1. Introduction
This paper investigates the potential for implementing smart mobility solutions in Kinshasa to alleviate severe traffic congestion and improve road safety. It begins by analyzing the current mobility challenges in Kinshasa, such as congestion, pollution, and inadequate public transportation, detailed in the Section on the city’s background and mobility issues.
This study offers a thorough literature review of global smart mobility concepts, emphasizing Mobility-as-a-Service (MaaS), car sharing, micro-mobility, Vehicle-as-a-Service (VaaS), and electric vehicles (EVs). These insights are detailed in the literature review Section. Following this review, the study proposes a conceptual framework for integrating these solutions into Kinshasa’s existing infrastructure, customized to local conditions, as elaborated in the conceptual framework Section.
A thorough feasibility analysis follows, evaluating technological, economic, social, cultural, environmental, regulatory, and legal aspects. This is covered in the methodology and feasibility analysis Section. The paper also examines successful smart mobility implementations in other cities, extracting lessons that can be adapted for Kinshasa, detailed in the case studies Section.
While numerical validation is often used to quantify the impact of smart mobility solutions, it is important to recognize that in the case of Kinshasa, specific quantitative data may not yet be available or applicable. The city’s infrastructure is still in a developmental phase, and smart mobility solutions have not been implemented on a scale that would allow for meaningful numerical validation. As a result, this study adopts a qualitative and conceptual approach, emphasizing feasibility through comparative case studies and expert insights. However, the study does examine data, learned lessons, and real-world experiences from other cities, such as Curitiba, Singapore, and many more, where sustainable mobility systems have been implemented, providing relevant benchmarks and lessons that Kinshasa can apply. These case studies offer indirect but valuable validation, allowing Kinshasa’s policymakers and planners to draw insights on potential outcomes without the need for direct numerical data at this stage.
The study concludes with strategic recommendations for implementing smart mobility solutions in Kinshasa, including policy adjustments and infrastructure investments. It also outlines future research directions to further investigate these solutions in rapidly urbanizing cities, as detailed in the recommendations and future research Section.
The paper addresses several research questions: What are Kinshasa’s main mobility challenges? How have other cities successfully implemented smart mobility solutions? What are the essential elements of a conceptual framework for Kinshasa? What is the feasibility of implementing various smart mobility solutions in Kinshasa? What strategic recommendations can ensure successful implementation? These questions are explored throughout the sections on background and context, literature review, conceptual framework, feasibility analysis, and recommendations.
- (A)
Overpopulation and its main impacts
Current global demographic trends show a substantial increase in urban populations, with forecasts predicting that the world’s urban population will hit 6.3 billion by 2050, marking a 75% rise from today [
1,
2]. This growth is driven by migration, natural population increases, and urbanization, which are causing a shift toward more urban living [
3,
4,
5]. As cities become more crowded, this overpopulation affects urban mobility, leading to issues like congestion, longer travel times, traffic jams, safety concerns, and environmental pollution [
6].
Overpopulation has a major impact on urban transport systems, creating a range of complex issues. It puts extra strain on infrastructure, leading to problems like overcrowded public transport, longer travel times, heavy traffic, and increased pollution [
5,
7]. As more people move to suburbs, new residential areas pop up along transport routes, which adds even more pressure to these systems [
8]. With cities growing denser, there is a growing need for effective and sustainable transport solutions. This means we need to develop strategies to improve public transport routes, explore new technologies, and upgrade road infrastructure and signage [
9]. To tackle these challenges, we need a comprehensive approach that looks at how population growth, infrastructure, and environmental impact all connect. In today’s digital age, smart mobility is becoming an essential part of sustainable urban development.
Smart mobility encompasses various essential components that play a vital role in enhancing urban transportation networks. These elements include integrating technology to boost efficiency and sustainability [
10], offering real-time traffic monitoring and management [
11], encouraging multi-modal transportation planning to enhance access to services [
12], and adopting innovative transportation methods like electric cars [
13] and urban bicycles. These measures help cut down greenhouse gas emissions and air pollution, leading to better air quality and healthier residents. Additionally, smart mobility enhances traffic safety by using advanced traffic monitoring systems and quick response mechanisms to reduce accidents and collisions, ultimately improving the quality of life for all city dwellers [
14].
A study on smart mobility in urban areas [
12] found that there is a noticeable gap in attention to smart mobility in developing countries, where rapid urbanization is driving a growing need for efficient transportation solutions. Implementing smart mobility solutions is vital for driving economic growth and development in low-income areas. These solutions address transportation challenges and improve access to crucial services [
10,
15].
- (B)
Background and context of Kinshasa’s current mobility challenges
Kinshasa, the rapidly expanding capital city of the Democratic Republic of Congo, is on track to become Africa’s largest megacity by 2030 [
16]. However, it faces numerous mobility challenges exacerbated by rapid urbanization and population growth. The city’s overpopulation intensifies the strain on its transportation infrastructure, leading to congestion, pollution, and inadequate public transit systems. These issues hinder efficient movement within the city, impacting the daily lives of millions of residents and stalling the city’s development [
17]. Addressing these multifaceted problems is critical to improving Kinshasa’s mobility landscape and overall development trajectory.
To understand the nature of these problems the key basic characteristics of the twenty most populous cities globally, including Kinshasa, were collected. These data are presented in
Table 1.
According to the data provided in
Table 1, Kinshasa has the highest persons-per-car ratio from the world’s twenty most populated cities. This indicates low car ownership relative to population size, which means that existing vehicles are overcrowded, and more people rely on alternative and shared transportation modes. The dense population and insufficient modern public transport options, such as a metro system, present major transportation challenges. Implementing a metro system could offer a high-capacity, efficient alternative to road transport, greatly alleviating traffic congestion. The absence of such a system means that residents have fewer alternatives to navigate within the city, exacerbating road congestion.
The public transport system in Kinshasa is underdeveloped and unreliable [
18]. The current transport options are inadequate for the growing population. Many residents have to depend on overcrowded and poorly maintained vehicles, which raises the risk of accidents and diminishes the overall efficiency of the transport system [
19].
The transport system is primarily characterized by its informality. It includes various modes such as buses, minibuses, motorcycles, and taxis. Many of these modes operate without regulation, leading to a chaotic and unpredictable environment, including for example “taxi pirates”—unlicensed taxi drivers who fill the gap left by insufficient formal transport services. The dynamic nature of this system adds a layer of complexity and uncertainty for both passengers and drivers.
This system emerged historically as a response to inadequate urban planning during the colonial era, which did not provide sufficient means for residents to navigate the city. Over the decades, the informal sector has grown substantially, largely due to the government’s inability to provide comprehensive public transport solutions. The result is a highly fragmented and unreliable transport network that struggles with regulation and safety issues [
17,
19].
Kinshasa faces traffic congestion management challenges due to chaotic driver behavior, inadequate road infrastructure, and limited funds for road maintenance and expansion [
17]. With a population density of 14,391 people per square kilometer, it is evident that a large number of people are living in a confined area. Rapid urbanization and a lack of adequate transport infrastructure are leading to significant congestion on the roads, especially during peak hours, as many people need to travel within a limited space [
18]. This congestion significantly delays travel times and impacts economic productivity. Major routes, such as Lumumba Boulevard, which connects the city center to the international airport, are chronically congested due to the lack of secondary roads and the poor condition of existing infrastructure.
Regular flooding further exacerbates these issues, causing decreased travel speeds, delays, and rerouting that disrupt public transit services. The diminished accessibility to jobs is leading to significant economic burdens for local commuters [
20]. This aspect is closely related to the inadequate traffic management and absence of safety regulations that are contributing to the chaotic state of its public transport system. The city’s roads are plagued by potholes, and the behavior of drivers is often unpredictable and unsafe. This situation is compounded by the lack of investment in traffic management infrastructure and systems, which leads to frequent traffic accidents and unsafe road conditions.
Another problematic area is the environmental impact of current transport solutions. On the one hand, it is a noise pollution. Traffic noise is a major issue in Kinshasa, with noise levels ranging from 73 to 79 dB along major roads. The high level of noise pollution causes significant annoyance and sleep disturbance among residents. A notable percentage of people are highly annoyed or experience sleep disturbances due to traffic noise, especially in areas with heavy traffic. Additionally, the transport system in its current state is a major contributor to air pollution. The significant presence of outdated and poorly maintained vehicles releases a considerable amount of pollutants, exacerbating health problems within the community. Furthermore, the discharge of untreated wastewater into the Congo River from transport and other activities has led to severe environmental degradation, impacting water quality and public health [
21].
