Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
Abstract
:1. Introduction
1.1. Need and Importance of Smart Mobility
1.2. Paper Organization
1.3. Main Contributions
- A comprehensive review of smart mobility solutions and related services proposed in the last few years.
- The contextualization of how smart mobility is framed within smart cities and its key benefits and attributes.
- Discussion on the opportunities for smart mobility to become a reality in the coming years as well as the major issues and challenges it its realization.
- Meaningful insights into the future of the mobility-as-a-service paradigm.
- Overview of the Enabling Technologies that will support smart mobility services and applications such as AI, IoT, blockchain, geospatial technology and big data.
- Future trends for smart mobility.
2. Research Progress in Smart Mobility and Gap Analysis
- Classification of Articles on the basis of the type: we have chosen only peer reviewed research, review and survey articles satisfying our criteria;
- Identification of Publication Year: articles from 2011 to 2020 only were analyzed in the literature review;
- Classification of Articles on the basis of keywords: we have combined the following keywords
- Smart City
- Urban Smart Mobility
- Enabling Technology.
2.1. Research Based on Smart Mobility Applications
2.2. Taxonomy, Surveys and Review Based Papers
2.3. Regional, Governance and Citizen Centric Papers
2.4. Summary and Research Gap Analysis
- User Privacy
- Data Integration issues
- Data Standardization issues
- Sensor characteristics
- Impact of external environment on sensing capabilities of sensors.
- Enforcing uniform and ubiquitous mobility laws, rules and regulations
- Citizen participation in mobility initiatives
- Crowdsensing in smart mobility
- Interoperability
- Legacy Infrastructure setups
- Amicable Cooperation between public-private mobility services players.
3. Smart Mobility in Smart Cities
3.1. Overview
3.2. Opportunities
3.3. Challenges
4. Smart Mobility Services and Applications
4.1. Mobility-as-a-Service
- High number of private cars within a city
- Little attractiveness for citizens when it comes to using public transport
- Little use of active modes of transport
- Less adequate location of specific places for bicycles, scooters and parking links and its lack of integration with the transport network
- Lack of accessibility to transport systems by people with disabilities or old people
- Lack of information in real time for citizens
- Lack of an integrated public transport platform, routes suitable for each user, on-demand parking, active mobility and unified payment systems for the entire transport network
- Lack of understanding of citizens’ mobility patterns
- Lack of information for citizens about the consequences of their fewer active habits in the carbon scab
- An easier way for citizens to plan, book and pay for mobility services (which will also facilitate citizens abandoning the private vehicle)
- Improving the efficiency of the transit network
- Reduced costs for citizens
- Decreased traffic congestion
- Reducing the ecological footprint
- Predicting demands
- Making personalized suggestions to citizens
- Allowing providers to plan ahead and meet citizens’ needs
- Increasing convenience, effectiveness and customer satisfaction
- Ease of payment
- Revenue growth for transportation service providers.
4.1.1. MaaS Implementation Challenges
4.1.2. Analysis of Security Threats
- Data Theft: Autonomous vehicles and self-driving cars form a part of a larger network of connected entities which continuously share data to provide a seamless user experience. Some of the data generated through different entities consist of personal user information such as financial details, contact details, travel habits and history. With this type of personal data at disposal, there are vulnerabilities related to data theft that can be used by attackers.
- Identity Theft: The concept of autonomous vehicles focuses on the transfer of control from the human drivers to the vehicles. In such scenarios, the connected vehicles become easy targets of cyber criminals and hackers. These vehicles are prone to identity theft where the hackers can obtain the vehicle identification information and misuse it.
- Device Hijacking: Vehicle hijacking is one of the biggest threats to autonomous vehicles. The attackers can gain control of the vehicle system and software and modify the algorithms to remotely control the vehicle. Once hijacked, the hackers can modify the important functioning units such as the navigation control unit, engine, brakes, heating systems, communication systems, vehicle camera and vision systems.
- Denial of Service: Attackers can take advantage of the vulnerabilities of the least secured devices on the network to gain control of the entire network and overwhelm the connected entities. These types of attacks can be launched on an individual node or vehicle-to-vehicle or vehicle-to-infrastructure, resulting in a communication system disruption.
- Privacy Infringement: Autonomous vehicles continuously generate data such as the vehicle location. These data provide personal information related to the user’s travel history and navigation, which can be misused to track the user’s current location and other whereabouts.
