Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directions

References

• Abadi, A., Rajabioun, T., & Ioannou, P. A. (2014). Traffic flow prediction for road transportation networks with limited traffic data. IEEE Transactions on Intelligent Transportation Systems, 16(2), 1–10. https://doi.org/10.1109/TITS.2014.2337238
   Web of Science ®Google Scholar
• Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transportation Research Record, 722, 1–9.
   Google Scholar
• Ahrabian, A., Kolozali, S., Enshaeifar, S., Cheong-Took, C., & Barnaghi, P. (2017). Stream data analysis as a web service: A case study using IoT sensor data [Paper presentation]. Proceedings of ICASSP2017.
   Google Scholar
• Aleko, D. R., & Djahel, S. (2020). An efficient adaptive traffic light control system for urban road traffic congestion reduction in smart cities. Information, 11(2), 119. https://doi.org/10.3390/info11020119
   Google Scholar
• Altintasi, O., Tuydes-Yaman, H., & Tuncay, K. (2017). Detection of urban traffic patterns from Floating Car Data (FCD). Transportation Research Procedia, 22, 382–391. https://doi.org/10.1016/j.trpro.2017.03.057
   Google Scholar
• Altintasi, O., Tuydes-Yaman, H., & Tuncay, K. (2018). Monitoring urban traffic from floating car data (FCD): Using speed or a LOS-based state measure [Paper presentation]. Scientific and Technical Conference Transport Systems Theory and Practice (pp. 163–173).
   Google Scholar
• Amado, M., Poggi, F., Ribeiro Amado, A., & Breu, S. (2018). E-city web platform: A tool for energy efficiency at urban level. Energies, 11(7), 1857. https://doi.org/10.3390/en11071857
   Web of Science ®Google Scholar
• Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72, 327–334. https://doi.org/10.1016/j.eswa.2016.10.055
   Web of Science ®Google Scholar
• Antsfeld, L. (2013). Shortest paths in networks. Association for the Advancement of Artificial Intelligence.
   Google Scholar
• Antsfeld, L., Walsh, T. (2012). Finding multi-criteria optimal paths in multi-modal public transportation networks using the transit algorithm [Paper presentation]. Proceedings of the 19th Its World Congress (p. 25).
   Google Scholar
• apporio home page. (n.d.). https://www.apporio.com/product/apporio-taxi-uber-clone-script/.
   Google Scholar
• Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-Khah, M., & Siano, P. (2016). Iot-based smart cities: A survey [Paper presentation]. 2016 Ieee 16th International Conference on Environment and Electrical Engineering (EEEIC) (pp. 1–6). https://doi.org/10.1109/EEEIC.2016.7555867
   Google Scholar
• astro home page. (n.d.). https://flyastro.com/.
   Google Scholar
• auto-bleue home page (n.d.). https://www.auto-bleue.org/en.
   Google Scholar
• Babu, N. V., Rao, P. J., Boyidi, S., Bendalam, S. (2019). A geospatial study on Visakhapatnam smart city based on hydrogeochemistry analysis [Paper presentation]. Proceedings of International Conference on Remote Sensing for Disaster Management (pp. 751–756).
   Google Scholar
• Bachmann, C. (2011). Multi-sensor data fusion for traffic speed and travel time estimation [A thesis for the degree of Master of Applied Science]. Department of Civil Engineering University of Toronto. (Unpublished doctoral dissertation)
   Google Scholar
• Baek, J., & Choi, Y. (2020). Smart glasses-based personnel proximity warning system for improving pedestrian safety in construction and mining sites. International Journal of Environmental Research and Public Health, 17(4), 1422. https://doi.org/10.3390/ijerph17041422
   PubMed Web of Science ®Google Scholar
• Bakas, I., Laurent, A., Clavreul, J., Saraiva, A. B., Niero, M., Gentil, E., & Hauschild, M. Z. (2018). LCA of solid waste management systems. In Life cycle assessment (pp. 887–926). Springer.