Given these above-described significant challenges, overpopulation, in particular, exacerbates the mobility-related issues and challenges. Overpopulation presents a critical challenge for Kinshasa’s mobility infrastructure. With the city’s population expected to reach over 20 million by 2030, the already-strained transportation system is under immense pressure. Rapid population growth has led to increased demand for transport services, outpacing the city’s ability to develop adequate public transportation and road infrastructure. As more residents rely on personal vehicles and informal transit systems, Kinshasa’s streets are frequently congested, leading to longer travel times, increased pollution, and a higher rate of road accidents. The overburdening of existing infrastructure also results in frequent breakdowns, worsened road safety conditions, and reduced accessibility, particularly for low-income communities. These impacts make it clear that conventional mobility solutions are no longer sufficient, and innovative approaches, such as smart mobility concepts, must be explored to accommodate the city’s growing population and future transportation needs.
Overall, in a nutshell, Kinshasa’s mobility challenges are largely driven by rapid urbanization, poor infrastructure development, and a severe lack of reliable public transportation options. Roads such as the “Lumumba Boulevard”, a major artery connecting key areas of the city, experience chronic congestion, with travel delays that can extend for hours during peak times. The absence of mass transit systems, such as metro or tram networks, forces residents to depend on personal vehicles or informal, overcrowded taxi services, further contributing to the traffic burden. Additionally, the city’s road conditions are often poor, with potholes and insufficient maintenance exacerbating travel inefficiencies. Compounding these issues, Kinshasa suffers from a lack of proper traffic management systems and frequent flooding, which regularly disrupts transportation flow. These problems are not just logistical but also significantly impact the quality of life for residents and the economic productivity of the city. As a result, addressing these mobility challenges through innovative, sustainable, and scalable solutions is essential for Kinshasa’s future.
- (C)
Rationale for Introducing Modern Smart Mobility Concepts in Kinshasa
The need to transform Kinshasa’s mobility landscape is driven not only by existing challenges in mobility but also by the necessity to adapt to the changing demands of a modern urban environment. Embracing modern smart mobility concepts can revolutionize Kinshasa’s transportation systems, leading to improved transport efficiency, sustainability, equity, economic growth, and a better quality of life for its residents.
In light of Kinshasa’s significant mobility challenges, traditional transportation approaches have proven insufficient to address the city’s rapidly evolving needs. The absence of effective public transit systems, combined with over-reliance on informal transport modes and personal vehicles, has led to chronic congestion, accidents, and environmental degradation. Smart mobility solutions, such as MaaS, EVs, and car-sharing systems, provide a pathway to resolving these issues. These innovative approaches leverage advanced technologies to improve traffic management, reduce reliance on private vehicles, and offer environmentally friendly alternatives. By incorporating real-time data, automated systems, and intelligent transportation networks, these solutions enable efficient, scalable, and sustainable urban transport. Additionally, lessons from global cities facing similar challenges demonstrate that smart mobility can dramatically improve urban flow, reduce pollution, and increase access to transportation for underserved populations. As Kinshasa looks to the future, embracing smart mobility offers a transformative opportunity to modernize its transportation infrastructure and create a more livable, sustainable urban environment.
In terms of efficiency, smart mobility improves travel routes, shortens wait times, and reduces congestion by utilizing real-time data and cutting-edge technologies [
22,
23]. Traffic jams and air pollution remain major issues, particularly in large cities, and this situation is unsustainable in the long run. Urban transport systems worldwide face challenges such as pollution and inefficient resource use, which often hinder economic growth. Simply constructing more roads will not solve these problems; there is a need to integrate urban infrastructure through smart connectivity [
24]. As a key component of a smart city, smart mobility has the potential to decrease traffic congestion, shorten commuting times, and reduce road accidents, while also allowing passengers to tailor their travel experiences. Indeed, developing smart mobility solutions is one of the top challenges for major cities globally. This involves a series of strategic actions supported by advanced technologies [
25]. To provide a comprehensive view of smart mobility, the various elements and dimensions are explored. Additionally, the trends, opportunities, and challenges associated with smart mobility are discussed. This paper emphasizes four key components of smart mobility: intelligent transport systems, open data, big data analytics, and citizen engagement. These interconnected elements are essential for the effective deployment of smart mobility initiatives [
15]. By enhancing the movement of people and goods, smart mobility boosts overall efficiency in transportation networks and smoothens urban operations.
As previously noted, Kinshasa is dealing with major environmental issues such as air pollution and carbon emissions. To address these challenges and improve sustainability, adopting a smart mobility strategy could be key. This approach would include integrating EVs, offering micro-mobility options like electric scooters, and expanding shared transportation services [
26]. By embracing these eco-friendly solutions, Kinshasa can move toward a greener city and a healthier living environment for its residents.
Smart mobility makes transportation more accessible to everyone, ensuring that people from all income levels and locations can enjoy convenient and affordable travel options [
27]. By connecting urban centers with more remote areas, these initiatives foster inclusivity and strengthen social ties [
28].
A well-functioning transportation system catalyzes economic activity and prosperity [
29]. Embracing smart mobility solutions can attract investment, create employment opportunities in emerging sectors such as EV infrastructure and mobility services, and stimulate overall economic growth through enhanced urban connectivity and efficiency [
30].
5. Methodology for Integrating Artificial Intelligence (AI) and Large Language Models (LLMs) into Smart Mobility Solutions for Kinshasa
Section 5 presents the quintessence of a comprehensive research framework for integrating AI and LLMs into smart mobility solutions for Kinshasa. The objective is to improve the city’s multi-modal public transportation system by employing AI-driven tools, simulations, and data analysis methods. The approach emphasizes a sequential process to enable informed decision-making for transportation management, infrastructure improvement, and policy interventions. For a non-expert, briefly explained, an LLM is an advanced type of artificial intelligence that can understand, generate, and interpret human language. It has been trained on vast amounts of text data from books, articles, and websites, which allows it to answer questions, create written content, and carry on conversations much like a person. LLMs are powerful tools used for tasks like translation, summarizing text, and even helping solve complex problems by processing large sets of information. Essentially, they help computers better understand and communicate using natural human language.
This overall complex methodological approach consists of five core steps, which are described in more detail in the remaining parts of
Section 5.
Here, we provide a comprehensive summary of the QUINTESSENCE all the main steps of the complex methodological approach, which is described further in Section 5:- ▪
Step 1: Understanding the Current System and Transport Dynamics while involving LLMs
Step 1 focuses on gaining a thorough understanding of the current transportation system in Kinshasa. This involves a detailed examination of various aspects such as the quality of infrastructure, traffic flow, existing transit routes, and public transportation services. To achieve this, data are collected using a range of methods, including sensors and IoT devices that provide real-time information about vehicle numbers, speeds, and congestion points. Satellite images and geographic data are also used to assess road conditions and detect traffic bottlenecks. Additionally, public feedback is gathered through surveys and questionnaires to understand how residents experience the transportation system.
LLMs are then employed to process the collected data. These models are particularly useful for analyzing vast datasets, helping to uncover patterns in congestion and identify recurring problem areas. LLMs also play a crucial role in interpreting urban planning documents and public feedback, making it easier to identify key insights and opportunities for improvement. The goal of this step is to build a comprehensive understanding of the transportation system, allowing for the identification of critical areas in need of attention and setting the foundation for future improvements.
- ▪
Step 2: Stakeholder Engagement Analysis while involving LLMs
Step 2 involves engaging all the relevant stakeholders to ensure that the goals of the project align with the needs and expectations of the community. Stakeholders include government authorities involved in urban planning, transport experts, and local communities who are directly affected by traffic congestion. LLMs are used to summarize stakeholder input effectively, providing decision-makers with the most relevant information. These models also help in building consensus by analyzing feedback and highlighting common areas of agreement or disagreement. This process facilitates focused discussions, ensuring that the priorities of different stakeholders are understood. The aim of this step is to establish clear and realistic goals that address the needs of all parties involved, fostering a more inclusive and collaborative approach to tackling traffic congestion.
