^{1}

^{2}

^{1}

^{3}

^{1}

^{2}

^{3}

The paper aims to build a hybrid personalized multicriteria model in the Indian transportation industry to identify the most feasible transport mode suitable for commuters’ customized preferences. A hybrid multicriterion model, i.e., Fuzzy Analytical Hierarchy Process (AHP), was used to compute the criteria weights, which were subsequently analyzed by three approaches, namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy TOPSIS, Evaluation Based on Distance from Average Solution (EDA), and Interpretive Ranking Process (IRP). The case of an Indian metropolitan city, Hyderabad, is taken to illustrate the proposed approach. The paper highlights the following transport modes: metropolitan train (unconventional mode) and conventional modes such as the car, public bus transport, and bikes for Hyderabad. Furthermore, sensitivity analysis is performed to identify the consistency in ranking with variation in weights, and the Ensemble Ranking and transportation experts validate the rankings.

India’s transportation industry has been growing steadily at a Cumulative Annual Growth Rate (CAGR) of 5.9% and is majorly dominated by roadways. India has a road network of 5.23 million kilometers and is expected to grow at a CAGR of 7% in the next five years [

Existing decision-making systems do not consider the customized preferences of the passengers and the transport planners to provide an automated decision regarding the vehicle mode is feasible, which is essential for a developing country and a price-sensitive market like India. Moreover, any country’s Central government accords paramount importance to the safety and security of the conventional modes of transportation apart from analyzing the economic feasibility and the environmental sustainability. Some factors like political consequences, cost-effectiveness, and the impact on the environment [

In this context, there is a need to choose a vehicle mode that satisfies all the stakeholders (users, operators, planners, and policymakers). The choice must also rationalize all the stakeholders’ conflicting perspectives for which the hybrid multicriteria model is adopted in this paper. The PESTLE framework (Political, Economic, Social, Technological, Legal, and Ecological) is adopted in this paper to identify the most suitable transport alternative with due consideration for all dimensions. This framework is appropriate since different stakeholders consider different perspectives. Users consider mainly the economic and social perspectives to decide on the choice of the vehicle mode. If the choice is studied ony from a user perspective, other (political, technological, and legel) considerations may not be factored in, thus resulting in a mopic viewpoint. However, in the context of a metropolitan city and for a problem statement like the vehicle mode choice, there is a need to address all the PESTLE factors to make a more informed decision. This is because transport operators, city planners, and policymakers also consider other political, legal, technological, and environmental concerns for promoting a vehicle mode. Hence, to cater to all stakeholders and capture the plethora of factors considered in this paper, a multistakeholder perspective is adopted without confining to a single perspective. The research objectives or motives of this paper are defined as follows:

To identify and evaluate the significant factors from the internal and external environments that influence India’s transport mode decision

To evaluate and identify the best-suited transport modes from a multistakeholder perspective (benefitting passengers, operators, planners, and policymakers) using a hybrid multicriteria model

To provide practical and policy implications to the stakeholders

Since there are many factors/criteria to be considered which may involve evaluating the benefit-cost analysis of the elements, a multicriteria [

The review of the existing literature involves identifying the factors and subfactors influencing the vehicle mode selection (Section

The existing studies that analyze the passenger preferences categorize the factors into (i) Political factors, (ii) Economic Factors, (iii) Social Factors, (iv) Technological Factors, (v) Legal Factors, and (vi) Environmental factors [

Factors that influence the vehicle mode selection.

The factors and subfactors that influence the selection of an available transport mode are as follows:

The political situation is a critical factor that determines the choice of transport. The enforcement and amendments made to significant transportation policies, including employment laws for staff, environmental legislation, vehicle taxation laws, health, and safety norms [

Political stability [

Government policy: the government policy issues refer to the introduction of competing for public transports and the implementation of newer unconventional modes of transport. The Ministry of Transportation and Roadways promulgates legislations to different route modes of transport, which impacts the city’s route landscape and, in turn, the choice of transport.

However, another set of factors that influences the feasibility of transport modes are economic factors. The financial issues include

Duties and taxes [

Economic growth: the growth of the economy influences lifestyle changes, and this, in turn, impacts the choice of the transportation mode.

Cost efficiency [

Workforce utilization/unemployment rate: the transportation industry is infrastructure-dependent and highly labor-intensive. Alternative transport modes especially are highly dependent on the low-cost labor market. At the same time, conventional transport modes cannot be completely phased out since the livelihood of the drivers and the staff (employees) will be disrupted.

Social factors include attributes like demography, education level, and income with the perception of health, safety, and security [

Health: the health conditions and issues faced by passengers and drivers influence the continuation of transportation modes’ operation.

Safety: the safety of the vehicle and the route adopted are both critical to choose a suitable vehicle mode.

Security: the measure of the risk exposure of the passengers and the staff to criminal activities like physical harassment, robbery, stealing, etc., is security. Passengers intuitively prefer highly secure modes of transport.