   Google Scholar
• Bao, X., Zhan, Y., Xu, C., Hu, K., Zheng, C., & Wang, Y. (2017). A novel dual microwave Doppler radar based vehicle detection sensor for parking lot occupancy detection. IEICE Electronics Express, 14(1), 20161087–20161087. https://doi.org/10.1587/elex.13.20161087
   Web of Science ®Google Scholar
• Barth, M., & Boriboonsomsin, K. (2008). Real-world carbon dioxide impacts of traffic congestion. Transportation Research Record: Journal of the Transportation Research Board, 2058(1), 163–171. https://doi.org/10.3141/2058-20
   Google Scholar
• Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., & Werneck, R. F. (2016). Route planning in transportation networks. In Algorithm engineering (pp. 19–80). Springer.
   Google Scholar
• Bast, H., Funke, S., & Matijević, D. (2006). Transit: Ultrafast shortest-path queries with linear-time preprocessing [Paper presentation]. 9th Dimacs Implementation Challenge—Shortest Path.
   Google Scholar
• Behrendt, F. (2016). Why cycling matters for Smart Cities. Internet of bicycles for intelligent transport. Journal of Transport Geography, 56, 157–164. https://doi.org/10.1016/j.jtrangeo.2016.08.018
   Web of Science ®Google Scholar
• BELL home page (n.d.). http://www.bellflight.com/company/innovation/air-taxi.
   Google Scholar
• Benevolo, C., Dameri, R. P., & D Auria, B. (2016). Smart mobility in smart city. In Empowering organizations (pp. 13–28). Springer.
   Google Scholar
• Biglia, A., & Belleflamme, P. (2015). Analyse prospective sur l’implémentation de la voiture autonome: Impact sur l’industrie automobile et le citoyen. Mémoire-recherche En vue de l’obtention du titre de Master en sciences de gestion. Université Catholique de Louvain.
   Google Scholar
• blueindy home page (n.d.). https://www.blue-indy.com/.
   Google Scholar
• BPA Research. (2013). The size and shape of the UK parking profession. BPA.
   Google Scholar
• Brendel, A. B., Lichtenberg, S., Brauer, B., Nastjuk, I., & Kolbe, L. M. (2018). Improving electric vehicle utilization in carsharing: A framework and simulation of an e-carsharing vehicle utilization management system. Transportation Research Part D: Transport and Environment, 64, 230–245. https://doi.org/10.1016/j.trd.2018.01.024
   Web of Science ®Google Scholar
• Brock, K., den Ouden, E., van der Klauw, K., Podoynitsyna, K., & Langerak, F. (2019). Light the way for smart cities: Lessons from Philips Lighting. Technological Forecasting and Social Change, 142, 194–209. https://doi.org/10.1016/j.techfore.2018.07.021
   Web of Science ®Google Scholar
• Bruglieri, M., Ciccarelli, D., Colorni, A., & Luè, A. (2011). Poliunipool: A carpooling system for universities. Procedia - Social and Behavioral Sciences, 20, 558–567. https://doi.org/10.1016/j.sbspro.2011.08.062
   Google Scholar
• Bruzzone, F., Scorrano, M., & Nocera, S. (2021). The combination of e-bike-sharing and demand-responsive transport systems in rural areas: A case study of velenje. Research in Transportation Business & Management, 40, 100570. https://doi.org/10.1016/j.rtbm.2020.100570
   PubMed Web of Science ®Google Scholar
• Burghout, W. (2004). Hybrid microscopic-mesoscopic traffic simulation [Unpublished doctoral dissertation]. KTH.
   Google Scholar
• Byon, Y.-J., Shalaby, A., Abdulhai, B., Cho, C.-S., Yeo, H., & El-Tantawy, S. (2019). Traffic condition monitoring with SCAAT Kalman filter-based data fusion in Toronto, Canada. KSCE Journal of Civil Engineering, 23(2), 810–820. https://doi.org/10.1007/s12205-018-0132-5
   Web of Science ®Google Scholar
• CAR2GO hame page. (n.d.). https://www.car2go.com.
   Google Scholar
• CBC User Guide. (n.d.). https://www.coin-or.org/Cbc/cbcuserguide.html.
   Google Scholar
• CES Fact Sheet and Logo. (n.d.). https://www.ces.tech/Media/fact-sheet-logo.aspx.
