Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment
<p>A simplified representation of the Mobility-as-a-Service concept.</p> "> Figure 2
<p>A simplified representation of the car-sharing concept.</p> "> Figure 3
<p>Electric vehicle characteristics compared to traditional combustion vehicles.</p> "> Figure 4
<p>The most common 1- and 2-wheeled micro-mobility vehicles.</p> "> Figure 5
<p>Schematic overview of a Vehicle-as-a-Service concept.</p> "> Figure 6
<p>Research design for smart mobility integration into an existing transport system.</p> ">
Abstract
:1. Introduction
- (A)
- Overpopulation and its main impacts
- (B)
- Background and context of Kinshasa’s current mobility challenges
- (C)
- Rationale for Introducing Modern Smart Mobility Concepts in Kinshasa
2. Literature Review
2.1. Review of Existing Literature on Smart Mobility Concepts
2.1.1. Technological Innovations and Integration in Smart Mobility
2.1.2. User-Centric Approaches and Behavior Change in Smart Mobility
2.1.3. Data-Driven Decision-Making in Smart Mobility
3. Conceptual Framework
3.1. Overview of Key Smart Mobility Concepts
3.1.1. Concept of “Mobility-as-a-Service”
3.1.2. Concept of “Car Sharing”
3.1.3. Concept of “Electric Vehicles”
- (a)
- Battery Electric Vehicles (BEVs) rely entirely on electricity stored in rechargeable battery packs and must be plugged into an external power source to recharge. Models like the Tesla Model S and the Nissan Leaf are prime examples of BEVs (situation in 2024).
- (b)
- Plug-in Hybrid Electric Vehicles (PHEVs) blend an electric motor with an internal combustion engine. These vehicles can be recharged using an external power source and are capable of operating purely on electric energy for a specific range before the internal combustion engine activates. Examples of such vehicles are the Chevrolet Volt and the Toyota Prius Prime (situation in 2024).
- (c)
- Hybrid Electric Vehicles (HEVs) integrate an electric motor with an internal combustion engine. However, unlike PHEVs, HEVs lack the ability to recharge their batteries from an external power source. Instead, HEVs rely on regenerative braking and the internal combustion engine to maintain battery power. Instead, the battery is charged through regenerative braking and the internal combustion engine. The Toyota Prius and Honda Insight are widely recognized HEVs (situation in 2024).
- (d)
- Fuel Cell Electric Vehicles (FCEVs) generate electricity on-board through a chemical reaction between hydrogen and oxygen in a fuel cell. FCEVs emit only water vapor and heat as byproducts, with the Toyota Mirai and Hyundai Nexo being prime examples (situation in 2024).
3.1.4. Concept of “Micro-Mobility”
3.1.5. Concept of “Vehicle-as-a-Service”
- (a)
- Subscription-Based Access: VaaS offers a flexible subscription or pay-per-use system, giving people or companies the chance to use vehicles when they need them instead of having to buy them. This strategy aligns with the growing market preference for access rather than ownership.
- (b)
- Diverse Vehicle Options: VaaS platforms provide a wide array of transportation options that extend far beyond the typical car. From electric bikes and scooters to vans and specialized vehicles designed for specific tasks such as deliveries or special events, these platforms ensure users have the flexibility to select the perfect vehicle for their unique needs.
- (c)
- Digital Platforms and Connectivity: VaaS relies heavily on digital platforms and mobile apps to facilitate seamless booking, payment, and vehicle access. Users can reserve vehicles, track their usage, and manage payments through intuitive interfaces, enhancing convenience and accessibility.
- (d)
- Integrated Services: Beyond vehicle access, VaaS platforms often integrate additional services such as insurance, maintenance, roadside assistance, and charging infrastructure for EVs. This holistic approach aims to provide a hassle-free and comprehensive mobility experience for users.
- (e)
- Flexibility and Scalability: VaaS solutions offer flexibility in terms of subscription plans, allowing users to adjust their vehicle usage based on changing needs. For businesses, VaaS offers scalability by providing fleet management tools and analytics to optimize vehicle utilization and costs.
- (f)
- Sustainability and Efficiency: By encouraging shared use and streamlining vehicle fleets, VaaS helps advance sustainability efforts by cutting down on total vehicle ownership, easing traffic congestion, and lowering emissions in cities. It also supports the switch to greener and more efficient transportation choices, including EVs.
- (g)
- Data-Driven Insights: VaaS platforms leverage data analytics to gather insights into user behavior, vehicle usage patterns, and operational efficiency. This data-driven approach enables continuous optimization of services, fleet management, and user experiences.
3.2. Integration of Modern Smart Mobility Concepts into Kinshasa’s Existing Infrastructure
3.2.1. Mobility-as-a-Service
3.2.2. Car Sharing
3.2.3. Micro-Mobility
3.2.4. Vehicle-as-a-Service
3.2.5. Electric Vehicles
3.2.6. Infrastructure Upgrades
- (a)
- Digital Infrastructure: enhancing Internet connectivity, expanding mobile network coverage, and implementing secure digital payment systems.
- (b)
- Transport Infrastructure: building and maintaining dedicated lanes for bikes and scooters, creating parking spaces for car-sharing vehicles, and establishing EV charging stations.
- (c)
- Data Management: developing integrated data platforms to manage real-time information from various transport services, ensuring seamless operation of MaaS.
- (d)
- Policy and Regulation: crafting supportive policies that promote innovation in mobility services, ensure safety and reliability, and provide incentives for sustainable practices.
4. Examination of Successful Smart Mobility Implementations in Other Cities
4.1. Studies on Successful Implementations of Specific Smart Mobility Concepts in Other Cities
4.1.1. Singapore’s and Helsinki’s Mobility-as-a-Service Ecosystems
4.1.2. Barcelona’s Superblocks
4.1.3. Medellín’s, Curitiba’s, and Cape Town’s Integrated Transportation Systems
4.2. Comparative Studies of Similar Initiatives in Other Cities
4.2.1. São Paulo, Brazil
4.2.2. Mumbai, India
4.2.3. Lagos, Nigeria
4.2.4. Jakarta, Indonesia
4.2.5. Manila, Philippines
4.2.6. Cairo, Egypt
4.2.7. Dhaka, Bangladesh
4.3. Identification of Key Success Factors for Smart Mobility
4.3.1. Leadership and Vision in Smart Mobility
- (a)
- Setting clear objectives: Kinshasa can benefit from defining clear objectives for its smart mobility initiatives, aligning them with principles of sustainability, equity, and community well-being. A clear vision offers a roadmap for making decisions and guarantees that efforts are focused on achieving concrete results.
- (b)
- Stakeholder engagement: Engaging diverse stakeholders, including government agencies, urban planners, community groups, and transportation experts, is crucial for building consensus and support for smart mobility initiatives. By fostering collaboration and inclusivity, Kinshasa can ensure that its mobility strategies address the diverse needs of its population.
- (c)
- Incremental implementation: Bogotá’s success was achieved through incremental and phased implementation of infrastructure projects, allowing for flexibility and adaptation to changing circumstances. Kinshasa can adopt a similar approach by starting with pilot projects or smaller-scale interventions to test the feasibility and effectiveness of smart mobility solutions before scaling up implementation.
