Transportation Resources Research Articles

A [formula omitted]-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources

With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A Q-Learning-based NSGA-II algorithm (QNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search; and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the QNSGA-II algorithm in solving such problems are validated.

Integrated optimization of train line planning and timetabling: a new method of changing train operation zone and direct-service of cross-line ODs

ABSTRACT Cross-line operation increases accessibility to the railway network. However, its long-distance operation is quite difficult due to the transport resource limitations. To provide good transportation plans for operators and more direct services for cross-line passengers, we propose a new method of changing operation zones of cross-line trains, which can be summarized as the integrated train line planning and timetabling problem. A model is established to minimize deviation of same-line trains from ideal timetables and maximize cross-line origin-destination direct services. A Lagrangian relaxation approach and an improved priority-rule heuristic algorithm are developed to solve large-scale problems and obtain feasible solutions. Reality-based instances are conducted on the Beijing-Shanghai high-speed railway and its connecting lines to verify the performance of the model and solution approach. The results show the benefit of changing train operation zones, and the model and approach can help to generate a good balance between operating profit and passenger demand.

A Fuzzy Wavelet Neural Network (FWNN) and Hybrid Optimization Machine Learning Technique for Traffic Flow Prediction

Traffic go with the flow forecasting is essential in urban planning and management, optimizing transportation structures and resource allocation. However, accurately predicting visitors glide is tough because of its inherent complexity, nonlinearity, and diverse uncertain factors. The trouble declaration underscores the issue in as it should be forecasting site visitors flow, mainly in urban environments characterized through dynamic and complex site visitor’s styles. In the existing paintings there are numerous traditional devices getting to know models used for visitors flow prediction, however those conventional strategies show off barriers in reaching excessive prediction accuracy. Therefore, the proposed work targets to put into effect hybrid optimization techniques for correct prediction in shipping machine. Here fuzzy wavelet neural community (FWNN) is used to address complicated nonlinear structures with uncertain conditions and hybrid optimization method called hybrid firefly and particle swarm optimization (HFO-PSO) which combines the exploration and exploitation talents of firefly and this fusion allows the version to capture intricate visitor’s styles efficiently and optimize the prediction technique, improving accuracy and efficiency. Moreover, the prediction performance of the proposed model is established and compared by means of the usage of distinct measures.

Exploring the role of Mobility-as-a-Service in morning commuting trips

Promising the seamless integration of multiple transportation modes, Mobility-as-a-Service (MaaS) has gained popularity over the years, yet its effectiveness in enticing private car users and improving travel efficiency remains uncertain. This study explores the competitiveness of MaaS-enabled multi-modal travel options versus private car usage through equilibrium analysis. In addition to pricing that affects the fixed cost for travel, we examine the often overlooked inconvenience cost associated with multi-modal trips. We establish our analysis for the commuting problem of a one-origin-one-destination network, where a highway and a mass transit line connect the residential area and the central business district (CBD) area. Travelers choose their departure time and travel mode among auto, Park-and-Ride (PnR), ride-hailing and transit (RnT) to minimize their total travel cost. Inconvenience costs associated with searching for parking and waiting for ride-hailing are explicitly modeled. We analytically provide the mode share and departure time windows under all possible equilibria. Our findings reveal the complex nature of mode choice, distinctly affected by fixed and inconvenience costs, with demand playing an even more significant role. Notably, fixed costs set an entry fee to adopt different modes, and the feature of inconvenience costs affects the utilization of available transportation resources. More importantly, to benefit the overall system, we encourage maintaining a balanced mode share by implementing pricing and capacity strategies rather than aiming for a completer transition of private car users to MaaS.