- ▪
Step 3: Identifying Upgrade Opportunities, with the support of LLMs
Step 3 is about identifying opportunities for improvement and mapping out potential solutions to address the challenges in Kinshasa’s transportation system. To do this, different scenarios are tested using advanced traffic simulation tools like VISSIM and SUMO, to name a few, which predict how traffic will flow under various conditions. These simulations help identify the most effective ways to reduce congestion, improve road safety, and optimize public transit routes.
LLMs assist by analyzing the data collected from these simulations and extracting valuable insights. They can look at historical congestion patterns and predict where future bottlenecks might occur. By comparing different datasets, LLMs can categorize congestion events based on their causes, such as poor road conditions or increasing population density.
In addition to understanding the current problems, this step focuses on identifying areas with the greatest potential for improvement. These areas could include upgrading road infrastructure, optimizing transit routes, or expanding services to underserved regions. By mapping these opportunities, the team can prioritize actions that will have the most significant impact on improving the overall efficiency and effectiveness of Kinshasa’s transportation system.
- ▪
Step 4: Introducing Smart Mobility Solutions, with the support of LLMs
Step 4 introduces the implementation of smart mobility solutions as a key strategy for addressing the challenges identified earlier in the process. The focus here is on integrating advanced technologies that improve the efficiency, sustainability, and convenience of the transportation system. One major initiative is the development of Mobility-as-a-Service (MaaS) platforms, which combine various public transport services into a single, easy-to-use system. This integration makes it simpler for residents to access different types of transportation, ultimately encouraging the use of public transit.
Another important aspect is the promotion of electric vehicles (EVs) and smart traffic management systems. Encouraging the use of electric buses and taxis can help reduce both congestion and environmental pollution. To support this transition, infrastructure investments such as charging stations for EVs are identified as crucial. Additionally, smart traffic signals that can adjust based on real-time traffic conditions are proposed to optimize the flow of vehicles through busy intersections.
LLMs assist in analyzing public sentiment and feedback, collected from sources such as social media and community surveys, to gauge the public’s perception of these smart mobility solutions. This ensures that any concerns or potential challenges are identified and addressed early in the process. By focusing on these advanced mobility solutions, the transportation system becomes more efficient and adaptable, meeting the evolving needs of Kinshasa’s residents while supporting a more sustainable urban environment.
Once the solutions have been defined, they are implemented in selected areas to assess how well they work in real-world conditions. This involves testing new routes, optimizing bus schedules, and improving traffic signal coordination in areas where traffic congestion is most severe. Data gathered during these pilot projects help to evaluate the effectiveness of the solutions in reducing congestion, improving travel times, and increasing overall service reliability.
Predictive models, powered by LLMs, are used to simulate potential outcomes of these pilot tests. These models forecast the longer-term effects of the interventions on traffic flow, fuel efficiency, and public satisfaction. As the pilot tests proceed, the dynamic nature of LLMs allows adjustments to be made based on real-time data, ensuring the solutions remain flexible and adaptable to any challenges that arise.
By testing and refining the proposed measures during the pilot phase, this step provides valuable insights into how the solutions can be scaled across the entire city, ensuring that they are both practical and beneficial for Kinshasa’s transportation system.
- ▪
Step 5: Community Involvement and Education, with the support of LLMs
LLMs do significantly support the “Community Involvement and Education” step by facilitating the engagement and communication between decision-makers and communities. LLMs can help simplify complex information about transport improvements and urban planning, making it easier for residents to understand proposed changes. Through natural language processing, LLMs can generate clear explanations, answer residents’ questions, and provide insights during scenario planning workshops. These models can also assist in creating personalized educational content, tailoring it to different community groups’ needs and preferences.
Moreover, LLMs can be integrated into interactive simulation platforms and virtual reality (VR) environments, where they can guide users through the proposed public transport improvements, station designs, and user journeys. By providing real-time feedback based on user interactions, LLMs can help participants explore different scenarios, understand trade-offs, and prioritize investments according to their preferences.
Additionally, in scenario planning workshops, LLMs can analyze community feedback, suggest alternative upgrade scenarios, and predict the outcomes of different decisions. They can also support tools like UrbanSim by interpreting the results of simulations, offering recommendations based on data, and assisting participants in evaluating the pros and cons of various options.
In summary, LLMs do play a crucial role in improving community involvement by simplifying information, enhancing simulation experiences, and actively participating in discussions and scenario planning. This can lead to more informed and inclusive decision-making processes.
- ▪
Step 6: Pilot Programs, Evaluation, and Continuous Improvement
LLMs are very useful for supporting Step 6 in the smart mobility project by summarizing pilot program results and generating insights from the data collected during the pilot phase. They do help analyze and interpret large amounts of information from AI-integrated mobility interventions and user feedback, offering clear summaries to facilitate decision-making on whether to expand these initiatives. Additionally, LLMs can aid in real-time data analysis and provide sentiment analysis from user feedback to improve service quality and system responsiveness. Overall, LLMs do enhance the efficiency and effectiveness of the continuous improvement cycle.
- ▪
Step 7: Policy Guidelines and Sustainable Funding
LLMs are highly beneficial for Step 7 by analyzing legislative documents and policy proposals to identify any potential barriers to implementing AI-powered smart mobility solutions. They can and do help generate clear policy recommendations that align with the project’s objectives and ensure regulatory compliance. Furthermore, LLMs can assist in exploring sustainable funding models by utilizing AI-driven data analytics to make informed recommendations for optimizing infrastructure investments and enhancing operational efficiency. This support can help policymakers make well-informed decisions, driving forward smart urban transportation initiatives in Kinshasa.
Globally, LLMs play a crucial role at every stage of the process by performing various essential functions. They analyze vast amounts of unstructured data, such as public feedback and policy documents, to extract valuable insights. LLMs also facilitate communication between diverse stakeholders, making collaboration more efficient by summarizing and organizing input from various sources. Additionally, they contribute to simulating outcomes, allowing for predictive modeling that tests different scenarios before physical implementation. Furthermore, LLMs enable dynamic adaptation by continuously adjusting strategies based on real-time data analysis.
This methodology integrates data-driven insights, stakeholder involvement, and the flexibility offered by LLMs to enhance Kinshasa’s public transport system. By focusing on technological innovation, particularly AI and LLMs, this approach ensures that the system becomes sustainable, efficient, and adaptable to the evolving needs of an expanding urban population. In a resource-constrained environment like Kinshasa, these tools offer a comprehensive solution for managing traffic congestion and improving public transit infrastructure.
5.1. Research Design and Approach for Smart Mobility Integration in Kinshasa: An AI (Artificial Intelligence) and LLMs (Large Language Models) Supported Research Approach and Simulation Studies
This sub-section delves into the comprehensive research design and approach required to significantly upgrade Kinshasa’s rather primitive multi-modal public transportation system, aligning it with the demands of a bustling metropolis. Efficiently integrating previously presented smart mobility concepts like MaaS, car sharing, micro-mobility, VaaS, and EVs into Kinshasa’s multi-modal road infrastructure demands a comprehensive research design and approach. The process of the implementation of these concepts should take place in a logical sequence of steps that are presented in
Figure 6.
A detailed description of these steps is provided below. This text outlines the methodologies, tools, and strategies essential for this transformative process. Highlighting the importance of comprehensive simulation research, this approach leverages advanced software, AI, and LLM technologies to support informed decision-making and achieve optimal outcomes.
The initial phase consists of a thorough examination of Kinshasa’s transportation ecosystem. Comprehensive data collection and analysis form the foundation for effective upgrade planning. This includes analyzing current road networks, public transit systems, traffic patterns, infrastructure deficiencies, and future population projections. To begin with, it is essential to conduct thorough data collection by compiling comprehensive information on current transportation modes, infrastructure status, ridership trends, and traffic patterns. This can be achieved through extensive surveys, IoT sensors, and satellite imagery. Following this, an infrastructure assessment is necessary. This involves analyzing the present condition of the infrastructure and pinpointing congestion areas, safety risks, and regions needing urgent attention. This assessment should be followed by a service evaluation that consists of an analysis of service reliability, frequency, accessibility, and affordability across different transport modes [
100]. This analysis is needed for a nuanced understanding of user experiences and pain points. By harnessing LLM technologies, such as natural language processing (NLP) for sentiment analysis of public feedback and understanding complex urban planning documents, researchers can gain nuanced insights into the multifaceted challenges of the city’s transportation landscape.