- (d)
- Data-informed decision-making: Leveraging data and technology for informed decision-making is essential in optimizing transportation planning and infrastructure investments. Kinshasa could leverage data analytics to understand traffic patterns, commuter habits, and infrastructure utilization, which would allow for more informed decision-making and better resource distribution.
4.3.2. Public Engagement and Co-Creation
- (a)
- Embrace community input: Kinshasa has an opportunity to draw lessons from Vienna’s approach by involving its community in the development and execution of smart mobility initiatives. By soliciting input from various stakeholders, including regular commuters, local enterprises, and advocacy groups, Kinshasa can gain a clearer insight into the particular mobility challenges and requirements of its population.
- (b)
- Utilize participatory platforms: Kinshasa can leverage digital platforms and social media channels to facilitate broader public participation in smart mobility initiatives. By creating easy ways for residents to give feedback and work together, the city can make sure that everyone, including marginalized groups, has their voices heard and taken into account in the decision-making process.
- (c)
- Co-create solutions: By involving residents in the co-creation of smart mobility solutions, Kinshasa can ensure that the final outcomes are user-centric and culturally relevant. Collaborative design workshops, pilot projects, and community-led initiatives can foster a sense of ownership and empowerment among residents, driving greater acceptance and adoption of smart mobility measures.
- (d)
- Build trust and transparency: Transparency and trust-building are essential pillars of successful public engagement. Kinshasa can prioritize open communication, accountability, and responsiveness to community feedback to build trust and confidence in its smart mobility initiatives.
4.3.3. Adaptive Regulation in Smart Mobility
4.3.4. Equity and Inclusivity in Smart Mobility
4.3.5. Achieving the Optimal Balance Between Data Privacy and Security in Smart Mobility
4.3.6. Fostering Cooperation for Smart Mobility Success
- (a)
- Government Leadership and Coordination—Example from Singapore: The Land Transport Authority (LTA) collaborates with private operators and technology companies to integrate various transportation modes seamlessly. Kinshasa can establish dedicated agencies or task forces to coordinate smart mobility efforts across government departments, fostering a cohesive approach to urban mobility planning.
- (b)
- Private Sector Engagement and Innovation—Example from Barcelona: Private companies play a crucial role in Barcelona’s superblocks initiative, contributing innovative solutions for urban mobility challenges. Kinshasa can incentivize private sector participation through public–private partnerships (PPPs) and innovation grants, encouraging the development of smart transportation solutions tailored to local needs.
- (c)
- Community Participation and Stakeholder Engagement—Example from Helsinki: Helsinki engaged citizens in co-creating smart mobility solutions through platforms like the Whim app, enhancing user experience and acceptance. Kinshasa can organize public consultations, focus groups, and participatory workshops to gather insights from residents, ensuring that mobility solutions align with community priorities and preferences.
4.3.7. Enhancing Interoperability in Smart Mobility Through Effective Data Governance
4.3.8. Behavioral Change in Smart Mobility
- Research and Analysis: conducting behavioral studies and surveys to understand commuting preferences, motivations, and barriers among residents.
- Nudging Techniques: applying behavioral “nudges,” such as incentives for carpooling, rewards for using public transit, or gamification elements in mobility apps to encourage sustainable choices.
- Stockholm’s Congestion Pricing: by implementing congestion pricing, Stockholm successfully nudged commuters toward alternative modes of transportation, reducing traffic congestion and emissions.
- Paris’s Vélib’ Bike-Sharing System: Paris’s Vélib’ program promoted cycling as a sustainable mode of transit through convenient and affordable bike-sharing services, influencing commuter behavior positively.
- New York City’s Transit Marketing Campaigns: NYC’s targeted marketing campaigns for public transit usage leveraged behavioral insights, showcasing the benefits of shared mobility options and encouraging transit ridership.
4.3.9. Infrastructure Investment as the Backbone of Smart Mobility
- Inner-city Highways: Well-designed inner-city highways are crucial to urban mobility, ensuring the efficient movement of vehicles and alleviating traffic congestion. Kinshasa can learn from cities that have implemented well-designed inner-city highways, optimizing traffic management and improving overall accessibility within urban centers.
- Charging Stations: As the number of EVs increases, establishing a comprehensive network of charging stations becomes essential to facilitate their broader acceptance. Kinshasa can learn from other cities that have effectively implemented charging infrastructure, thereby encouraging the use of EVs and cutting down on transportation-related carbon emissions.
- Bike Lanes: Investing in dedicated bike lanes promotes active transportation and reduces reliance on motor vehicles, thereby mitigating congestion and enhancing air quality. Kinshasa can prioritize the development of bike-friendly infrastructure, encouraging cycling as a viable mode of urban mobility and improving public health outcomes.
- Transit Hubs: Transit hubs serve as vital nodes within urban transport systems, facilitating seamless transfers between different transportation modes. Kinshasa can learn from other cities that have emphasized the development of well-integrated transit hubs, thereby enhancing connectivity, simplifying travel for commuters, and promoting the use of public transportation.
- Underground Metros and Inner-city Railways: Subterranean metro systems and inner-city railways offer rapid, efficient, and sustainable mass transit options, reducing congestion and greenhouse gas emissions. Kinshasa can explore the feasibility of underground transit networks, leveraging their capacity to move large volumes of passengers swiftly across the city.
5. Methodology for Integrating Artificial Intelligence (AI) and Large Language Models (LLMs) into Smart Mobility Solutions for Kinshasa
- A Comprehensive Summary of Section 5:
- ▪
- Step 1: Understanding the Current System and Transport Dynamics while involving LLMsStep 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 LLMsStep 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 LLMsStep 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 LLMsStep 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 LLMsLLMs 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 ImprovementLLMs 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 FundingLLMs 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.
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
- 1. Step: Understanding the Current System and Complex Transport Dynamics
- 2. Step: Stakeholder Engagement
- 3. Step: Identification of Upgrade Opportunities
- 4. Step: Research about Smart Mobility Concepts Implementation
- 4. Step/Phase I. Data-driven Insights
- 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.
- 4. Step/Phase II. Simulation Studies with AI Integration
- Advanced simulation software like VISSIM (See URL: https://www.ptvgroup.com/fr/produits/ptv-vissim; accessed on 1 April 2024), Aimsun (See URL: https://www.aimsun.com/), or SUMO (See URL: https://eclipse.dev/sumo/; accessed on 1 April 2024) integrated with AI techniques such as advanced machine learning algorithms, deep learning models, and forecasting methodologies.
- 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.
- 4. Step/Phase III. Designing Integrated Smart Mobility Solutions
- 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.
- 4. Step/Phase IV. Technological Innovations: Enhancing with AI
- 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.
- 5. Step: Community Involvement and Education
- 6. Step: Pilot Programs, Evaluation, and Continuous Improvement
- 7. Step: Policy Guidelines and Sustainable Funding
5.2. Maximizing Data Insights and Decision-Making with Large Language Models (LLMs) in the Smart Mobility Integration for Kinshasa
5.2.1. Surveys Revolutionized by LLMs
5.2.2. Interviews Enhanced with LLM Insights
5.2.3. Case Studies Empowered by LLM Analysis
5.2.4. Field Sensor Data Transformed with LLM Analytics
5.2.5. Integrated Approach with LLM Technologies
6. Feasibility Analysis
6.1. Methodology: Criteria for Feasibility Analysis Selection
6.2. A Comprehensive Guide to Feasibility Analysis
6.2.1. Practicality of the Technology Implementation
- 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
6.2.3. Regulatory and Legal Feasibility
- 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.
