Decision-Support Framework for Selecting the Optimal Road Toll Collection System

Milenković, Marina; Glavić, Draženko; Mladenović, Miloš N.

doi:https://doi.org/10.1155/2018/4949565

Journal of Advanced Transportation

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Abstract Introduction Discussion and Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2018 | Article ID 4949565 | https://doi.org/10.1155/2018/4949565

Decision-Support Framework for Selecting the Optimal Road Toll Collection System

Marina Milenković,¹Draženko Glavić,¹and Miloš N. Mladenović²

Academic Editor: Jose E. Naranjo

Received08 Jan 2018

Accepted22 Apr 2018

Published27 May 2018

Abstract

One of the central decision-making questions in planning road tolling is the selection of the optimal toll collection system (TCS). The question of TCS selection arises in the situation when the existing TCS is to be upgraded or when TCS is selected for a newly constructed road. Considering that there are multiple TCS available nowadays, with their particular advantages and disadvantages, and that there is a range of often conflicting criteria for TCS selection, this decision-making issue belongs to the group of multicriteria decision-making (MCDM) problems. The MCDM-based methodology used in this research integrates Strengths-Weakness-Opportunities-Threat (SWOT) analysis and Fuzzy Preference Ranking Organization Method for Enrichment Evaluations (F-PROMETHEE). The expert-based decision-support framework includes a procedure for defining evaluation criteria and their weights, scoring of alternatives, and sensitivity analysis. Presented decision-support framework is tested with fourteen toll systems. Results indicate that the best-ranked TCS is the dedicated short-range communication multilane free flow. Decision-support framework is developed for transferability to different contexts, where local features can be taken into account by choosing specific alternatives, criteria, and criteria values. Finally, this development opens up opportunities for further analysis of criteria values and considerations of user attitudes in road pricing scheme planning.

1. Introduction

Road pricing scheme represents the direct charges collected for the road use, such as distance or time-based fees, and urban congestion pricing [1–5]. These charges often depend on the vehicle type, number, and configuration of vehicle axles, vehicle dimensions, trailer types, and vehicle emission class. In general, road pricing has two purposes. One purpose of road pricing is demand management, while the other purpose is a mechanism for financing road infrastructure construction, operation, and maintenance. With its strong origins in the economic theory, road pricing has been advocated as an efficient policy to account for externalities and infrastructural funding. Despite its sound purposes, road pricing is a typical example of a theoretically well-developed transport policy that has been shown difficult to implement [6]. Since road pricing is a type of a policy that radically shifts distribution of costs and benefits, this policy is often value-laden and politely charged. One of the crucial aspects in planning road pricing scheme is public acceptability of road pricing overall. A range of previous research indicates the low public acceptability of road pricing, pointing out to the infringement of freedom and fairness [7–10]. Along with the questions of public acceptability, another crucial issue with road pricing is political acceptability and support [11–13]. For example, politicians often perceive road pricing as complicated new charge for something that is free [12]. Moreover, political unacceptance of road pricing might originate from preference for policies with highly visible benefits and distributed and low-visibility costs [13, 14]. Consequently, planning road pricing scheme often requires balancing contradicting objectives that planners and infrastructure managers might have with respect to users.

An important part in the delicate process of road pricing planning is the selection of tolling system, as an important ancillary asset. Just as different optimal tool schemes relate to different policy objectives [15], toll collection system (TCS) directly affects the user’s experience and consequently acceptance of road pricing scheme. Additional challenge in the decision-making process emerges from the fact that the choice of a particular TCS assumes the operational responsibility for the next 10 to 20 years. This long-term responsibility for TCS asset is particularly important for agencies with large road pricing systems, often responsible for large-scale investment into a single TCS. In practice, the agency is facing a choice between a multitude of TCS currently in use. Nowadays, TCS used worldwide include manual and automatic coin machine (ACM), vignettes, dedicated short-range communication (DSRC) with barriers, DSRC multilane free flow (MLFF), barcode, radiofrequency identification (RFID) with barriers, RFID open road tolling (ORT), global navigation satellite system/cellular network or vehicle positioning system (GNSS/CN or VPS), automated number plate recognition system (ANPR), infrared, tachograph, smart card, and smartphones (Figure 1). These systems differ in their performance, if one considers, for example, accuracy, costs, or interoperability. Bearing in mind the many and diverse interests at stake, each TCS has a range of advantages and drawbacks from the standpoint of road manager and user. For example, different TCS respond differently to the need to maximize the toll revenue, which is often an important criterion for road managers. On the contrary, different TCS cater differently to the user needs for reducing delays and costs or improving level of service (LOS) and safety.

