A Systematic Review of the Role of Land Use, Transport, and Energy-Environment Integration in Shaping Sustainable Cities

Abstract

Land use, transport, and energy-environment integration (LUTEI) is receiving considerable attention as an elaborate approach to improving urban resilience. Research evidence on this multidisciplinary topic tends to be fragmented, hindering constructive analysis of its role in shaping sustainable cities. This paper addresses this by undertaking a holistic systematic review to consolidate diverse perspectives. The analysis of 195 reviewed papers identified four main clusters of knowledge that include methodological approaches, policy instruments, urban design elements, and impacts of interventions. The analysis revealed that a growing body of literature is increasingly focused on improving accessibility planning, transit-oriented development, and policy integration to achieve sustainable and healthy transport as a vital element of resilience in cities. The review found, however, that the integration of environment and energy into land use and transport models is still at a nascent stage of development and has largely been overlooked in traditional LUTI models. This can lead to unreliable assessments of the impacts of low-carbon mobility solutions, emerging green transport technologies, and long-term changes in energy consumption affecting sustainable mobility futures. This paper concludes by connecting LUTEI dimensions to the UN’s sustainable development goals (SDG), outlining future directions to ignite meaningful research on the topic and providing a transparent path for decision-makers to adopt LUTEI-informed planning.

1. Introduction

The interactions between transport, land use, and energy-environment have a significant impact on the functioning and development of metropolitan cities [1]. As these interactions become increasingly complex and interrelated, a deep understanding of the dynamics is crucial for the introduction of sustainable and innovative mobility solutions [2]. Also, transport, economic growth, and CO₂ emissions are mutually dependent, and environmental degradation is significantly impacted by economic growth in urban areas [3]. The idea of sustainable mobility is rooted in the seamless connection of land use and transportation systems, while green mobility prioritizes energy conservation and reducing environmental impact [4]. This approach is critical in achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 11, which aims to create inclusive, integrated, and resilient cities [5]. Sustainability and resilience are sometimes complementary in LUTEI, and although they are sometimes employed synonymously in literature, they have distinct assumptions about urban functionality. In LUTEI literature, sustainability mostly refers to the ability of cities to reduce their carbon emissions and to improve energy efficiency over the long-term, while resilience refers to their ability to adapt to demographic, economic, and climate changes while maintaining sustainable low-carbon mobility [6,7,8,9,10]. However, sustainable mobility also has a direct or indirect relationship with other SDGs [11]; thus, a comprehensive understanding of sustainable mobility is essential to identify the opportunities and constraints within the SDGs framework. In other words, sustainable mobility encompasses various other dimensions such as accessibility, affordability, and safety, which are all essential for creating liveable and sustainable cities. These dimensions are not only important for achieving SDG 11 but also for other SDGs such as SDG 9 on Industry, Innovation, and Infrastructure, SDG 3 on Good Health and Well-being, and SDG 7 on Affordable and Clean Energy [12,13].

On the other hand, the LUTI framework is a common approach used in urban planning to analyze the relationship between land use and transportation, but it does not typically consider the environmental and energy implications of these decisions. Many papers argue that comprehensive understanding of urban systems cannot be attained through the examination of these factors in isolation, particularly when the objective is to mitigate the effects of climate change and promote resilience in urban environments [14,15,16,17]. Therefore, this paper highlights the importance of integrating environmental and energy considerations into the traditional Land Use–Transportation Interaction (LUTI) framework in order to make more sustainable and environmentally friendly decisions about how cities function and evolve [1,14,18]. The literature analysis shows that urban mobility’s environmental impact is affected by technology and location. Combining urban and spatial data with life cycle assessment methodology can reduce environmental impact. However, more improvements are necessary in spatially and territorially focused LCA methods [15,19]. Additionally, the analysis shows that a tailored approach is necessary to address the diversity in regional characteristics and constraints of urban mobility, as a one-size-fits-all approach may lead to suboptimal solutions. These findings are a significant achievement in the field of environmental sustainability and emphasize the importance of context-specific methods [20].

After critical examination of LUTEI literature, this research also highlights the significance of the strategies such as promoting transit-oriented developments [20,21,22], encouraging safe active transportation on a less crowded, well-balanced route [23,24], and increasing public transport connectivity and acceptability in achieving sustainable accessibility and reducing traffic congestion [20,25]. However, upon further analysis of the literature, it has become apparent that research on recent green mobility technologies and autonomous vehicle (AV) adoption have mostly focused on the short-term impact on congestion and emission, overlooking the long-term impact on regional land use interactions, job accessibility, alternate mobility scenarios, livability, the transition period with human drivers, and environmental effects [26,27,28]. Un-controlled AV adoption could increase dependence and emissions; compensating policies are needed to prevent unsustainable outcomes [29].

The research questions that the paper aims to answer are designed to provide a detailed understanding of the current state of research in this area, identify key literature and its contribution, evaluate the strengths and limitations of existing methodologies, and suggest potential areas for future research. This information can be used to guide future research and policy decisions in the field, thus filling an important research gap in land use, transportation, environment, and energy.

This article is divided into several sections, each with a specific focus. In Section 2, the systematic review process is described in detail, including the research objectives, questions, and databases that were consulted. The criteria used to determine which studies were included and excluded in the review are also outlined. This section provides a clear understanding of the methods used to conduct the review, which is important for evaluating the credibility and reliability of the findings. In Section 3, a bibliometric analysis is conducted to identify the most influential research on the topic, and the concepts and themes that have evolved over time. It also assesses the connection between different categories of research on the topic and classifies and establishes a cognitive network to consolidate the fragmented literature. This section provides a broad overview of the state of research on the topic and helps to identify key areas of focus and gaps in the literature. Section 4 discusses the integration of findings to identify limitations, challenges, and research priorities for future research directions. It also elaborates on the construction of the framework that links LUTEI determinants with the UN’s Sustainable Development Agendas. This section provides insight into the practical applications of the research and demonstrates how it can be used to inform policy and decision-making. Finally, Section 5 provides a concise summary of the main findings of the review and offers reflections on their significance and implications. Overall, this article provides a comprehensive overview of the research on the LUTEI, from the systematic review process to the integration of findings and their practical applications.

2. Materials and Methods

This paper presents the results of a systematic literature review which aimed to identify relevant information about a specific topic. The review began by establishing research goals, objectives, and key questions. Search criteria were defined using a specific strategy, and the methods of evaluation used in the review are outlined. Conclusions and future research opportunities are also discussed.

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Data sources identification and selection of relevant data;

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Identification and application of inclusion and exclusion criteria;

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Evaluating the quality of the selected studies and reviewing the results;

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Evaluating the literature through bibliometric data analyses, and assessing the cognitive structure of the corpus of knowledge using established bibliometric visualization and citation analyses;

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Results interpretation to draw conclusions and identify future research opportunities.

2.1. Research Objectives and Key Questions

The systematic review presented in this paper aimed to identify key literature on the topic to help investigate the following research questions:

RQ1.

What are the main points of discussion in the literature on the integration of land use, transport, and the environment, and how has this research progressed over time?

RQ2.

Which studies have had the most influence on the evolution of this literature to date?

RQ3.

What are the different knowledge areas in the land use, transport, and environment integration studies that can contribute to sustainable mobility and resilient cities?