6.2.4. Cultural and Social Viability
6.2.5. Environmental Feasibility
7. Discussion
8. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Gu, D.; Andreev, K.; Dupre, M.E. Major Trends in Population Growth Around the World. CCDCW 2021, 3, 604–613. [Google Scholar] [CrossRef] [PubMed]
- Arana, V. Population Behavior and Land Occupation Trends. In Water and Territory in Latin America: Trends, Challenges and Opportunities; Arana, V., Ed.; Springer International Publishing: Cham, Switzerland, 2016; pp. 5–31. ISBN 978-3-319-30343-7. [Google Scholar]
- Keyfitz, N.; Philipov, D. Migration and Natural Increase in the Growth of Cities. Geogr. Anal. 1981, 13, 287–299. [Google Scholar] [CrossRef]
- Nauman, E.; VanLandingham, M.; Anglewicz, P. Migration, Urbanization and Health. In International Handbook of Migration and Population Distribution; White, M.J., Ed.; Springer Netherlands: Dordrecht, The Netherlands, 2016; pp. 451–463. ISBN 978-94-017-7282-2. [Google Scholar]
- Bolaños, I.G.M.; Terán, M.; Paspuezán, R.A.P. Revisión del impacto de la movilidad urbana. Visión Empres. 2019, 9, 128–134. [Google Scholar] [CrossRef]
- Zhong, Z.; Tayakasu, H.; Takayasu, M. Novel Approaches to Urban Science Problems: Human Mobility Description by Physical Analogy of Electric Circuit Network Based on GPS Data. Sci. Rep. 2024, 14, 13380. [Google Scholar] [CrossRef] [PubMed]
- Makarova, I.; Khabibullin, R.; Belyaev, E.; Mavrin, V. Managing an Urban Transport System in Enhancing the Area Stability. In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems, Rome, Italy, 23–24 April 2016; pp. 112–117. [Google Scholar]
- Roy, T.; Budhadev, R. Urban Sprawl and Transport Sustainability on Highway Corridors Using Stake Holder Analysis. In Advances in Finance & Applied Economics; Bhanumurthy, N.R., Shanmugan, K., Nerlekar, S., Hegade, S., Eds.; Springer: Singapore, 2018; pp. 63–71. ISBN 9789811316968. [Google Scholar]
- Kuo, Y.-H.; Leung, J.M.Y.; Yan, Y. Public Transport for Smart Cities: Recent Innovations and Future Challenges. Eur. J. Oper. Res. 2023, 306, 1001–1026. [Google Scholar] [CrossRef]
- Khamis, A.; Malek, S. Smart Mobility for Sustainable Development Goals: Enablers and Barriers. In Proceedings of the 2023 IEEE International Conference on Smart Mobility (SM), Thuwal, Saudi Arabia, 19–21 March 2023; pp. 173–185. [Google Scholar]
- Prakash, A. Smart Mobility Solutions for a Smart City. IEEE Potentials 2021, 40, 24–29. [Google Scholar] [CrossRef]
- Mitieka, D.; Luke, R.; Twinomurinzi, H.; Mageto, J. Smart Mobility in Urban Areas: A Bibliometric Review and Research Agenda. Sustainability 2023, 15, 6754. [Google Scholar] [CrossRef]
- Tundys, B.; Wiśniewski, T. Smart Mobility for Smart Cities—Electromobility Solution Analysis and Development Directions. Energies 2023, 16, 1958. [Google Scholar] [CrossRef]
- Butler, L.; Yigitcanlar, T.; Paz, A. How Can Smart Mobility Innovations Alleviate Transportation Disadvantage? Assembling a Conceptual Framework through a Systematic Review. Appl. Sci. 2020, 10, 6306. [Google Scholar] [CrossRef]
- Bıyık, C.; Abareshi, A.; Paz, A.; Ruiz, R.A.; Battarra, R.; Rogers, C.D.F.; Lizarraga, C. Smart Mobility Adoption: A Review of the Literature. J. Open Innov. Technol. Mark. Complex. 2021, 7, 146. [Google Scholar] [CrossRef]
- Word Bank. Kinshasa’s Path Towards Resilient Urban Development; Word Bank: Washington, DC, USA, 2023. [Google Scholar]
- Kayisu, A.; Joseph, M.K.; Kyamakya, K. ICT and COMPRAM to Assess Road Traffic Congestion Management in Kinshasa. In Proceedings of the 2017 IST-Africa Week Conference (IST-Africa), Windhoek, Namibia, 31 May–2 June 2017; pp. 1–10. [Google Scholar]
- Iyenda, G. Street Enterprises, Urban Livelihoods and Poverty in Kinshasa. Environ. Urban. 2005, 17, 55–67. [Google Scholar] [CrossRef]
- Pierre, M.B.; Patrick, M.K.; Barthélémy, B.K.M.; Francy, P.P.; Nzau, V.N.; Arthur, M. Typology and Risk Factors of Public Road Accidents in Kinshasa with Its Two Peripheral Areas (Mongata and Kasangulu) from March to May 2017. JTTs 2022, 12, 619–634. [Google Scholar] [CrossRef]
- He, Y.; Thies, S.; Avner, P.; Rentschler, J. Flood Impacts on Urban Transit and Accessibility—A Case Study of Kinshasa. Transp. Res. Part D Transp. Environ. 2021, 96, 102889. [Google Scholar] [CrossRef]
- Mangenda, H.H.; Kunyima, K.C.; Nedeff, V.; Capsa, D.; Felegeanu, D.-C.; Tomozei, C. Potential Enviromental Impacts of Geo-Materials Explotation in the City of Kinshasa, Democratic Republic of Congo. Environ. Eng. Manag. J. 2014, 13, 1605–1609. [Google Scholar] [CrossRef]
- Müller-Eie, D.; Kosmidis, I. Sustainable Mobility in Smart Cities: A Document Study of Mobility Initiatives of Mid-Sized Nordic Smart Cities. Eur. Transp. Res. Rev. 2023, 15, 36. [Google Scholar] [CrossRef]
- Paiva, S.; Ahad, M.A.; Tripathi, G.; Feroz, N.; Casalino, G. Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges. Sensors 2021, 21, 2143. [Google Scholar] [CrossRef] [PubMed]
- Cao, Y.; Derrible, S.; Le Pira, M.; Du, H. Advanced Transport Systems: The Future Is Sustainable and Technology-Enabled. Sci. Rep. 2024, 14, 9429. [Google Scholar] [CrossRef]
- Rivera, R.; Amorim, M.; Reis, J. Public Transport Systems and Its Impact on Sustainable Smart Cities: A Systematic Review. In Industrial Engineering and Operations Management, Proceedings of the XXVI IJCIEOM (2nd Edition), Rio de Janeiro, Brazil, 22–24 February 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 33–47. ISBN 978-3-030-78569-7. [Google Scholar]
- Rodrigues, P.; Real, E.; Barbosa, I.; Durães, L. Rethinking Smart Mobility: A Systematic Literature Review of Its Effects on Sustainability. In Marketing and Smart Technologies, Proceedings of the ICMarkTech 2022, Santiago, Spain, 1–3 December 2022; Reis, J.L., Peter, M.K., Varela González, J.A., Bogdanović, Z., Eds.; Springer Nature: Singapore, 2023; pp. 219–232. [Google Scholar]
- Tomaszewska, E.J.; Florea, A. Urban Smart Mobility in the Scientific Literature—Bibliometric Analysis. Eng. Manag. Prod. Serv. 2018, 10, 41–56. [Google Scholar] [CrossRef]
- Brzeziński, Ł. Social Aspects of Smart Urban Mobility. Encyclopedia 2024, 4, 864–873. [Google Scholar] [CrossRef]
- Litman, T. Smart Transportation Economic Stimulation: Infrastructure Investments That Support Economic Development; Victoria Transport Policy Institute: Victoria, BC, USA, 2009. [Google Scholar]
- Economic Growth. 2023. Available online: https://www.un.org/sustainabledevelopment/economic-growth/ (accessed on 4 May 2024).