The evolution of technology developed ways to quantify the maintenance of vehicles. There are several technological factors of which the factors “upgradation in fuel technology” and the “rate of fuel efficiency” [

Maintenance: the extent to which a vehicle is damage-free and is in good condition for operability on the roads is called maintenance. Maintenance is also related to the safety of the vehicle and is of high importance.

Fuel efficiency: the distance travelled by a vehicle viz-a-viz the fuel consumed to complete the trip is a measure of how fuel-efficient the vehicle is. The higher the value, the more economical the vehicle will be.

Laws governing the transport authorities and vehicle plying policies are essential to determine the transport mode. Legal factors can be:

Restricted movement: the legal factors deal with regulatory operating bodies, statutory rules, and the policies regarding “Restricted Movement” [

Legislation: the traffic routing policies and the extent of stringent measures taken to administer the functioning of the unconventional modes of transport also influence vehicle mode choice.

The ecological dimension is captured by the factor of environmental pollution [

Air pollution: the vehicle modes that release poisonous pollutants into the air like carbon monoxide and nitrous oxides have a damaging effect on the environment leading to hazards like global warming, acid rain, and ozone layer depletion if not curbed at early stages.

Noise pollution: the vehicles also can contribute to a major portion of the community noise, particularly disruptive in the residential areas. The traffic flow speed is directly proportional to the number of decibels of noise emitted and needs to be controlled.

The existing studies on vehicle alternative selection are discussed below:

Christiansen [

Gössling [

Daisy et al. [

Ashmore et al. [

Tarabay and Abou-Zeid [

Van Ristell et al. [

Stoilova [

Roorda et al., [

Jian et al. [

Luo et al. [

Chen and Wang [

Onstein et al. [

To study how fuel efficiency impacts vehicle mode, binary logit models were adopted by Krishna et al. [

Böcker et al. [

Furthermore, the viability of an unconventional mode of transport, namely, e-bike share vehicle, is assessed by Campbell et al. [

Chee and Fernandez [

Donald et al. [

Gebeyehu and Takano [

The transport preferences of school-going children were analyzed by Kamargianni et al. [

Madhuwanthi et al. [

Transportation mode choice is investigated in Kharagpur and Asansol by Majumdar et al. [

Santos et al. [

The above studies adopt existing choice models to identify the best-suited vehicle mode in respective country scenarios. However, there are some limitations in the choice of the methodology used.

These limitations are thus discussed below:

The above studies consider single or multiple factors for determining the choice of vehicle selection. However, a multifactor approach is not adopted to analyze the cumulative impact of all the factors considered for the transport choice decision. Determining the essential factors will help take proactive steps by road transport authorities to provide transport services at subsidized rates to retain frequent passengers.

Figure

Research gaps.

Secondly, since a single factor is not sufficient to decide on the most feasible transport choice, there is a need to implement a multifactor approach to capture commuters’ personalized preferences. Furthermore, existing choice models only capture the economic perspective of the choice of transport mode while other dimensions like political, social, and ecological factors are not considered. Furthermore, in a multistakeholder and hierarchical scenario where multiple conflicting objectives are applicable, there is a need for a more robust model to capture all the factors influencing the choice of the vehicle mode. Though there are some existing multicriteria approaches to make such decisions, there is a need to validate these approaches’ outcome and perform a sensitivity analysis for further whetting the results. A hybrid multicriteria model that computes weights shows ranking, and validation using different multicriteria methods can provide a more optimized decision.

For overcoming the above limitations, a hybrid multicriteria model is adopted in the paper. The data collection procedure and research methodology adopted are discussed in

This section elucidates the data collection procedure (

Existing frameworks like the SWOT capture the strengths, weaknesses, opportunities, and threats but do not factor in the ecological and the social dimensions. The different transport’s survivability is driven by several external factors like government policies, carbon footprint, unfavorable laws, and constantly updating technologies. The PESTLE framework [

PESTLE Analysis framework stands for, Political, Economic, Social, Technological, Legal, and Environmental factors within the system to aid decision-making.

Furthermore, although there are existing techniques like choice models [

Secondly, there is a need to identify a suitable vehicle alternative from a host of conflicting objectives across stakeholders and across multiple hierarchy levels (criteria are further subdivided into subcriteria).

Thus, the hybrid multicriteria model is recommended over state-of-the-art choice models [

Fuzzy AHP is adopted to compare the factors and alternatives by computing fuzzy scores assigning weights to the criteria. This methodology is very useful for multiple criteria decision-making in uncertain environments where the relative importance of factors influencing the transport mode cannot be assigned a crisp score. However, a fuzzy score with lower, medium, and higher bounds can be estimated for the factors. Hence, Fuzzy AHP is adopted. This Ranking is accomplished by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [

Overall, to present a consolidated picture of the various rankings and to arrive at a robust consensus ranking to choose the most feasible vehicle mode, the novel Ensemble Ranking [

Thus, a hybrid approach [

The methodology adopted is divided into the following phases:

To analyze the alternative modes of transport in the metropolitan city of Hyderabad, initially, the data collection phase was initiated. A “complete participation” interview approach [

The above responses provided by the passengers are then transformed to fuzzy scores according to the fuzzy scale provided in Table

Sample demographics.