Engagement with diverse stakeholders is pivotal for success. This includes government entities, private sector players, urban planners, transportation experts, academia, and community representatives. Collaborative partnerships ensure a holistic approach, alignment of goals, and integration of local insights into planning processes. Engaging key stakeholders through data-driven insights ensures alignment with city-wide goals. It is needed to demonstrate the potential benefits and impacts of proposed upgrades, aiding decision-makers in policy formulation and budget allocation to government bodies. Then, transport operator consultation on service improvements should be realized, to assess fleet performance, route efficiencies, and capacity optimizations. It should be followed by ensuring community participation using integrating simulation-based interactive platforms that would allow residents to visualize proposed changes, provide feedback, and participate in co-designing transportation solutions. Also, LLM technologies can be used to aid in synthesizing vast amounts of stakeholder input and generating actionable recommendations for decision-makers.
Within upgrade initiatives, it is important to support evidence-based decision-making, for example, in the form of simulation studies. They can serve to quantify congestion reductions, travel time savings, and environmental benefits that will be a result of Infrastructure Enhancement. Then, the analyses and performance simulations for different vehicle types (e.g., electric buses, mini-vans) should be conducted to determine optimal fleet compositions balancing cost-effectiveness, emissions reductions, and passenger comfort a lifecycle cost. Subsequently, robust IT systems, mobile applications, and communication platforms should be designed to improve user experience and operational efficiency. This aspect involves modernizing the vehicle fleet by integrating advanced technologies. Utilizing real-time simulations of passenger movements, fare collections, and service disruptions can achieve this modernization.
This pivotal phase in the smart mobility implementation process involves researching how smart mobility concepts can be applied in specific conditions. Understanding the impact of these concepts on the entire urban mobility ecosystem is essential. The findings will serve as the foundation for strategic planning and decision-making. A blend of simulation methods and empirical research can be employed. To provide context and a deeper understanding of user needs and preferences, conducting surveys and interviews with stakeholders and target groups is recommended. These qualitative insights will complement the data obtained from simulations. Before implementing system-wide infrastructure changes and policy interventions, it is advisable to simulate commuting behavior, travel patterns, and transport mode choice using agent-based modeling (ABM) [
101]. Equally important is the modeling of traffic dynamics, signal timing and transit operations, optimization of route designs, bus schedules and intersection configurations through traffic flow simulation [
102]. Microsimulation tools such as VISSIM or SUMO can be used for this purpose.
The study of implementing smart mobility concepts can be broken down into a series of interconnected stages, each necessitating its own distinct set of tools and methodologies:
A mix of quantitative and qualitative methods such as surveys, GPS tracking, and travel diaries to capture real-time mobility data, commuter preferences, and pain points.
Utilizing data analytics and visualization tools can provide valuable insights into traffic patterns, congestion points, transportation mode preferences, and peak travel periods. Additionally, LLM technologies can analyze unstructured data from sources like social media, news articles, and online forums to detect underlying trends and sentiments related to transportation challenges.
AI-powered simulations enable researchers to model complex traffic scenarios, predict demand patterns, optimize route planning for MaaS users, and dynamically adjust traffic signals based on real-time data. LLM technologies have the capability to improve simulation outcomes by producing synthetic data to address deficiencies in real-world datasets and by simulating a more extensive array of scenarios.
Selection of the most appropriate tools depends on the smart mobility concept that would be implemented into the existing transport system. The recommended tools for each of the smart mobility services are as follows:
Mobility-as-a-Service Integration: AI-driven platforms for seamless integration of various transport modes, optimizing journey planning, real-time updates, and personalized recommendations for users. LLM technologies can aid in generating natural language descriptions of recommended routes and services, enhancing user understanding and engagement.
Car Sharing and VaaS Solutions: AI algorithms for fleet management optimization, demand prediction, dynamic pricing, and route optimization for shared mobility services. LLM technologies can assist in generating personalized communication with users, providing trip suggestions, promotions, and feedback prompts.
Micro-Mobility Solutions: AI-driven solutions for fleet management, optimal positioning of stations, demand-responsive services, and improving safety for micro-mobility vehicles. LLM technologies can process user-generated data to pinpoint high-demand areas for micro-mobility services and create easily understandable explanations of safety protocols and regulations.
EV Infrastructure: AI-powered analytics to identify optimal locations for EV charging stations based on usage patterns, integrate smart grid technologies for efficient energy management, and forecast charging demand for effective infrastructure planning. LLM technologies can assist in summarizing technical reports and regulations related to EV infrastructure deployment, facilitating decision-making for policymakers and stakeholders.
Intelligent Traffic Management: AI-driven traffic signal optimization, adaptive traffic flow control, predictive congestion management, and anomaly detection for proactive maintenance. LLM technologies can analyze historical traffic data and generate insights on traffic patterns, bottlenecks, and potential interventions.
Smart Parking Solutions: AI-powered parking management systems for real-time parking availability updates, dynamic pricing adjustments, automated valet services, and efficient space utilization. LLM technologies can analyze user reviews and feedback to identify areas for improvement in parking services and generate personalized recommendations for drivers.
The next step consists of empowering communities in the decision-making process. For this purpose, interactive simulation platforms can be used. Proposed public transport improvements, station designs, and user journeys can be presented to residents through virtual reality (VR) simulation. Furthermore, it is appropriate to organize scenario planning workshops to explore different upgrade scenarios, evaluate trade-offs, and prioritize investments based on community priorities and preferences. Scenario modeling tools such as UrbanSim can be used for this purpose.
Continuous monitoring and adaptive strategies ensure ongoing performance optimization. Initiate pilot projects in targeted areas to test AI-integrated smart mobility interventions in real-world scenarios. Collect user feedback, monitor AI-driven system performance metrics, and conduct rigorous evaluations to refine strategies and address challenges before full-scale implementation. LLM technologies can assist in summarizing pilot program results and generating insights for scaling successful initiatives to other areas of the city. After implementing smart mobility solutions, it is important to perform real-time data analysis. This involves monitoring the performance of the transport system, detecting anomalies, and triggering adaptive responses, such as dynamic adjustments to traffic routing during peak hours. For this purpose, the integration of IoT sensors and platforms is required. It is also advisable to implement user feedback mechanisms within mobile applications and customer service channels, using sentiment analysis and AI-based insights to iteratively improve service quality, responsiveness, and inclusiveness.
Collaborate intensively with policymakers to create regulations and incentives that advance AI-powered smart mobility solutions while ensuring data privacy and security. Encourage innovation in AI technologies for urban transportation. Investigate sustainable financing models that utilize AI-driven data analytics to optimize infrastructure investments and enhance operational efficiency. Utilize LLM technologies to analyze legislative documents and policy proposals, identify implementation barriers, and formulate policy recommendations.
This proposed research framework, supported by comprehensive simulation studies, sets the stage for a significant enhancement of Kinshasa’s multi-modal public transport system. Utilizing data-driven insights, fostering stakeholder partnerships, employing cutting-edge simulation technologies, and engaging the community, Kinshasa can develop a contemporary, efficient, and eco-friendly transportation network. This initiative aims to improve mobility, minimize environmental impact, and elevate the quality of life for residents, establishing a model for smart urban transportation in the region.
5.2. Maximizing Data Insights and Decision-Making with Large Language Models (LLMs) in the Smart Mobility Integration for Kinshasa
The effective modernization of Kinshasa’s public transport system and the integration of smart mobility solutions require sophisticated data collection methodologies. This comprehensive approach includes surveys, interviews, case studies, and field sensor data and makes strategic use of LLMs [
103]. The following text outlines the innovative and qualitative advantages of these methods over traditional, non-LLM-based approaches.
5.2.1. Surveys Revolutionized by LLMs
Conventional survey analysis typically depends on preset categories and manual coding, which restricts the breadth of insights. Utilizing advanced LLMs equipped with complex natural language processing (NLP) abilities enables the analysis of open-ended survey responses. This approach reveals nuanced sentiments and emerging themes, transcending the restrictions of predetermined categories. This dynamic analysis enables researchers to capture subtle shifts in public opinion, preferences, and concerns related to transportation, providing a richer understanding of informed decision-making [
104]. In practical terms, this means that instead of relying solely on preset survey questions, LLMs can process and interpret responses in a more human-like manner. They can monitor shifts in sentiment over time, assess the impact of external events on public opinion, and uncover subtle trends that conventional analysis methods might overlook. For example, LLMs can determine whether the public’s attitude toward a new transit project is favorable because of enhanced accessibility or unfavorable due to disturbances in current routes, offering valuable insights for policymakers.