RQ4.

What are the strengths and limitations of LUTEI research, and what are the major approaches that can provide the opportunity for sustainable mobility?

RQ5.

Which future application and research streams can be pursued to achieve low-carbon mobility within an adequately integrated plan for a sustainable future?

2.2. Data Sources and Inclusion/Exclusion Criteria

The primary literature search was conducted utilizing Scopus, as it exhibited the highest coverage of the topic and better functionality [30]. The Web of Science (WoS) was also utilized as a supplementary source for comprehensive coverage. The keyword-based query used in both databases was “Land use and Transportation Integration, or Interaction, and Sustainability or Environment” as illustrated in Figure 1. Snowballing was employed to identify additional records to ensure a comprehensive review [31].

A keyword-based query was used to select documents containing “land use and transport,” and at least one of the terms “integration” or “interaction” and “sustainability” or “environment” in the title, abstract, or keywords from Scopus and WoS. The research focus was on land use and transport integration and its relation to sustainability and environment in urban areas, specifically road and rail transport [14]. English journal articles published between 1995 and August 2021 were considered, and articles that did not consider interactions between land use, transport, and environment were excluded. A total of 195 articles were retrieved and analyzed using a four-step screening process outlined in the PRISMA method [32,33] in Figure 1, standard Scopus analyses, and visual bibliometric analysis software including VOS viewer v.1.6.16 [34] and Gephi v.0.9.2 [35].

2.3. Descriptive Analysis

The historical distribution of the 195 articles that were used in the analysis is provided in Figure 2. The diagram shows that the number of relevant articles that met the search and inclusion/exclusion criteria fluctuated within a narrow band of less than 10 articles per year between 1995 and 2016 and started to increase substantially over the past six years reaching 27 articles to date in 2021. Overall, and despite the fluctuations, contributions to LUTEI show an increasing trendline over the given period. The number of publications rapidly increased from 6 papers in 2016 to 27 in 2021 which reflects the increasing recognition of the role of transport in SDG11 since 2015 [12] and the significance of linking transport planning to land use, thus answering RQ1 [2,36].

The geographic distribution of publications, comprising 44 countries, is provided in

Table 1. Geographic and journal classification distribution of LUTEI literature.

Countries with Highest Citations	No.	Countries with Highest Number of Papers	No.
United States	6690	United States	43
United Kingdom	1822	China	34
Canada	1354	United Kingdom	26
Australia	785	Australia	24
Netherlands	672	Canada	17
China	496	Hong Kong	13
Hong Kong	315	Netherlands	13
Spain	257	Spain	10
New Zealand	255	Germany	6
Colombia	153	Japan	6

. The United States had the highest number of publications on this subject (43 articles), followed by China (34 articles), and the United Kingdom (26 articles). Articles published by researchers from the US also had the most citations (6690 citations) as presented in Table 1.

The Scopus analysis also included journal classification based on subject areas. This included Social Sciences (40.4%), Environmental Sciences (21.7%), and Engineering (16.4%) amongst the principal themes influencing LUTEI literature. This was followed by the fields of Energy (5.8%), Decision Sciences (3.3%), Health (3.3%), Biological Sciences (2.8%), Economics (1.5%), and Computer Sciences (0.8%). As mentioned earlier, this multi-disciplinary topic is integrative, and most of the studies considered spanned over several domains. Although these classifications are rather general, they represent boundaries of disciplines covered in this body of knowledge and main subject areas (thus contributing to answering RQ1).

2.4. Bibliometric Analysis

The research identified 78 journals that published literature on land use and transport integration and environment. Though no specific journal was dominant, the Journal of the American Planning Association, Transport Policy, and Annals of Behavioral Medicine received the most citations in the analysis period (

Table 2. Distribution of and citation analysis of LUTEI literature across journals.

Sources with Highest Citations	No.	Sources with Highest Number of Papers	No.
Journal of the American Planning Association	3636	Journal of Transport Geography	13
Transport Policy	1762	Sustainability (Switzerland)	12
Annals of Behavioral Medicine	1466	Transport Policy	12
Journal of Transport Geography	501	Journal of Transport and Land Use	11
Transportation	405	Transportation Research Part A: Policy and Practice	11
Land Use Policy	294	Transportation Research Part D: Transport and Environment	9
Transportation Research Part A: Policy and Practice	270	Transportation	8
Transport Reviews	218	International Journal of Sustainable Transportation	6
Computers, Environment and Urban Systems	197	Land Use Policy	6
Journal of Transport and Land Use	189	Environment and Planning B: Planning and Design	4

).

The Journal of the American Planning Association had the highest citations (3636) which were mainly attributed to four key papers [18,37,38,39]. Transport Policy, which had 12 related publications, attracted 1762 citations for articles covering mobility goals of land use and transport integration (thus answering RQ2). The citation analysis in

Table 3. Top researchers by citation impact and publication output.

Authors with Highest Citations	No.	Authors with Highest Number of Papers	No.
Frank, L.D.	2489	Frank, L.D.	4
Sallis, J.F.	2467	Sallis, J.F.	4
Cervero, R.	2213	Bertolini, L.	4
Saelens, B.E.	2201	Kii, M.	4
Ewing, R.	2053	Furlan, R.	4
Banister, D.	1291	Miller, E.J.	3
Waddell, P.	871	Curtis, C.	3
Bachman, W.	735	Doi, K.	3
Chapman, J.E.	735	Pfaffenbichler, P.	3
Conway, T.L.	735	Zhao, P.	3

also lists the authors with highest influence in this topic.

The authors with the highest number of citations were Lawrence Douglas Frank (2489 citations), James F. Sallis (2467), and Robert B. Cervero (2213 citations) with the most contributed topics including the built environment, pedestrians, leisure time, travel behavior, mode choice, active transport, and urban form.

2.5. Bibliometric Network Analysis: Co-Occurrence

The bibliometric analysis identified influential concepts and trends in LUTEI and sustainable mobility research, showing changes and structure over time [40] using VOS viewer software on 195 selected articles. The 92 most frequent keywords from a total of 589 author keywords are shown in Figure 3 and

Table 4. Top ten keyword occurrences in LUTEI—A bibliometric network analysis.

#	Author Keyword	Occurrence Weight	Total Link Strength	Average Year of Publication
1	Built environment	22	43	2018
2	Land use	19	39	2014
3	Urban form	11	20	2016
4	Public transport	10	21	2017
5	Sustainability	10	15	2014
6	Transportation	10	21	2016
7	Accessibility	9	12	2018
8	Urban planning	9	15	2016
9	Transit-oriented development	7	14	2019
10	Cycling	6	16	2018

, which highlights the frequency of co-selection for each keyword in LUTEI publications answering RQ1 and RQ2.

The keywords with highest weights have a more prominent appearance in the network visualization shown in Figure 3. This includes keywords such as “Built environment”, “Land use”, “Urban form”, and “Public transport”.

The VOS viewer analysis shows that links represent co-occurrences between keywords, as shown in Table 4, which illustrates the number of articles in which the keywords appear together. The links’ strength is visualized using Gephi in Figure 3. For instance, “built environment” has a strong connection with “travel behavior”, “residential self-selection” and “walking”. Similarly, “public transport” is strongly drawn to concepts of “cycling”, “participation”, and “sustainability”.