- Wolniak, R. Smart Mobility in a Smart City Concept. Sci. Pap. Silesian Univ. Technol. 2023, 2023, 679–692. [Google Scholar] [CrossRef]
- Ceder, A.; Jiang, Y. Personalized Public Transport Mobility Service: A Journey Ranking Approach for Route Guidance. Transp. Res. Procedia 2019, 38, 935–955. [Google Scholar] [CrossRef]
- Wang, B.; Yang, M.; Feng, T.; Yang, Y.; Yuan, Y. Heterogeneous Choice of Personalized Mobility-as-a-Service Bundles and Its Impact on Sustainable Transportation. Transp. Res. Part D Transp. Environ. 2024, 131, 104224. [Google Scholar] [CrossRef]
- Morgan, J. Electric Vehicles: The Future We Made and the Problem of Unmaking It. Camb. J. Econ. 2020, 44, 953–977. [Google Scholar] [CrossRef]
- City of Amsterdam. Program Smart Mobility Amsterdam 2019–2025. Available online: https://amsterdamsmartcity.com/updates/project/program-smart-mobility-amsterdam-2019-2025 (accessed on 17 July 2024).
- Robinson, T.; Ji, M. Twenty-One Million Trips a Day: The Seoul Mass Transit Revolution. In Sustainable, Smart and Solidary Seoul: Transforming an Asian Megacity; Robinson, T., Ji, M., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 75–98. ISBN 978-3-031-13595-8. [Google Scholar]
- Said, R.M.; Rasoul, A.S.A. Integration of Smart Urban Mobility Systems. Benha J. Appl. Sci. 2023, 8, 249–260. [Google Scholar] [CrossRef]
- Sadeghian, S.; Wintersberger, P.; Laschke, M.; Hassenzahl, M. Designing Sustainable Mobility: Understanding Users’ Behavior. In Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Seoul, Republic of Korea, 17 September 2022; ACM: New York, NY, USA; pp. 34–44. [Google Scholar]
- Lyons, G.; Hammond, P.; Mackay, K. The Importance of User Perspective in the Evolution of MaaS. Transp. Res. Part A Policy Pract. 2019, 121, 22–36. [Google Scholar] [CrossRef]
- Vereinte Nationen. Sustainable Mobility and Smart Connectivity: UNECE Nexus; United Nations: Geneva, Switzerland, 2021; ISBN 978-92-1-117251-5. [Google Scholar]
- Zawieska, J.; Pieriegud, J. Smart City as a Tool for Sustainable Mobility and Transport Decarbonisation. Transp. Policy 2018, 63, 39–50. [Google Scholar] [CrossRef]
- Zapolskyte, S.; Burinskienė, M.; Trépanier, M. Evaluation Criteria of Smart City Mobility System Using MCDM Method. Balt. J. Road Bridge Eng. 2020, 15, 196–224. [Google Scholar] [CrossRef]
- Lee, D.; Camacho, D.; Jung, J. Smart Mobility with Big Data: Approaches, Applications, and Challenges. Appl. Sci. 2023, 13, 7244. [Google Scholar] [CrossRef]
- Munhoz, P.; Dias, F.; Chinelli, C.; Azevedo Guedes, A.L.; Neves, J.; Silva, W.; Soares, C. Smart Mobility: The Main Drivers for Increasing the Intelligence of Urban Mobility. Sustainability 2020, 12, 10675. [Google Scholar] [CrossRef]
- Murati, E. The MaaS Paradigm: From Its Origin to a European Mobility Model. In Regulating Mobility as a Service (MaaS) in European Union: A Legal Analysis of Digitalization, Competition, and Multimodality; Murati, E., Ed.; Springer Nature: Cham, Switzerland, 2023; pp. 21–44. ISBN 978-3-031-46731-8. [Google Scholar]
- Barreto, L.; Amaral, A.; Baltazar, S. Mobility in the Era of Digitalization: Thinking Mobility as a Service (MaaS). In Intelligent Systems: Theory, Research and Innovation in Applications; Jardim-Goncalves, R., Sgurev, V., Jotsov, V., Kacprzyk, J., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 275–293. ISBN 978-3-030-38704-4. [Google Scholar]
- Liimatainen, H.; Mladenović, M.N. Developing Mobility as a Service—User, Operator and Governance Perspectives. Eur. Transp. Res. Rev. 2021, 13, 37. [Google Scholar] [CrossRef]
- Santos, G.; Nikolaev, N. Mobility as a Service and Public Transport: A Rapid Literature Review and the Case of Moovit. Sustainability 2021, 13, 3666. [Google Scholar] [CrossRef]
- Kriswardhana, W.; Esztergár-Kiss, D. A Systematic Literature Review of Mobility as a Service: Examining the Socio-Technical Factors in MaaS Adoption and Bundling Packages. Travel Behav. Soc. 2023, 31, 232–243. [Google Scholar] [CrossRef]
- Butler, L.; Yigitcanlar, T.; Paz, A. Barriers and Risks of Mobility-as-a-Service (MaaS) Adoption in Cities: A Systematic Review of the Literature. Cities 2020, 109, 103036. [Google Scholar] [CrossRef]
- Arias-Molinares, D.; García-Palomares, J.C. The Ws of MaaS: Understanding Mobility as a Service Fromaliterature Review. IATSS Res. 2020, 44, 253–263. [Google Scholar] [CrossRef]
- Kolleck, A. Does Car-Sharing Reduce Car Ownership? Empirical Evidence from Germany. Sustainability 2021, 13, 7384. [Google Scholar] [CrossRef]
- Shaheen, S.; Cohen, A. Innovative Mobility: Carsharing Outlook Carsharing Market Overview, Analysis, and Trends; UC Berkeley Transportation Sustainability Research Center: Berkeley, CA, USA, 2020. [Google Scholar]
- Mitropoulos, L.; Kortsari, A.; Ayfantopoulou, G. A Systematic Literature Review of Ride-Sharing Platforms, User Factors and Barriers. Eur. Transp. Res. Rev. 2021, 13, 61. [Google Scholar] [CrossRef]
- Nansubuga, B.; Kowalkowski, C. Carsharing: A Systematic Literature Review and Research Agenda. J. Serv. Manag. 2021, 32, 55–91. [Google Scholar] [CrossRef]
- Outlook for Electric Mobility—Global EV Outlook 2024—Analysis. Available online: https://origin.iea.org/reports/global-ev-outlook-2024/outlook-for-electric-mobility (accessed on 16 July 2024).
