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A Special Issue of Sustainability (ISSN 2071-1050) belonging to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 21076

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Editors

Dr. Andrea Pompigna

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Faculty of Technological & Innovation Sciences, Universitas Mercatorum, Piazza Mattei 10, 00186 Rome, Italy
Interests: traffic flow theory, traffic data management and analysis, transportation modelling and simulation, traffic engineering operations and highway capacity, time-dependent queue theory in transportation engineering, technologies and applications for smart and sustainable roads
Special Issues, Collections and Topics in MDPI journals

Dr. Giulia Del Serrone

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Department of Civil, Building and Environmental Engineering, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Interests: sustainable transport infrastructure; railway infrastructure sustainability and LCA; road safety and traffic operations; intelligent transportation systems (ITS); pavement engineering and asset management; climate-resilient and cool pavements; advanced mobility and emerging transport infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban and interurban mobility face growing challenges due to population growth, urbanization, and increased demand for transportation. These pressures necessitate innovative and sustainable solutions to optimize transportation systems and infrastructures, improve efficiency, and reduce environmental impacts. This Special Issue focuses on transportation system and infrastructures optimization, transport planning, and traffic management, aiming to provide a holistic perspective that addresses both operational efficiency and environmental sustainability.

Advancements in technology, including intelligent transportation systems (ITSs), machine learning, and real-time data analysis, offer new opportunities for optimizing traffic management and transport networks. This Issue seeks to expand the current literature by presenting novel models and methodologies that enhance operational efficiency, promote equitable access to mobility services, and minimize the ecological footprint of transportation systems and infrastructures. It emphasizes strategic transport planning that integrates sustainability goals by balancing socio-economic, environmental, and technological factors.

We invite scientific contributions exploring advanced methods and case studies in areas such as capacity optimization, sustainable transport planning, real-time traffic management, and multimodal transport integration. By fostering scientific discourse and innovation, this Special Issue aims to contribute to the development of resilient, efficient, and equitable mobility systems.

Dr. Andrea Pompigna
Dr. Giulia Del Serrone
Guest Editors

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Keywords

transportation system and infrastructures optimization
transport planning
traffic and infrastructure management
sustainable mobility
intelligent transportation systems (ITS)
traffic simulation
congestion mitigation
multimodal integration
big data in transportation
road safety and risk assessment

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Related Special Issue

Transportation Systems and Infrastructures Planning, Optimization, and Management: 2nd Edition in Sustainability (2 articles)

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21 pages, 2363 KB

Open AccessArticle

Probabilistic Modeling of Inter-Vehicle Spacing on Two-Lane Roads: Implications for Safety-Oriented and Sustainable Traffic Operations

by Andrea Pompigna, Giuseppe Cantisani and Giulia Del Serrone

Sustainability 2026, 18(6), 2896; https://doi.org/10.3390/su18062896 - 16 Mar 2026

Viewed by 504

Abstract

Accurate characterization of inter-vehicle spacing is fundamental for safety assessment and sustainable operation of road networks, particularly on two-lane rural roads where monitoring infrastructure is limited. Unlike temporal headways, vehicle spacing directly reflects physical vehicle interactions and roadway occupancy, making it a more appropriate variable for evaluating collision risk and operational efficiency. This study develops a probabilistic framework for modeling vehicle spacing based on the statistical isomorphism between Event Flows and Linear Fields of Random Points. Using a calibrated microscopic simulation model, spacing distributions are generated for unidirectional traffic over flow rates from 100 to 1300 veh/h. A Pearson Type III distribution is shown to consistently reproduce the observed asymmetry, kurtosis, and non-zero minimum spacing across traffic regimes. Distribution parameters are estimated via maximum likelihood and validated using a heuristic Kolmogorov–Smirnov procedure suitable for large samples. Results demonstrate systematic relationships between spacing distribution parameters and macroscopic traffic variables, enabling estimation of the probability of unsafe spacing conditions from commonly available traffic data. The proposed framework supports sustainability-oriented traffic management by providing a quantitative basis for safety evaluation and operational control without requiring extensive sensing infrastructure. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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18 pages, 6421 KB