Figure 3 illustrates the direct relationships between keywords using arrows pointing from the source keyword to the target keyword, representing co-occurrence patterns. For example, “cycling,” “policy integration,” and “governance” are sources of “public transport” research subjects, and “public transport” is a source of “sustainability” topics and research. The visualization map also shows chronological trends of keywords based on their most frequent publication years. It shows that between 2000–2010, there was more attention towards “transport,” “urban transport,” “policy integration,” and “environment.” From 2010–2015, the focus shifted towards “sustainable accessibility,” “sustainable mobility,” “mode choice,” and “transit.” From 2015, there was increased interest in “land use and transport integration” research which shifted the focus towards “built environment,” “urban planning,” “transport planning,” “travel behavior,” “public transport,” “walkability,” and “cycling.” However, recent research trends in 2020 and 2021 focused on “transit-oriented developments,” “interaction effects,” “public transit,” and “simulation.”

2.6. Bibliometric Network Analysis: Co-Occurrence

To understand the evolution of this topic’s multidisciplinary knowledge, assessing intellectual connections and similarities between studies was necessary to classify and extract themes. Multiple methods, such as bibliographic coupling and co-citation analysis, can be used to map the literature’s network and structure [41,42]. Despite the similarities of these methods [43], co-citation analysis is viewed as a more effective method to establish the literature’s scholarly cognitive structure [41,44,45], thus it was applied to classify the main clusters emerging from LUTEI and sustainable mobility literature. Using VOS viewer, this analysis determines the frequency of co-citation among authors in a list of references for citing articles [46,47], resulting in formation of clusters of articles with similar themes or focus [48]. Higher co-citation frequency increases the likelihood of these papers being in the same cluster [49].

To form clearly defined clusters, a minimum of four citations was used as a threshold. This resulted in 996 nodes representing all references and 110,156 edges representing co-citation frequency. The software VOS viewer was used to visualize four clusters, with a representative label selected for each cluster to reflect its key theme in Figure 4. The authors then examined the articles in each cluster to propose a theme based on common features. Each cluster has a combination of themes, with one theme being more prevalent and defining the general theme, content, or approach of the cluster [50,51]. This resulted in the identification of the following dominant themes for each cluster: Cluster one Methodology Framework (276 items, 27.1%), Cluster two Policy Instruments (248 items, 24.9%), Cluster three Urban Design (251 items, 25.2%), and Cluster four Impact (218 items, 21.9%). Using co-citation analysis, the identified four distinct clusters of knowledge areas within LUTEI studies are relevant to answering RQ3. In Section 3, we will explore each cluster in detail, highlighting key themes and knowledge areas that shed light on different aspects of LUTEI.

3. Results

To determine the key issues of discussion in each cluster, the authors analyzed the articles in each cluster in depth and identified the common issues to be discussed including similar practices and methodologies in each article. Eventually, the most relevant subjects that best described each cluster were assigned by the authors based on research goals and in compliance with previous bibliometric analysis of research trends shown in Section 2. The data and evidence used in the analysis is available online to ensure transparency and replicability. More details about each cluster are provided next to help answer RQ3, RQ4, and RQ5.

3.1. Cluster 1: Methodology Framework

One of the clusters uncovered in this analysis is related to the use of transport modelling and simulation using advanced dynamic models [14,52,53,54]. The current trends in LUTI modelling include the shift towards the use of multi-agent activity-based simulation to adequately predict environmental impacts and evaluate urban resilience [6,14,55]. The evolution of these models can be categorized into three generations of models as demonstrated in

Table 5. Evolution of LUTI methodologies: A summary of three generations over time [14,55,56].

Generations	Main Attributes	Model Example
1st-GEN	(a) Defining accessibility for the spatial allocation of activities; relationship between mobility and land use; and population disaggregation (b) Travel origin and destination predictions to manage travel conditions and overcome traffic congestion	Gravity Model [57], TOMM [58], PLUM [59], ITLUP [60]
2nd-GEN	(a) Econometric approach to capture individual choices and disaggregated behaviors (b) Introduction of random utility models and four-step travel demand models associated with land use	DELTA [61], MUSSA [62], TRANUS [63], MEPLAN [64]
3rd-GEN	(a) Influenced by computational advances, disaggregated models, and comprehensive microsimulation of urban systems (b) Introduction of cell-based models based on spatial evolution of travel demand and movement (c) Extensions to incorporate environmental and energy sub-models	ILUMASS [65], ILUTE [66], UrbanSim [18], WILUTE [6]

.

LUTI models have dealt with various challenges such as the need to address multitudes of scenarios and stochastic variations in disaggregated models; dealing with uncertain outputs [67,68]; applying various methods for evaluation of accessibility [14]; interactions between different activities and travel scenarios [69]; and inclusion of energy, environmental and health factors and challenges [6,14]. Recent 3rd generation LUTEI models have brought research focus to energy consumption management, climate change, social equity [70], and urban health [71]. This has been facilitated by software advances that cleared the path for enhanced network modelling, data calibration, integration, and problem identification [18,55,72]. In these models, accessibility is a fundamental factor for ensuring social equity considerations in sustainable mobility [14,73].

The key LUTEI considerations are demonstrated in Figure 5. Despite the importance of energy and environmental factors, a limited number of models have taken them into consideration. Examples include TRESIS [74] which simulates location and commute choices combined with environmental strategy measures. Another example includes ILUMASS which simulates traffic flow associated with land use changes including considerations of environmental sub-models [54]. UrbanSim [18] also incorporates accessibility modelling and environmental regulations in scenario assumptions. Similarly, WILUTE [6] evaluates the sustainability of integrated land use and transport policies with reference to environmental impacts, energy consumption, and general effect on public health.

A review of LUTEI models showed that the sub-models for environment and energy have not been fully integrated into land use and transport models. Recent studies have used a combination of models and software to address these gaps in analysis. For example, a study by François, Gondran, and Nicolas [15] used SIMBAD for land use and transport integration, EcoInvent for vehicle life cycle assessment, and COPERT for pollution and emission modelling. Another study by Shahumyan and Moeckel [75] analyzed complex model integration using SILO for land use, MSTM for transport modelling, and BEM and MEM for emission modelling.

3.1.1. Comparative Studies

Comparing different systems allows for identification of advantages and disadvantages. Comparative studies can focus on models/policies or built environments. Studies often compare the impact of built environments on travel and environment in various locations such as cities, CBDs, and suburbs. However, while evaluations of individual locations can provide useful references, they lack practicality in establishing LUTEI typology [20,76,77]. For instance, a study by Aston et al. [20] evaluated mode-location bias in multiple networks of Melbourne, Boston, and Amsterdam and demonstrated that it is essential for transport mode or demand analysis to be associated with various urban forms to improve the validity of LUTEI models. On the other hand, evaluation of the impact of various policies or modelling approaches in addressing urban sustainability issues is essential for efficiency analysis [78]. Various models are used to compare the effectiveness of policies. For instance, Paul Waddell [18] developed the UrbanSim model to evaluate the various real estate policy’s effects on locational choices. Alonso, Monzon, and Wang [79] successfully compared different public transport, telecommunication, and re-densification policies using MARS (Metropolitan Activity Relocation Simulator). Another debated study by Echenique et al. successfully applied 26 sustainability metrics to compare various policies of smart growth in different English city regions, proving that some strategies like compact cities do not necessarily reduce energy consumption and environmental impacts [78].