Gender | Number | Percentage |
---|---|---|

Males | 200 | 66 |

Females | 100 | 34 |

Age | ||

<30 years | 230 | 76.6 |

30–50 years | 70 | 23.4 |

High class | 10 | 3.3 |

Middle class | 220 | 73.3 |

Lower middle class | 70 | 23.4 |

Private employees | 120 | 40 |

Government employees | 100 | 33.3 |

Students | 50 | 16.6 |

Miscellaneous | 30 | 10 |

300 |

The sample involves 300 passengers belonging to different localities, age groups, professions, and gender. Out of the total sample, there were 200 male and 100 female respondents. Almost 23.4 percent of the respondents were aged between 30 and 50 years, while the majority of the commuters are in the age group of <30 years and constitute 76.6% of respondents. Table

Based on their responses, the weights computed are provided as inputs to the ranking models TOPSIS/Fuzzy TOPSIS/EDA/IRP below. But before that, the next phase, phase 2, involves classification of the factors into benefit and cost factors for inputting to the ranking models TOPSIS, Fuzzy TOPSIS, and EDA. For instance, political instability is considered a cost factor, implying that the higher the value, the more disruptive it is to the vehicle mode. At the same time, a positive sign (+) is provided for fuel efficiency, which indicates that the higher the value, the more beneficial is the factor. The impacts of these criteria depicted in the criteria evaluation matrix were quantified on a 1–9 scale (Saaty scale) as follows: 1 (no effect) and 9 (highest impact), while for the alternatives, the rating score provided by the passengers was in a 1–10 scale as follows: 1 (lowest) and 10 (highest). Factors like fuel and |cost efficiency are measured per kilometer, while the operating cost is calculated on a monthly basis.

The issues were then categorized into factors and subfactors based on the PESTLE (Political, Economic, Social, Technological, Legal, Environmental) framework. The relative importance of the elements is computed by the Fuzzy AHP (Fuzzy Analytic Hierarchy Process). The best alternative mode of transport is assessed and ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), Fuzzy TOPSIS, EDA, and IRP methods.

The next phase, phase 3, involves computing the weights for each factor classified above using the Fuzzy Analytical Hierarchy Process (Fuzzy AHP). Before understanding the functionality of the Fuzzy AHP, the fuzzy set theory and the Analytical Hierarchy Process (AHP) are detailed below.

Any event, process, or function that is continuously variable and cannot be definitively categorized into true or false is said to be Fuzzy. Fuzzy logic implements the above principle to degrees of truth rather than the usual true/false or 1/0 like the Boolean logic. In fuzzy systems, the values are indicated between 0 (untrustworthy) to 1 (right).

Fuzzy Logic principles in set theory constitute Fuzzy Set Theory [

The Analytical Hierarchy Process (AHP) is a multicriterion approach adopted to assign relative weights to each criterion. AHP performs cost-benefit analysis based on absolute priorities [

The AHP technique for computing the weights works as follows:

Step 1: the pairwise comparison criteria matrix

Each entry ^{th} factor to the ^{th} factor. If ^{th} criterion is more critical, and if ^{th} criterion is not considered as necessary as the ^{th} criterion. If two criteria have the same level of importance, then the entry

The entries

Obviously,

The relative importance between the two criteria is quantified from 1 to 9, as shown in Table

The interpretations are reflective of the qualitative evaluations of how important a criterion is over the other.

Step 2: once the matrix

where

Step 3: finally, the factor weight vector

Fuzzy linguistic terms with defined triangular scales [

Saaty scale | Definition | Fuzzy triangular scale |
---|---|---|

1 | Equally important | (1,1,1) |

3 | Weakly important | (2,3,4) |

5 | Fairly important | (4,5,6) |

7 | Strongly important | (6,7,8) |

9 | Absolutely important | (9,9,9) |

Fuzzy Analytic Hierarchy Process (F-AHP) [

The linguistic terms are defined in terms of the following sets of fuzzy triangular scales:

Table

The weights computed by the Fuzzy AHP are validated by calculating two consistency ratios (CRm and CRg). These ratios are computed by the following procedure [

Step 1: subdivide the fuzzy pair-wise comparison matrix (used for computing weights) into two matrices:

The first matrix is formed from the middle element “

The second matrix is derived as the geometric mean of the lower bound and the higher bound elements of the above fuzzy comparison matrix denoted by formula:

Step 2: the priority weight vector ^{th} root of the elements in each of the above matrices:

where _{i} = ^{th} is the root value of the criterion “^{th} root values of all criteria