   Google Scholar
• Chai, H., Ma, R., & Zhang, H. M. (2019). Search for parking: A dynamic parking and route guidance system for efficient parking and traffic management. Journal of Intelligent Transportation Systems, 23(6), 541–556. https://doi.org/10.1080/15472450.2018.1488218
   Web of Science ®Google Scholar
• Chang, F.-S., Wu, J.-S., Lee, C.-N., & Shen, H.-C. (2014). Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert Systems with Applications, 41(6), 2947–2956. https://doi.org/10.1016/j.eswa.2013.10.026
   Web of Science ®Google Scholar
• Chen, X., Chen, H., Yang, Y., Wu, H., Zhang, W., Zhao, J., & Xiong, Y. (2021). Traffic flow prediction by an ensemble framework with data denoising and deep learning model. Physica A: Statistical Mechanics and Its Applications, 565, 125574. https://doi.org/10.1016/j.physa.2020.125574
   Web of Science ®Google Scholar
• Cheng, B., Longo, S., Cirillo, F., Bauer, M., & Kovacs, E. (2015). Building a big data platform for smart cities: Experience and lessons from Santander [Paper presentation]. Big Data (Bigdata Congress), 2015 IEEE International Congress on (pp. 592–599).
   Google Scholar
• Chen, X., & Liu, N. (2016). Smart parking by mobile crowdsensing. International Journal of Smart Home, 10(2), 219–234. https://doi.org/10.14257/ijsh.2016.10.2.21
   Google Scholar
• Chen, X., Santos-Neto, E., Ripeanu, M. (2012). Crowdsourcing for on-street smart parking [Paper presentation]. Proceedings of the Second ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (pp. 1–8). https://doi.org/10.1145/2386958.2386960
   Google Scholar
• Chen, L.-C., Sheu, R.-K., Peng, W.-Y., Wu, J.-H., & Tseng, C.-H. (2020). Video-based parking occupancy detection for smart control system. Applied Sciences, 10(3), 1079. https://doi.org/10.3390/app10031079
   Google Scholar
• Chou, Y.-T., Hsia, S.-Y., & Lan, C.-H. (2017). A hybrid approach on multi-objective route planning and assignment optimization for urban lorry transportation [Paper presentation]. In Applied System Innovation (Icasi), 2017 International Conference on (pp. 1006–1009).
   Google Scholar
• Combs, T. S., Sandt, L. S., Clamann, M. P., & McDonald, N. C. (2019). Automated vehicles and pedestrian safety: Exploring the promise and limits of pedestrian detection. American Journal of Preventive Medicine, 56(1), 1–7. https://doi.org/10.1016/j.amepre.2018.06.024
   PubMed Web of Science ®Google Scholar
• Communauto home page (n.d.). https://www.communauto.paris.
   Google Scholar
• Cugurullo, F., Acheampong, R. A., Gueriau, M., & Dusparic, I. (2021). The transition to autonomous cars, the redesign of cities and the future of urban sustainability. Urban Geography, 42(6), 833–859. https://doi.org/10.1080/02723638.2020.1746096
   Web of Science ®Google Scholar
• Dameri, R. P. (2014). Comparing smart and digital city: Initiatives and strategies in Amsterdam and Genoa. Are they digital and/or smart? In Smart city (pp. 45–88). Springer.
   Google Scholar
• Dib, O., Manier, M.-A., Moalic, L., & Caminada, A. (2017). A multimodal transport network model and efficient algorithms for building advanced traveler information systems. Transportation Research Procedia, 22, 134–143. https://doi.org/10.1016/j.trpro.2017.03.020
   Google Scholar
• Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271. https://doi.org/10.1007/BF01386390
   Google Scholar
• Dimitrakopoulos, G. (2017). Current technologies in vehicular communication. Springer.
   Google Scholar
• Djahel, S., Doolan, R., Muntean, G.-M., & Murphy, J. (2015). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials, 17(1), 125–151. https://doi.org/10.1109/COMST.2014.2339817
   Web of Science ®Google Scholar
• Djahel, S., Jabeur, N., Nait-Abdesselam, F., & Wolstencroft, T. (2020). A wave based and collaboration driven framework for reduced traffic congestion in smart cities. IEEE Intelligent Transportation Systems Magazine, 13(4), 251–261.
   Web of Science ®Google Scholar
• Djahel, S., Jones, A., Hadjadj-Aoul, Y., & Khokhar, A. (2018). Critic: A cognitive radio inspired road traffic congestion reduction solution [Paper presentation]. 2018 Wireless Days (WD), April (pp. 151–157).