Taking into account the importance of TCS in the effectiveness of the road pricing scheme, as well as large-scale investment and conflicting criteria related to a multitude of alternative TCS, transport planners face a challenging question—which TCS should we select? Previously, only one study has dealt with the issue of selecting the optimal TCS [16]. This study from India considered five alternative TCS including barcode-based ETC, RFID-based ETC, ANPR ETC, VPS ETC, and active infrared system. A multicriteria decision-making method fuzzy VIKOR was used, concluding that the ETC system based on RFID technology was the best of all considered options. Thus, with this gap in the previous research, the objective of this paper is the development of a decision-support framework for selecting the optimal TCS. The next section will present an expert-based methodological framework centered on Fuzzy Preference Ranking Organization Method for Enrichment Evaluations (F-PROMETHEE) multicriteria decision analysis technique. Moreover, the methodological framework integrates Strengths-Weakness-Opportunities-Threat (SWOT) analysis. Decision-support framework is tested with fourteen toll systems, in the context of the newly constructed motorway. Following the results from the implemented decision-support framework (DSF), the discussion of implications is provided, including recommendations for future research.

2. Methodology

Considering the multitude of conflicting criteria stemming from both planners and users’ perspective, as well as a range of advantages and disadvantages that each TCS has, decision-making is not intuitive. Lack of intuitiveness originates from the fact that TCS selection problem cannot be easily organized due to large number of interdependencies and is thus not easily specified in advance. As TCS selection does not have a conventional mathematical formulation, the choice of the optimal TCS belongs to the category of semistructured decision problems [17]. The challenge of semistructured problem is contrasted with the fact that agency often has additional requirements. These agency’s requirements might include the need that comparison of alternatives is analytically based and that there is cross-referencing to the relevant sources of information, transferability among locations or agencies, and communication of evaluation results in a multifaceted form [18]. On the contrary, transport planners and road managers often have extensive experience with their transport system. Consequently, this expert knowledge can be an essential resource for aiding the selection of the optimal TCS.

Considering the semistructured nature of the TCS selection problem and the extensive expert knowledge about the transport system, there is an opportunity for a DSF based on multicriteria decision-making (MCDM) methodology. MCDM refers to the process of making decisions between a number of alternatives by defining the decision criteria and their weights. The application of MCDM results in the ranking of alternatives, from the most to the least favorable, thus allowing comparison of alternatives. Some common methods for MCDA in transport applications include the technique for order of preference by similarity to ideal solutions (TOPSIS) [19, 20], analytic hierarchy process (AHP) [21–23], grey evaluation method (GE) [24, 25], simple additive weighting (SAW) [26], and elimination and choice expressing reality ELECTRA [27]. In particular, MCDM methods are often used for formulating DSFs for optimal technological system selection [28–36].

This DSF centers on Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), as the outranking method. When contrasted with other multicriteria analysis methods, PROMETHEE performs better in dealing with the problems having a large number of alternatives [37–41]. On the contrary, the capability to deal with a large number of alternatives is not the case with the AHP method [42]. In practice, PROMETHEE is a group of methods, where PROMETHEE I is used for partial ranking of alternatives and PROMETHEE II is used for complete ranking of alternatives [37]. In order to overcome the uncertainty in the process of the selecting optimal toll collection system, we utilize the extension of the PROMETHEE with fuzzy numbers (F-PROMETHEE). As a fuzzy subset of real numbers, fuzzy numbers are included to deal with subjective dilemma that comes from using linguistic variables for problem representation [43]. As a result, the use of fuzzy numbers allows consideration of vagueness inherent in this decision-making context through the user friendliness from linguistic evaluations. The integration of PROMETHEE I and II, as well as other elements into a DSF, is presented in the following section.