3.1.2. Large-Scale Simulation Models

This type of methodology usually involves big data that might complicate the LUTEI computations, but it can enable patterns of mobility and locational choice by forming essential dynamics of differences over space and time [72,80,81]. For instance, Batty and Milton developed the QUANT model that required big data because of the spatial extent and outlined a dynamic pattern of how employment and travel costs affect transport mode choices [72]. These large-scale models tackle rapid development, access to fast computation, precision, and link to different models on various scales [72,81,82]. For example, in this case, Batty and Milton suggested linking to other models such as LUISA suggested by Echenique et al. [16] to handle an extensive network.

3.1.3. General Equilibrium Models

General equilibrium models in econometrics included some pioneering work of spatial interaction analysis of LUTI that provided balanced predictions of land use and travel demand [83,84]. Some well-known examples of these models include MUSSA [62,85] which usually involves transit policies as an essential component and is based on the paradigm of static market equilibrium [83,85]. Another example is the RELU-TRAN, Regional Economy Land Use, and Transportation model, by Anas and Liu [86] which considered road traffic, housing and job market, and real-estate value associated with the microeconomic approach [86,87]. Several studies stated that comparative static equilibrium can be convenient in computations but are inadequate to model vehicle technology advancements and can hardly evaluate the temporal extent of urban development [84,88]. Therefore, they are more suitable for more steady urban growth conditions [86,88,89], and not suited for analyzing medium- to long-term trends in faster urban transformation [89].

3.1.4. Methodologies Based on Survey

The survey-based studies are usually focused on users’ travel experiences and perceptions of the built environment [23,89,90]. The main factors considered in these types of papers include the validity of the LUTEI measures and model calibration [21,23,26]. However, data mining for larger-scale analysis can be time-consuming [26]. Therefore, recent researchers preferred using online platforms (e.g., social media, smart phone data, trajectory data) or open-source algorithms for evaluating adequacy of intervention using variety of available data [23,24,26]. An example of this is a study by Desjardins et al. [24] that used the CycleStreet routing algorithm to analyze origin–destination zone characteristics that impact cycling trip flows.

3.1.5. Sustainable Mobility Methodologies

A major area of emphasis in recent sustainable mobility methodologies is a surge in the integration of active travel modes including cycling and walking into recent micro-mobility models [6,68,91], mostly due to increased endeavors to develop healthier cities [92]. A study by Zhao and Li [91] focused on sustainable transport mode integration, including walking, cycling, and public transport in the built environment.

The recent surge in techniques to analyze the suitability of micro-scale urban structures for walkability, such as Regression models [93,94], Walk Score [94], and Space Syntax [95,96], also confirms the trend towards active transport as a sustainable mode of commuting. In addition, the pandemic has affected household travel and activities [97] due to increased virtual accessibility and shift towards remote work, altering the landscape of LUTEI developments [98,99]. However, the long-term impact of these changes on land use, travel behavior, and the environment is uncertain and should be closely monitored and studied in the coming years.

3.2. Cluster 2: Policy Instruments

The role of policy instruments in managing land use and transport interactions featured prominently in the literature and resulted in a substantial cluster. To achieve sustainable mobility goals, a range of policy instruments are reported including demand management, reducing travel distance, encouraging sustainable modes of travel, enabling environmentally friendly technologies, and public awareness [2,29,100]. Several policies are explained next to identify research gaps [7].

3.2.1. Reducing Environmental Impacts

The impacts of transport and land use interactions are usually assessed through three major external costs of traffic congestion, emissions, and regional air pollution [2,8,100,101]. Efforts to reduce the environmental impacts often rely on policies to integrate planning, economics, and technological advancement [102]. Commonly suggested policies reported in the literature include:

• Land use policies focusing on increasing mixed-use, high-density, and transit-oriented developments aimed at reducing the physical distance of activities, and restrictive policies to preserve natural environments;
• Transport-related policies that focus on modal shifts to public transport, walking, and cycling. This can be applied through demand management, speed limits for personal vehicles, road pricing and congestion charging, and parking control. In these approaches, integration with land use policies is essential;
• Increasing transport efficiency through a gradual increase in fuel taxes and technological innovations, i.e., clean energy, alternative fuels, and improved vehicle engine designs. Related policies also focus on affordability, stakeholder involvement in decision-making to increase implementation and commitment to the process of change [2,102].

A study in Europe compared the effectiveness of the mentioned policies [103]. Combined with an increase in fuel prices, travel demand management policies were reported to be the most effective, resulting in an average vehicle travel demand reduction of 0.8% per year. However, investment policies for technological advancement were found to be only partially effective [102,103]. Another study in China, which compared two scenarios of having no strict policy compared to policies that ban internal combustion engine (ICE) vehicles, was found to have the potential to increase electric vehicle (EV) market share to 70% by 2050 [104]. With EV (Electric Vehicle) policy support there will be valuable changes in the energy mix and environmental impacts when fossil fuels no longer dominate mobility [105]. Research has found that the adoption of electric vehicles alone may not reduce traffic congestion and may even lead to more personal travel. To reduce emissions and their impact on local environments, both demand management measures and technology regulations need to be implemented simultaneously along with promoting public transportation and other low-carbon mobility solutions.

3.2.2. Public Transport Related Policies

Policies for restricting unsustainable mobility solutions must be accompanied by viable alternatives such as public transport (PT) [2]. Effective PT demand models should consider costs, socio-economic factors, land use integration, and accessibility [106]. Government funding plays a crucial role in making public transportation a preferred mode of choice, as it has traditionally not received the same level of funding as motorways and tunnels [107,108]. User-pay road pricing, such as the London Congestion Charging Scheme, has been shown to increase public transportation usage by 38% [109,110,111]. Revenues can be used for PT network improvements. Value capture [109] is another government funding policy gaining support in cities worldwide. In this case, governments and property developers collaborate to invest in public transport infrastructure, leading to increased property values and financial recovery for investors. Projects, such as the Buenos Aires subway line, are often funded through beneficiary funds, such as a 5% property tax and a 2.4% increased value tax for properties near stations [112]. Hong Kong uses the “rail-plus-property” model, which combines investment in public transport infrastructure and Transit-Oriented-Development projects, to improve less desirable railway stations and hubs [112,113].

To increase the appeal of PT, fare payments were improved through E-ticketing and smart card technology [108,114] to encourage more patronage. Also, this provided policymakers with evidence-based insights into PT networks that can be critically used for public transport system evaluation and surrounding area improvements. Other related policies focused on reducing the impact of public transport on the environment [74,109,115]. For example, the Stockholm Regional Public Transport Authority reduced the environmental impact of public transport projects by using green fuel for 58% of buses and by investing in land use integration projects [115]. Effective public transport policy also requires proper planning, implementation, and stakeholder participation.