A ridge-based detection algorithm with filament overlap identification for 2D mycelium network analysis

Many terrestrial ecosystems engage mycorrhizal symbiotic associations, potentially to enhance nutrition, increase resistance to soil-borne pests and diseases, and improve resilience and soil structure. Mycorrhizal fungi create dynamic networked structures through branching and anastomosis that connect multiple plants and consent to transport resources underground from nutrient-rich patches to demanding plants. Controlled laboratory experiments are fundamental to improving our knowledge of mycelium network growth dynamics and further understanding its role in preserving ecological niches. We propose a method for highly automated analysis of the mycelium network structure and other morphological properties, such as hyphal length, hyphal density, and number of crossing and branches, in 2D microscopy images of fungal samples. Available tools for automated network analyses suffer from overestimating network connectivity since filament crossings are not considered. In particular, we propose a) a ridge-based mycelium detection algorithm and b) a geometrical-based approach to identify overlapping filaments crossing each other. The algorithmic solution is evaluated on a total of 135 real mycelium sample images over different validation steps, originating from different datasets and having different characteristics, including background, contrast, image acquisition system, fungal species, and clearness (e.g., level of transparency, homogeneity, dirtiness of the medium) of the sample. Results show that 1) the proposed detection method can be used to measure the length of mycelium in an image, replacing manual tracing and allowing for less laborious analysis ρ̂c=0.96, 2) the filament detection is on par with state-of-the-art techniques F1=0.88−0.94 with a more intuitive parameterization, and 3) the proposed algorithm correctly identifies filament crossings F1=0.89 in most common cases, yielding a reduction in the overestimation of network connectivity. The latter feature consents to applying the proposed fully automated solution to complex and irregular fungal structures, advancing mycelium detection and reconstruction performance accuracy with respect to the state-of-the-art.

Two-level balancing multi-objective algorithm for trapezoidal type-2 fuzzy flexible job shop problems

Uncertainty remains a critical issue in realistic applications in many fields. However, there is little literature considering trapezoidal interval type-2 fuzzy set (TIT2FS) in scheduling problems. To fill this gap, we propose a two-level balancing multi-objective optimization algorithm for a distributed flexible job shop scheduling problem (DFJSP), wherein two typical constraints are considered: the TIT2FS processing time and the transportation resource limitation. Two objectives are to be minimized: the TIT2FS fuzzy makespan and energy consumption. A two-level evolution mechanism is employed. The first level comprises two populations to optimize the two respective objectives, while the second level is composed of two populations to perform the convergence and diversity balancing tasks. Next, an efficient problem-specific initialization method with several heuristics is presented. Subsequently, different types of mutation and crossover operators are proposed under the consideration of problem knowledge. To further improve the performance of exploration and exploitation abilities, we present a balancing enhanced local search method. Finally, we performed detailed comparisons with four efficient algorithms. The results show that the two-level optimization algorithm was efficient in both convergence and diversity abilities, indicating that our algorithm can efficiently achieve a balance between these abilities.

Spatial Mapping of On-road Traffic Emission for Air Quality Management: A Case of Vinh Phuc Province

Vinh Phuc province is in the northern key economic region of Vietnam. The province has been facing challenges in air quality management as the number of vehicles has rapidly increased to meet the rising demand for transportation resources. This research was aimed to conduct an emission inventory and to build a spatial emission map for traffic activities in an attempt to improve air quality management in Vinh Phuc province. The vehicles were categorized into 5 groups: motorcycles, cars, light-duty vehicles (LDVs), heavy-duty vehicles (HDVs) and buses. Meanwhile, the streets were also categorized into 5 groups: highways, rural roads, urban streets, suburban streets and industrial streets. The results showed that motorcycles were the main means of transportation (93% of total vehicles) and they were also the major contributors to total emissions of NOx, CO, VOCs (volatile organic compounds), CH4, NMVOCs (non-methane volatile organic compounds) and especially in the cases of CO, CH4, VOC and NMVOC emissions which contributed more than 90% of emissions. Cars were the main source of SO2 emissions, contributing 51% of total SO2 road traffic emissions. The emissions of TSP (total suspended particulate), PM10 and PM2.5 were mostly generated from buses (about 32%), followed by motorcycles (about 18%). LDVs and HDVs contributed 18% and 15% to total particulate matter emissions, respectively. Spatial distribution analysis of CO, NOx, SO2, TSP, NMVOC and PM2.5 which involved visual identification in polluted areas, showing that high emissions were in the southeast part of the province and the most polluted areas were Vinh Yen city, followed by Binh Xuyen district and Phuc Yen city. These results provide suggestions for local governments on how to design effective air quality control strategies.