2.1. Decision-Support Framework

The DSF involves a procedure, presented in the Figure 2, to be organized as an expert-based workshop. The figure shows subprocesses as well as outputs from each subprocess. Involvement of stakeholders in the decision-making process has been especially highlighted before [44] and is thus an important aspect of the proposed DSF. As Figure 2 shows, first step includes description of the MCDM problem with a definition of the TCS goals. During workshops, the authors first defined a set of alternatives, as TCS used worldwide. Then, SWOT analysis, including a discussion about strengths, weakness, opportunities, and threats of each alternative, was conducted to help experts get better understanding of the alternative. In general, groups of experts should be between four and six people, to allow sufficient diversity of opinions while still having possibility for simultaneous discussion. In addition, by bringing in experts with different roles and backgrounds, discussion allows for complementary knowledge exchange in a constructive setting framed by SWOT. In this particular workshop, four tolling experts from the fields of transport planning, traffic operations, transport policy, and transport economics have participated. This combination has allowed for a sufficient diversity of opinion to emerge, while still being able to have a common understanding on some of the core issues. SWOT-related processes are colored blue in Figure 2. TSC characteristics: in the workshop, the experts discussed the MCDM task, tolling options, definition of criteria and their weights. Next, sets of the criteria were defined, as well as the criteria weights. The values of the criteria weights were determined using subjective fuzzy evaluations by experts. Then, the experts scored alternatives per each criterion using subjective fuzzy evaluations. Processes using fuzzy logic are colored green in Figure 2. The F-PROMETHEE was used for ranking the alternatives. While implementing the PROMETHEE, the preference functions were defined for each of the considered criteria, as well as the values of the parameters for the selected preference functions. This method determined the values of the positive and negative outranking flows, as well as the net outranking flow, which led to the ranking of the alternatives. Following the alternative ranking, the sensitivity analysis was conducted in order to determine the stability interval for each of the considered TCS alternatives. Processes that rely on PROMETHEE are colored red in Figure 2. The following subsections elaborate on each of the DSF subprocesses.

2.1.1. Establishing Decision Context

The first subprocess involves the definition of TCS goal and all the possible alternative TCS, as well as the criteria list for evaluating the alternatives. The definition of the criteria is preceded with a SWOT analysis of the TCS alternatives. SWOT analysis is a structured planning method that helps evaluate the strengths, weaknesses, opportunities, and threats of each TCS, as follows: Strengths: characteristics (relative) of the toll alternative that give it an advantage over other alternatives Weaknesses: characteristics (relative) of the toll alternative that represent disadvantage relative to other alternatives Opportunities: characteristics (absolute) of the toll alternative that could be used for its advantage during implementation Threats: characteristics (absolute) of the toll alternative that could be a cause of issues during implementation.

In this DSF, the objective of SWOT is to enable experts to better understand alternatives and their scores and as well define criteria. SWOT is selected as a technique that has minimum structure, thus not overburdening the discussion with its process but allows focus on substance. Having a divergent method is particularly important in this subprocess, because it allows expert knowledge acquisition from both explicit and implicit knowledge, as well as from knowledge about transport system from different aspects. Procedurally, as experts are discussing strengths, weaknesses, opportunities, and threats of different TCS alternatives, they are exchanging knowledge and identifying issues and potential criteria to be discussed, selected, and categorized. Consequently, although SWOT analysis is an output in itself, the process of getting to this output is important for knowledge development among the group of experts. As a result, this DSF subprocess results in refinement of alternatives, their scores, and criteria, as well as the objective per criterion, i.e., minimization or maximization.

2.1.2. Determination of Criteria Weight

Determination of the weights is the second subprocess in this DSF. SWOT analysis is used to assist the process of criteria weighting by deepening knowledge of toll alternative characteristics. The relevant linguistic terms are defined in order to assess the criteria individually, instead of using pairwise comparisons. These determined linguistic terms are than expressed by triangular fuzzy numbers. Linguistic definitions for the criteria weight and their corresponding fuzzy numbers are shown in Table 1. Graphical interpretation of a triangular fuzzy number is presented in Figure 3, where is the lower and is the upper limit, while is the point where membership function is equal to one.

Experts evaluate the criteria weights in the form of linguistic terms. In order to define the weight factor of each criterion, each expert is presented with a list of criteria defined in the previous step. Then, linguistic expert evaluations are converted into related fuzzy numbers. Next, the importance of each criterion , taking into account the estimates of all experts , is determined as follows: The fuzzy numbers were defuzzified by using the distance minimization method suggested by Asady and Zendehman [58] as given below:Defuzzified values are then normalized as follows:where denotes number of criteria ; denotes number of decision-makers ; denotes number of alternatives ; denotes left threshold of fuzzy number estimated by expert for criteria ; denotes midpoint of fuzzy number estimated by expert for criteria ; denotes right threshold of fuzzy number estimated by expert for criteria ; denotes weight of criteria .