   Google Scholar
• Do, Q., Martini, B., & Choo, K.-K R. (2018). Cyber-physical systems information gathering: A smart home case study. Computer Networks, 138, 1–12. https://doi.org/10.1016/j.comnet.2018.03.024
   Web of Science ®Google Scholar
• DriveNow Car Sharing & Car Club. (n.d.). https://www.drive-now.com/en.
   Google Scholar
• Duret, A., & Yuan, Y. (2017). Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework. Transportation Research Part B: Methodological, 101, 51–71. https://doi.org/10.1016/j.trb.2017.02.008
   Web of Science ®Google Scholar
• Enríquez, F., Soria, L. M., Álvarez-García, J. A., Velasco, F., Déniz, O. (2017). Existing approaches to smart parking: An overview [Paper presentation]. International Conference on Smart Cities (pp. 63–74).
   Google Scholar
• Escriva, D. M. L., Torres-Sospedra, J., & Berlanga-Llavori, R. (2018). Smart outdoor light desktop central management system. IEEE Intelligent Transportation Systems Magazine, 10(2), 58–68. https://doi.org/10.1109/MITS.2018.2806643
   Web of Science ®Google Scholar
• Essien, A., Petrounias, I., Sampaio, P., & Sampaio, S. (2021). A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. World Wide Web, 24(4), 1345–1368. https://doi.org/10.1007/s11280-020-00800-3
   Google Scholar
• Ezzedine, H., Bonte, T., Kolski, C., & Tahon, C. (2008). Integration of traffic management and traveller information systems: Basic principles and case study in intermodal transport system management. International Journal of Computers Communications & Control, 3(3), 281–294. https://doi.org/10.15837/ijccc.2008.3.2396
   Web of Science ®Google Scholar
• Fabian, T. (2013). Parking lot occupancy detection using computational fluid dynamics [Paper presentation]. Proceedings of the 8th International Conference on Computer Recognition Systems Cores 2013 (pp. 733–742).
   Google Scholar
• Fan, S.-K S., Su, C.-J., Nien, H.-T., Tsai, P.-F., & Cheng, C.-Y. (2018). Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Computing, 22(17), 5707–5718. https://doi.org/10.1007/s00500-017-2610-y
   Web of Science ®Google Scholar
• Fountain, H. (n.d.). A slow ride toward the future of public transportation. https://www.nytimes.com/2016/11/08/science/finland-public-transportation-driverless-bus.html.
   Google Scholar
• Fraifer, M., & Fernström, M. (2016). Investigation of smart parking systems and their technologies [Paper presentation]. Thirty Seventh International Conference on Information Systems. IoT Smart City Challenges Applications (ISCA 2016), Dublin, Ireland (pp. 1–14).
   Google Scholar
• Frampton, C. (2017, July). Average driver spends nearly two days a year searching for parking. https://www.admiral.com/magazine/news-and-current-affairs/drivers-spend-fourty-four-hours-a-year-searching-for-a-parking-space.
   Google Scholar
• Frias-Martinez, V., Moumni, Y., Frias-Martinez, E. (2014). Estimation of traffic flow using passive cell-phone data [Paper presentation]. Proceedings of the International Workshop on Data Science for Macro-Modeling (pp. 1–2). https://doi.org/10.1145/2630729.2630739
   Google Scholar
• Fujitsu. (n.d.). Fujitsu launches new location data service spatiowl. https://www.fujitsu.com/global/about/resources/news/press-releases/2011/0614-01.html.
   Google Scholar
• Gao, H., Yun, Q., Ran, R., & Ma, J. (2021). Smartphone-based parking guidance algorithm and implementation. Journal of Intelligent Transportation Systems, 25(4), 412–422. https://doi.org/10.1080/15472450.2020.1871339
   Web of Science ®Google Scholar
• García-Nieto, J., Alba, E., & Olivera, A. C. (2012). Swarm intelligence for traffic light scheduling: Application to real urban areas. Engineering Applications of Artificial Intelligence, 25(2), 274–283. https://doi.org/10.1016/j.engappai.2011.04.011
   Web of Science ®Google Scholar
• Gardner, M. W., & Dorling, S. (1998). Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14–15), 2627–2636. https://doi.org/10.1016/S1352-2310(97)00447-0
   Web of Science ®Google Scholar
• Geisberger, R., Sanders, P., Schultes, D., & Delling, D. (2008). Contraction hierarchies: Faster and simpler hierarchical routing in road networks. In C. C. McGeoch (Ed.), Experimental algorithms (pp. 319–333). Springer.