Step 3: the sum of product of all priority weight vector elements with their corresponding criteria column-wise sum computes the lambda-max for both the above matrices as follows:

where

Step 4: compute the consistency index (CI) for each matrix from the corresponding lambda-max by the formulae:

for the number of criteria “

Step 5: compute the consistency ratio (CR) by dividing the Consistency index (CI) values by the Random Index (RI), where RI is specified by Gogus and Boucher [_{m} for the number of criteria = 3 is 0.489 while

Step 6: check for CR_{m} and

The vehicle transport alternatives are then ranked using the TOPSIS, Fuzzy TOPSIS, EDA, and IRP techniques detailed below:

TOPSIS is a multicriteria decision analysis method, which was originally developed by Hwang and Yoon [

The proposed technique works as follows:

Step 1: define the evaluation matrix consisting of

where ^{th} alternative on the ^{th} criterion.

Step 2: the matrixes normalized to the matrix

where

Step 3: compute the weighted normalized decision matrix

Step 4: calculate the worst and best alternatives

if the

if the

where

Step 5: calculate the largest distance between the target alternatives

Similarly, compute the best distance between

Step 6: calculate the similarity

where

Step 7: the alternatives are ranked according to the

The first ranked alternative is the best choice among the modes of transport.

Fuzzy TOPSIS is one of the best methods to get an ideal solution when there is uncertainty in the selection process. Fuzzy TOPSIS (F-TOPSIS) integrates the fuzzy concept to TOPSIS [

In the paper, the vehicle modes are ranked using Fuzzy TOPSIS technique in the

This technique ranks the alternatives in terms of being the closest to the best-case and farthest from the worst-case scenario (nadir) solution [

Step 1: select the best factors defining the alternatives.

Step 2: construct the alternative-criteria matrix

where

Step 3: determine the mean solution

where

Step 4: the PDA and the NDA are computed differentially for cost and benefit criteria, shown as follows:

, if the

if the

where

Step 5: compute

where

Step 6: compute the normalized values of

Step 7: evaluate

Step 8: the alternatives are ranked in the descending order of the average score,

The IRP is a ranking procedure used for validation and is adopted due to the perfect blending of rational selection processes with rudimentary intuitive processes.

The IRP is a novel ranking method [

In the IRP, expert inputs that impact the interpretive logic for factor dominance are applied for paired comparison. The IRP makes it easy to distinguish the influence of interactions rather than the variables in an abstract sense.

The basic steps of the IRP are as follows:

Categorize the variables into two sets—one to be ranked, second the criteria for ranking

Identify the relationship between the two sets of variables

Construct a cross-interaction matrix between the two sets of variables

Interpret the binary relationships by converting to a cross-interpretive matrix

Translate the matrix into a dominating interactions matrix representing the relative dominance of one actor over the other

Rank the actors or alternatives based on the net dominant score

The final phase is the validation of the rankings for which the Ensemble Ranking procedure is adopted. The Ensemble Ranking technique is constructed from the above four methods (TOPSIS, Fuzzy TOPSIS, EDA, and IRP) using the methodology entailed in Mohammadi and Rezaei [

The methodology aims to compute a consolidated ranking system from different individual ranking systems to maximize the rankings’ consensus and validity.

Consider “_{1}, _{2}, _{3}, …, _{n} to a particular alternative based on their respective methodology. Consider an assumed consolidated ranking of

For this purpose, a quadratic minimizer function is constructed as

An optimal weighted Ensemble ranking procedure is formulated by assigning individual weights

Each auxiliary variable representing each individual ranking method is denoted by

The weights are computed as the normalized form of auxiliary variables by the formula:

The consolidated ranking is the sum-product of each rank with respective weightage:

Figure

Methodology adopted for the study.

The results are enclosed in Section

The model determines the best vehicle mode constructed based on a tree structure with level 1 (top-most root) as the multicriteria objective of choosing the most feasible and viable transport mode alternative for passengers. Secondly, the vehicle alternatives are specified in level 2, and criteria in level 3.

Figure

The proposed model.

Hyderabad, the capital of Telangana, the newly formed 29th state of India, has been selected as a case in point for metropolitan cities. The population is 13 million, of which around 13% are below the poverty line and earn an income of Rs. 4 Lakh per capita [

The suitability of the vehicle modes is not evaluated in terms of absolute importance. They are evaluated from the point of view of relative applicability in Hyderabad.

This study was conducted on the busy route connecting Nagole, an eastern residential suburb in the city outskirts of Hyderabad, to the other end of the city, Hitech City, the financial and technological hub employing millions of software professionals and analysts. Figure

The modes of transport, their routes, and the study area (in clockwise order, public bus, metro train, bike, and car).

This study considers four modes of transportation, locally known as–Metro train, Public bus, Bike, and Car as alternatives. Bike is a two-wheeler micro-mobility vehicle mode controlled by a handlebar. Rickshaws constitute 30% of the terrestrial vehicles and 23% of commuters with an average trip length of 8.76 km [

The phases illustrated above in Methodology are implemented below for the scenario:

The criteria are defined and the pair wise matrix is evaluated with relative weightages assigned to each ordered tuple of criteria

The weights of all criteria computed by Fuzzy AHP is illustrated in Table

Weights computed from the fuzzy AHP.