- Sanguesa, J.A.; Torres-Sanz, V.; Garrido, P.; Martinez, F.J.; Marquez-Barja, J.M. A Review on Electric Vehicles: Technologies and Challenges. Smart Cities 2021, 4, 372–404. [Google Scholar] [CrossRef]
- Muñoz-Villamizar, A.; Montoya-Torres, J.; Faulin, J. Impact of the Use of Electric Vehicles in Collaborative Urban Transport Networks: A Case Study. Transp. Res. Part D Transp. Environ. 2017, 50, 40–54. [Google Scholar] [CrossRef]
- Vanus, J.; Bilik, P. Research on Micro-Mobility with a Focus on Electric Scooters within Smart Cities. World Electr. Veh. J. 2022, 13, 176. [Google Scholar] [CrossRef]
- Comi, A.; Polimeni, A. Assessing Potential Sustainability Benefits of Micromobility: A New Data Driven Approach. Eur. Transp. Res. Rev. 2024, 16, 19. [Google Scholar] [CrossRef]
- The Benefits and Challenges of Micro-Mobility—European Commission. Available online: https://urban-mobility-observatory.transport.ec.europa.eu/news-events/news/benefits-and-challenges-micro-mobility-2021-10-01_en (accessed on 16 July 2024).
- Ma, J.; Zhu, Y.; Chen, D.; Zhang, C.; Song, M.; Zhang, H.; Chen, J.; Zhang, K. Analysis of Urban Electric Vehicle Adoption Based on Operating Costs in Urban Transportation Network. Systems 2023, 11, 149. [Google Scholar] [CrossRef]
- Vehicle-as-a-Service|Deloitte Global. Available online: https://www.deloitte.com/global/en/Industries/automotive/perspectives/vehicle-as-a-service.html (accessed on 16 July 2024).
- Narayanan, S.; Chaniotakis, E.; Antoniou, C. Shared Autonomous Vehicle Services: A Comprehensive Review. Transp. Res. Part C Emerg. Technol. 2020, 111, 255–293. [Google Scholar] [CrossRef]
- Chen, X.; Deng, Y.; Ding, H.; Qu, G.; Zhang, H.; Li, P.; Fang, Y. Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities. IEEE Commun. Surv. Tutor. 2024, 26, 2048–2081. [Google Scholar] [CrossRef]
- Mahrez, Z.; Sabir, E.; Badidi, E.; Saad, W.; Sadik, M. Smart Urban Mobility: When Mobility Systems Meet Smart Data. IEEE Trans. Intell. Transp. Syst. 2021, 23, 6222–6239. [Google Scholar] [CrossRef]
- Moving MaaS: The Situation in Singapore; The Eno Center for Transportation: Washington, DC, USA, 2024.
- Diao, M. Towards Sustainable Urban Transport in Singapore: Policy Instruments and Mobility Trends. Transp. Policy 2018, 81, 320–330. [Google Scholar] [CrossRef]
- Smart Nation: Singapore’s Intelligent Transport System (ITS). Available online: https://theaseanpost.com/article/smart-nation-singapores-intelligent-transport-system-its (accessed on 23 July 2024).
- Qiao, S.; Huang, G.; Yeh, A.G.-O. Mobility as a Service and Urban Infrastructure: From Concept to Practice. Trans. Urban Data Sci. Technol. 2022, 1, 16–36. [Google Scholar] [CrossRef]
- Smith, G.; Hensher, D.A. Towards a Framework for Mobility-as-a-Service Policies. Transp. Policy 2020, 89, 54–65. [Google Scholar] [CrossRef]
- Wolniak, R. Smart Mobility in Smart City—Copenhagen and Barcelona Comparision. Sci. Pap. Silesian Univ. Technol. Organ. Manag. Ser. 2023, 2023, 679–697. [Google Scholar] [CrossRef]
- Helsinki Ecosystem. Available online: https://mobilitylab.hel.fi/helsinki-ecosystem/ (accessed on 17 July 2024).
- Hielkema, H.; Hongisto, P. Developing the Helsinki Smart City: The Role of Competitions for Open Data Applications. J. Knowl. Econ. 2012, 4, 190–204. [Google Scholar] [CrossRef]
- Audouin, M.; Finger, M. The Development of Mobility-as-a-Service in the Helsinki Metropolitan Area: A Multi-Level Governance Analysis. Res. Transp. Bus. Manag. 2018, 27, 24–35. [Google Scholar] [CrossRef]
- Peres, M.; Silva, J.; Vilafañe, C. Medellin (Colombia): A Case of Smart City. In Proceedings of the ICEGOV’13: Proceedings of the 7th International Conference on Theory and Practice of Electronic Governance, Seoul, Republic of Korea, 22–25 October 2013. [Google Scholar] [CrossRef]
- Corburn, J.; Asari, M.; Jamarillo, J.; Gaviria, A. The Transformation of Medellin into a ‘City for Life’: Insights for Healthy Cities. Cities Health 2019, 4, 13–24. [Google Scholar] [CrossRef]
- Biczynska, E. The Smart City of Medellín, Its Achievements and Potential Risks. Urban Dev. Issues 2019, 62, 29–38. [Google Scholar] [CrossRef]
- Prestes, O.M.; Ultramari, C.; Caetano, F.D. Public Transport Innovation and Transfer of BRT Ideas: Curitiba, Brazil as a Reference Model. Case Stud. Transp. Policy 2022, 10, 700–709. [Google Scholar] [CrossRef]
- Macedo, J. Planning a Sustainable City: The Making of Curitiba, Brazil. J. Plan. Hist. 2013, 12, 334–353. [Google Scholar] [CrossRef]
- Zingoni de Baro, M.E. Curitiba Case Study. In Regenerating Cities: Reviving Places and Planet; Zingoni de Baro, M.E., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 117–162. ISBN 978-3-030-90559-0. [Google Scholar]
- Boulle, M.; Ryneveld, P. Unpacking Implementation—The Case of the MyCiTi Bus Rapid Transit in Cape Town. Technical Report, MAPS Programme. 2015. Available online: https://www.researchgate.net/publication/318395148_Unpacking_implementation_-_The_case_of_the_MyCiTi_Bus_Rapid_Transit_in_Cape_Town?channel=doi&linkId=596764af458515e9af9ea0c8&showFulltext=true (accessed on 17 July 2024).