5.2.2. Interviews Enhanced with LLM Insights
Conducting interviews generates qualitative data crucial for understanding stakeholders’ perspectives. LLMs streamline interview analysis by automating transcription, extracting key phrases, and performing sentiment analysis at scale. This automation reduces human bias, speeds up analysis processes, and uncovers latent insights buried within interview transcripts. By integrating LLM insights, researchers can identify sentiment trends, sentiment shifts, and outliers, enriching qualitative data with quantitative dimensions for comprehensive analyses [
105]. In contrast to manual interview analysis, where researchers may overlook subtle cues or spend excessive time transcribing and coding data, LLMs offer a scalable and accurate solution. They can discern not only positive and negative sentiments but also subtle emotions like uncertainty, excitement, or frustration, offering a comprehensive perspective on stakeholders’ attitudes. This nuanced analysis is particularly valuable when exploring complex topics such as the public perception of new mobility solutions or community responses to infrastructure changes.
5.2.3. Case Studies Empowered by LLM Analysis
Typically, the examination of case studies requires the manual extraction and synthesis of information, a method that is often labor-intensive and prone to mistakes. LLMs are adept at handling unstructured case study data, uncovering patterns, correlations, and causal relationships across varied datasets. Utilizing machine learning algorithms, LLMs can forecast potential outcomes based on historical data, enabling decision-makers to simulate different scenarios, evaluate risks, and refine strategies before execution [
106]. This data-driven approach minimizes uncertainty and maximizes the effectiveness of infrastructure investments and policy decisions.
Examining case studies is crucial for grasping best practices and insights gained from cities that have effectively modernized their transportation systems and incorporated smart mobility solutions. Traditionally, this task requires manual extraction and synthesis of pertinent information, making it laborious and prone to human error. However, incorporating LLMs revolutionizes this analytical process by automating data extraction, enhancing accuracy, and delivering more profound insights.
LLMs can analyze extensive volumes of unstructured data from case studies, uncovering significant patterns, correlations, and causal links that human analysts might miss [
107]. By leveraging natural language processing capabilities, LLMs can sift through extensive documents, extracting relevant details about strategies, challenges, outcomes, and contextual factors that influenced the success or failure of mobility initiatives. Moreover, LLMs enable the synthesis of information across multiple case studies, providing a comprehensive view of various approaches and their effectiveness. For example, LLMs can compare the impact of different policy measures, technological implementations, and community engagement strategies across diverse urban contexts. This comparative analysis helps in identifying common success factors and potential pitfalls, offering valuable insights for tailoring solutions to Kinshasa’s unique environment. LLMs also facilitate predictive analytics by applying machine learning algorithms to historical data from case studies [
108]. This capability allows decision-makers to simulate various scenarios and assess potential outcomes based on empirical evidence. For instance, by understanding how similar cities managed traffic congestion or integrated EVs, stakeholders in Kinshasa can predict the likely impact of similar interventions and optimize their strategies accordingly. Furthermore, the use of LLMs in case study analysis supports evidence-based decision-making by providing robust, data-driven recommendations. These insights empower policymakers to prioritize initiatives, allocate resources efficiently, and craft interventions that are scalable and sustainable. The capability to swiftly process and analyze vast amounts of qualitative data guarantees that the lessons drawn from global best practices are thoroughly and effectively incorporated into Kinshasa’s transportation planning and execution strategies.
The main potential of LLMs consists in revolutionizing the analysis of case studies by automating data extraction, enhancing the accuracy of insights, enabling comparative and predictive analysis, and supporting evidence-based decision-making [
103]. By adopting this forward-thinking strategy, Kinshasa can harness international expertise to create effective and locally tailored smart mobility solutions, resulting in a transportation system that is more efficient, sustainable, and inclusive.
5.2.4. Field Sensor Data Transformed with LLM Analytics
Field sensor data, including traffic cameras, GPS devices, and environmental sensors, capture real-time insights critical for transportation management. LLMs enhance sensor data analysis by identifying traffic patterns, predicting congestion hotspots, and correlating environmental factors with mobility trends. Advanced deep-learning models integrated with LLMs can detect anomalies, optimize resource allocation, and support predictive maintenance strategies for infrastructure [
109]. This proactive approach ensures efficient traffic flow, reduced emissions, and improved overall mobility experience for residents and commuters.
LLMs analyze vast datasets, uncovering traffic patterns and congestion points to optimize road networks and signal timings [
110]. Predicting future congestion allows for proactive measures like dynamic traffic management and congestion pricing, improving traffic flow and reducing travel times. LLMs also correlate environmental sensor data with traffic patterns, aiding city planners in promoting EVs and green spaces to enhance air quality. Integrating data from smart mobility services, such as ride-sharing and bike-sharing platforms, enhances analytical capabilities. LLMs can analyze usage data from these services to discern demand trends and optimize service distribution. For example, studying ride-sharing data helps in adjusting vehicle availability and positioning, while bike-sharing data aids in determining the optimal locations for bike stations. Advanced deep-learning models can identify anomalies in sensor data, swiftly pinpointing traffic incidents or infrastructure issues for immediate action, ensuring safety and minimizing disruptions. LLMs also enhance resource allocation by fine-tuning public transit schedules and guiding infrastructure placement, thereby reducing wait times and alleviating overcrowding. Furthermore, LLMs facilitate predictive maintenance by analyzing sensor data to predict infrastructure failures, allowing for timely repairs and prolonging asset lifespans. This holistic approach improves traffic flow, mitigates congestion, and enhances overall mobility experiences.
Integrating LLMs with field sensor data and smart mobility service data transforms transportation management. This approach provides actionable insights that enhance efficiency, sustainability, and the quality of life for Kinshasa’s residents.
5.2.5. Integrated Approach with LLM Technologies
The integrated approach combining traditional data collection methods with LLM technologies represents a leap forward in data-driven decision-making for urban mobility. LLMs facilitate a seamless flow of insights across datasets, enabling the cross-validation of findings and comprehensive trend analysis [
111]. By leveraging advanced machine learning algorithms within LLM frameworks, stakeholders can build predictive models for demand forecasting, optimize service delivery, and tailor mobility solutions to evolving needs. This agility and foresight empower cities like Kinshasa to adapt to dynamic challenges, optimize resource utilization, and enhance urban livability sustainably.
Integrating LLMs into various data collection techniques significantly enhances the precision, quality, and speed of data analytics within Kinshasa’s transportation sector. Leveraging LLMs’ strengths in natural language processing, machine learning, and predictive analytics enables stakeholders to gain comprehensive insights into transportation patterns, user behaviors, and infrastructure demands. This data-centric strategy ensures that Kinshasa’s mobility projects adapt effectively, promoting sustainable urban growth, improving public services, and elevating the quality of life for its citizens.
6. Feasibility Analysis
The feasibility analysis framework in this study is built upon a comprehensive, multi-dimensional approach that assesses smart mobility solutions through the lenses of technological, economic, regulatory, social, and environmental perspectives. Each dimension is thoroughly evaluated to ensure that the proposed solutions are not only innovative but also sustainable, scalable, and well-suited to the unique challenges faced by Kinshasa. The process begins by identifying Kinshasa’s specific urban mobility challenges, such as congestion, inadequate infrastructure, and environmental concerns, and then systematically evaluates each of the five key factors.
The first dimension, technological feasibility, assesses the compatibility of Kinshasa’s existing infrastructure with proposed smart mobility technologies and considers the availability, integration, and scalability of these technologies within the local context. Economic feasibility focuses on the financial sustainability of these solutions by examining their cost–benefit ratios, long-term economic impact, and potential funding sources, ensuring that the city’s limited resources are used effectively. The regulatory feasibility component reviews the alignment of smart mobility solutions with current government policies and regulations, also emphasizing the importance of stakeholder engagement and public–private partnerships to facilitate implementation.
The social feasibility aspect is crucial in determining public acceptance, accessibility, and safety. This dimension ensures that the proposed solutions are inclusive and cater to the needs of all demographics, particularly underserved populations, while also addressing cultural attitudes toward new technologies. Finally, environmental feasibility evaluates the potential of smart mobility solutions to reduce emissions, improve air quality, and support Kinshasa’s long-term sustainability goals. By addressing these interdependent factors, the analysis provides a holistic view of the viability of implementing smart mobility concepts in Kinshasa, ensuring that the solutions are both technically and socially adaptable to the city’s evolving needs.