Criteria | Fuzzy set (Si) | Weightages | Normalized weights |
---|---|---|---|

Political factors | (0.198, 0.329, 0.523) | 0.349 | 0.388 |

Economic factors | (0.065, 0.102, 0.162) | 0.121 | 0.121 |

Social factors | (0.111, 0.192, 0.323) | 0.209 | 0.231 |

Technological factors | (0.070, 0.111, 0.181) | 0.110 | 0.134 |

Legal factors | (0.033, 0.049, 0.081) | 0.054 | 0.059 |

Environmental factors | (0.035, 0.053, 0.091) | 0.059 | 0.066 |

Table

The weights computed above are validated for consistency by computing the consistency ratios as elucidated above in methodology _{m} and lambda-max_{g} are first computed as: lambda-max_{m} = 6.22 and

Furthermore, the consistency index values are: CI_{m} = 0.044 and

Furthermore, for the number of criteria = 6 (in study), the corresponding Random index (RI) values according to Gogus and Boucher [_{m} = 1.19 and

Therefore, the consistency ratio values CR_{m} and _{m} = CI_{m}/RI_{m} = 0.044/1.19 = 0.0368 (3.7%)

Both ratios are less than 0.1 (10%), which implies that the weights computed by the above Fuzzy AHP procedure are valid.

Furthermore, to analyze the subcriteria weights, the pair-wise comparison matrices for all the subfactors were similarly constructed to calculate their relative contribution toward the main factors.

Table

Relative weights of the subfactors.

Criteria | Subcriteria | Weights of subcriteria |
---|---|---|

Political factors | Political instability | 0.187 |

Government policies | 0.101 | |

Economic factors | Duties and taxes | 0.032 |

Economic growth | 0.034 | |

Employment | 0.049 | |

Cost efficiency | 0.082 | |

Social factors | Health | 0.052 |

Safety | 0.104 | |

Security | 0.078 | |

Technological factors | Maintenance | 0.079 |

Fuel efficiency | 0.090 | |

Legal factors | Restricted movement | 0.035 |

Legislations | 0.034 | |

Environmental factors | Air pollution | 0.036 |

Noise pollution | 0.023 |

It is found that Political Instability (Political Factor) is the major barrier followed by Safety (Social Factor), Government policies (Political Factor), and Fuel efficiency (Technological Factor). This corroborates the above findings in Table

First, the weights are provided as input to the TOPSIS model after categorization into beneficial and nonbeneficial subcriteria. For instance, the subcriteria Political Instability, Government Policies, and Pollution are nonbeneficial and are considered as negative drivers for choosing the vehicle alternative while Fuel Efficiency, Cost Efficiency, and Employment are positive drivers or beneficial factors. Subsequently, the Positive and Negative Distance from the ideal solutions, i.e., the PIS and the NIS, were calculated through the weighted normalized decision matrix. As these ideal solutions represent the hypothetical scenario, the distance of each alternative from these extremes was calculated (Di+ and Di-). Based on these distances, the relative closeness index, Ci, was computed to rank these alternatives using TOPSIS in Table

TOPSIS results for the respondents.

Alternative mode | Di^{+} | Di^{−} | Ci | Rank |
---|---|---|---|---|

Metro | 0.089 | 0.228 | 0.718 | 1 |

Public bus | 0.197 | 0.095 | 0.325 | 2 |

Bike | 0.226 | 0.101 | 0.309 | 3 |

Car | 0.225 | 0.096 | 0.299 | 4 |

Similarly, the weighted criteria and alternative evaluation matrix are input in

Criterion- alternative evaluation matrix.

Weights | 0.388 | 0.121 | 0.231 | 0.134 | 0.060 | 0.066 |

Alternative mode | Political factors | Economic factors | Social factors | Technological factors | Legal factors | Environmental factors |

Metro | 4 | 6 | 9 | 10 | 9 | 9 |

Public bus | 8 | 6 | 8 | 7 | 10 | 10 |

Bike | 7 | 9 | 10 | 5 | 7 | 7 |

Car | 8 | 5 | 7 | 8 | 5 | 5 |

Fuzzy TOPSIS results for the respondents.

Alternative mode | Rank |
---|---|

Metro | 1 |

Public bus | 2 |

Bike | 4 |

Car | 3 |

The evaluation matrix above represents the weights assigned to the criteria and the alternative scores (out of 10) assigned for each of the alternative transport modes; for instance, Metro is assigned a score of 10 for Technological Factors. In contrast, the Public bus is assigned a score of 7, Bike a score of 5, and Car a score of 8.