Open AccessArticle

Interventions to Motorised Traffic to Promote Sustainable and Low Traffic Neighbourhoods

by Scott Bradley, Finlay Mcbride, Mason Stephenson and Shohel Amin

Sustainability 2026, 18(6), 2693; https://doi.org/10.3390/su18062693 - 10 Mar 2026

Viewed by 632

Abstract

The increasing reliance on motorised traffic has led to significant environmental, health and urban mobility challenges for pedestrians and cyclists. Despite growing awareness of the benefits of active travel, including improved public health, reduced carbon emissions, and enhanced urban liveability, many cities struggle to implement effective interventions that prioritise non-motorised mobility due to inadequate infrastructure, safety concerns and car-oriented policies. It is essential to introduce strategic interventions, such as traffic calming measures, dedicated cycle lanes, pedestrian-friendly infrastructure and policy reforms to encourage sustainable mobility choices. This paper examined the impacts of bicycle and pedestrian infrastructure schemes on sustainability and Low Traffic Neighbourhoods (LTNs) at the Trafford Road corridor in Greater Manchester and Wood Street in Wakefield city centre, respectively. Most of the projected trips from the hypothetical office building will occur on the western and northern leg of the Haden Circus roundabout, with approximately 50% on the inward traffic of the western leg towards the roundabout and approximately 40% going outwards. The bicycle infrastructure scheme in the Trafford Road corridor observed an increase of up to 34% in bicycle traffic flow. On the other hand, the pedestrian infrastructure scheme on Wood Street caused a gradual increase in bicycle traffic on Wood Street from 174 to 356 per hour but had an insignificant influence on the pedestrian flow. Many United Kingdom (UK) councils have proposed traffic calming schemes in the city centre to enhance accessibility for pedestrians and cyclists, improve urban air quality and promote business and economic development. This paper examines how the schemes increase pedestrian and cyclist footfall within the traffic-calming zone while increasing traffic on adjacent roads. Restricting motorised traffic to prioritise cycling and walking improves public health, reduces pollution, enhances road safety, boosts local economies, and creates more liveable urban spaces, all while promoting sustainable and efficient transportation. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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18 pages, 3091 KB

Open AccessArticle

Glare Impact from Photovoltaic Systems on Driver Safety

by Mieczysław Słowik, Przemysław Skrzypczak and Krzysztof Wandachowicz

Sustainability 2026, 18(5), 2541; https://doi.org/10.3390/su18052541 - 5 Mar 2026

Viewed by 876

Abstract

This article examines the potential risk of impaired visibility for drivers caused by sunlight reflecting off the surfaces of PV panels installed within the right-of-way of motorways and expressways. A literature review was conducted to describe the current state of knowledge and identify the requirements applicable to this area. The procedures for measuring the reflective properties of PV panels using a goniophotometer and a luminance camera (imaging luminance measuring device—ILMD) were evaluated. The measurement results for three PV panels with different surface structures are presented, allowing the properties of PV panels to be determined in terms of their potential impact on driver safety. A computer application was developed to determine whether the sun’s rays will reflect off a photovoltaic panel’s surface toward a vehicle’s direction of travel. The application graphically displays information on whether the sun will be reflected in the direction of a moving vehicle and whether this reflection poses a threat to driver safety. A comprehensive procedure for assessing the risk of glare caused by sunlight reflecting off the surfaces of photovoltaic panels was developed, along with detailed requirements. This study supports sustainable development by promoting renewable energy deployment in motorway corridors while simultaneously ensuring road safety. It integrates environmental (renewable energy use), social (driver safety), and technical (quantitative glare assessment methods) dimensions of sustainability. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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21 pages, 2695 KB

Open AccessArticle

A Comparative Analysis of the Effect of Route Set Size in Logit and Weibit-Based Stochastic Traffic Assignment

by Seungkyu Ryu

Sustainability 2025, 17(24), 11144; https://doi.org/10.3390/su172411144 - 12 Dec 2025

