As both developing and developed countries continue to urbanize, rapid urban growth is anticipated, particularly in low- and middle-income countries [1]. This will inevitably lead to enhancements in economic development, as well as the expansion of production, population, employment, travel demand, and freight transport demand in urban road networks. Transport can potentially lead to multiple social and environmental effects, such as difficulty with access, social severance, pedestrian accident risk, higher noise levels, greater air pollution, global warming, climate change, and other adverse consequences [2]. These effects strongly influence the health and well-being of the residents of urban areas [3]. To address these problems, a sustainable urban land use and transport planning (SULT) process is essential for ensuring sustainable and livable cities and societies [4].

Recently, the UNDP announced 17 sustainable development goals (SDGs) in association with 169 targets to promote a balance among the economic, social, and environmental elements of sustainable development and encourage the execution of important actions in the future [5]. Some examples of the targets of the SDGs that are closely related to the social and environmental issues associated with transport are the following: Target 3.6 of SDG 3 aims to reduce the number of global deaths and injuries from road crashes by half; Target 11.6 of SDG 11 proposes diminishing the adverse environmental consequences of cities; Target 13.2 of SDG 13 proposes the incorporation of climate change measures in national policies, strategies, and planning [5]. Sustainable urban mobility planning (SUMP) is a new strategic and integrated approach to urban transport planning. It can potentially contribute to sustainable urban mobility goals, such as air quality improvement, better accessibility, road safety improvement, traffic noise mitigation, climate change alleviation, and enhanced quality of life [6,7]. The implementation of suitable SUMP policy measures can allow the targets associated with these three to be reached.

Medium-sized cities (with less than one million people) in developing countries are residential places for approximately 25% of the global population, and those in Asian and African countries have the fastest rate of urbanization [8]. Such cities have experienced various challenges related to transport, such as adverse environmental consequences and a lack of sufficient resources [8]. Under such circumstances, medium-sized cities in developing countries critically need to appropriately prioritize and evaluate road segments according to the levels of adverse environmental consequences of their transport systems and to allocate limited budgets for the improvement of those road segments. The Central Business District (CBD) road network in Khon Kaen Metropolitan Municipality (KKMM), Khon Kaen City (KKC), Thailand, was selected as the study area. KKC, one of the largest and fastest-growing regional cities, is a medium-sized city in Thailand. With the rapid growth of its travel and freight demand, KKC has suffered from various adverse transport-related problems, such as traffic congestion, road accidents (e.g., pedestrian accident risk), adverse environmental impacts (e.g., PM_2.5 concentrations and noise levels), inefficient energy consumption, and greenhouse gas (e.g., CO₂) emissions [9]. The comprehension, prioritization, and evaluation of such transport-related environmental consequences are critical for ensuring the development of sustainable and livable cities. Based on direct interviews with decision-makers and administrators, KKMM has rarely performed suitable processes of prioritization and evaluation of all road segments according to the degrees of their separated and combined environmental consequences. An efficient decision support model (DSM) framework is indispensable in the understanding, ranking, and assessment of problematic road segments, identification of the possible causes (transport-related environmental criteria) of the problems with those road segments, and the appropriate allocation of limited budgets for their proper treatment.

The assessment of such adverse environmental effects of transport is difficult and complicated. This is because when the combined environmental effects of several road segments are estimated, multiple criteria must be simultaneously determined, and each road segment commonly experiences different levels of adverse environmental consequences (ranging from psychological effects to direct physical and health impacts) for each criterion [2]. In addition, the residents’ perception (and, therefore, their relative weights) of such criteria will be altered with the road class and land use type [10,11]. Furthermore, such complex decision-making processes must normally deal with uncertain and obscure (fuzzy) information and judgments.