- Financial Fraud: In countries such as India where toll taxes are automatically deducted using RFID tags on the vehicles, there are serious concerns related to the financial security of the linked bank accounts. Any insecure network or device may result in banking frauds and other related attacks such as ransomware.
4.2. Traffic Flow Optimization
- Optimization of the use of road infrastructure
- Integration of different domains (air, land and sea)
- Conducting predictive traffic analysis, with emphasis on moments when a greater flow of vehicles is expected
- Performing data analysis that allows traffic planning and control in real time
- Optimizing the location of electric car charging stations to the places where they are most in demand
- Developing predictive models that understand the needs of citizens in terms of new mobility models (including e-scooters, e-bikes, carsharing, carpooling).
4.3. Optimization of Logistics
- Vehicles that deliver not being fully filled in terms of their transport capacity
- Inefficient route optimization to make deliveries
- Lack of coordination between transport providers in order to achieve an integrated way that includes several fleets
- Little awareness by consumers and suppliers of the carbon footprint implications of the large number of deliveries
- Collision of the delivery times with the hours of greatest traffic congestion in the cities
- Deliveries made using vehicles that use fossil fuels
- Inefficient coordination of the different means of transport (sea, air and land)
- Use of private transport by citizens when shopping, instead of using public transport.
4.4. Autonomous Vehicles
- Lane marking: these markings are not the best for today’s vehicles. It is an aspect that will necessarily have to be improved to the point that they can be read efficiently by machines.
- Roadside sensors: this type of sensor should be included in sidewalks, curbs and lanes. In this way, it will be possible for AVs to be aware of the environment that surrounds them and thus act in a preventive way to possible situations of danger.
- Smart signage: image recognition is currently used to read traffic signs. In the future, it is expected that the signals will be able to send a signal that can be read by machines and thus facilitate the reading of the signals by the autonomous vehicles.
- Providing a safer and more reliable means of transport,
- Reducing the number of accidents,
- Reducing the need for human intervention during driving,
- Reducing traffic congestion
- Allowing elderly people or people with disabilities to make their lives easier
- Elimination of traffic lights as autonomous vehicles will be able to efficiently set priorities
- Reducing/eliminating the time spent searching for parking spaces.
- Perception, planning, control and ethics
- Data privacy
- Transmission security
- Processing latency
- Energy efficiency.
4.4.1. Levels of Autonomous Driving
4.4.2. Vehicle Sensors
4.4.3. Vehicle Communication Protocols
- Safe routing: the security of messages is, in all systems, delicate and of enormous importance. In this specific case, illegal message tampering can have very serious consequences and even have an impact on life-or-death situations;
- Reliable communication: link ruptures can suddenly happen due to some VANETs’ characteristics such as high mobility, intermittent connectivity or obstacles in cities, and what to do in case of packet losses is a challenge;
- Determining the optimal path from a source to a destination, taking into account the density of traffic and the shortest distance;
- Determining a routing strategy that adapts to the two distinct environments of VANETs: city environment and motorways.
4.4.4. Ethical Issues
4.5. Outdoor Navigation Technologies
5. Enabling Technologies to Support Smart Mobility
5.1. Overview of Enabling Technologies
- Real-time information about the location collected from smartphones that can be used for various services on people’s mobility can be a major barrier in terms of privacy;
- The collection of images of people obtained from services for mapping and recognition purposes;
- The on-board units of the vehicles might be associated with plate numbers which are considered indirect identifiers;
- The lack of verification of the accuracy and relevance of the information collected.
- Port scales can be optimized if data are shared in advance in order to improve planning and resource allocation. In Hamburg, for example, electric vehicles and real-time navigation are used to ensure the smooth flow of traffic and thus reduce congestion;
- Traffic lights can be controlled based on traffic flow if the necessary data are collected. In this way, traffic in cities can be optimized and congestion can be reduced. This is an example currently in practice in the city of Hong Kong;
- IoT sensors and CCTV cameras can also be used for traffic management, thus reducing congestion;
- Open data about mobility can be of added value to citizens that can be aware of real time traffic data and plan their day with more information so it can be more efficient;
- Parking spaces can be monitored, and drivers can reduce parking time and also CO2 emissions;
- On-demand and more adaptive modes of capacity planning and operations can be achieved using data from sensors, cameras and vehicles so that information to commuters can be given so they can plan their routes more efficiently;
- Fleet efficiency can be obtained by combining real-time traffic data to produce route optimization using AI techniques, reducing wait times and optimizing energy consumption;
- Increase citizens’ security by preventing anomaly detection and anticipating incidents.