Cited by 2 | Viewed by 740

Abstract

This study presents a comprehensive comparative analysis of the effect of route set size on stochastic user equilibrium (SUE) traffic assignment, focusing on both logit-based (Multinomial Logit (MNL) and Path Size Logit (PSL)) and weibit-based models (Multinomial Weibit (MNW) and Path Size Weibit (PSW)). The primary objective is to investigate the influence of route set size on traffic patterns and determine the minimum requisite number of routes for flow stabilization within the SUE framework. The analysis, conducted on the Winnipeg network using a customized Self-Regulated Averaging (SRA) scheme, yields three key findings. First, all models successfully converged, but the weibit-based models (MNW and PSW) converged faster than the logit-based models. Second, an analysis of perceived total travel time demonstrated that the majority of efficiency gains from route inclusion diminish after a threshold of approximately maximum 30 routes to 40 routes per O-D pair, indicating this number is sufficient for achieving stable SUE results in both model families. Third, the weibit-based model was found to be more sensitive to route overlap effects, continuing to adjust flow patterns up to maximum 45 routes per O-D pair, and exhibiting a greater tendency to allocate flow to less overlapping outer roads. This highlights the superior capability of the weibit formulation, which accounts for heterogeneous perception variance, to achieve a more behaviorally realistic equilibrium compared to the logit models. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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23 pages, 4211 KB

Open AccessArticle

Developing a Capacity Model for Roundabouts Using SIDRA Calibrated via Simulation-Based Optimization

by Duygu Erol and Ozgur Baskan

Sustainability 2025, 17(22), 10289; https://doi.org/10.3390/su172210289 - 17 Nov 2025

Cited by 3 | Viewed by 1363

Abstract

Various intersection structures are utilized in city-wide traffic network infrastructure by local transportation authorities to handle the exponentially increasing traffic loads in developing countries. In this regard, numerous studies have considered the notable positive contribution of the modern roundabouts in intersection performance as a prominent method utilized widely in our contemporary world. Properly designed roundabouts are vital components of sustainable transportation planning, as they significantly influence traffic efficiency, safety, and environmental performance. Accurate estimation of roundabout capacity is essential to ensure that they can accommodate anticipated traffic volumes without causing congestion, thereby contributing to energy efficiency and reducing emissions. Moreover, sustainable roundabout design supports the development of safer and more inclusive transportation networks by improving accessibility for all road users, thus strengthening the overall sustainability of urban mobility. The SIDRA (version 8.0), a traffic simulation software, is frequently employed in performance analysis and determining the effects of possible outcomes of different scenarios of roundabouts in today’s world. On the other hand, driver behaviors are found to play a significant role in software performance during the analysis process of roundabout capacity and performance. Therefore, in order to optimize the environmental factor (EF) representing driver behaviors in the SIDRA software, a Differential Evolution Algorithm-Based Bi-Level Calibration Model (DEBCAM) was introduced. Observation data collected from eight different modern-structured roundabouts through drones were run into the SIDRA simulation software; the average delays obtained were employed to estimate optimum EF values through DEBCAM. Observed average delay values were taken into consideration with respect to the delay values obtained as a result of the SIDRA calibration by using the GEH statistics. GEH values indicate the consistency of vehicle delay data obtained via the DEBCAM with observed data. Acquired results clearly suggest that the SIDRA software needs to be calibrated so that it can represent drivers’ behaviors. After determination of the optimum values of the EF parameter for calibration of the SIDRA software, the regression analysis was conducted through the Partial Least Squares (PLS) method. As a result of the analysis, a capacity estimation model was developed, which displayed a significant conformity with the SIDRA capacity estimation results. Our findings suggested that the parameter requirement for the roundabout capacity estimation can be decreased by employing the appropriate EF value for the roundabout that needs to be analyzed. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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27 pages, 5464 KB

Open AccessArticle

Application of Restricted Lanes Within Three Legs Roundabout at King Abdulaziz University (KAU)

by Alaa R. Sindi, Hatan S. Turkstani and Ahmed S. Alzahrani

Sustainability 2025, 17(22), 10132; https://doi.org/10.3390/su172210132 - 12 Nov 2025