3.2.3. Walking and Cycling Policies

Transport policies for active transport and micro-mobility focus on infrastructure design and promoting diverse, multi-functional neighborhoods through land use planning [116]. Key factors of active transport and micro-mobility management strategies include:

• Pathway management policies: Visual signs for cycling infrastructure, equipment requirement including speed limits for cycling, limitations on devices to avoid cluttering, service limitations, and device parking locations;
• Equitable accessibility: Concession, discounts, alternative payment for public micro-mobility devices, and enhanced accessibility for people with disability;
• Enforcement: Convenience and safety considerations along streets, intersections, and pedestrian/cyclist encounters along the sidewalks [117].

The availability of open data and real-time information is critical for monitoring performance standards and assessing the impact of services [24,117]. In 2009, the City of Hamilton introduced a 20-year horizon bicycle master plan implementing 20 km cycling facilities each year. Research by Desjardins et al. [24] revealed that short-distance travel is a significant predictor of active transportation mode choice and that policies promoting integrating bicycle and PT encourage usage [91,118].

3.2.4. Urban Freight

A sustainable freight system is an essential function of prosperous cities [119]. Wisconsin’s State Smart Transportation Initiative (SSTI) [120] researched policies that have been applied across the US to manage road freight aiming to ease congestion within urban contexts. Key aspects of the initiative included:

• Designing urban freight routes to decrease pollution impact on residents, and considerations for pedestrian and bicycle;
• Modal shift to rail-oriented transport which has been successfully applied in Chicago;
• Land use policies including multi-modal urban freight within the CBD, using zoning regulations such as designated areas for freight activities, and adequate parking zones for freight vehicles using suitable timing and pricing limits [120].

National plans for road safety and sustainable mobility policies have traditionally not incorporated considerations for freight transportation [119,120,121], but recent policies have shifted towards their consideration in environmental impact action plans [121]. Evaluation of effectiveness of subsidies and carbon pricing policies for Madrid’s urban freight also confirmed that this would influence energy prices and green technology adoption rates in the long term [122].

3.2.5. Policy Implementation

Policy settings for urban roads as open public spaces should consider equity by prioritizing policies for sustainable public transport and non-motorized movements [118]. This paradigm is shifting towards objective interventions that prioritize stakeholder participation [2]. For example, to improve consistency in the Netherlands transportation program, civil servants connect policymakers and stakeholders by interpreting visions, development strategies, budgets, and plan implementations [116]. Several organizations have attempted to improve stakeholder participation in transportation policy through initiatives such as the Brookings Institution’s accessibility efforts, the promotion of 20-min neighborhoods by the Victorian State Government, Wisconsin’s State Smart Transportation Initiative (SSTI) for efficient, equitable, and environmentally friendly mobility policies, and the Spatial Network Analysis for Multimodal Urban Transport Systems (SNAMUTS) system for collecting public transport policy feedback. The key factors to improve the acceptability of these policies are:

• Improve availability of information by providing awareness of positive impacts through media, education, or campaigns;
• Stakeholder involvement and increasing the level of flexibility between expectations and results;
• Restrictive policies need to be coupled with incentives;
• To improve commitment to changes, policies need to be applied through several stages and measurable action plans to provide time to reflect opinions;
• Long-term action plans for reducing environmental impacts need to be consistent. This includes consideration of road pricing, green technology subsidies and incentives which should serve to achieve preferred outcomes;
• During monitoring and risk assessment, policies’ flexibility to alterations is essential if the objective does not match the outcome [2,102].

3.3. Cluster 3: Urban Design

Sustainable development involves a learning process to reduce car dependency and its associated impacts on society and the environment [37,123]. Therefore, sustainable mobility requires an integrated transport network promoting intercity movement and easy accessibility to transport [4]. The key themes connecting urban design strategies to sustainable mobility are described next.

3.3.1. Transit-Oriented Developments (TOD)

TOD is an effective planning strategy for managing large public transportation systems and high land utilization [22,76] with three dimensions of functionality [76,124] measured by diversity, density, and design [37]. Key findings of TOD strategies for successful integration are:

• Public transport: Maximizing ridership and passenger flow and convenience through improved services and supply;
• Accessibility: Pedestrian-oriented development with concepts of 10-min walking distances from the public transport hubs;
• Land use efficiency: Functional mix-use and compact developments;
• Environmental impact: Minimizing land use conflict and traffic congestion effect on the environment [22,124].

Since TODs focus on balance and integration, they are an effective strategy for planners to identify development opportunities, public health, and quality of life [4]. A study of Tehran’s Bus Rapid Transit (BRT) stations by Pezeshknejad et al. [125] showed that low-level integration resulted in a weaker choice value and attraction, while improved integration provided better accessibility. Several studies for Doha’s new metro system [21,126,127] also demonstrated the importance of TOD as a prioritized strategy to achieve the Qatar National Vision (QNV-2030) for reducing car dependence. This is typically achieved through micro-mobility-oriented development with main facilities and amenities located within a radius of 400–800 m from public transport hubs.

3.3.2. Social Knowledge and Perception Integration Strategy

This category explores the link between perceived social knowledge and the development of urban design strategies for land use and transport integration [123]. Studies reported that an individual’s perception of built environment factors such as mobility, density, and diversity influences mode of travel choices. A study in Switzerland found that a 50% increase in perceived comfort led to an 8% increase in public transport market share [128]. Another study by Guo and He [23] demonstrated how nearly 70% of individuals prefer using dockless bike-sharing when the perceived accessibility to public transport stations is positively integrated. Subjective measures in these studies were well-researched, but implementation in practice was hindered by lack of clear measurability [37,129]. Objective metrics were more manageable but still faced practical challenges. For instance, a study by Boisjoly and El-Geneidy [130] found that 52% of practitioners surveyed did not implement accessibility measures due to a lack of knowledge, and 34% cited data deficiency as a significant barrier to informed decision-making. Te Brömmelstroet and Bertolini [123] implemented a five-stage intervention plan in the Netherlands to bridge the gap between policy and practice by involving modelers and members of the public in LUTI strategy-making. These included problem identification, discussion of the desired outcome through integration, selection of suitable indicators, generating relevant scenarios, and internalization and assessment of the impact of interventions.

3.3.3. Accessibility, Walkability, and Integration of Active Transport

Studies under this theme examined how linking metropolitan railways to other urban transport systems, such as bus stations, and integrating them with nearby residential neighborhoods can improve sustainable accessibility [131]. Low-carbon sustainable accessibility strategies were found to be closely related to reduced vehicle-kilometers-travelled (VKT) and walking and cycling integration with public transport [116]. Zhao and Li [91] study found that individuals feel comfortable cycling distances of 1.2–3.7 km to and from PT stations. Distinctive features such as separation from other users, speed limits, bike racks, and parking lots can increase the perception of safety and encourage more individuals to share the road. [23,91]. Also, a secure and adequate number of bicycle parking lots around PT stations based on passenger volume contributes to higher bicycle-PT travel integration [89]. Another factor that negatively affects walkability and active transport integration is bicycle-pedestrian crashes [23,117]. Guo and He [23] crowded shared pedestrian-cycling paths decrease the use of docked bike systems due to perceived safety issues, while perceived bicycle-vehicle crashes do not have a significant impact on usage. To improve walkability in neighborhoods, effective design strategies include land use mix [132], optimized street density and continuous sidewalks [129] with pedestrian safety considerations [94].

3.4. Cluster 4: Impacts of Interventions

This cluster includes studies that looked at evaluating the impacts of LUTEI interventions.