Reconceptualising transport-related social exclusion in rural China

In this paper, we propose a conceptual framework to elucidate transport-related social exclusion (TRSE) in rural China. Drawing from participant observations across 146 villages in Guangdong (2643 rural residents), we illuminate the multifaceted nature of TRSE in Chinese villages. In term of interregional transport challenges, rural residents are socially excluded due to inadequate transport infrastructure and services. In contrast, within the microcosm of intravillage transport, residents adeptly harness available transport resources to craft exclusionary strategies.This emergent form of TRSE, distinct from previous paradigms, sees villagers deploying transport as an instrument to deliberately exclude the Other based on the depth and nuances of their interpersonal relationships. This exclusion manifests through unique characteristics: the nebulous contours of marginalised groups, mutual exclusion between rural residents, ethical ambivalence, and its profound resonance with traditional cultural norms. Given the intricate layers that seem to eclipse the phenomena observed in urban contexts, we underscore the imperative for a more nuanced exploration of TRSE in rural landscapes.

Examining the impact of station location on dockless bikesharing-metro integration: Evidence from Beijing

To address the unbalanced distribution of dockless bike sharing around metro stations, existing studies usually examine local-scale characteristics while neglecting the correlation between station location and travel demand which exert a more substantial influence on travel behavior. This study bridges this gap by conducting a comprehensively analysis of the impacts and underlying mechanism of station location on DBS-metro integrated demand. The results uncover a notable negative correlation between station location − measured by both proximity to the city center and distance from subcenters and DBS-metro integration demand. Moreover, job-housing imbalance and public transport allocation around metro stations, inherently influenced by station location, further shape DBS-metro integration demand. Notably, our results show significantly higher demand during the morning peak compared to the evening and at metro stations with medium-level ridership, highlighting the heterogeneous patterns. In addition, subcenters compensate to some extent for the decline in transport resources caused by distance from the city center. These insights bear critical implications for policy formulation concerning on-demand public transport services and the exploration of transport equity, facilitating a more informed approach towards rectifying transportation disparities.

Optimization of mixed cargo allocation and stowage with heterogeneous vehicles

In order to solve the problem of low efficiency and high loading costs in business logistics and distribution operations, this paper classifies non-standard cargoes based on their spatial and temporal characteristics. Based on this, a dual-layer planning model for multi-vehicle cargoes loading and transportation is constructed to jointly optimize the loading process. This paper designs a space-inspired heuristic algorithm to solve the model and verifies the feasibility of the model. The results show that compared to a single vehicle type, the joint optimization of multi-vehicle combinations for loading and transportation has stronger adaptability and can better accommodate changes in cargo volume. Compared to the single vehicle type, not only did the loading rate increase by 18%, but also effectively reduced the transportation cost by 3%. Different proportions of spatial and temporal characteristic cargoes have different effects on the loading and transportation. Adjusting the proportion of cargoes that can be placed on both upper and lower layers to 50% can better utilize transportation resources such as vehicles. Increasing the proportion of urgent cargoes can improve the economic efficiency of loading and transportation.

Facilitators and barriers to supply-side maternal, newborn, and child health service availability in DRC: a systematic review and narrative synthesis

Background This study is a systematic review of barriers and facilitators of supply-side maternal, newborn, and child health (MNCH) service availability in the Democratic Republic of the Congo (DRC). Given gaps in understanding of the factors affecting supply-side service availability, authors reviewed the literature to describe the DRC’s landscape and inform future research and programming. # Methods Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, authors searched two databases and grey literature, using the Quality Assessment with Diverse Studies tool to assess quality. Authors identified key themes and used a narrative approach to analyze extracted data. # Results The search identified 2160 articles, with 26 meeting the inclusion criteria. Authors grouped them according to the following key themes: facility readiness, human resources for health (HRH), geography and transportation, conflict, financing, and governance. MNCH service availability was hindered by lack of supplies, logistical challenges, weak infrastructure, unmet staffing needs, HRH turnover, poor HRH distribution, career and payment obstacles, long distances to facilities and poor transportation, system strain and disruption due to conflict, low government expenditure, high out of pocket costs, fragmented stakeholders, and low government prioritization. Authors found facilitating factors in each key theme, including parallel supply chains and buffer stocks, proactive staffing policies, motivated HRH, novel transportation methods, health posts, concentrations of stakeholders in conflict zones, universal health coverage policies, provider payment reforms, stakeholder coordination, and horizontal integration efforts. # Conclusions The DRC faces substantial supply-side barriers to MNCH service availability. Progress towards the Sustainable Development Goals and better MNCH outcomes requires additional work to build on existing facilitators and investments in service availability. Additional research on demand-side factors and quality of care could inform these efforts.