5.2. Role of Enabling Technologies in Smart Mobility Services and Applications
5.2.1. Role in Mobility-as-a-Service
5.2.2. Role in Traffic Flow Optimization and Optimization of Logistics
5.2.3. Role in Autonomous Vehicles
6. Future Trends on Smart Urban Mobility
- Mobility in degraded vision
- Electric vehicles
- Alternate fuels
- Mobility solutions in natural calamities and disasters
- Mobility for differently abled citizens
- Inclusive, environment friendly, sustainable and efficient transportation
- IoT based dynamic traffic management
- Transparent and distributed traffic management
- Security of citizens, devices and vehicles.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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S. No. | Paper (Year) | Focus |
---|---|---|
1. | Amoretti, M. et al. (2017) | Smart mobility application “UTravel” based on “Universal Profiling and Recommendation (UPR)”. |
2. | Belbachir, A. et al. (2019) | Traffic control system based on cooperative agents. |
3. | Benevolo, C. et al. (2016) | Novel action taxonomy involving a systematic approach to smart mobility and analysis of the role of ICT. |
4. | Mirri, S. et al. (2016) | Smart Mobility for All (SMAll)—Framework for developing and implementing smart mobility applications. |
5. | Lopez, D. et al. (2020) | Six-layer blockchain architecture to handle the issues regarding security, privacy, scalability and management of “Smart Mobility Data-market (BSMD)”. |
6. | Bravo, Y. et al. (2016) | Framework “HITUL” for assistance in the decision making of traffic control management. |
7. | Aletà, N. et al. (2017) | Spectrum of development of “Spanish Smart City” measures with a view to mobility and environmental concerns. |
8. | Camero, A. et al. (2018) | Prediction of the occupancy rate of car parking space premised on deep learning with “Recurrent Neural Networks (RNNs)”. |
9. | Cledou, G. et al. (2018) | Taxonomy for the formulation of smart city services. |
10. | Dameri, R. P. (2017) | Novel action taxonomy concerning an extensive methodology related to Smart Mobility. |
11. | Bucchiarone, A. (2019) | Collective Adaptation Engine (CAE)—Distributed adaptation method for ensemble-based systems in smart mobility context. |
12. | Del Vecchio, P. et al. (2019) | Application of system dynamics to simulate substitutes for conventional human mobility. |
13. | Debnath, A. K. et al. (2014) | Realistic structure to design a comparative analysis, which gauges cities based on the smartness of their transport frameworks. |
14. | Docherty, I. et al. (2018) | Analysis of the likely transition of present issues of mobility governance with the aim to safeguard and increase the public value. |
15. | Torres-Sospedra, J. et al. (2015) | “SmartUJI APP” and “SmartUJI AR” to enhance the positioning element of smart mobility in a smart university setting acting as a representative for a smart city. |
16. | Garau, C. et al. (2015) | Mobility indicators to assess smart urban mobility in Italian cities. |
17. | Groth, S. (2019) | Transformation from an automobile community to a multimodal community fostered by the advent of smart mobility powered by ICT in a German region. |
18. | Battarra, R. et al. (2018) | Assessment of the possibility and degree of applying the smart city model with the aim to boost the effectiveness and living conditions of urban areas. |
19. | Ilarri, S. et al. (2015) | SemanticMOVE—distributed system for mobility data management and semantic improvement. |
20. | Papa, E. et al. (2015) | Multi-disciplinary and collective methodology to smart mobility to enable the shift to a “smarter mobility” to improve the city development and citizens’ quality of life. |
21. | Jeekel, H. (2017) | Relationship between smart mobility and social sustainability. |
22. | Kronsell, A. et al. (2020) | Theoretical assessment of experimental governance with respect to smart mobility in Sweden. |
23. | Garau, C. et al. (2016) | Quantitative approach for assessing urban mobility in the Italian area of Cagliari. |
24. | Kudo, H. (2016) | Framework of citizen engagement and efficiency of Japanese Smart Communities to contemplate the collaborative design and development of a smart mobility framework. |
25. | Longo, A. et al. (2019) | Holistic methodology to model the efficiency of public transport facilities schemed as a whole in a multi-stakeholder context from end-to-end perspective. |
26. | Lyons, G. (2018) | Analysis of the meaning of “smart” in the context of smart urban mobility and the relationship between smartness and sustainability. |
27. | Mangiaracina, R. et al. (2017) | Analysis of the role played by “Intelligent Transport Systems (ITS)” in assisting urban smart mobility. |
28. | Mboup, G. (2017) | Highlighting the factors that link citizens to different facilities, especially mobility and ICT frameworks in the Senegalese city of Dakar. |
29. | Melo, S. et al. (2017) | Case study in the Portuguese city of Lisbon which establishes a performance assessment of passenger and commercial vehicle redirection. |