- Bulman, A.; Greenwood, G.; Kingma, R. Myciti Bus Rapid Transit It Is Not Just about the Bus. In Proceedings of the 33rd Southern African Transport Conference (SATC 2014), Pretoria, South Africa, 7–10 July 2014. [Google Scholar]
- Hadders, R. Access to Livelihood Opportunities Through MyCiTi BRT in Cape Town, South Africa. Master’s Thesis, Universiteit Utrecht, Utrecht, The Netherlands, 2019. [Google Scholar]
- Miller, M. MyCiTi BRT: A Tod Approach in Transforming Cape Town’s Built Environment. Master’s Thesis, Stellenbosch University, Stellenbosch, South Africa, 2018. [Google Scholar]
- Noy, K.; Givoni, M. Is ‘Smart Mobility’ Sustainable? Examining the Views and Beliefs of Transport’s Technological Entrepreneurs. Sustainability 2018, 10, 422. [Google Scholar] [CrossRef]
- Maluf, A. Development of Sao Paulo Integrated Bus Rapid Network: 2002–2013. Journeys Shar. Urban Transp. Solut. 2013, 2014, 47–57. [Google Scholar]
- Abdul, S.; Sattar, S. Mobility and Transport Infrastructure in Mumbai Metropolitan Region: Growth, Exclusion and Modal Choices Mobility and Transport Infrastructure in Mumbai Metropolitan Region: Growth, Exclusion and Modal Choices. Urban Plan. Transp. Res. 2023, 11, 2212745. [Google Scholar] [CrossRef]
- Paul, O.; McSharry, P. Public Transportation Demand Analysis: A Case Study of Metropolitan Lagos. arXiv 2021, arXiv:2105.11816. [Google Scholar]
- Jauregui-Fung, F. BRT Transjakarta: Phasing in, Performing and Expanding a New System Within a Consolidated Urban Area: Report for the “Inclusive and Sustainable Smart Cities in the Framework of the 2030 Agenda for Sustainable Development” Project; German Institute of Development and Sustainability (IDOS): Bonn, Germany, 2022. [Google Scholar] [CrossRef]
- Gaspay, S.M.; Tiglao, N.C.; Tacderas, M.A.; Tolentino, N.J.; Ng, A.C. Reforms in Metro Manila’s Bus Transport System Hastened by the COVID-19 Pandemic: A Policy Capacity Analysis of the EDSA Busway. Res. Transp. Econ. 2023, 100, 101305. [Google Scholar] [CrossRef]
- Tarek, S.; Ibrahim Nasreldin, T. Towards Applying Smart Mobility Solutions in Egypt: An Integrative Framework and a Case Study Application. Ain Shams Eng. J. 2023, 14, 101987. [Google Scholar] [CrossRef]
- Karmaker, A.K.; Islam, S.M.R.; Kamruzzaman, M.; Rashid, M.M.U.; Faruque, M.O.; Hossain, M.A. Smart City Transformation: An Analysis of Dhaka and Its Challenges and Opportunities. Smart Cities 2023, 6, 1087–1108. [Google Scholar] [CrossRef]
- Roblek, V. The Smart City of Vienna. In Smart City Emergence; Elsevier: Amsterdam, The Netherlands, 2019; pp. 105–126. ISBN 978-0-12-816169-2. [Google Scholar]
- Tang, C. The Cost of Traffic: Evidence from the London Congestion Charge. J. Urban Econ. 2020, 121, 103302. [Google Scholar] [CrossRef]
- Glazener, A.; Wylie, J.; van Waas, W.; Khreis, H. The Impacts of Car-Free Days and Events on the Environment and Human Health. Curr. Environ. Health Rep. 2022, 9, 165–182. [Google Scholar] [CrossRef]
- Karami, Z.; Kashef, R. Smart Transportation Planning: Data, Models, and Algorithms. Transp. Eng. 2020, 2, 100013. [Google Scholar] [CrossRef]
- Barr, S.; Lampkin, S.; Dawkins, L.; Williamson, D. Smart Cities and Behavioural Change: (Un)Sustainable Mobilities in the Neo-Liberal City. Geoforum 2021, 125, 140–149. [Google Scholar] [CrossRef]
- Kussl, S.; Wald, A. Smart Mobility and Its Implications for Road Infrastructure Provision: A Systematic Literature Review. Sustainability 2023, 15, 210. [Google Scholar] [CrossRef]
- Alanazi, F.; Alenezi, M. A Framework for Integrating Intelligent Transportation Systems with Smart City Infrastructure. J. Infrastruct. Policy Dev. 2024, 8, 3558. [Google Scholar] [CrossRef]
- ElBanhawy, E.Y.; Dalton, R.; Anumba, C. The Agent Based Modeling of E-Mobility. In Proceedings of the 2014 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA, 15–18 June 2014; pp. 1–6. [Google Scholar]
- Modeling and Design of Smart Mobility Systems. In Urban Systems Design: Creating Sustainable Smart Cities in the Internet of Things Era; Elsevier: Tokyo, Japan, 2020; pp. 163–197.
- Zhang, Z.; Sun, Y.; Wang, Z.; Nie, Y.; Ma, X.; Sun, P.; Li, R. Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks. arXiv 2024, arXiv:2405.02357. [Google Scholar]
- Minaee, S.; Mikolov, T.; Nikzad, N.; Chenaghlu, M.; Socher, R.; Amatriain, X.; Gao, J. Large Language Models: A Survey. arXiv 2024, arXiv:2402.06196. [Google Scholar]
- Naveed, H.; Khan, A.U.; Qiu, S.; Saqib, M.; Anwar, S.; Usman, M.; Akhtar, N.; Barnes, N.; Mian, A. A Comprehensive Overview of Large Language Models. arXiv 2024, arXiv:2307.06435. [Google Scholar]
- Papers with Code—Smart-Infinity: Fast Large Language Model Training Using Near-Storage Processing on a Real System. Available online: https://paperswithcode.com/paper/smart-infinity-fast-large-language-model (accessed on 23 July 2024).
- Papers with Code—Where Would I Go Next? Large Language Models as Human Mobility Predictors. Available online: https://paperswithcode.com/paper/where-would-i-go-next-large-language-models (accessed on 23 July 2024).
- Zhang, Y.; Chen, X.; Jin, B.; Wang, S.; Ji, S.; Wang, W.; Han, J. A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery. arXiv 2024, arXiv:2406.10833. [Google Scholar]
- Prepare for Truly Useful Large Language Models. Nat. Biomed. Eng. 2023, 7, 85–86. [CrossRef] [PubMed]
- Fang, X.; Xu, W.; Tan, F.A.; Zhang, J.; Hu, Z.; Qi, Y.; Nickleach, S.; Socolinsky, D.; Sengamedu, S.; Faloutsos, C. Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding—A Survey. arXiv 2024, arXiv:2402.17944. [Google Scholar] [CrossRef]
- Liu, O.; Fu, D.; Yogatama, D.; Neiswanger, W. DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models. arXiv 2024, arXiv:2402.02392. [Google Scholar]
- Costescu, D.; Roman, E.A. Challenges in the Development of Urban Intermodal Mobility Systems. In Handbook of Research on Promoting Sustainable Public Transportation Strategies in Urban Environments; IGI Global: Hershey, PA, USA, 2023; pp. 98–119. [Google Scholar] [CrossRef]
- Chen, L.; Wang, Y.; Qi, G.; Lv, H.; Han, G.; Li, F. Research on Smart Mobility in Public Transportation and Solutions for Mobility. Highlights Sci. Eng. Technol. 2023, 56, 498–505. [Google Scholar] [CrossRef]
- Musa, A.A.; Malami, S.I.; Alanazi, F.; Ounaies, W.; Alshammari, M.; Haruna, S.I. Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainability 2023, 15, 9859. [Google Scholar] [CrossRef]
- Paalosmaa, T.; Shafie-Khah, M. Feasibility of Innovative Smart Mobility Solutions: A Case Study for Vaasa. World Electr. Veh. J. 2021, 12, 188. [Google Scholar] [CrossRef]
- Turečková, K.; Nevima, J. The Cost Benefit Analysis for the Concept of a Smart City: How to Measure the Efficiency of Smart Solutions? Sustainability 2020, 12, 2663. [Google Scholar] [CrossRef]
- Bulut, U.; Frye, E.; Greene, J.; Li, Y.; Lee, M. The Hidden Price of Convenience: A Cyber-Inclusive Cost-Benefit Analysis of Smart Cities. In Research in Mathematics and Public Policy; Lee, M., Chesler, A.N., Eds.; Association for Women in Mathematics Series; Springer: Berlin/Heidelberg, Germany, 2020; pp. 81–92. ISBN 978-3-030-58747-5. [Google Scholar]
- Governance of the Smart Mobility Transition; Emerald Publishing Limited: Bradford, UK, 2018.