Generally, the evaluation of the transport-related environmental effects of each road segment is an unstructured decision-making problem involving multiple (objective and subjective) criteria: dealing with a certain number of alternatives, considering group judgments, and considering uncertain, incomplete, and ambiguous (fuzzy) information. Hence, the multiple-attribute decision-making (MADM) method matches the nature of such an evaluation [12]. Various MADM techniques have been developed, such as the simple additive weight (SAW), analytic hierarchy process (AHP), fuzzy AHP (FAHP), analytic network process (ANP), technique for order preference by similarity to an ideal solution (TOPSIS), fuzzy TOPSIS (FTOPSIS), evaluation based on distance from the average solution (EDAS), and data envelopment analysis (DEA) [12,13]. Each of these methods is unique in terms of its potential applicability, strengths, drawbacks, and limitations.

Keshavarz-Ghorabaee [14] conducted a study to evaluate initiatives aimed at reducing air emissions from transportation by using the Stepwise Weight Assessment Ratio Analysis II (SWARA II) technique. Zarandi et al. [15] utilized the fuzzy analytic network process (FANP) to evaluate the environmental implications of PM_2.5 concentrations in Tehran, Iran. Borza et al. [16] utilized the analytic hierarchy process (AHP) and technique for order of preference by similarity to the ideal solution (TOPSIS) to conduct a multi-criterion analysis of traffic pollution at various congested intersections in Sibiu, Romania. Broniewicz et al. [17] utilized the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Ratio Estimation in Magnitudes or decibels to Rate Alternatives which are Non-Dominated (REMBRANDT), and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methodologies to assess the concerns with the development of sustainable transport in association with the construction of a national road and an expressway in Northeastern Poland. Jovanovic et al. [18] performed an environmental impact assessment (EIA) using several multiple-attribute decision-making (MADM) approaches, including the AHP, AHP Entropy, TOPSIS, VIKOR, and Entropy VIKOR. Only the AHP and AHP Entropy approaches were recommended for future use in EIA. According to this concise literature review, several MADM approaches have recently been utilized in the field of EIA. A comparable pattern is anticipated for the future. The main difficulty lies in selecting the optimal combination of multiple MADM techniques for EIA and decision-making challenges, specifically for medium-sized cities in developing nations.

Recently, the hybrid MADM (HMADM), which combines various simple and beneficial algorithms, was utilized to provide more precise and better outcomes at the expense of greater difficulty and complexity [19]. HMADM was applied to address this decision-making problem. In many HMADM studies [16,20,21], the FAHP was adopted to consider the relative weights of each criterion in a fuzzy environment but not to rank alternatives. The fuzzy scoring method (FSM) [22] can be used to transform linguistic (fuzzy) scores into corresponding numerical (crisp) scores [2,10]. TOPSIS can be applied to determine the composite scores of all alternatives when the relative weights of all criteria and the performance scores of all alternatives in association with each criterion are given. TOPSIS has been successfully applied to various domains and subject matter [23].

Although an efficient decision support model (DSM) framework is needed to rank and assess the multiple criteria of transport-related environmental effects (in a fuzzy environment) of various road segments in the urban road networks of medium-sized cities in developing countries, there is a lack of research that has attempted to perform such an important task by integrating applicable mathematical modeling methods (MMMs) for each environmental criterion with powerful HMADM techniques in a fuzzy environment. Consequently, this research aims to fill this gap by setting its main objective as the first proposal of a novel integrated DSM framework based on the combination of five robust MMM models (namely, models for the prediction of the CO concentration (COC), the CO₂ emissions (CO2Es), the PM_2.5 concentration (PM2.5C), the noise level (NOLs), and the pedestrian accident risk (PAR)) and a rigorous HMADM technique (which includes the FAHP, FSM, and TOPSIS) to efficiently prioritize and assess each separate criterion and the multiple criteria of transport-related environmental effects in the fuzzy environment of road segments in the urban road network of a medium-sized city (KKC) in a developing country (Thailand). In addition, this DSM framework can be used to identify the possible causes (transport-related environmental criteria) of problems with those road segments and to appropriately allocate limited resources for their suitable remediation.

2. Criteria of Transport-Related Environmental Effects

3. HMADM Approach