinguish the critical obstacles. As shown in Table 8, 20 attributes have a
S. Rajak et al. / Ecological Indicators 71 (2016) 503–513
511
F(x)
FTS
STS
0
1
2
3
TS
4
5
ETS
VTS
6
7
8
9 -
10 x FTSI
Fig. 2. Linguistic levels to match Fuzzy Transport Sustainability Index [STS (0, 1.5, 3); FTS (1.5, 3, 4.5); TS (3.5, 5, 6.5); VTS (5.5, 7, 8.5); ETS (7, 8.5, 10)]. Table 8 Fuzzy performance importance indexes of Transport Sustainable attributes.
Transport Sustainable attribute
Aggregated fuzzy performance rating
W ijk = [(1, 1, 1) − Wijk ]
FPII
Ranking score
TS111 TS112 TS123 TS121 TS122 TS123 TS131 TS132 TS133 TS141 TS142 TS143 TS151 TS152 TS153 TS211 TS212 TS213 TS221 TS222 TS223 TS231 TS232 TS233 TS241 TS242 TS243 TS251 TS252 TS253 TS261 TS262 TS263 TS271 TS272 TS273 TS311 TS312 TS313 TS321 TS322 TS323 TS331 TS332 TS333 TS341 TS342 TS343 TS351 TS352 TS353 TS411 TS412 TS413 TS421 TS422 TS423 TS431 TS432 TS433
(3, 5, 7) (5, 6.5, 8) (3, 5, 7) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (7, 8, 9) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (5, 6.5, 8) (7, 8, 9) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8) (7, 8, 9) (5, 6.5, 8) (5, 6.5, 8) (3, 5, 7) (3, 5, 7) (5, 6.5, 8) (5, 6.5, 8)
(0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.3, 0.50, 0.7) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.3, 0.50, 0.7) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.2, 0.35, 0.5) (0.1, 0.20, 0.3) (0.2, 0.35, 0.5)
(0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.7, 1.60, 2.7) (0.7, 1.60, 2.7) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.7, 1.60, 2.7) (0.7, 1.60, 2.7) (1, 2.28, 4) (0.6, 1.75, 3.5) (1, 2.28, 4) (1, 2.28, 4) (0.3, 1.00, 2.1) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.5, 1.30, 2.4) (0.6, 1.75, 3.5) (1, 2.28, 4) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.3, 1.00, 2.1) (0.3, 1.00, 2.1) (0.3, 1.00, 2.1) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.9, 2.50, 4.9) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (0.5, 1.30, 2.4) (0.3, 1.00, 2.1) (0.9, 2.50, 4.9) (1, 2.28, 4) (1, 2.28, 4) (0.5, 1.30, 2.4) (0.5, 1.30, 2.4) (1, 2.28, 4) (0.6, 1.75, 3.5) (0.6, 1.75, 3.5) (0.5, 1.30, 2.4) (1, 2.28, 4)
1.85 1.35a 1.85 1.35a 1.35a 1.85 1.35a 2.35 1.85 1.35a 2.35 1.85 2.35 1.63 1.63 1.35a 1.85 2.35 1.63 1.63 2.35 1.85 2.35 2.35 1.07a 1.35a 1.85 1.85 2.35 1.35a 2.35 1.35a 1.85 2.35 1.85 1.85 1.85 1.85 1.07a 1.07a 1.07a 1.35a 2.35 2.63 1.85 1.85 1.85 1.35a 1.35a 1.07a 2.63 2.35 2.35 1.35a 1.35a 2.35 1.85 1.85 1.35a 2.35
a
Weaker attributes.
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lower performance than the threshold. These 20 attributes represent significant contributions and it could be enhanced to achieve transport sustainability. For example, Regular check-up and maintenance could reduce the oil leaks and enhance the operational efficiency; Availability and quality of affordable various modes and types of travel could be enhanced by proper planning; Parking facilities could be enhanced by proper parking management; Compact development and mixed use development could enhance Land use mix; Workforce development and Employment stability could be ensured by effective manpower planning; Control on Air pollution could be enhanced by proper guidelines and government policy (e.g., Government of India has developed “Auto Fuel Policy (AFP) for India”); Control on Noise pollution could be enhanced by design modification (Help of experts in design) and also by using personal protective equipments Reduce in dissemination transport waste such as asbestos, rubber, gasoline, lubricants could enhance Control on Water pollution. 7. Conclusions This article presented the fuzzy based approach to evaluation of urban transport sustainability performance. The purpose of the study is the contribution of a conceptual model for transport sustainability performance evaluation, computation of FTSI and identification of weaker attributes. The study begins with the gathering of inputs ratings and weights from experts and decision makers. Since conventional transport sustainability performance measurement is associated with vagueness and uncertainties, fuzzy logic approach has been used in the study. Triangular fuzzy numbers are used in performance evaluation. The inputs are fuzzified and The FTSI has been computed as (5.05, 6.62, 8.12). Based on Euclidean distance computation, the enterprise is found to be ‘Very Transport Sustainable’. The computed result has been validated using the conventional crisp approach. Besides computing transport sustainability index, the weaker areas for improvement also have been identified. FPII of transport sustainability attributes were computed. 20 attributes out of 60 attributes were found to be weaker; proposals have been developed for the transport sustainability improvement. The conduct of the study provided guidelines for practitioners to systematically quantify and analyze transport sustainability performance. The approach could be applied to any type of enterprise/region. 7.1. Limitations and future scope The conceptual model for transport sustainability evaluation could also be extended beyond 20 criteria. Trapezoidal fuzzy functions also could be used to improve the effectiveness of results. Also, dedicated knowledge based expert system and Global Sensitivity and Uncertainty Analyses (GSUA) could be developed to enhance the effectiveness of computation and analysis. References Aotearoa, T.A.M., 2009. Cultural Indicators for New Zealand. Ministry for Culture and Heritage, Te Manatu¯ Taonga, Wellington New Zealand, ISBN 978-0-478-18466-2. Awasthi, A., Chauhan, S.S., 2011. Using AHP and DempstereShafer theory for evaluating sustainable transport solutions. Environ. Modell. Software 26, 787–796. Banister, D., 1996. Barriers to Implementation of Unban Sustainability. European regional science association 36th European congress, ETH Zurich, Switzerland, pp. 26–30, August 1996. Bartlett, A.A., 2012. The Meaning of Sustainability, teachers clearinghouse for science and society education newsletter. vol. 31, no. 1, winter 2012, pp. 1–17. [email protected]. Black, W.R., 1996. Sustainable transportation: a US perspective. J. Transp. Geogr. 4 (3), 151–159.
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