Forecasting Road Freight and Passenger Transport Demands in Türkiye Using the Exponential Smoothing

Exponential smoothing is a popular technique used to analyze and forecast trends in various industries such as road transportation and passenger transportation. This method is used to calculate weighted averages using historical data and adjust forecasts based on recent trends. In the Turkish context, forecasting transportation demands using exponential smoothing provides valuable information for transportation planning and resource allocation. The increasing number of vehicles has caused many negative environmental consequences. This study analyzed ten years of data on road freight and passenger transportation on a regional scale in order to make future predictions. In the study, solution suggestions are presented based on the findings and the policies that should be implemented to solve the problem are evaluated.

Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources

As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

Game Study on Morning Peak Road Congestion Problem

The morning peak road congestion problem has always been a hot issue in urban traffic management. Road congestion during the morning peak not only affects the travel efficiency of travelers, but also has a serious negative impact on the urban environment and economic development. This article starts from the perspective of game theory and focuses on the problem of road selection. This article considers government supervision and builds a game model from the two game subjects of the driver and the traffic controller. This article analyzes the relationship between the driver and the driver, the driver and the traffic controller. This paper studies the impact of traffic time, whether the driver investigates in advance, and whether the traffic commander conducts road command on the stable strategy of the driver's road choice game. The research results of this article show that drivers and drivers can achieve Nash equilibrium by choosing different roads, and drivers adopting collaborative strategies during the morning peak period can effectively alleviate road congestion. Under the condition of ensuring the cost of arranging traffic commanders for drivers and the government, drivers can investigate the road in advance and traffic commanders can direct road 2, which can effectively promote the smooth operation of the traffic system. At the same time, this article puts forward targeted policy suggestions, aiming to optimize the allocation of transportation resources, improve road traffic efficiency, reduce morning peak road congestion, and provide scientific reference and decision-making support for urban traffic management.

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.

The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study's objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation. ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated. All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients' transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified "Transported" cases (False Positive). The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.

Addressing uncertainty in closed-loop supply chain networks: a multi-objective approach to integrated production and transportation problems

Purpose This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model. Design/methodology/approach In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts. Findings The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management. Research limitations/implications This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management. Originality/value This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

Family health assessment in Ifugao, Philippines: a baseline study for Health In-tervention Program

A study was conducted to assess the health status of families in selected communities in Ifugao, Philippines. Interviews were done using a guide questionnaire adopted from the Family Health Management Manual for Nursing Students by Lydia Capistrano-Viet. The findings showed that the majority of households are nuclear, consisting of 5–10 people, and have an average monthly family income of 5,000–10,000 Philippine peso. The majority of the families possessed their own lots and homes made of wood and concrete, with enough space for the entire family. The majority of them disposed of their trash by burning it or giving it to animals. Nearly majority of them feature closed drainage systems and flush toilets. The majority reported that their homes lacked bug and rodent breeding grounds and shared a water supply. Additionally, they felt that their families' health was good as they had only experienced colds and the common cold over the previous six months. As shown by the fact that all of the respondents visit doctors and the health center for their medical requirements, 80% of them have complete immunization records. More or less seventy percent of people said they also follow healthy habits. The findings also demonstrate the availability of transportation, communication, and health resources in the neighborhood. A health intervention program aimed at enhancing the health and well-being of family clients in the community will be developed based on the study's findings.