3.4.1. Impact on Urban Health

Studies in this category found that 38 SDG targets are relevant to urban health based on the relationship between SDGs and integrated land use, transport, and environmental planning [5]. Clean urban environments promoting walking and cycling as physical activity can improve quality of life and mental health through increased active transport [39,91,117,118,133]. A study in Barcelona assessed health-related effects of physical activity of walking, cycling, and running for a new riverside project [134]. The authors collected data from 5737 participants using health surveys, epidemiological studies, and Barcelona’s health records to model health impacts. The results showed that if 50% of users performed physical activity daily in Besòs riverside trail for at least an hour, there will be 4.8 fewer deaths annually from diseases such as diabetes or heart conditions. Another study evaluated the impact of cycling infrastructure expansion alongside mixed land use and network connectivity on public health across Europe [92]. The results showed that a 10% increase in cycling infrastructure contributed to avoiding 31 premature deaths annually in Vienna; 21 in Rome 21; and 18 in London. Other interventions, like Barcelona’s Superblocks, were also reported as innovative integrated planning strategy that also contributed to improving public health [5,135]. The 503 Superblocks designed by the Urban Ecology Agency, aligned with SDG11, promoted active mobility and connected neighborhood which reduced unsustainable transport behaviors by 19%, reduced NO2 levels and were reported to have accounted for 667 fewer premature deaths per year [135].

The connection between health science and land use, transport, and environmental planning is important, but studies show a lack of evidence-based data to evaluate urban health. More effort is needed to integrate physical activity and the physical environment to achieve sustainable development. [5,93,109,136].

3.4.2. Impact on Traffic Congestion and Vehicle-Kilometers-Travelled (VKT)

The impact of various policies on travel demand can be examined through LUTEI [137] to estimate causes of congestion through urban forms [138]. Some studies assumed that policies supporting dense and diverse urban forms would encourage walking and cycling and consequently result in decreased VKT [124,132,139]. However, a study covering 157 US cities demonstrated that only a fraction of VKT is influenced by density, and that VKT is more affected by accessibility and connectivity efficiency [140]. Furthermore, policies aimed at reducing congestion through LUTI often focus on specific areas such as CBDs or high-density zones, which can lead to conflicting impacts at local and regional levels, as shown in the Madrid case study [137]. Strategies to reduce environmental impact by increasing energy-efficient vehicles can also increase congestion. For example, a simulation study for Berlin found that VKT would increase by 13% with the introduction of shared autonomous vehicles [27], and Zhuge and Wang [26] predict heavier congestion after the adoption of autonomous and electric vehicles due to an increase in empty travel. Similar findings were reported for Melbourne, where it was found that AVs would result in at least a 10% increase in VKT, even when shared [141].

3.4.3. Impact on Accessibility

Recent integrated urban studies have shifted focus from transport planning and mobility to accessibility planning [116,142,143]. At a macro scale, equitable accessibility should provide a spatial distribution of transport networks and PT in a way that ensures well-connected network and dynamic reciprocity between home and job location [144]. Therefore, some metropolitan-integrated plans target areas with higher social exclusion, like the “London Transport Plan 2025” and “St Paul 2040 Transportation Plan”, to have 45 min accessibility to occupations via PT [143]. At a micro scale, an urban structure providing walkable and micro-mobility accessibility to PT would lead to higher ridership, as shown in collaborative research covering 17 cities and 12 countries, which demonstrated that equitable accessibility to various destinations was positively correlated with less than 150 m walking distance to PT or destination and higher residential density [145]. However, a highly compact urban form may not support micro-mobility accessibility for longer distances [117,145]. In addition, emerging EV and AV are expected to have a substantial impact on accessibility components. While the exact impact is yet to be determined, high-income households might benefit more from this improved accessibility. There will likely be less advantages for low-income households that are more likely to use the walking, cycling, and public transport system [26].

3.4.4. Impact on Travel Mode Choice

Studies that explored the relationship between urban form and transport tried to minimize the adverse impact of vehicle-oriented urban areas such as high rates of crashes and emissions by suggesting more sustainable alternatives [132,146]. It is evident from several studies that well-established neighborhoods with higher density and land use mix have better access to services with walkable distances, and residents choose more sustainable transport modes [145,147,148,149]. A study in the San Francisco Bay Area used Mixed Multidimensional Choice models to confirm that higher highway density is co-related with more auto ownership levels while higher street block densities encourage sustainable modes of transport [150]. Furthermore, the implications of green vehicle technologies such as EV and AEV (Automated Electric Vehicle) are mostly centered on potential impacts [151,152] with studies showing mixed results on the effects on public transportation and travel demand [26]. While it is commonly noted that these emerging technologies will eventually influence urban form [26,29], how these changes would eventuate and what their actual impacts will be are still unknown.

4. Discussion

LUTEI models have faced various challenges such as addressing multiple scenarios and variations in models, uncertain outputs, accessibility, and considering energy, environmental, and health factors. Recent third generation models have focused on energy management, climate change, social equity, and urban health, thanks in part to advances in software. Policy instruments are used to manage land use and transport interactions and achieve sustainable mobility goals. The relationship between health and urban design is important but lacks robust data, researchers are using interdisciplinary methods to study it, and more research is needed to improve urban population’s health through evidence-based policies and interventions. The introduction of EVs and AVs is expected to have a significant impact on accessibility, but the exact nature and extent of this impact is not yet fully understood. EVs and AVs have the potential to improve accessibility by reducing traffic congestion, travel times, air pollution, and noise levels, but may also exacerbate existing inequalities in accessibility and have an uncertain impact on urban form and transportation. It is important to monitor these developments closely to ensure that they are implemented in a way that benefits all members of society.

4.1. Contributions

This paper presents a cohesive overview of research on integrated land use, transport, and environmental knowledge (LUTEI) and serves as a reference on the topic. The bibliometric evaluations identify key research concentrations and influential efforts in shaping future directions of LUTEI research for sustainable mobility. The interconnections of the four main clusters identified in the research, which included “Methodology Framework”, “Policy Instruments”, “Urban Design”, and “Impacts of Interventions”, are shown in Figure 6. The relationships between clusters were determined thorough an examination of overlap between clusters and the analysis of the content of the articles themselves. As demonstrated in the figure, all clusters are dependent on the “Methodology Framework” showing a strong connection with other clusters, with some articles contributing to multiple clusters. This means that “Methodology Framework” contained the main structure of LUTEI modelling tools for evaluation and reflection. This paper presents a unified framework for interpreting and assessing research on LUTEI and their role in sustainable mobility. It utilizes various viewpoints and consolidates fragmented literature to provide a well-balanced argument. The original goal of LUTI was to improve accessibility and reduce travel needs, but LUTEI also addresses energy and environmental issues in land use transport analyses. Previous studies mainly focused on spatial systems and accessibility, lacking consideration of the environment-energy aspect. Some suggested incorporating environmentally friendly modes into LUTI, while others emphasized congestion reduction strategies, but little attention was given to the long-term effects of congestion on energy consumption and the promotion of environmentally friendly modes and strategies. Few studies have assessed the environmental impact of traffic generation and human activities on residential neighborhoods in the context of LUTEI.