30. | Ning, Z. et al. (2017) | Concept of “Vehicular Social Networks (VSNs)” focusing on the importance of highly efficient and secure smart city transmission in VSNs. |
31. | Orlowski, A. et al. (2019) | Establishing an indicator for assessing the degree of smart mobility solutions deployed in urban areas. |
32. | Pangbourne, K. et al. (2018) | Analysis of “Mobility as a Service (MaaS)” to evaluate its potential impact for city policymakers with regards to governance and sustainability. |
33. | Peprah, C. et al. (2019) | Evaluating the smartness of transport systems in the cities of Ghana and illustrating the realization of the notion. |
34. | Pereira, J. et al. (2019) | General framework for the implementation of fog computing features in “Vehicular ad-hoc Networks (VANET)” context. |
35. | Schlingensiepen, J. et al. (2016) | “Autonomic transport management system”—ICT based system for the management of transport. |
36. | Faria, R. et al. (2017) | Analysis of existing IoT methods and notions concerning smart cities and smart mobility. |
37. | Turetken, O. et al. (2019) | Implementation of “Service-Dominant Business Model Radar (SDBM/R)” in the context of smart mobility. |
38. | Yigitcanlar, T. et al. (2019) | Correlation between urban intelligence and sustainable mobility forms for the municipalities in Australia. |
39. | Zawieska, J. et al. (2018) | Analysis of the association between the deployment of the smart city notion and the sustainable mobility notion. |
40. | Singh, Y. J. (2020) | Notion of shared mobility focusing on gender parity. |
Attributes | Significance |
---|---|
Flexibility | Allows users to choose from the multiple modes of transportation to suit their needs using smart and dynamic navigation. |
Efficiency | Provides efficient mobility options with minimum disruptions, low cost and minimum commute time. |
Integration | Ensures end-to-end route plans independent of the transportation modes. |
Sustainability | Promotes cleaner and sustainable operations with minimum emissions. |
Security and Safety | The efficient data sharing and connectivity models ensure road safety. |
Social Benefits | Provides equal opportunities to citizens to use public transport. Ensuring quality of life to all. |
Automation | Facilitates automation in all processes. |
Connectivity | The entities in the network are connected. |
Accessibility | Affordable to all. |
User Experience | The efficient processes ensure a better user experience. |
S. No. | Challenges | Solution |
---|---|---|
1 | Multiple Services integration | Initiating goal directed discussions amongst the various service providers to ensure cross border integrations. |
2 | Payment System | Technological interventions based on emerging technologies such as blockchain, can be used to ensure the security and transparency of the financial systems. |
3 | Subscription Models in MaaS | Government and other regulating bodies must promote collaborations through various pilot programs. Customized subscription models need to be developed to enhance the user experience. |
4 | Data and information sharing amongst service providers | Data sharing models can be developed regulating the general principles of data and information sharing and ensuring agreements on the levels of data sharing between service providers. |
5 | Legal Challenges | There should be globally recognized standards and laws governing the various services provided under the MaaS Platform. |
6 | Adoption Challenges | The MaaS platform developers must take measures to build trust amongst the users and create awareness about the financial, societal and environmental benefits of MaaS. |
7 | Scalability | International governmental bodies must collaborate for the development of laws and standards governing the mobility services and to facilitate joint ventures across cities and nations. |
8 | Trust and Collaboration amongst stakeholders | The governing bodies should ensure transparency in the processes by drafting well-defined liabilities and mutual contractual agreements documenting the agreed-upon actions and objectives. |
Levels | Target Systems | Disrupted Services |
---|---|---|
Sensors | Camera, GPS, LiDAR, Radar, proximity sensors, ultrasonic sensors | Parking assistance, object identification, navigation, collision avoidance, traffic signal identification, cruise control |
Device | Access control systems | Anti-theft system, keyless entry systems, signal jamming, replay attack |
Software | In Vehicle protocols: LIN, CAN, FlexRay | Communication system |
Sensor | Location | Purpose |
---|---|---|
Long-Range Radar | Front-center of the vehicle | Emergency Braking Pedestrian Detection Collision Avoidance Adaptative Cruise Control |
LIDAR | Front, back and 4 corners (diagonals) | Environment mapping |
Camera | Front, back and 2 sides | Traffic Sign Recognition Lane Departure Warning Surround View Digital Side Mirror Rear View Mirror |
Short-Medium Range Radar | Front, back and rear corners | Cross Traffic Alert Rear Collision Warning |