- Quan, X.; Solheim, M.C.W. Public-Private Partnerships in Smart Cities: A Critical Survey and Research Agenda. City Cult. Soc. 2023, 32, 100491. [Google Scholar] [CrossRef]
- Simon, N.; Csiszar, C. The Quality of Smart Mobility: A Systematic Review. Sci. J. Silesian Univ. Technol. Ser. Transp. 2020, 109, 117–127. [Google Scholar] [CrossRef]
- Pop, M.-D.; Proștean, O.; Prostean, G. Analysis of Policies and Regulations for a Sustainable Smart Mobility System. In Proceedings of the 32nd International Business Information Management Association Conference (IBIMA), Seville, Spain, 15–16 November 2018. [Google Scholar]
- Obaid, M.; Torok, A. Macroscopic Traffic Simulation of Autonomous Vehicle Effects. Vehicles 2021, 3, 187–196. [Google Scholar] [CrossRef]
- Regulating Smart Mobility: Addressing Challenges and Opportunities in the Digital Transition of Mobility—Prosjektbanken. Available online: https://prosjektbanken.forskningsradet.no/project/FORISS/283327 (accessed on 23 July 2024).
- Silva, T.; Verde, D.; Paiva, S.; Barreto, L.; Pereira, A.I. Accessibility Strategies to Promote Inclusive Mobility through Multi-Objective Approach. SN Appl. Sci. 2023, 5, 150. [Google Scholar] [CrossRef]
- Ranchordás, S. Smart mobility, transport poverty and the legal framework of inclusive mobility. In Smart Urban Mobility: Law, Regulation, and Policy; Springer: Berlin/Heidelberg, Germany, 2020; pp. 61–80. [Google Scholar] [CrossRef]
- The Technology That Is Driving the Future of Smart Mobility. Available online: https://www.smartcitiesworld.net/special-reports/the-technology-that-is-driving-the-future-of-smart-mobility (accessed on 23 July 2024).
- Anedda, M.; Fadda, M.; Girau, R.; Pau, G.; Giusto, D. A Social Smart City for Public and Private Mobility: A Real Case Study. Comput. Netw. 2023, 220, 109464. [Google Scholar] [CrossRef]
- Allam, Z.; Newman, P. Smart Cultural and Inclusive Cities: How Smart City Can Help Urban Culture and Inclusion. In Revising Smart Cities with Regenerative Design; Allam, Z., Newman, P., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 77–99. ISBN 978-3-031-28028-3. [Google Scholar]
- Obaid, M.; Torok, A.; Ortega, J. A Comprehensive Emissions Model Combining Autonomous Vehicles with Park and Ride and Electric Vehicle Transportation Policies. Sustainability 2021, 13, 4653. [Google Scholar] [CrossRef]
- Terdiman, M.; Angert, T. Green and Smart Cities in the Developing World. In The Palgrave Encyclopedia of Urban and Regional Futures; Brears, R.C., Ed.; Palgrave Macmillan: Cham, Switzerland, 2023. [Google Scholar] [CrossRef]
- Ling, S.; Ma, S.; Jia, N. Sustainable urban transportation development in China: A behavioral perspective. Front. Eng. Manag. 2022, 9, 16–30. [Google Scholar] [CrossRef]
- World Economic Forum. Why Developing Nations Are Defining the Smart Cities of the Future. World Economic. 2023. Available online: https://www.weforum.org/agenda/2023/01/smart-cities-developing-nations-davos23/ (accessed on 4 May 2024).
City | Country | Population (2024) | Density (People/km2) | Number of Cars | Metro System | Cars per 1000 People |
---|---|---|---|---|---|---|
Kinshasa | DR Congo | 14,342,000 | 14,391 | 1,500,000 | No | 104.59 |
Cairo | Egypt | 20,901,000 | 19,376 | 2,220,000 | Yes | 106.22 |
New York City | USA | 18,804,000 | 10,430 | 2,000,000 | Yes | 106.36 |
Tokyo | Japan | 37,393,000 | 6363 | 4,547,000 | Yes | 121.60 |
Lagos | Nigeria | 14,368,000 | 6871 | 1,800,000 | No | 125.28 |
Shanghai | China | 27,058,000 | 3854 | 3,620,000 | Yes | 133.79 |
Osaka | Japan | 19,165,000 | 12,200 | 2,620,000 | Yes | 136.71 |
Dhaka | Bangladesh | 21,006,000 | 29,346 | 2,900,000 | Yes | 138.06 |
Chongqing | China | 15,872,000 | 382 | 2,600,000 | Yes | 163.81 |
Manila | Philippines | 13,923,000 | 18,000 | 2,300,000 | Yes | 165.19 |
Mumbai | India | 20,411,000 | 20,482 | 3,400,000 | Yes | 166.58 |
Kolkata | India | 14,850,000 | 24,000 | 2,500,000 | Yes | 168.35 |
Tianjin | China | 13,580,000 | 1135 | 2,700,000 | Yes | 198.82 |
Buenos Aires | Argentina | 15,154,000 | 14,469 | 3,200,000 | Yes | 211.17 |
Karachi | Pakistan | 16,094,000 | 4421 | 3,600,000 | No | 223.69 |
Mexico City | Mexico | 21,782,000 | 6000 | 5,500,000 | Yes | 252.50 |
Istanbul | Turkey | 15,190,000 | 2848 | 4,000,000 | Yes | 263.33 |
Beijing | China | 20,463,000 | 1326 | 5,972,000 | Yes | 291.84 |
Delhi | India | 30,291,000 | 11,297 | 11,400,000 | Yes | 376.35 |
São Paulo | Brazil | 22,043,000 | 7398 | 8,500,000 | Yes | 385.61 |
Lesson | Description |
---|---|
Customized regulatory approaches | Tailor regulations like congestion pricing to address traffic bottlenecks and promote sustainable transportation. |
Public engagement and awareness | Initiatives like car-free days for fostering public awareness and participation in alternative transportation. |
Infrastructure investments | Prioritize investments in public transit, cycling lanes, and pedestrian-friendly infrastructure. |
Data-driven decision making | Use data analytics to monitor regulatory effectiveness and optimize traffic management strategies. |
Lesson | Description |
---|---|
Design Transit-Oriented Developments (TOD) | Design plans centered on transit hubs to encourage mixed-use development, affordable housing, and convenient public transportation access. |
Prioritize Universal Design | Incorporate universal design in infrastructure to accommodate diverse mobility needs, including elderly and disabled individuals. |
Engage Communities in Planning | Involve local communities, advocacy groups, and stakeholders in planning to ensure projects meet residents’ mobility needs and aspirations. |
Lesson | Description |
---|---|
Establishing Clear Data Governance Policies | Develop clear policies for data collection, storage, sharing, and anonymization in smart mobility projects. |
Engaging Stakeholders | Involve citizens, advocacy groups, and experts in shaping data governance to foster transparency and trust. |
Conducting Privacy Impact Assessments (PIAs) | Conduct periodic Privacy Impact Assessments (PIAs) to detect and address potential privacy concerns in smart mobility initiatives. |
Secure Data Storage Facilities | Invest in secure data storage with robust cybersecurity measures to protect sensitive mobility data. |
Encrypted Communication Networks | Implement encrypted communication for data transmission between devices, sensors, and control systems. |
Transparent Data Practices | Communicate openly about data collection purposes, usage policies, and privacy safeguards to citizens. |
Engagement and Education | Conduct public awareness campaigns and workshops on data privacy, cybersecurity, and smart mobility benefits. |
Lesson | Description |
---|---|