This SLR highlighted the role of rail transport in integrated modelling, specifically in the context of TOD as an effective strategy for sustainable development. Most studies focused on the long-term effects of design strategies on reducing reliance on private vehicles and optimizing flexibility and adaptability of land uses around stations. Those rail-based studies that did reflect on LUTEI more specifically regarding incorporated energy use, emission per passenger-kilometer, external environmental costs for different modes of transport, urban density, transport-related energy consumption, and the effectiveness of public transport.

Overall, few state-of-art papers were more innovative in their approach by integrating Life Cycle Assessment (LCA) to analyze the relationship between urban characteristics, mobility behaviors, and air pollution. Recent studies have paved the way for sustainable solutions by analyzing the impact of green vehicle technology adoption.

4.2. Linkage of the LUTEI Framework to SDGs

This study used the UN’s SDG framework and goals to link each dimension of the conceptual LUTEI framework to urban-related sustainability issues. The SDGs have explicit functional goals for urbanization and instruments for action at a local scale. After conducting a thorough review of the relevant literature, we determined that the SDGs were the most appropriate framework for our study, given the substantial attention and support they have received recently. While other frameworks such as C40 Cities (a network of mayors collaborating to confront the climate change challenges) or the New Urban Agenda (which acknowledges the role of urban areas and human activities in utilizing opportunities for sustainable development) may align with the objectives of this paper, the SDG framework is considered the most comprehensive due to its high profile and its ability to bring together various urban sustainability elements and stakeholders.

The authors analyzed the link between the LUTEI framework and the SDGs by examining each sustainable goal target in detail, utilizing information from LUTEI literature and co-citation analysis.

Table 6. Linkage of the LUTEI framework to the UN’s sustainable development goals.

LUTEI Dimensions	Directly Connected SDGs	Indirectly Connected SDGs
Methodology Framework	9.1. Develop reliable urban infrastructure that provides sustainable access to roads, sufficient number of transport modes, and comfortable access to economic opportunities 11.2. Provide safe and inclusive accessibility to sustainable transport 11.3. Promote integrated planning 13.2. Include environmental and climate change considerations in policies and plans 17.18. Enhance quality and reliability of disaggregated data	4.7. Promote knowledge and skills of sustainable development 7.a. Promote investment in clean fuel research and clean energy technology 10.7. Ensure well-organized plan for people’s mobility and migration
Policy Instruments	3.6. Improve road safety 7.1. Improve affordability of clean energy resources and services 7.a. Promote investment in clean fuel research and clean energy technology 7.b. Facilitate infrastructure for upcoming technologies and sustainable energy 11.1. Ensure affordable housing and basic services 11.2. Provide safe and inclusive accessibility to sustainable transport 11.3. Promote integrated planning 11.6. Reduce adverse impact of urban areas on environment 11.b. Adopt an integrated policy to improve urban resilience and risk management 13.2. Include environmental and climate change considerations in policies and plans 17.7. Promote funding for green technologies	1.4. Equal rights to resources and access to services 1.5. Develop resilience in vulnerable urban areas 4.7. Promote knowledge and skills of sustainable development 5.5. Inclusive opportunity of participation and decision-making of stakeholders 10.3. Ensure equality in policies 10.7. Ensure well-organized plan for people’s mobility and migration 12.2. Sustainable management of consumption of natural resources 12.8. Promote public awareness of sustainable development and lifestyles 16.7. Make sure that management is inclusive and representative of all groups
Urban Design	11.2. Provide safe and inclusive accessibility to sustainable transport 11.3. Promote integrated planning 11.7. Ensure safe and inclusive access to green and public areas	3.6. Improve road safety 5.5. Inclusive opportunity of participation and decision-making of stakeholders 7.b. Facilitate infrastructure for upcoming technologies and sustainable energy 10.7. Ensure well-organized plan for people’s mobility and migration 16.7. Ensure inclusive and representative management
Impacts of Interventions	3.6. Improve road safety 3.9. Reduce adverse impact of urban activities on health 3.d. Risk reduction management 9.1. Develop reliable urban infrastructure that provide sustainable access to roads, sufficient number of transport modes, and comfortable access to economic opportunities 11.6. Reduce adverse impact of urban areas on environment 13.2. Integrate environment and climate change factors into policies and plans	1.5. Develop resilience in vulnerable urban areas 6.3. Minimizing impact of urban pollutants on water quality 12.2. Sustainable management of consumption of natural resources

Table source: Author developed and produced from publicly available data [5,12,13,146,153,154,155,156,157,158].

illustrates this connection, showing both direct and indirect patterns of association between SDGs and clusters.

Table 6 demonstrates the connection of LUTEI dimensions to SDGs with at least 26 targets corresponding to 14 SDG. The strength of this relationship is mainly captured by SDG 7 (sustainable energy), SDG 9 (resilient infrastructure), SDG 11 (sustainable cities), SDG 13 (environmental impact and climate change), and SDG 17 (policy implementation). However, there are other SDGs that are indirectly related to LUTEI denoted by dotted lines in Figure 7.

Our analysis demonstrates that LUTEI policy instruments have the highest correlation with SDG targets, with the importance of integrated planning specifically mentioned in SDG 11 and 13. Other SDGs have also referenced the significance of accessibility to resources, basic services, and sustainable mobility. However, urban access in SDGs’ framework is supply-oriented and it is more about availability of transport infrastructure than accessibility to activities. This is further confirmed in a study by Brussel et al. [11] that SDG indicators are unable to capture transport reality comprehensively. Also, based on the UN’s [13] report, the pandemic has exacerbated some urban inequalities such as access to safe and reliable transport. Despite the importance of LUTEI, there is no specific SDG target that directly recommends land use, transport, and environment integration.

4.3. Limitations

The scope of this study was limited to the Scopus and WOS databases, resulting in 195 journal articles being analyzed. The literature search was based on relevant keywords to address the research questions, but it is acknowledged that other keywords may yield different results. Only peer-reviewed publications were included in the analysis, with a limited number of grey literature articles being used as supplementary sources. The use of a limited amount of grey literature may have constrained the comprehensiveness of the evaluation, but it ensured the quality of the identified corpus of knowledge. Additionally, the clustering of articles into themes may have some ambiguity due to hybrid articles that could pertain to multiple clusters. However, the themes identified are still considered valid and justified in the analysis. It is acknowledged that the theme labels provided are subjective and other researchers may classify them differently. These limitations should be taken into account when interpreting the findings of the study.

Below, we discuss the limitations identified during the comprehensive analysis of LUTEI literature, answering RQ4. However, for a more detailed analysis of LUTEI research limitations, please refer to section three, where each sub-cluster is examined in-depth to provide a comprehensive understanding of the challenges faced in achieving sustainable mobility.