   Google Scholar
• Gentile, G. (2015). Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks. Transportation Research Procedia, 5, 66–81. https://doi.org/10.1016/j.trpro.2015.01.011
   Google Scholar
• Gentile, G., et al. (2010). The General Link Transmission Model for dynamic network loading and a comparison with the DUE algorithm. New Developments in Transport Planning: Advances in Dynamic Traffic Assignment, 178, 153.
   Google Scholar
• Geroliminis, N., & Daganzo, C. F. (2008). Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Research Part B: Methodological, 42(9), 759–770. https://doi.org/10.1016/j.trb.2008.02.002
   Web of Science ®Google Scholar
• Gomez, D., Marin-Plaza, P., Hussein, A., Escalera, A., & Armingol, J. (2016). Ros-based architecture for autonomous intelligent campus automobile (ICAB). UNED Plasencia Revista de Investigacion Universitaria, 12, 257–272.
   Google Scholar
• Goodall, W., Dovey, T., Bornstein, J., & Bonthron, B. (2017). The rise of mobility as a service. Deloitte Review, 20(1), 112–129.
   Google Scholar
• Goudarzi, S., Kama, M., Anisi, M., Soleymani, S., & Doctor, F. (2018). Self-organizing traffic flow prediction with an optimized deep belief network for internet of vehicles. Sensors, 18(10), 3459. https://doi.org/10.3390/s18103459
   PubMed Web of Science ®Google Scholar
• Goumiri, S., Benboudjema, D., & Hakimi, Y. (2022). Estimating parking availability using decision tree regressor [Paper presentation]. 2022 First International Conference on Big Data, Iot, Web Intelligence and Applications (BIWA) (pp. 1–6). https://doi.org/10.1109/BIWA57631.2022.10037810
   Google Scholar
• Goumiri, S., Benboudjema, D., & Pieczynski, W. (2021). One convolutional layer model for parking occupancy detection [Paper presentation]. 2021 IEEE International Smart Cities Conference (ISC2) (pp. 1–5). https://doi.org/10.1109/ISC253183.2021.9562946
   Google Scholar
• Guo, J., Huang, W., & Williams, B. M. (2014). Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies, 43, 50–64. https://doi.org/10.1016/j.trc.2014.02.006
   Web of Science ®Google Scholar
• Guo, J., Williams, B. M., & Smith, B. L. (2007). Data collection time intervals for stochastic short-term traffic flow forecasting. Transportation Research Record: Journal of the Transportation Research Board, 2024(1), 18–26. https://doi.org/10.3141/2024-03
   Google Scholar
• Gurczik, D.-I. G. (2015). Bluetooth-based floating car observer: Model evaluation using simulation and field measurements [Paper presentation]. Proceedings Young Researchers Seminar.
   Google Scholar
• Habtie, A. B., Abraham, A., & Midekso, D. (2016). A neural network model for road traffic flow estimation. In Advances in nature and biologically inspired computing (pp. 305–314). Springer.
   Google Scholar
• Hajiahmadi, M., van de Weg, G. S., Tampère, C. M., Corthout, R., Hegyi, A., De Schutter, B., & Hellendoorn, H. (2016). Integrated predictive control of freeway networks using the extended link transmission model. IEEE Transactions on Intelligent Transportation Systems, 17(1), 65–78. https://doi.org/10.1109/TITS.2015.2460695
   Web of Science ®Google Scholar
• Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107. https://doi.org/10.1109/TSSC.1968.300136
   Web of Science ®Google Scholar
• Hart, P. E., Nilsson, N. J., & Raphael, B. (1972). Correction to a formal basis for the heuristic determination of minimum cost paths. ACM SIGART Bulletin, 37(37), 28–29. https://doi.org/10.1145/1056777.1056779
   Google Scholar
• Himpe, W., Corthout, R., & Tampère, M. C. (2016). An efficient iterative link transmission model. Transportation Research Part B: Methodological, 92, 170–190. https://doi.org/10.1016/j.trb.2015.12.013
   Web of Science ®Google Scholar
• Hodgkiss, J., Djahel, S., Hadjadj-Aoul, Y. (2019). An advanced coordination protocol for safer and more efficient lane change for connected and autonomous vehicles [Paper presentation]. 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC) (pp. 1–4).