The above responses average the rating provided to the subcriteria under each of the criteria based on questions asked to the passengers (questionnaire of interview enclosed in Appendix). For instance, the above response 10 for Political Factors is the aggregated average of the rating provided to each of the Political Factors, i.e., Political instability and Government Policies (out of 10) by the passengers. The aggregated Evaluation Matrix in Table

The same evaluation matrix in Table _{i} and NSN_{i}, the final appraisal score (AS_{i}) was calculated to rank the alternatives in Table

EDA results.

Alternative mode | SP_{i} | NSP_{i} | SN_{i} | NSN_{i} | AS_{i} | Rank |
---|---|---|---|---|---|---|

Metro | 0.08985 | 0.23505 | 0.52052 | 0.71514 | 0.61783 | 1 |

Public bus | 0.17261 | 0.04153 | 1 | 0.12635 | 0.56317 | 2 |

Bike | 0.16389 | 0.02335 | 0.94948 | 0.07105 | 0.51027 | 4 |

Car | 0.01734 | 0.32868 | 0.10044 | 1 | 0.55022 | 3 |

The Interpretative Ranking Procedure (IRP) is implemented based on the SAP-LAP framework (Situation-Actor-Processes- Learning- Actions-Performance) where the above vehicle alternatives are considered as actors that are mapped to processes as shown in the framework in Table

Variables of SAP-LAP in the context of choosing a suitable vehicle mode.

Components | Variables | |
---|---|---|

Situation | External | S1- growth of unconventional transportation modes |

Internal | S2- strong technological developments | |

Actors | External | A1-car |

A2-bike | ||

A3-metro | ||

A4-public bus | ||

Processes | Internal | P1- technology and business strategy alignment |

External | P2-offering feasible transport alternative to commuters |

The SAP-LAP framework in Table

The dominance matrix in Table

Dominant matrix of actors with respect to processes.

A1 | A2 | A3 | A4 | No. dominating ( | Rank | ||
---|---|---|---|---|---|---|---|

A1 | 1 | 1 | 0 | 3 | |||

A2 | 0 | −3 | 4 | ||||

A3 | 1 | 1 | 1 | 3 | 2 | 1 | |

A4 | 1 | 1 | 1 | 2 | |||

No. being dominated ( | 1 | 3 | 1 | 0 | 5 |

The overall ranking of the vehicle alternatives under TOPSIS, Fuzzy TOPSIS, EDA, and IRP is summarized in Table

Comparison of ranking from different techniques.

Alternative | EDA | TOPSIS | Fuzzy-TOPSIS | IRP |
---|---|---|---|---|

Metro | 1 | 1 | 1 | 1 |

Public bus | 2 | 2 | 2 | 2 |

Bike | 4 | 3 | 4 | 4 |

Car | 3 | 4 | 3 | 3 |

The Metro and Public bus alternatives are consistently found to be the top 2 recommended vehicle modes under all the four techniques (EDA, TOPSIS, Fuzzy TOPSIS, and IRP), the Car is found to be the next preferred alternative under EDA and Fuzzy TOPSIS, and the TOPSIS method is found to rank the Bike as the next preferred alternative.

A sensitivity analysis of the models is performed below:

The sensitivity analysis is used to investigate the results’ stability over a varied range of input variable values. There are 13 subfactors involved in the current study, but the analysis of over 13 weight patterns became cumbersome. Therefore, to better understand the results, the top 5 subfactors were selected for the final analysis based on their relative importance. The factors chosen for the study are Political Instability, Cost Efficiency, Safety, Government policies, and Fuel Efficiency. The results’ stability is analyzed by testing the model over five different sets of weights (indicated by P1-P5) of the top 5 subcriteria. The weights of the subcriteria computed in Table

Sets of weights used for sensitivity analysis.

Weights | Political instability | Cost efficiency | Safety | Government policies | Fuel efficiency |
---|---|---|---|---|---|

P1 | 0.187 | 0.110 | 0.104 | 0.101 | 0.080 |

P2 | 0.172 | 0.156 | 0.103 | 0.091 | 0.095 |

P3 | 0.155 | 0.134 | 0.091 | 0.089 | 0.117 |

P4 | 0.194 | 0.121 | 0.106 | 0.137 | 0.105 |

P5 | 0.191 | 0.109 | 0.117 | 0.095 | 0.114 |

The sensitivity analysis charts are plotted for each of the individual multicriteria methods, namely, TOPSIS, Fuzzy TOPSIS, EDA, and IRP. The vertical bars are colored blue for the metro, orange for the public bus, gray for the bike, and yellow for the car. The bars’ size is inversely proportional to ranking (longer the bar, lower it is in terms of alternative ranking).

In TOPSIS, the Metro as an alternative is found to be consistently in the top 2 alternatives, the Car is found to be consistently in the 3rd or 4th position, and the rankings of Public bus and Bike are found to be less stable.

In the Fuzzy TOPSIS, Metro is again found to be stable in the top two while the other modes of transport are varying and fluctuating.