Development of Carpooling Website using Machine Learning

In the face of urban congestion and environmental concerns, Carpooling has emerged as a sustainable solution for optimizing transportation resources. This abstract presents the architecture and functionality of a Carpooling Website designed to facilitate efficient ride- sharing among users. The proposed web Website offers a user-friendly interface allowing individuals to register, create profiles, and list their travel preferences including departure times, routes, and passenger capacity.

Assessment of the integrated disease surveillance and response system implementation in health zones at risk for viral hemorrhagic fever outbreaks in North Kivu, Democratic Republic of the Congo, following a major Ebola outbreak, 2021

BackgroundThe Democratic Republic of the Congo (DRC) experienced its largest Ebola Virus Disease Outbreak in 2018–2020. As a result of the outbreak, significant funding and international support were provided to Eastern DRC to improve disease surveillance. The Integrated Disease Surveillance and Response (IDSR) strategy has been used in the DRC as a framework to strengthen public health surveillance, and full implementation could be critical as the DRC continues to face threats of various epidemic-prone diseases. In 2021, the DRC initiated an IDSR assessment in North Kivu province to assess the capabilities of the public health system to detect and respond to new public health threats.MethodsThe study utilized a mixed-methods design consisting of quantitative and qualitative methods. Quantitative assessment of the performance in IDSR core functions was conducted at multiple levels of the tiered health system through a standardized questionnaire and analysis of health data. Qualitative data were also collected through observations, focus groups and open-ended questions. Data were collected at the North Kivu provincial public health office, five health zones, 66 healthcare facilities, and from community health workers in 15 health areas.ResultsThirty-six percent of health facilities had no case definition documents and 53% had no blank case reporting forms, limiting identification and reporting. Data completeness and timeliness among health facilities were 53% and 75% overall but varied widely by health zone. While these indicators seemingly improved at the health zone level at 100% and 97% respectively, the health facility data feeding into the reporting structure were inconsistent. The use of electronic Integrated Disease Surveillance and Response is not widely implemented. Rapid response teams were generally available, but functionality was low with lack of guidance documents and long response times.ConclusionSupport is needed at the lower levels of the public health system and to address specific zones with low performance. Limitations in materials, resources for communication and transportation, and workforce training continue to be challenges. This assessment highlights the need to move from outbreak-focused support and funding to building systems that can improve the long-term functionality of the routine disease surveillance system.

Synergies and Potential of Industry 4.0 and Automated Vehicles in Smart City Infrastructure

The integration of Industry 4.0 and automated vehicles into the smart cities concept is a topical issue in the urbanization of cities and technological innovation within cities. As it is a relatively modern issue, many aspects of this field have not yet been explored; as a consequence, this paper is concerned with the search for synergies between Industry 4.0 and automated vehicles in smart city infrastructures. There is a lack of contributions in this field that summarize these synergies in a single article and address a wide range of aspects, including transport, energy, communication, and citizen participation. As the field lacks a complete and clear summary of what is already known, which would help multiple stakeholders, the authors decided to conduct this review. The article elucidates the above-stated aspects through a clear and in-depth literature review, which is complemented by specific examples from practice. Of course, the article also includes a description of the synergy potential and the impact on the inhabitants, the environment, and, last but not least, on the overall city life. The main hypothesis of this article is that the integration of Industry 4.0 technologies and automated vehicles within smart city infrastructure will result in significant improvements in transportation efficiency, resource utilization, and overall urban sustainability. The article discusses the positives and negatives of such integration, highlighting, on the one hand, the benefits in terms of reducing environmental impact and improving citizens’ quality of life, but on the other hand, also highlighting the various ethical, legal, and social issues that such integrations may bring. Several methods have been used within the article, namely analysis, synthesis, comparison, and historical interpretation. The final discussion highlights the benefits, as well as the challenges, that such integration faces and must deal with if it is to be successful. It can be concluded that the synergistic potential of automated vehicles and Industry 4.0 in smart city infrastructure is enormous and that such integration offers promising solutions for enhancing transportation efficiency, energy management, and overall urban sustainability. It is also highlighted in the article that, in order to reap the benefits of such synergies, a wide-ranging collaboration of policymakers, industry stakeholders, and urban planners is needed.