2.1.3. Scoring of Alternatives

In addition to assessing the weight of the criteria, tolling experts are responsible for scoring alternatives, also partially relying on information provided from SWOT analysis. In order to evaluate the performance of each alternative on the basis of the defined criteria, the expert opinions are collected as linguistic terms, defined in Table 1. Due to the fact that determination of analytical scoring is not possible for such a large number of criteria and alternatives, linguistic scale is used. Linguistic expressions are then transformed into the corresponding fuzzy numbers (Table 1). The values of the fuzzy numbers boundaries are the same as those during the activity of criteria weighting. The estimates of all experts were taken into account using (1). The obtained fuzzy numbers were than defuzzified using (2) and normalized using (3).

2.1.4. Evaluation of Alternatives

The detailed procedure for evaluation of alternatives using PROMETHEE II method consists of five steps [see [38, 39]]. The subprocess starts with the determination of deviations based on pairwise comparisons. This step is then followed by applying a relevant preference function for each criterion defined in subprocess 2 above. Step three involves calculating global preference index, while stop four involves calculating positive and negative outranking flows for each alternative and partial ranking. The subprocess ends in step five, with the calculation of net outranking flow for each alternative and with complete ranking [38, 39].

PROMETHEE is based on pairwise comparisons and the specificity of this method is preference function. Its rule is that difference in values for each pair of alternatives leads to the range from zero to one, per criterion. In this way, normalization of values and obtaining information about the preferences of each alternative in relation to each, according to each of the criteria, are achieved.

The PROMETHEE I partial ranking is based on the computation of two preference flows. Phi+ (positive flow) is a measure of the strength of the alternative, while Phi− (negative flow) is a measure of the weakness of the alternative. PROMETHEE I partial ranking allows incomparability, which usually happens when one alternative is good in a subset of criteria in which another one is weak and vice versa [59]. The possibility of incompatibility is excluded by introducing the characteristic of net outranking flow which denotes the difference between the positive and negative outranking flows. Thus, alternative is preferred to alternative in the PROMETHEE II ranking only if it is preferred to b according to the net preference flow. In this particular case, only if [60]. Thus, PROMETHEE II provides a complete ranking of the alternatives from the best to the worst ones [59].

2.1.5. Sensitivity Analysis

As the last subprocess, sensitivity analysis is essential for determining the changes of alternative ranking due to changes in the value of particular criteria weight. As part of PROMETHEE, weight sensitivity analysis can be done using Walking Weights or Stability Intervals analysis [60]. For this DSF, we have implemented Stability Intervals, which shows how the Phi score and the PROMETHEE II ranking vary as a function of the weight of a criterion and identifies the interval of stability of the first place and complete ranking.

3. Application of the Decision-Support Framework

Decision-support framework is tested with fourteen toll systems, in the context of the newly constructed motorway. DSF has been implemented in a workshop setting, with participation by four tolling experts from the fields of transport planning, traffic operations, transport policy, and transport economics. Experts’ number has been decided based on the general recommendations that group activity is usually most effective in groups of four to six people.

3.1. Description of the Alternative TCS

Toll collection has changed historically from manual toll collection to various forms of electronic toll collection. The development of digital technology has been extremely dynamic in recent years and has led to the rapid application of numerous technologies in toll collection. Short description of alternative TCS is given in Table 2.

3.2. SWOT Analysis of Toll Alternatives

Results of SWOT analysis conducted during the workshop are represented in Table 3. Resulting SWOT matrix has assisted experts in further DSF steps, when defining the criteria and scoring the alternative per criterion.

3.3. Criteria List and Groups

During the workshop, experts have decided on the list of criteria which were then grouped. As Table 4 shows, the groups of criteria have been divided into technical operation, traffic operation, financial, environmental, and socioeconomic criteria. Each group contains three to five criteria that cover all characteristics of TCS alternatives, while total number of criteria is twenty. Table 4 also provides a brief description of each criterion.

3.4. Criteria Weights

During the workshop, experts have also assigned criteria weights using the fuzzy-based process described in Section 2. Table 5 provides the evaluations of four experts participating in the study expressed by linguistic terms. The final defuzzified and normalized values of criteria weight are shown in Table 6.