   Google Scholar
• Housing, U., & Otb, M. S. (2018). Smart cities Ranking of European medium-sized cities. https://www.smart-cities.eu/download/smart_cities_final_report.pdf
   Google Scholar
• Hoyer, R., Czogalla, O., Naumann, S. (2006). Improvement of real time passenger information by floating car observers [Paper presentation]. Proceedings of the 13th World Congress on Intelligent Transport Systems (its2006).
   Google Scholar
• Huang, Y., & Chen, Y. (2020). Survey of state-of-art autonomous driving technologies with deep learning [Paper presentation]. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (Qrs-c) (pp. 221–228). https://doi.org/10.1109/QRS-C51114.2020.00045
   Google Scholar
• Hussein, A., García, F., Armingol, J. M., & Olaverri-Monreal, C. (2016). P2v and v2p communication for pedestrian warning on the basis of autonomous vehicles [Paper presentation]. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (pp. 2034–2039). https://doi.org/10.1109/ITSC.2016.7795885
   Google Scholar
• Hwang, I., Jang, Y. J., Ko, Y. D., & Lee, M. S. (2018). System optimization for dynamic wireless charging electric vehicles operating in a multiple-route environment. IEEE Transactions on Intelligent Transportation Systems, 19(6), 1709–1726.
   Web of Science ®Google Scholar
• Iacobucci, R., Donhauser, J., Schmöcker, J.-D., & Pruckner, M. (2023). The demand potential of shared autonomous vehicles: A large-scale simulation using mobility survey data. Journal of Intelligent Transportation Systems, 1–22. https://doi.org/10.1080/15472450.2023.2205021
   Google Scholar
• INRIX. (2017, July). Searching for parking costs the UK £23.3 billion a year. http://inrix.com/press-releases/parking-pain-uk/.
   Google Scholar
• Izquierdo, A., del Val, L., & Villacorta, J. J. (2021). Feasibility of using a mems microphone array for pedestrian detection in an autonomous emergency braking system. Sensors, 21(12), 4162. https://doi.org/10.3390/s21124162
   PubMed Web of Science ®Google Scholar
• Jain, A. K., Acharya, R., Jakhar, S., & Mishra, T. (2018). Fifth Generation (5G) Wireless Technology “Revolution in Telecommunication [Paper presentation]. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1867–1872). https://doi.org/10.1109/ICICCT.2018.8473011
   Google Scholar
• Jang, J., Kwag, S. I., & Ko, Y. D. (2023). Eco-friendly platooning operation algorithm of the electric vehicles. Journal of Intelligent Transportation Systems, 1–18. https://doi.org/10.1080/15472450.2023.2209911
   Google Scholar
• Jeekel, H. (2017). Social sustainability and smart mobility: Exploring the relationship. Transportation Research Procedia, 25, 4296–4310. https://doi.org/10.1016/j.trpro.2017.05.254
   Google Scholar
• Jin, W.-L. (2015). Continuous formulations and analytical properties of the link transmission model. Transportation Research Part B: Methodological, 74, 88–103. https://doi.org/10.1016/j.trb.2014.12.006
   Web of Science ®Google Scholar
• Jo, H., & Yoon, Y. I. (2018). Intelligent smart home energy efficiency model using artificial Tensorflow engine. Human-Centric Computing and Information Sciences, 8(1), 18. https://doi.org/10.1186/s13673-018-0132-y
   Google Scholar
• JUMP home page. (n.d.). https://jump.com/.
   Google Scholar
• Kankaria, R. V., Jain, S. K., Bide, P., Kothari, A., & Agarwal, H. (2020). Alert system for drivers based on traffic signs, lights and pedestrian detection [Paper presentation]. 2020 International Conference for Emerging Technology (INCET) (pp. 1–5). https://doi.org/10.1109/INCET49848.2020.9154167
   Google Scholar
• Kaul, A., Xue, L., Obraczka, K., Santos, M. A., & Turletti, T. (2018). Handover and load balancing for distributed network control: Applications in ITS message dissemination [Paper presentation].2018 27th International Conference on Computer Communication and Networks (ICCCN) (pp. 1–8). https://doi.org/10.1109/ICCCN.2018.8487373
   Google Scholar
• Kawasaki, A. (2015). Fujitsuś approach to smart mobility. Fujitsu Scientific & Technical Journal. 51(4), 3–7.