6.1. Methodology: Criteria for Feasibility Analysis Selection
The purpose of the feasibility study is to evaluate the prospects of implementing smart mobility solutions and an intermodal public transportation system within Kinshasa’s road infrastructure. This assessment relies on stringent selection criteria to ensure a thorough examination of the proposed initiatives. It considers technical, economic, environmental, social, and institutional dimensions, aiming to deliver a comprehensive perspective on the project’s feasibility (see
Table 8).
From a technical perspective, the analysis examines the compatibility of existing infrastructure, evaluating whether the current road systems can accommodate new technologies and intermodal systems without requiring significant alterations [
112]. The assessment also considers the presence and advancement of smart mobility technologies, including IoT, real-time data analytics, and mobile apps for public transportation. Another crucial element is operational efficiency, focusing on potential enhancements in traffic management, congestion alleviation, and the optimization of public transit schedules via intelligent systems [
113]. Furthermore, the scalability of the smart mobility solutions is considered to ensure their capacity for future expansion as the city’s population and transportation demands grow.
Economic feasibility is determined by conducting a comprehensive cost–benefit analysis, weighing the investment and operational expenses against anticipated economic gains, such as decreased travel time and fuel costs. This analysis pinpoints possible funding avenues, including government allocations, international assistance, and private sector investments, while also evaluating the project’s long-term financial viability. Additionally, market demand is examined by considering factors like population density, urbanization rates, and current transportation trends to assess the need for smart mobility solutions among Kinshasa’s residents.
Institutional feasibility involves reviewing existing policies and regulations to identify potential barriers or facilitators for implementing smart mobility solutions and recommending necessary adjustments to the legal framework. The capacity of local government agencies and institutions to manage, operate, and maintain the new systems is evaluated, identifying training and capacity-building needs. Given the importance of stakeholder collaboration, this study involves identifying essential stakeholders—such as governmental agencies, private sector partners, and community organizations—and evaluating their respective roles.
Social feasibility focuses on how the proposed smart mobility solutions improve accessibility for all residents, including marginalized and underserved communities. Public acceptance of smart mobility concepts is measured using surveys, focus groups, and stakeholder engagement initiatives to gauge perceptions and receptiveness. Additionally, safety and security are evaluated to explore the potential for enhancing road safety and reducing accidents through improved traffic management and real-time monitoring systems.
Environmental feasibility involves evaluating the potential environmental benefits of reducing emissions through improved traffic management and increased use of public transportation. The proposed smart mobility solutions are designed to be sustainable by aligning with sustainable development goals and minimizing the transportation sector’s carbon footprint in the city. Moreover, the utilization of resources is meticulously assessed to minimize environmental impact throughout both the deployment and upkeep stages of these smart mobility systems.
The criteria for the feasibility analysis aim to establish a comprehensive framework for assessing the implementation and integration of smart mobility solutions and an intermodal public transportation system in Kinshasa. By evaluating technical, economic, environmental, social, and institutional factors, this methodology ensures a thorough and balanced analysis, facilitating informed decision-making and effective execution.
6.2. A Comprehensive Guide to Feasibility Analysis
The process of feasibility analysis for the successful implementation of smart mobility solutions in urban environments can be divided into the evaluation of individual feasibility aspects. Their prioritizing is crucial for effective decision-making. The recommended order based on best practices in urban planning, transportation studies, and sustainability research, is the following: Technological feasibility, Economic feasibility, Regulatory and Legal Feasibility, Social and Cultural feasibility, and Environmental feasibility. The main aspects of these analytic studies are summarized in
Table 3. The different dimensions of the feasibility analysis are summarized in
Table 9.
The following text explores the essential aspects of each category of the feasibility analysis. It outlines the necessary steps for their effective realization and provides advice on possible threats and challenges.
6.2.1. Practicality of the Technology Implementation
Evaluating the technological preparedness of current infrastructure is crucial for the success of transportation projects. This assessment involves examining the city’s digital framework, telecommunications systems, and the alignment of existing infrastructure with contemporary technologies. Integrating data analytics, IoT technology, and digital platforms can drastically enhance the effectiveness and dependability of transportation systems. These advanced tools and technologies contribute to more streamlined operations and improved service reliability [
114]. Moreover, ensuring interoperability between different systems is essential to creating a cohesive and integrated transportation network [
115].
For the effective realization of a technological feasibility study the following steps are recommended:
Perform a thorough evaluation of the transportation infrastructure to pinpoint areas needing enhancement and modernization. This involves examining the state of roads, bridges, and public transit facilities.
Invest in upgrading digital infrastructure and telecommunications networks to support smart mobility initiatives. This includes expanding broadband access and deploying IoT devices for real-time data collection.
Encourage collaborations between public entities and private enterprises to seamlessly incorporate smart mobility solutions into current transportation frameworks. Partnering with technology firms and startups can introduce cutting-edge innovations and advancements.
Focus on designing transportation systems with user-centric principles to guarantee accessibility and inclusivity for all. This involves soliciting feedback from diverse stakeholders, including commuters with disabilities and marginalized communities.
Launch pilot initiatives to evaluate the practicality and scalability of novel transportation solutions prior to widespread implementation. This approach enables incremental enhancements and reduces the risks linked to substantial investments.
6.2.2. Economic Feasibility
Smart mobility solutions offer a promising avenue toward achieving more efficient, sustainable, and accessible urban transportation networks. However, alongside technological advancements and innovation, there lies the crucial challenge of assessing the economic feasibility of implementing these solutions. It is important to be aware of the complexities of performing cost–benefit analyses (CBA), identifying potential funding sources and exploring different financial models tailored to smart mobility initiatives.
Cost–benefit analyses serve as the cornerstone for evaluating the economic viability of smart mobility projects [
116]. The process begins by meticulously identifying and quantifying both the direct and indirect costs associated with implementation. Direct costs encompass the initial investment in technologies, infrastructure development, and ongoing operational expenses. Indirect costs, though more nuanced, must not be overlooked, encompassing environmental impacts, societal benefits, and potential externalities. Simultaneously, it is imperative to assess the myriad benefits that smart mobility solutions promise to deliver. These benefits extend beyond mere monetary gains and encompass reductions in traffic congestion, travel time savings, improvements in air quality, and enhanced societal well-being. Quantifying these benefits often involves sophisticated modeling techniques, drawing upon data analytics, and scenario planning to forecast the impacts accurately [
117]. The heart of cost–benefit analysis (CBA) lies in assessing costs versus benefits. Metrics such as net present value (NPV), benefit–cost ratio (BCR), and internal rate of return (IRR) give stakeholders insights into the project’s economic viability over its entire lifecycle. Additionally, sensitivity analysis strengthens these assessments by highlighting how the project’s outcomes respond to different assumptions and external influences.
Obtaining sufficient financial resources is essential for the successful implementation of smart mobility initiatives [
118]. There are numerous funding sources available, each with unique considerations and requirements. Traditional methods of financing include public budgets, grants from both governmental and non-governmental organizations, and international development funds. However, innovative approaches such as public–private partnerships (PPPs) and venture capital investments offer alternative pathways, fostering collaboration between the public and private sectors while mitigating financial risks [
119]. Moreover, investigating value capture methods provides a chance to match the funding of smart mobility initiatives with the value they create. Approaches like tax-increment financing, land value capture, and development impact fees allow municipalities to claim a share of the enhanced property values and economic advantages arising from transportation enhancements.
Choosing suitable financial models is crucial for maintaining the long-term viability of smart mobility projects. Implementing user fees, congestion pricing, and road tolls can create revenue streams, control demand, and encourage sustainable travel habits. Additionally, revenue-sharing partnerships with private sector entities offer further funding opportunities, utilizing their expertise and resources to expedite project execution. Integrating innovative financing methods like green bonds and social impact bonds ensures that funding aligns with broader goals of sustainability and social equity [
120]. These tools allow investors to back projects that produce tangible environmental and social advantages, thereby accessing fresh sources of funding for smart mobility initiatives.
6.2.3. Regulatory and Legal Feasibility
In an era marked by rapid urbanization and technological advancement, the landscape of public transportation is undergoing a profound transformation. The emergence of smart mobility solutions promises increased efficiency, sustainability, and accessibility. However, this evolution necessitates a careful examination of existing regulatory frameworks to ensure alignment with the demands of this new paradigm.