Cited by 1 | Viewed by 1637

Abstract

The three-leg 50-Years Roundabout at King Abdulaziz University (KAU) is known for its vibrance and important location as it is located at the center of several major buildings and hospitals. In recent years, the roundabout is witnessing a huge demand that influences the university road networks’ level of service, “LOS”, which in return, has negative impacts on students and faculties in terms of delay and travel time. Several treatments can be implemented along the roundabout. One of those treatments is applying restrictions during morning peak hours such as blocking and restricting specific lanes. This treatment has the advantage of reducing conflict points that cause sudden and frequent stops at the roundabout; as a result, delay and congestion occur. By reducing conflict points, traffic flow can be improved, in addition to enhancing safety and promoting sustainability. This paper examines the base condition of the 50-Years Roundabout in terms of traffic flow, LOS, delay, capacity, and toxic emissions, and proposes traffic system management (TSM) strategies through applying restricted and designated lanes to improve traffic condition. The study employs PTV Vissim, SIDRA Intersection, and Surrogate Safety Assessment Model “SSAM” to examine the base and proposed conditions. The results show a significant improvement through the reduction in conflict points, so that reflects the positive impacts on sustainability, congestion, delay, travel time, LOS, and overall toxic emissions. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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21 pages, 2533 KB

Open AccessArticle

Application of the Holt–Winters Model in the Forecasting of Passenger Traffic at Szczecin–Goleniów Airport (Poland)

by Natalia Drop and Adriana Bohdan

Sustainability 2025, 17(14), 6407; https://doi.org/10.3390/su17146407 - 13 Jul 2025

Cited by 3 | Viewed by 3486

Abstract

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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25 pages, 5850 KB

Open AccessArticle

Simulation-Based Modeling of the Impact of Left-Turn Bay Overflow on Signalized Intersection Capacity

by Deana Breški and Biljana Maljković

Sustainability 2025, 17(12), 5397; https://doi.org/10.3390/su17125397 - 11 Jun 2025

Cited by 2 | Viewed by 1397

Abstract

The motorized vehicle methodology in the Highway Capacity Manual (HCM) does not account for the effect of left-turn bay overflow, which is stated as a limitation of the methodology. In this study, an adjustment factor was developed to quantify the impact of left-turn bay length on the through lane capacity at signalized intersections. The adjustment factor was modeled based on a large number of scenarios generated using the CORSIM microsimulation model. These scenarios covered intersection geometries typical for two-phase signal control and included a wide range of traffic parameters (number of lanes, traffic volume, left-turn volume, left-turn bay length, cycle length, and green ratio). By comparing the capacity values obtained with a short left-turn bay to those with an infinitely long bay under identical other traffic conditions, it was possible to develop an adjustment factor that reflects the impact of turn bay overflow. A regression-based model was created and validated, showing very good agreement with the simulated values. The new adjustment factor provides an enhancement of the HCM estimation methodology that improves the accuracy of capacity and delay estimates in intersection evaluations as well as supports more effective intersection design and sustainable mobility. More accurate capacity estimation reduces congestion, travel delays, and vehicle stopping, directly contributing to sustainable transportation goals, lowering emissions, and supporting environmentally responsible urban mobility systems. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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21 pages, 1309 KB

Open AccessArticle

Quantum-Inspired Spatio-Temporal Inference Network for Sustainable Car-Sharing Demand Prediction

by Nihad Brahimi, Huaping Zhang and Zahid Razzaq

Sustainability 2025, 17(11), 4987; https://doi.org/10.3390/su17114987 - 29 May 2025

Cited by 4 | Viewed by 1585

Abstract

Accurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges for conventional models. These models typically struggle to capture nonlinear and high-dimensional patterns. Existing methods struggle to model entangled relationships across these modalities and lack scalability in dynamic urban environments. This paper presents the Quantum-Inspired Spatio-Temporal Inference Network (QSTIN), an enhanced approach that builds upon our previously proposed Explainable Spatio-Temporal Inference Network (eX-STIN). QSTIN integrates a Quantum-Inspired Neural Network (QINN) into the fusion module, generating complex-valued feature representations. This enables the model to capture intricate, nonlinear dependencies across heterogeneous mobility features. Additionally, Quantum Particle Swarm Optimization (QPSO) is applied at the final prediction stage to optimize output parameters and improve convergence stability. Experimental results indicate that QSTIN consistently outperforms both conventional baseline models and the earlier eX-STIN in predictive accuracy. By enhancing demand prediction, QSTIN supports efficient vehicle allocation and planning, reducing energy use and emissions and promoting sustainable urban mobility from both environmental and economic perspectives. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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21 pages, 4151 KB