Integrated Transport Systems | Develop systems connecting various transit modes for seamless mobility. Collaborate with transport operators, city planners, and technology providers to optimize routes and enhance accessibility. |
Smart Infrastructure Investments | Invest in smart traffic management, real-time data analytics, and digital signage to improve traffic flow and safety. Collaborate with technology companies and research institutions on innovative solutions. |
Interdisciplinary Partnerships | Promote collaboration between urban planners, engineers, data scientists, and social scientists to develop well-rounded mobility solutions. |
Capacity Building and Knowledge Sharing | Organize workshops and training to build capacities and share best practices and trends in smart mobility. |
Lesson | Description |
---|---|
Adopting Open Data Standards | Implement open data standards for integration and sharing of data from transportation networks, IoT devices, and urban sensors. |
Promoting Data Collaboration | Encourage data sharing among government, private sector, and academic institutions, ensuring privacy and security. |
Creating Transparent Data Guidelines | Develop robust data governance frameworks with well-defined policies for data collection, storage, sharing, and usage, ensuring compliance with international standards like GDPR. |
Emphasizing Data Security and Privacy | Implement robust data security measures, encryption protocols, and anonymization techniques to protect sensitive information and build citizens’ trust. |
Building Data Infrastructure | Invest in data centers, cloud computing infrastructure, and edge computing technologies to manage and analyze vast amounts of mobility data in real time. |
Deploying IoT and Sensor Networks | Implement IoT devices and sensor networks across transportation infrastructure to capture real-time data on traffic flows, vehicle movements, and passenger behaviors. |
Lesson | Description |
---|---|
Prioritizing Public Transit Accessibility | Invest in efficient public transit systems, including BRT routes, reliable schedules, and user-friendly payment systems. |
Promoting Active Transportation | Develop cycling lanes, pedestrian pathways, and safe infrastructure for micro-mobility options like e-scooters and shared bikes. |
Integrating Technology Solutions | Implement smart mobility apps, real-time transit information, and seamless intermodal connectivity for enhanced user experience. |
Awareness Campaigns | Launch campaigns to highlight the benefits of using shared and sustainable transportation modes. |
Education Initiatives | Partner with schools, businesses, and communities to educate about transportation options and the importance of emission reductions and behavioral change. |
Category of Analysis | Selection Criteria | Main Content |
---|---|---|
Technological Feasibility | Infrastructure compatibility | Assessing current road infrastructure and its ability to support new technologies without extensive modifications. |
Availability and maturity of smart mobility technologies | Applying IoT, real-time data analytics, and mobile applications for public transport. | |
Economic Feasibility | Cost-benefit analysis | Comparing investment and operational costs against expected economic benefits (e.g., reduced travel time, fuel savings). |
Potential funding sources | Finding government budgets, international aid, and private sector investments. | |
Financial sustainability | Evaluating long-term viability of the project. | |
Market demand | Considering population density, urbanization rates, and existing transportation patterns. | |
Regulatory and Legal Feasibility | Policy and regulation review | Identifying potential barriers or facilitators for implementing smart mobility solutions and recommending legal adjustments. |
Capacity of local government | Evaluating ability to manage, operate, and maintain new systems; identifying training and capacity-building needs. | |
Stakeholder collaboration | Identifying key stakeholders and assessing their roles. | |
Social and Cultural Feasibility | Accessibility | Improving accessibility for all residents, including marginalized and underserved communities. |
Public acceptance | Gauging through surveys, focus groups, and stakeholder engagement activities. | |
Safety and security | Assessing potential for improving road safety and reducing accidents through better traffic management and real-time monitoring. | |
Environmental Feasibility | Emission reduction | Evaluating potential environmental benefits of improved traffic management and increased use of public transportation. |
Sustainability | Aligning with sustainable development goals and reducing the overall carbon footprint. | |
Resource efficiency | Ensuring minimal environmental disruption during implementation and maintenance. |
Category of Feasibility Analysis | Content | Main Output |
---|---|---|
Technological Feasibility | Evaluation of the technological infrastructure required for smart mobility concepts | Assessment of the compatibility of existing systems with modern technologies |
Economic Feasibility | Cost–benefit analysis of implementing smart mobility solutions | Identification of potential funding sources and financial models |
Regulatory and Legal Feasibility | Review of current regulations and legal structures pertaining to smart mobility | Recommendations for necessary regulatory adjustments |
Social and Cultural Feasibility | Analysis of social acceptance and cultural adaptability of smart mobility concepts | Potential impact of smart mobility concepts on local communities and residents |
Environmental Feasibility | Assessment of the environmental impact of public transportation and smart mobility solutions | Information for minimizing negative environmental impacts and maximizing overall sustainability |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kayisu, A.K.; Mikusova, M.; Bokoro, P.N.; Kyamakya, K. Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment. Sustainability 2024, 16, 9371. https://doi.org/10.3390/su16219371
Kayisu AK, Mikusova M, Bokoro PN, Kyamakya K. Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment. Sustainability. 2024; 16(21):9371. https://doi.org/10.3390/su16219371
Chicago/Turabian StyleKayisu, Antoine Kazadi, Miroslava Mikusova, Pitshou Ntambu Bokoro, and Kyandoghere Kyamakya. 2024. "Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment" Sustainability 16, no. 21: 9371. https://doi.org/10.3390/su16219371
APA StyleKayisu, A. K., Mikusova, M., Bokoro, P. N., & Kyamakya, K. (2024). Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment. Sustainability, 16(21), 9371. https://doi.org/10.3390/su16219371