3.5. Scoring of Alternatives and Base Matrix of Input Data

Experts were scoring alternatives per criterion, but due to large number of criteria and alternatives, Table 7 shows an example of scoring for one criterion, namely “interoperability”. Defuzzified values of alternatives’ scores per criterion estimated by experts are shown in Table 8.

Table 9 shows the type of particular criteria, which preference function was selected for each criterion, and what the values of each parameter for each preference function are.

3.6. PROMETHEE I and II Alternative Ranking

The PROMETHEE Table (Table 10) displays the Phi, Phi+, and Phi− scores. The alternatives are ranked according to the PROMETHEE II complete ranking.

Analyzing the flows given in Table 10 it can be concluded that two best-ranked tolling options according to PROMETHEE I partial ranking and according to PROMETHEE II complete ranking are DSRC MLFF (Phi = 0.0425) and RFID ORT (Phi = 0.0454). They are followed by the alternatives with slightly lower values of net flow (GNSS/CN and Smartphone), while the other alternatives have significantly poorer values of positive, negative, and net flow. The lowest ranked alternative is the Manual TCS.

3.7. Sensitivity Analysis

Table 11 shows the sensitivity analysis results, as the possible range of criteria weights values needed to keep the complete ranking unchanged or for the first place to remain unchanged.

4. Discussion and Conclusion

Planning road pricing scheme is a delicate and often politically charged process, highly dependent upon user and political acceptability for a successful implementation. In this context, toll collection system is crucial for effectiveness of road pricing scheme, both from the standpoint of road managers and from the standpoint of road users. Moreover, toll collection system installation is usually a large-scale investment. Thus, the selection of the optimal toll collection system is a very important decision in the process of planning a road pricing scheme. However, decision-makers are often faced with a choice between a range of alternatives, which often have conflicting advantages and disadvantages. Considering the significance of the toll collection system selection, this research provides a decision-support framework. The discussion here will focus on elaborating the application of this decision-support framework under the assumption of inter- and intraorganizational learning.

In addition to the described framework, results include a systematized analysis of fourteen toll collection systems using SWOT analysis, including their technical and operation characteristics. From the SWOT matrix, one can observe that this analysis itself provides decision-makers with a deeper understanding of advantages and limitations of toll collection systems. Here, it is important to highlight those features that are common to many of toll collection systems. One could then easily identify groups of technologies based on their effect on the traffic flow. For example, manual tolling certainly belongs to the group of technologies that require full stopping for the road pricing transaction to occur. On the other hand, technology such as ANPR with barriers might require deceleration (approximately 30–50 km/h), while GNSS-CN does not require any reduction in vehicle’s velocity. Nonetheless, despite its usefulness to organizational learning, SWOT analysis itself is insufficient to help decision-makers select the optimal toll collection system.

Results indicate that the DSRC MLFF, GSNN-CN, and RFID ORT are grouped in the top of ranking. In particular, DSRC MLFF toll system has a narrow advantage over RFID ORT toll technology, while the GSNN-CN technology achieved somewhat worse value for financial criteria (Capex and Opex). Furthermore, smartphone technology got something worse score for traffic criteria (traffic safety). However, when the final ranking is compared to the ranking obtained in the study of Vats et al. (2014), some differences can be noticed. Vats et al. (2014) determined that the optimal alternative for India was the ETC system based on RFID technology, while ANPR and VPS occupied the second and third place, respectively. It should be noted that the research presented here includes additional technologies which were not included in the study by Vats et al. (2014). Moreover, the study presented here contains a larger number of criteria, as well as a greater number of experts from various fields that participated in the evaluation.

In addition to the ranking results, sensitivity analysis provides several other conclusions. First, regarding sensitivity to changes in the technical criteria, one can conclude that the first place is stable in a range from 0%–8.84% up to 0%–100% depending on the criterion. Second, regarding sensitivity to changes in the traffic operations criteria, one can conclude that the first place is stable in a range from 2.89%–15.25% up to 0%–100% depending on the particular criterion. Third, regarding sensitivity to changes in the financial criteria, one can conclude that the ranking of the first place remains stable with increase within 0.00%–8.02% for criterion “average cost for user”, within 1.39%–100% for criterion “revenue/cost ratio”, and within 7.73%–27.62% for criterion “Capex and Opex”, respectively. Fourth, with respect to changes in the environmental criteria, the ranking of the first place is stable in the range up to 100%, depending on the criterion. Lastly, regarding sensitivity to changes in the socioeconomic group of criteria, the ranking of the first place is stable in range from 0.00%–12.06% up to 0%–100%, depending on the criterion.