Open AccessArticle

Research on Resource Consumption Standards for Highway Electromechanical Equipment Based on Monte Carlo Model

by Linxuan Liu, Wei Tian, Xiaomin Dai and Liang Song

Sustainability 2025, 17(10), 4640; https://doi.org/10.3390/su17104640 - 19 May 2025

Cited by 4 | Viewed by 1174

Abstract

The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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17 pages, 2907 KB

Open AccessArticle

ST_AGCNT: Traffic Speed Forecasting Based on Spatial–Temporal Adaptive Graph Convolutional Network with Transformer

by Rongjun Cheng, Mengxia Liu and Yuanzi Xu

Sustainability 2025, 17(5), 1829; https://doi.org/10.3390/su17051829 - 21 Feb 2025

Cited by 6 | Viewed by 2076

Abstract

Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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21 pages, 3769 KB

Open AccessArticle

Effects of Lane Imbalance on Capacity Drop and Emission in Expressway Merging Areas: A Simulation Analysis

by Kai Zhang, Jian Rong, Yacong Gao and Yue Chen

Sustainability 2024, 16(23), 10388; https://doi.org/10.3390/su162310388 - 27 Nov 2024

Cited by 2 | Viewed by 2651

Abstract

Lane imbalance does not provide sufficient space for merging vehicles to adjust their speed and change lanes smoothly. This leads to improper driving behavior that disrupts mainline traffic flow stability, resulting in capacity drops and increased vehicle emissions. However, quantitative analyses, specifically the effects of lane imbalance on capacity and emissions, remain limited. Existing traffic simulation platforms struggle to capture the effects of geometric design changes on capacity. To address these gaps, we developed a simulation method incorporating interactions between geometric design and traffic flow demand into an XGBoost model, enhancing the predictive accuracy for driving behavior parameters. Implemented within the TESS NG platform, this model enables real-time adjustments in driving behavior parameters as traffic demand varies under different lane balance conditions. The simulation results indicated a 42.4% capacity drop and a 34.9% increase in CO₂ emissions when the balanced merging area was shifted to lane imbalance. Conversely, shifting to lane balance increases capacity by 8.2% and reduces CO₂ emissions by 39.8% under severe congestion conditions. Under lane imbalance, vehicle speeds are lower across all traffic demand levels. When the demand exceeds 1300 pcu/h/ln, lane changes occur closer to the end of the acceleration lane, with higher speed differentials. These insights underscore the potential of lane balance optimization to mitigate capacity drops and emissions, providing a valuable simulation approach for the design and evaluation of merging areas. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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Review

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52 pages, 6163 KB

Open AccessReview

Advancing Inclusive, Multimodal, Climate-Resilient Planning for Rural Networked Transport Infrastructure

by Brooke Segerberg and Abbie Noriega

Sustainability 2026, 18(6), 2842; https://doi.org/10.3390/su18062842 - 13 Mar 2026

Cited by 1 | Viewed by 1600

Abstract

Rural communities in many low- and middle-income countries (LMICs) remain isolated from reliable access to critical sites and social services due to inadequate transport connectivity. Formal planning approaches to improve rural networked transport infrastructure (RNTI) remain limited, underfunded and deprioritized relative to urban systems. Where resources do exist, they largely emphasize roads, despite the fact that nearly one-third of the global rural population lives more than two kilometers from an all-weather road and relies primarily on walking and intermediate modes of transport (IMTs), such as bicycles, motorcycles, and animal-powered vehicles. This review examines planning approaches for RNTI with a focus on non-car-centric, multimodal mobility. It assesses prioritization frameworks, including multi-criteria analysis, that incorporate social, environmental, accessibility, and economic considerations. Long-term outcomes are strengthened by participatory methods, multimodal planning and cross-sectoral integration that align transport investments with health, education, agriculture, and renewable resource goals. Addressing persistent barriers such as funding constraints, data gaps, and maintenance challenges requires improved spatial mapping and travel-time analysis to better identify mobility needs and guide investment decisions. The limited body of formal literature on the topic of RNTI necessitates the inclusion of grey literature and practitioner sources and underscores the call for additional research. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

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