Requirements | Key Concerns | Description |
---|---|---|
Human agency and oversight |
| AVs must allow a level of autonomy to the human drivers. They should be allowed to override the decision of the machine intelligence. Autonomy requires drivers to be informed. On the other hand, AVs should monitor the driver’s state and block the autonomy if their state could cause risks (e.g., they are drunk, sleepy, etc.). Finally, humans outside the vehicle should also be able to preserve their autonomy. |
Technical robustness and safety |
| AVs must be robust to external cybersecurity attacks (e.g., Hijacking, Abuse, Passive behavioral attacks). Data communications between vehicles and servers must not reveal private data that could affect the behavior of the vehicle. Moreover, AVs must provide an “automatic safe condition state” for managing emergency situations and minimizing the risks. |
Privacy and data governance |
| Autonomous vehicles continuously capture information to enhance their artificial intelligence systems. These data contain information related to user behavior and travel history. Sharing the personal user data must comply with the GDPR. These personal data are further vulnerable to thefts and misuse. Moreover, the type of the information collected from the AVs influences their capabilities. Thus, the kind of data and their scope must be specified (e.g., geolocalisation data for navigation, biometric data for user recognition and driver’s state evaluation, driver behavioral data for analysis). Data storage leads to the following legal issues: transparency (privacy policies must clearly describe which data are collected and why), explicit consent, sharing with third parties, compliance with data protection standards and regulations. |
Transparency |
| Transparency is strictly related to privacy and data governance. The manufacturers must provide information about data collection and their use. Moreover drivers must be aware of the AV’s mechanisms for accountability. Transparency also regards the right to explanation. Drivers need to trust AVs, thus transparency and communication of the underlying functionality must be clearly explained in a way humans can understand. Explainable Artificial Intelligence studies these aspects that are crucial in smart cities where humans and machines continuously interact [100] |
Diversity, non-discrimination and fairness |
| No distinction between individuals must be applied. This could seem obvious, since it is clearly stated in the Universal Declaration of Human Rights, but it is also well known that systems based on artificial intelligence can be biased by the data used to train their models. It has already happened, in different domains, that “intelligent systems” have discriminated against some ethnic groups. |
Societal and environmental wellbeing |
| The use of AVs must ensure an increase in public health and mobility, reduce the traffic flow, and decrease carbon emissions. However, there is uncertainty regarding the spreading in using AVs. This could cause an increase in total pollution and congestion. The introduction of AVs must be combined with infrastructure changes aimed to facilitate and optimize the AV experience. |
Accountability |
| As previously discussed, the attribution of liability and responsibility is an open issue. Who will be responsible for the actions of the autonomous vehicle in situations where the entire control is in hands of the vehicle itself? Is it the responsibility of the human operator, the car company or the algorithms ?In case of accidents, AVs cannot be responsible, since they are not moral agents. The full deployment of autonomous vehicles on the road needs well established laws and regulations governing the liability and responsibilities. Moreover, once the vehicle’s control is in the hands of an algorithm or software, what will be the criteria to define risky and safe driving? How will the insurance companies handle the claims related to accidents and road safety issues? [101] |
Autonomous Vehicles | Optimization of Logistics | Mobility-as-a-Service | Traffic Flow Optimization | |
---|---|---|---|---|
Blockchain | [139,140,141] | [137,138] | [125,126,140] | [131] |
Geospatial technologies | [142,143] | [133,134] | [127,128] | [133,134] |
Big Data | [144,145] | [134] | [127,128,129] | [134] |
Clean Energy | [146] | [132] | [130] | [132] |
Artificial Intelligence | [147,148,149] | [133,134] | [126] | [133,134] |
IoT | [142,145] | [135,136] | [128] | [135,136] |
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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. https://doi.org/10.3390/s21062143
Paiva S, Ahad MA, Tripathi G, Feroz N, Casalino G. Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges. Sensors. 2021; 21(6):2143. https://doi.org/10.3390/s21062143
Chicago/Turabian StylePaiva, Sara, Mohd Abdul Ahad, Gautami Tripathi, Noushaba Feroz, and Gabriella Casalino. 2021. "Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges" Sensors 21, no. 6: 2143. https://doi.org/10.3390/s21062143