In EDA, the Metro is the most consistently superior alternative, while the next stable choice is the Bike. The ranking of the Public bus and the Car are highly unstable; similar is the case for the IRP process.

Overall, from the sensitivity analysis depicted in Figures

Sensitivity analysis using TOPSIS.

Sensitivity analysis using Fuzzy TOPSIS.

Sensitivity analysis using EDA.

Sensitivity analysis using IRP.

Furthermore, the above rankings are validated by the Ensemble Ranking technique, which assigns weights to each of the ranking algorithms, namely, TOPSIS, Fuzzy TOPSIS, EDA, and IRP to compute a consolidated ranking system for the tables.

The weights for each of the ranking algorithms are optimized using an inbuilt solver function, and the results of the Ensemble Ranking are illustrated in Table

Ensemble ranking technique results.

Alternative | EDA | TOPSIS | Fuzzy-TOPSIS | IRP | Final rank | |
---|---|---|---|---|---|---|

Metro | 1 | 1 | 1 | 1 | 1 | 1 |

Public bus | 2 | 2 | 2 | 2 | 2 | 2 |

Bike | 4 | 3 | 4 | 4 | 4 | 4 |

Car | 3 | 4 | 3 | 3 | 3 | 3 |

Weights computed | 0.33 | 0.069 | 0.33 | 0.271 | Confidence index | 0.85 |

Trust level | 1.000 |

The weightages for each ranking system are optimized by an inbuilt Excel macro solver, which on clicking a button, automatically (through its inbuilt macro solver code) assigns optimal weights to each ranking algorithm ensuring that the confidence index and trust level of the algorithms are maximized [

The confidence index is a measure of extent to which all the four models, namely, TOPSIS, Fuzzy TOPSIS, EDA and IRP, are in concordance with the aggregate ranking

Trust level metric is an indicator of the reliability of the final ranking, which is very high, i.e., 100% (1).

Overall, from the Ensemble Ranking, it can be estimated with a high confidence index and high trust level that the most feasible vehicle alternative is the Metro.

Having computed each of the rankings, performing a sensitivity analysis to ensure their stability, and aggregating the results with Ensemble Ranking method, the 4 vehicle modes have been ranked in the descending order based on customized preferences of the passengers filling the survey data. There is now a need to present these findings to the transportation researchers for a final whetting. A report of all the above results was sent to the research and development teams in the Road Transport Corporation to corroborate their domain knowledge about the vehicle mode applicability and the final rankings arrived at by using the multicriteria techniques.

The rankings above were corroborated with the transportation researchers in the Telangana Road Transport Corporation, and the results in Table

Comparison of final experts’ ranking with the above-computed rankings.

Alternative | Ranking | Validation | ||||
---|---|---|---|---|---|---|

TOPSIS | EDA | Fuzzy TOPSIS | IRP | Ensemble ranking | Domain expert validation | |

Metro | 1 | 1 | 1 | 1 | 1 | 1 |

Public bus | 2 | 2 | 2 | 2 | 2 | 2 |

Bike | 3 | 4 | 4 | 4 | 4 | 4 |

Car | 4 | 3 | 3 | 3 | 3 | 3 |

It was found that the Ensemble Ranking results were consistent with the expert rankings.

This paper thus computed each factor’s individual priorities influencing supply chain resilience from the relative importance values through Fuzzy AHP. From the criteria weightages and the scores assigned to each company on these criteria, the companies were ranked by TOPSIS, Fuzzy TOPSIS, EDA, and IRP. The rankings’ consistency was stabilized by sensitivity analysis and aggregated to a consolidated Ensemble Ranking system with high trust level and confidence index. The final rankings were also successfully whetted by domain experts and the final ranking reveals that the Metro is the most feasible alternative. The implications are discussed below:

In this paper, a novel hybrid multicriteria model is developed to choose the most feasible transport modes wherein the Fuzzy Analytical Hierarchy Process is used to compute the weights of criteria or factors considered by passengers to select the transport mode. The weights are then utilized to rank the transport modes for which three multicriteria ranking models, namely, TOPSIS, Fuzzy TOPSIS, and EDA are adopted. The rankings are further granularly analyzed using sensitivity analysis, which examines the stability of the ranking and the sensitivity impact of criteria on the ranking system. Furthermore, the results are validated by IRP and experts. The analysis reveals that the Metro train transport mode is consistently preferred in the top 2 alternatives, while other alternatives are sensitive to the variation in weights of the criteria adopted.

Therefore, this study employs a hybrid and robust multicriteria model, which can be recalibrated and adapted in different contexts to determine the most feasible transport mode.

The implications for practice are twofold: first on the transport policy and management, and second on the passengers.