• The requirement to account for a variety of scenarios and variations in a disaggregated manner is a limitation faced by LUTEI models. These models need to consider a wide range of scenarios, which can make it challenging to develop a model that is capable of accurately depicting all these factors and their interactions. Additionally, the computation of LUTEI models often involves a significant amount of data, which could lead to complexity and prolong the computation time.
• The challenge of interpreting uncertain outputs resulting from the complexity of interactions is a limitation that LUTEI models encounter. The uncertain nature of the results can impede the ability to make accurate predictions based on the outcomes of the model.
• The evaluation of accessibility in LUTEI models can be accomplished through various methodologies, each possessing unique advantages and limitations. This complexity in methodology can impede the ability to compare the outcomes of various studies, making it challenging to determine the most suitable method for evaluating accessibility.
• The integration of energy, environmental, and health factors within LUTI models can lead to complexity and difficulty in interpretation. Furthermore, incorporating environment and energy sub-models into land use and transport models can prove challenging, potentially resulting in a lack of precision in predictions and recommendations.
• The absence of a unified methodology package for the evaluation of LUTEI models presents a challenge, as different studies have employed various methods and tools, hindering the ability to compare the outcomes, and determining the most effective approach.
• Comparative studies on LUTEI typology tend to lack a pragmatic approach, often emphasizing the identification of the pros and cons of different models or built environments, rather than providing a concrete methodology for establishing LUTEI typology.
• The integration of general equilibrium models in econometrics with LUTEI models is challenging due to LUTEI models requiring a high level of detail and a large amount of data which can be incompatible with the simplifications used in general equilibrium models. LUTI models also often deal with spatial interactions and require high integration between sub-models, which can be difficult to achieve using general equilibrium models.
• Survey-based studies on LUTEI lack capturing users’ travel experiences and perceptions of the built environment and self-reported data can be biased; also, they are limited in scope to a specific population or area, which can result in a lack of precision in predictions and recommendations made by LUTI models.
• The implementation of subjective measures, such as an individual’s perception of mobility, density, and diversity of the built environment, which have been shown to be influential factors in travel behavior, can prove challenging due to their vague and intangible nature, making them difficult to measure in practice.
• Objective metrics are manageable but still pose practical challenges in implementation, with a significant proportion of practitioners not integrating accessibility measures due to lack of knowledge and data deficiency. Studies have also shown difficulty in bridging the gap between policy and practice in LUTEI; even with interventions such as involving modelers and members of the public in strategy-making, limitations in implementation remain.

5. Implications for Future Research

Future research avenues within the domain of LUTEI have been proposed based on an extensive examination of the extant literature and the classification of various dimensions of LUTEI. These research directions were informed by the literature specific to each cluster, and through a thorough critical analysis and evaluation of the corpus of knowledge and the identified gaps therein. In the process of reviewing each paper, the authors emphasized the limitations and proposed potential research agendas, which were subsequently consolidated with our own critical evaluations, resulting in the formulation of research directions for LUTEI.

5.1. Methodology Framework

Future methodological approaches can benefit from more reliable performance measures through higher-quality data, such as that provided by Internet of Things and big data methodologies. Additionally, these models should prioritize spatial equity as an important component of SDG targets that emphasize availability and accessibility to minimum standard services and urban facilities. Future hybrid models should consider both observable and unobservable factors, including user perception, to accurately predict choices despite the complexity of psychometric measurements. Recent advancements in modelling have introduced new computational possibilities, yet microsimulation of disaggregated data remains limited by a lack of a comprehensive framework. This complexity has led to a reliance on multiple individual models. Also, there has been limited integration of energy and environmental sub-models. Future research should focus on developing a unified platform with practical coupling toolkits to integrate a large number of components and sub-models. Moreover, a more robust bottom-up methodology to better incorporate potential effects of climate change policies and green energy technology, virtual accessibility, and micro-mobility strategies in LUTEI modelling tools is encouraged. Finally, LUTEI methodology should be expanded to simulate working from home, in order to examine short- to long-term effects on urban resilience in the aftermath of shocks such as the pandemic.

5.2. Policy Instruments

Future studies can address organizational barriers and develop a holistic interaction framework for effective testing and implementing policies. This includes examining the flexibility of macro-level policies at a local level and increasing stakeholder consultation before implementation. Evaluating the level of each stakeholder involvement is a necessary step to developing engaging LUTEI policies. Policies promoting R&D investment should be tailored to specific cities for optimal results. Successful policies that encourage and facilitate sustainable travel behavior will depend on an enhanced understanding of sociodemographic groups’ requirements, and the short-term of voluntary travel behavior change [159,160,161,162]. Research is needed to examine the long-term effects of policies related to access or ownership of green vehicle technology on sustainable mobility, including potential increases in VKT, to inform decision-making [9,163].

5.3. Urban Design

The urban design literature is mostly focused on micro-level strategies. The collection and evaluation of subtle design data for large-scale regions is challenging, and variations in strategy between CBDs and suburbs must be considered for evidence-based planning. Future studies should develop objective and transparent design dimensions with a holistic approach for sustainable mobility, considering specific characteristics and time contexts. Particularly, priority should be given to dynamic models for public transport, passenger estimation models, and integration of micro-mobility and public transport strategies [164,165]. Moreover, design measurements can be biased if the stakeholders’ participation is limited. This is when “modifications” strategies are viewed as “improvements” without considering how they affect the community in the long term. Therefore, follow-up research should focus on comparative analysis of design taxonomy, smart decision support tools, dynamic monitoring, perceptive classification systems, and updated indicators based on the environmental and socioeconomic settings to ensure accurate criteria implementation.

5.4. Impacts of Interventions

Future studies should integrate environmental variables with the urban health framework to assess the short- and long-term impacts of land use and transport policies on public health and the environment, and to encourage sustainable transportation and active lifestyles [105,166]. Moreover, research in the future should prioritize the integration of walking/cycling, as well as public transport with an emphasis on spatial variation and accessibility. The interaction between urban form and sustainable mobility needs more empirical data. The serviceability of green vehicle technology adoption studies should also be integrated with well-conceptualized LUTEI models and engineered designs to support informed sustainable mobility investments, accounting for potential congestion, VKT increase, and discouraged PT usage.

6. Conclusions

This paper presents an analysis of perspectives on LUTEI, a model for integrating environmental and energy considerations into land use and transportation to achieve sustainability. The paper consolidates academic debate, identifies major gaps, and prioritizes research prospects in the field. The analysis covers many research areas, providing a valuable contribution to the understanding of the integration of sustainable mobility.

This paper highlights the gradual evolution of LUTEI literature and confirmed the importance of sustainable mobility in social, environmental, and engineering factors with a sudden surge in energy and healthy academic debate. While the topics of fundamental studies that formed the direction of this research such as policy integration, sustainable accessibility, and travel behavior analysis are still subjects of interest, the trend is shifting towards more active transport, transit-oriented development, and advanced simulations at the micro-level.

Co-citation analysis revealed diverse research areas of LUTEI that support sustainable mobility challenges. The methodology mainly focuses on simulating equitable accessibility and predicting mode choice, but emerging vehicle technologies have led to a rise in the literature to develop tools for integrating environment and energy evaluations into these models.

Recent literature shows that the presence of sustainable mobility infrastructure and planning strategies can enhance public health. Despite the indication that health science is becoming increasingly important in urban planning, there is limited evidence on the long-term effects of integrated planning on public health.

This systematic review provides more reliable findings than traditional literature reviews due to its ability to minimize selection biases and consider various related concepts. In closing, research gaps and future research directions mentioned in this research will provide a dynamic framework to refine the reliability and comprehensiveness of LUTEI methodology for researchers; rethink the compliance and usability of LUTEI-informed policy to achieve sustainable mobility; and help policymakers to review the transparency of planning strategies towards inclusive, healthier, and more resilient urban areas.