Here, it is important to highlight the importance of defining and weighting criteria for evaluation of alternative TCS. The suggested decision-making process presented in this study indicates that agency’s staff should incorporate their knowledge of specific expert areas along with the agency’s objectives. One way this knowledge is incorporated is by defining criteria as well as by grouping them. With this in mind, the presented decision-support framework provides an extensive list of possible criteria for evaluation. These criteria and their group have to be based on the long-term goals that specific transport agency has for its overall system. For example, an agency that is responsible for dominantly regional and intercity motorway network might have different objectives from an agency that is dominantly responsible for suburban or urban motorway tolling. In addition, expert knowledge is particularly important in determining criteria weights. Special attention should be paid to determining criteria weight since they have a crucial role in the analytical procedure for determining the final ranking of alternatives.

In addition to the elements of the proposed decision-support framework above, it is important to highlight also the aspects of the process itself, in the context of organizational learning. In particular, special attention should be paid to the expert knowledge acquisition. The decision-support process itself should aim at maximizing the performance of experts’ decisions by minimizing human cognitive errors. In addition, the process should be able to generate several alternative plans by making changes in the decision criteria weights, which would allow experts to evaluate impacts of those changes in a matter of exploratory what-if analysis. One example of such exploratory analysis would be completely excluding a group of criteria. Such exploratory analysis has particular potential in the case of the existence of TCS in the particular area, where some aspects, such as interoperability, might be worthy of a more focused investigation.

It is important to highlight here that this is ultimately a decision-support and not a decision-making framework. Thus, joint discussion between experts during the workshop is an important aspect of supporting the decision-making process. Such a discussion is ultimately necessary for transparent and uniform presentation of alternative decisions. Moreover, experts should be encouraged from the start to understand the decision-making process as an iterative learning experience, where mutually agreed alternative is sought as the ultimate goal. Thus, approaching decision-making process as an iterative one allows for a range of perspectives and agency’s objectives to be taken into account. Here, the process can benefit from experts with various backgrounds, such as planning, economics, operations, or maintenance. However, as the proposed decision-support framework uses fuzzy logic, subjective uncertainty can be filtered out for increasing coherence in decision-making.

The methodological framework presented here is transferable, as it can be applied to different case studies, while adopting to local specifics. As objective implementation of MCDM procedure is of the utmost importance, special attention should be paid to identifying relevant experts to join the workshop. However, bearing in mind the need to select one alternative, there is a range of requirements that an agency needs to take into account in its decision-making procedures. The framework presented here can help in organizing road toll planning workshops by providing analysis based and transparent comparison of alternatives. Moreover, approaching these workshops as an opportunity to enhance inter- and intraorganizational learning, the framework presented here can be used as a communication medium.

On the contrary, one has to recognize that a large number of alternatives and criteria are also a disadvantage from the perspective of workload scope. In addition, for many of the alternatives, it is difficult to determine their scores per criterion in an analytical manner. Thus, the lack of analytically determined data in the scoring procedure for each of the alternatives per criterion is a limitation of this effort. Including as wide as possible range of experts can help in overcoming the challenge of missing alternative data. However, since group dynamics in workshops cannot be properly maintained with a significant number of participants, decision-makers will have to seek other solutions. Thus, one potential direction for future research is investigating the potential of using microsimulation techniques for evaluating some toll collection system effects, such as environmental pollution. In addition, when selecting the optimal toll collection system, it is also extremely significant to consider the attitudes of the highway users. In order to achieve this, agency can conduct a survey to determine the users’ attitudes towards particular toll collection systems. Surveying users would provide information about their expectations, which would contribute to the TCS ranking methodology from the users’ point of view. Finally, exploration of implications for the development of emerging self-driving vehicle technology in relation to different existing toll collection technologies is an important future effort.

Data Availability

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

Conflicts of Interest

All the authors declare that there are no conflicts of interest regarding the publication of this paper.

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Copyright © 2018 Marina Milenković et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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