From the weightage computation results using the Fuzzy AHP, it is found that Political factors are the most important, followed by Social factors and Technological factors and then Economic Factors. This implies that, for the transport authority to implement a new transport mode (in this case, metro), the political clearances need to be obtained, and the political party in power should ratify the launch of a new transport mode. Second, the authority should analyze the impact of the latest transport mode on society, particularly the commuters. Subsequently, the technological know-how is to be examined by consulting operations research experts and engineers to design a transport mode with state-of-the-art technology. The economic feasibility is to be analyzed, keeping in mind the price-sensitive market of India and the commuters’ economic conditions. Environmental considerations and sustainability need to be taken care of, keeping in mind the commuter’s perspective.

The subcriteria weight analysis reveals that the transport authority needs to overcome the major barrier of Political Instability (Political Factor). Consequently, assuring the Safety (Social Factor) of the commuters is of high importance. The authorities need to comply with the Government policies (Political Factor) and take care of technological factors like Fuel efficiency (Technological Factor) by designing state-of-the-art transport systems in consultation with technology experts. Overall, the transport authorities need to analyze factors like safety, government policies, and technological factors like fuel efficiency for ensuring the successful implementation of the new transport mode.

Considering the consistent performance of the metro train alternative with respect to the ranking models (TOPSIS, Fuzzy TOPSIS, EDA, and IRP), sensitivity analysis results, and validation results from Ensemble Ranking and transportation experts, it is recommended to advocate and spread awareness about the need to use the Metro as a cost-effective, environmental-friendly, safe, and fast mode of transport among all the employees, students, and other citizens.

The policy implications are thus outlined below:

Firstly, regularization and expansion of Metro routes are recommended, especially across busy and high traffic-density routes where other conventional modes of transport like Bus, Car, and Bike cause traffic jams.

Secondly, there is a need to define the hierarchy and areas of operation for the integrated use of an unconventional transport mode like the Metro and other defined conventional transport modes since eliminating the conventional transport modes with immediate action is not feasible. Busier and long-distance routes can be well-connected by the Metro. In contrast, for shorter distances and across traffic-sparse routes, conventional modes can continue to be operated to secure the livelihood of the staff of the Public bus corporations and for Bike and Bus drivers.

Thirdly, route planning can be optimized, and the use of pooling operations, especially for Cars, can be promoted to minimize the environmental damage caused by the use of private modes of transport like Cars.

Inter-modal transfer hubs can be initiated where for each metro station, a Car-pooling system can be arranged for short distances by using Car rental solutions. For instance, if a passenger (an employee of a private IT organization) needs to commute from Uppal (an eastern suburb locality in Hyderabad) to Hitech City (IT hub of the city), a Metro line from Nagole (near Uppal) to Miyapur (around 5 kilometers from Hitech City) can be routed. At the Miyapur Metro station, a Carpool rental, or a Bike or, for high traffic dense routes, a Bus can be arranged at nominal prices to safely drop the passengers at the desired location, i.e., Hitech city. This may lead to a win-win situation in the form of a public-private partnership for the Metro staff, Bus drivers, Car rental drivers, and Bike rental drivers; thus, they can collaboratively provide transport solutions to the people of Hyderabad.

Passengers, primarily working professionals and students, are motivated by the results to prefer the Metro to save commuting time, for safety, and for achieving cost and fuel efficiency. Metro is particularly useful for long-distance commuting from one end of the city to the other, and proves to be politically noncontroversial, economically viable, socially safe, healthy, technologically fuel-efficient, legal, and ecologically sustainable.

This paper devises a hybrid Fuzzy AHP-TOPSIS/Fuzzy TOPSIS/EDA/IRP/Ensemble Ranking model to evaluate which vehicle alternative to choose from, keeping in mind the passenger preferences. The study focuses on the Indian metro city context with Hyderabad as a case in point.

It is hoped that this paper would benefit the passengers, transport authorities, and researchers for constructing an intelligent transportation selection model based on factors prioritized by the stakeholders using a novel hybrid multicriteria model. The simulation model provides a platform to weigh different factors developed from the PESTLE framework and select the most feasible mode of transportation. This paper is confined to a particular metropolitan city in the southern part of India, and the alternative ranking is prescribed keeping in view the local requirements of passengers and taking into consideration the relative traffic densities of the particular city of Hyderabad. However, the factors considered to evaluate the most suitable vehicle mode are applicable for all city and country scenarios, and the model can be thus recalibrated and extended to all study areas(cities) in the world with different traffic and different population densities.

The hybrid multicriteria model is developed in the context of Indian metropolitan cities, and no prior work of the subject matter dealt in this paper is found in existing studies. The subcriteria considered were the vehicle selection problem: Political Stability, Government Policy, Duties and Taxes, Economic Growth, Unemployment, Cost Efficiency, Health, Safety, Security, Maintenance, Fuel Efficiency, Restricted Movement, and Pollution. These factors were taken into account to rank the vehicle modes in the descending order. The relative importance of the criteria and alternatives is further analyzed using sensitivity analysis and is validated by Ensemble Ranking and expert decision-makers. Thus, this paper demonstrates a methodology to determine an appropriate transportation mode, keeping in mind the passengers’ personalized preferences.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare no conflicts of interest.