The current regulatory landscape surrounding public transportation is a patchwork of laws and policies designed for a different era. While these regulations have served their purpose, they often fail to accommodate the complexities of smart mobility systems [
121]. From ride-sharing platforms to autonomous vehicles, new technologies are challenging traditional notions of transportation regulation [
122]. Key areas of concern include data privacy, liability, and interoperability standards [
123].
To facilitate the seamless integration of smart mobility solutions into existing transportation ecosystems, several regulatory adjustments are recommended:
Flexible Frameworks: Regulators should adopt agile, technology-neutral frameworks that can adapt to rapidly evolving innovations. This approach enables regulators to foster innovation while safeguarding public safety and consumer rights.
Collaborative Governance: Effective regulation requires collaboration between government agencies, industry stakeholders, and community representatives. Establishing multi-stakeholder task forces can facilitate dialogue and consensus building around regulatory reforms.
Data Governance: As smart mobility systems rely on vast amounts of data, regulators must establish robust data governance frameworks. These frameworks should prioritize privacy protection, data security, and transparency to build public trust in emerging technologies.
Liability Standards: Establishing clear liability standards is crucial for assigning responsibility when accidents or malfunctions occur within smart mobility systems. Regulatory bodies need to create liability frameworks that both encourage innovation and ensure accountability.
Interoperability Mandates: To ensure interoperability between different smart mobility services, regulators should mandate open standards and protocols. Interoperability facilitates seamless integration between various modes of transportation, enhancing user experience and system efficiency.
As public transportation develops into a more integrated and intelligent network, adaptable regulations become essential. Policymakers can maximize the potential of smart mobility by actively tackling regulatory issues and adopting innovative approaches, leading to safer, more efficient, and sustainable transport systems for everyone.
6.2.4. Cultural and Social Viability
In the fast-changing environment of modern cities, incorporating smart mobility principles into public transport systems is crucial for boosting efficiency, sustainability, and convenience. Nonetheless, the social and cultural acceptance of these innovations often remains underappreciated. A thorough examination of how these technological advancements align with various social groups and cultural contexts is essential. Grasping the views, preferences, and worries of community members is vital for the smooth adoption and enduring success of smart mobility solutions.
A critical component of this analysis involves assessing accessibility. This entails investigating how effortlessly all community members, irrespective of their socio-economic background or physical capabilities, can utilize these services [
28]. Creating smart mobility solutions that are not only physically accessible but also accommodate cultural and linguistic diversity is crucial [
124]. This might involve providing multi-language interfaces, accommodating diverse mobility needs such as wheelchair accessibility, and ensuring that routes and schedules cater to the needs of different demographic groups. Another crucial factor to evaluate is the impact of smart mobility on social equity. It is essential to scrutinize whether these advancements inadvertently exacerbate existing disparities or promote inclusivity. A thorough examination is needed to guarantee that such innovations do not unintentionally increase the divide between advantaged and disadvantaged groups [
125]. This could entail targeted subsidies for low-income residents, flexible fare structures, or initiatives to address the digital divide by providing access to technology and digital literacy programs [
126].
Closely tied to accessibility is the issue of affordability. Although smart mobility offers efficiency and cost savings, affordability is still a key consideration for widespread acceptance [
127]. Maintaining affordability for these solutions across all societal sectors is crucial to cultivating fair and inclusive transportation systems.
Moreover, cultural factors greatly influence how smart mobility initiatives are perceived [
128]. Each community possesses its unique set of norms, values, and habits, which must be carefully navigated to garner acceptance. From the integration of traditional modes of transport to respecting cultural practices related to communal travel, sensitivity to these nuances is vital for fostering positive engagement.
Equally important is the acknowledgment of potential disruptions to existing modes of transportation and local economies. While smart mobility offers numerous benefits, it may also challenge established norms and industries, necessitating strategies for mitigating negative impacts and fostering collaboration. The success of smart mobility hinges on its ability to align with the social fabric and cultural ethos of the communities it serves.
6.2.5. Environmental Feasibility
In a sustainable future, the integration of public transportation and smart mobility solutions can significantly mitigate environmental degradation. This thorough evaluation explores the environmental viability of integrating EVs and other green alternatives, with the goal of assessing their potential to lower carbon emissions and promote a more sustainable, eco-friendly atmosphere.
EVs, heralded as the cornerstone of eco-friendly transportation, promise a remarkable decrease in greenhouse gas discharges when compared to their traditional fossil fuel counterparts. By leveraging renewable energy sources and advancements in battery technology, EVs present a concrete route toward decarbonizing the transport industry [
129]. Incorporating EVs into public transportation fleets can greatly reduce air pollution and enhance urban air quality. Nonetheless, environmental viability requires more than just the adoption of EVs. It also involves evaluating other smart mobility solutions, such as ride-sharing services, micro-mobility options like electric scooters and bicycles, and efficient public transit systems. By encouraging multimodal transportation and decreasing dependence on single-occupancy vehicles, these approaches not only help ease traffic congestion but also lower carbon emissions and support sustainable urban growth.
The evaluation highlights the crucial role of infrastructure development and policy measures in promoting the adoption of environmentally friendly transportation options. Essential steps such as investing in charging stations, offering incentives for EV purchases, and enforcing emission regulations are vital to achieving the environmental advantages of public transport and smart mobility initiatives. The shift toward sustainable transportation solutions necessitates a unified effort from both policymakers and the public. By adopting EVs, encouraging smart mobility alternatives, and prioritizing environmental factors in urban planning, we can pave the way for a cleaner, healthier future for upcoming generations. This evaluation can act as a roadmap toward a more sustainable transportation framework.
7. Discussion
Efficient, sustainable transportation solutions are essential in modern urban environments. Kinshasa currently faces significant transportation challenges, including severe congestion, inadequate infrastructure, and limited public transportation options. These issues result in increased travel times, decreased road safety, environmental degradation, and economic inefficiencies. Addressing these challenges is crucial for the city’s sustainable development. Smart mobility presents a holistic approach to tackling these transportation challenges. An integrated approach is essential for developing efficient and sustainable urban transportation systems. By integrating technology, data-driven insights, and innovative transportation models, cities can optimize existing resources, enhance accessibility, and reduce carbon emissions. Kinshasa, as it moves toward the future of transportation, must carefully integrate smart mobility concepts into its infrastructure.
Through an examination of various smart mobility projects in cities globally, including those in less developed regions, Kinshasa has the opportunity to gain valuable insights, identify effective strategies, and tailor solutions to address its specific mobility challenges and goals for sustainable urban growth. Drawing lessons from these international examples can help guide policy decisions, infrastructure investments, and community involvement efforts to create a more connected, efficient, and enjoyable urban environment in Kinshasa and beyond.
The transition toward smart mobility in Kinshasa requires leveraging a wide range of global experiences and knowledge from cities around the world, both advanced and developing. By analyzing the successes and obstacles encountered by cities like Singapore, Barcelona, Helsinki, Medellín, Curitiba, Cape Town, and others, Kinshasa can develop informed strategies that suit its distinctive urban landscape and mobility challenges. From Singapore’s groundbreaking Mobility-as-a-Service model to Barcelona’s innovative superblocks and Helsinki’s seamless transportation integration through the Whim app, each city offers specific insights that can be incorporated into Kinshasa’s mobility planning. Comparative analyses can explore regulatory frameworks, data-sharing protocols, public–private collaborations, user engagement rates, and the societal and economic impacts of these projects, providing a detailed understanding for Kinshasa’s policymakers and city planners.
Moreover, exploring the integrated transportation systems in Medellín, the sustainable urban planning model in Curitiba, and the multimodal MyCiTi BRT system in Cape Town can provide valuable perspectives for Kinshasa on infrastructure investments, fair access to transportation, and environmental sustainability. By learning about spatial planning, optimizing public transit, promoting active transportation, and engaging with the community from these cities, Kinshasa can work toward a more connected, efficient, and enjoyable urban environment. The lessons learned from global examples go beyond just technical solutions to include policy frameworks, governance structures, and approaches to citizen participation. Kinshasa can use these insights to make informed policy decisions that prioritize sustainable mobility, allocate resources efficiently, and promote collaboration between government agencies, private sector organizations, and community stakeholders.
By conducting a thorough feasibility analysis and drawing inspiration from successful case studies worldwide, a list of comprehensive recommendations for implementing specific smart mobility concepts in Kinshasa was prepared.
The results of the feasibility analysis suggest that while smart mobility solutions present significant potential for alleviating Kinshasa’s urban mobility challenges, their implementation must be carefully aligned with the city’s broader socio-political landscape. Kinshasa’s current governance structures and regulatory frameworks, particularly those relating to urban transport and infrastructure development, require reform to fully support the integration of these solutions. The capacity of local government agencies to manage and operate smart mobility systems remains a critical concern, as does the need for effective collaboration between governmental bodies, private-sector partners, and international stakeholders. Furthermore, the analysis reveals potential regulatory hurdles, such as outdated transportation policies and limited stakeholder engagement, which could hinder the adoption of innovative mobility technologies. Addressing these issues through policy reforms, capacity building, and inclusive stakeholder dialogue is crucial for ensuring the long-term success of smart mobility solutions in Kinshasa.
Enhancing the city’s transportation system calls for the creation of a Multimodal MaaS platform. This platform would effortlessly integrate multiple transportation modes, offering users a unified payment system, real-time updates, and convenient trip planning. Furthermore, enhancing the public transit system should be a priority. This can be achieved by investing in new buses, optimizing routes for efficiency, and implementing real-time tracking systems. Introducing bus rapid transit (BRT) lanes could also help prioritize public transit and reduce travel times significantly.
To tackle parking challenges, implementing smart parking solutions is essential. Utilizing dynamic pricing and real-time availability data can effectively regulate parking demand, especially in high-traffic areas. This approach encourages turnover and discourages prolonged parking in prime spots, thereby alleviating traffic congestion. Additionally, incorporating EVs into the transportation system is crucial for reducing emissions and promoting sustainable mobility. This could involve setting up EV charging stations and providing incentives and policies to encourage the adoption of EVs.
Implementing sophisticated traffic management systems is essential for enhancing traffic flow and alleviating congestion. These systems use real-time data, sensors, cameras, and AI to efficiently monitor and control traffic. Adopting data-driven strategies through analytics and predictive modeling can optimize route planning, enhance service reliability, and address the dynamic needs of transportation. Additionally, partnering with local universities and research institutions to gather and analyze transportation data will support informed decision-making processes. The integration of LLM technologies into the research process is instrumental in deriving the core findings of this study. These technologies can be used to process and analyze vast datasets, including real-time traffic patterns, commuter preferences, and infrastructure usage, collected through surveys, GPS tracking, and IoT devices. The LLMs do enable the synthesis of these unstructured data sources into actionable insights, identifying key mobility challenges and opportunities for improvement. For example, LLMs can be employed to simulate and predict traffic congestion trends, optimize vehicle fleet compositions, and forecast the impact of different smart mobility interventions. This data-driven approach, facilitated by LLM technology, does allow for a more nuanced understanding of Kinshasa’s unique urban mobility dynamics, leading to targeted recommendations for policy and infrastructure investments. Additionally, LLMs can be used to assess public sentiment and preferences by analyzing open-ended survey responses, further informing the study’s conclusions on the feasibility of proposed solutions.
Raising public awareness is essential for educating people about the advantages of smart mobility and encouraging sustainable transportation practices. To do this effectively, various communication platforms such as social media, radio, and community events should be leveraged to connect with a broad audience and enhance understanding of smart mobility solutions. Prioritizing community engagement in the design and implementation of smart mobility projects is key. Engaging the community via forums, workshops, and online platforms collects valuable feedback on transportation needs and preferences, facilitating the creation of solutions customized to the local context. Promoting active transportation options like walking and cycling can decrease dependence on motor vehicles and improve public health. Creating dedicated lanes and facilities for pedestrians and cyclists ensures safer and more sustainable transportation modes. Additionally, advocating for ride-sharing and carpooling is crucial to lessen the number of single-occupancy vehicles on the road, leading to more efficient use of current transportation resources.
Accessibility is a key criterion in the implementation of smart mobility and must not be overlooked. To make smart mobility solutions inclusive for all societal members, including individuals with disabilities and low-income communities, it is essential to provide transportation options like wheelchair-accessible vehicles. Additionally, introducing affordable fare programs can help ensure that everyone has equitable access to mobility services.
The primary obstacles encountered in implementing smart mobility solutions can be categorized into two main groups. The first involves addressing the varied needs and preferences of the population, ensuring accessibility for everyone, and overcoming potential resistance to behavioral changes. To effectively introduce smart mobility solutions in a city, it is crucial to focus on community involvement, data-informed decision-making, and increasing public awareness. Another group of possible challenges could include resistance from traditional stakeholders, regulatory hurdles, and potential safety concerns. To enhance the integration of smart mobility solutions within a city, it would be very beneficial to establish public–private partnerships. To fully harness the potential of smart mobility, it is crucial to engage with technology companies, transportation providers, and investors. These collaborations can tap into their specialized knowledge, extensive resources, and substantial investment opportunities. This collaboration would encompass joint pilot programs, research initiatives, and infrastructure development to expedite the innovation and deployment of smart mobility projects.
Additionally, establishing a robust policy and regulatory framework is critical to promoting the adoption of smart mobility solutions. This framework must tackle regulatory hurdles while safeguarding the well-being of all users. A thorough review and possible revision of current transportation policies, zoning laws, and land-use plans are necessary to effectively integrate emerging mobility trends and technologies.
Beyond technological progress, a comprehensive approach to smart mobility research must also address socio-economic and environmental considerations. Examining how transportation policies influence equity, affordability, and environmental sustainability is crucial to guarantee that smart mobility projects advantage all societal groups while reducing negative environmental impacts.
8. Conclusions
Kinshasa’s current transportation ecosystem is marked by challenges, including traffic congestion, unreliable public transport, and limited infrastructure. Traditional modes of transportation, such as informal minibus networks and taxis, struggle to keep pace with the city’s rapid urbanization, leading to inefficiencies and frustrations for commuters. The existing infrastructure is strained, often unable to accommodate the growing population’s needs adequately. A comprehensive assessment of Kinshasa’s public transport infrastructure reveals deficiencies in capacity, reliability, and accessibility. The informal nature of the minibus networks, while providing a degree of flexibility, also leads to issues such as overcrowding, irregular schedules, and safety concerns. Additionally, the lack of designated lanes for public transport exacerbates congestion, further hampering efficiency.
Adopting smart mobility strategies provides a promising solution to tackle Kinshasa’s transportation issues. Implementing MaaS, along with car-sharing, bike-sharing, and micro-mobility programs, can significantly improve connectivity, alleviate congestion, and enhance urban living conditions. These smart mobility initiatives are crucial as the city experiences rapid growth and expansion. By prioritizing sustainability, equity, and innovation, Kinshasa can transform its transportation infrastructure, ushering in a new era of urban development and unlocking fresh opportunities for progress.
However, this process is fraught with many challenges that must be overcome. Resolving them requires thoughtful planning, collaboration, and a strong commitment to achieving sustainable and efficient smart mobility solutions. Advancing smart mobility in Kinshasa is a complex task that requires collaboration across different disciplines and creative thinking. Successful implementation requires collaboration between government agencies, private partners, and the community. As the city moves toward a more efficient transportation system, working together and being adaptable is crucial.
To implement Mobility-as-a-Service (MaaS) and other smart mobility solutions in Kinshasa, several key policy reforms and infrastructure investments are required. First, the city must prioritize the development of a Multimodal Mobility-as-a-Service platform that integrates all transportation modes with a unified payment system and real-time updates. This would necessitate the creation of dedicated public transit lanes, investment in charging stations for EVs, and data-sharing protocols across service providers. Additionally, regulatory reforms are needed to support public–private partnerships, enabling technology companies and transportation providers to collaborate effectively on smart mobility projects. Establishing policies that incentivize EV adoption and improve traffic management systems will also be critical in ensuring the success of these initiatives.
By concentrating on the research areas outlined in this paper, Kinshasa can lay the groundwork for an urban transportation system that is more efficient, equitable, and sustainable. This strategy can boost economic growth and improve residents’ quality of life. By fostering a clear vision and teamwork, the city can turn transportation challenges into opportunities for progress. Although the path forward may present difficulties, the aim of establishing a dynamic and inclusive Kinshasa with unlimited mobility is attainable. Embracing smart mobility provides an opportunity to shape Kinshasa’s future and create a legacy of innovation for generations to come. Also, further inspiration, especially related to smart city concepts, can be obtained from various sources such as Refs. [
127,
128,
129,
130,
131,
132].