   Web of Science ®Google Scholar
• Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760–766). Springer.
   Google Scholar
• Ketabi, R., Alipour, B., & Helmy, A. (2017). En route: Towards vehicular mobility scenario generation at scale [Paper presentation]. 2017 IEEE Conference on Computer Communications Workshops (Infocom Wkshps) (pp. 839–844).
   Google Scholar
• Keyvan-Ekbatani, M., Kouvelas, A., Papamichail, I., & Papageorgiou, M. (2012). Exploiting the fundamental diagram of urban networks for feedback-based gating. Transportation Research Part B: Methodological, 46(10), 1393–1403. https://doi.org/10.1016/j.trb.2012.06.008
   Web of Science ®Google Scholar
• Khan, M. S., Woo, M., Nam, K., & Chathoth, P. K. (2017). Smart city and smart tourism: A case of Dubai. Sustainability, 9(12), 2279. https://doi.org/10.3390/su9122279
   Web of Science ®Google Scholar
• Ki, Y.-K., Choi, J.-W., Joun, H.-J., Ahn, G.-H., & Cho, K.-C. (2017). Real-time estimation of travel speed using urban traffic information system and CCTV [Paper presentation]. Systems, Signals and Image Processing (IWSSIP), 2017 International Conference on (pp. 1–5).
   Google Scholar
• Kianpisheh, A., Mustaffa, N., Limtrairut, P., & Keikhosrokiani, P. (2012). Smart parking system (SPS) architecture using ultrasonic detector. International Journal of Software Engineering and Its Applications, 6(3), 55–58.
   Google Scholar
• Kirchler, D. (2013). Efficient routing on multi-modal transportation networks [Unpublished doctoral dissertation]. Ecole Polytechnique X.
   Google Scholar
• KITTYHAWK home page. (n.d.). https://kittyhawk.aero/.
   Google Scholar
• Knoop, V. L., Hoogendoorn, S. P. (2014). Network transmission model: A dynamic traffic model at network level [Paper presentation]. Transportation Research Board 91th Annual Meeting (pp. 13–17).
   Google Scholar
• Knoop, V., Tamminga, G., Leclercq, L. (2016). Network transmission model: Application to a real world city [Paper presentation]. Transportation Research Board 95th Annual Meeting (pp. 1–17).
   Google Scholar
• Koglin, T., & Rye, T. (2014). The marginalisation of bicycling in Modernist urban transport planning. Journal of Transport & Health, 1(4), 214–222. https://doi.org/10.1016/j.jth.2014.09.006
   Web of Science ®Google Scholar
• Kokolaki, E., Karaliopoulos, M., Kollias, G., Papadaki, M., & Stavrakakis, I. (2014). Vulnerability of opportunistic parking assistance systems to vehicular node selfishness. Computer Communications, 48, 159–170. https://doi.org/10.1016/j.comcom.2014.04.001
   Web of Science ®Google Scholar
• Kokolaki, E., Kollias, G., Papadaki, M., Karaliopoulos, M., & Stavrakakis, I. (2013). Opportunistically-assisted parking search: A story of free riders, selfish liars and bona fide mules [Paper presentation]. 2013 10th Annual Conference on Wireless on-Demand Network Systems and Services (WONS) (pp. 17–24). https://doi.org/10.1109/WONS.2013.6578315
   Google Scholar
• Kong, X., Xia, F., Ning, Z., Rahim, A., Cai, Y., Gao, Z., & Ma, J. (2018). Mobility dataset generation for vehicular social networks based on floating car data. IEEE Transactions on Vehicular Technology, 67(5), 3874–3886. https://doi.org/10.1109/TVT.2017.2788441
   Web of Science ®Google Scholar
• Koutitas, G. (2018). The smart grid: Anchor of the smart city. In Smart cities (pp. 53–74). Springer.
   Google Scholar
• Kuflik, T., Minkov, E., Nocera, S., Grant-Muller, S., Gal-Tzur, A., & Shoor, I. (2017). Automating a framework to extract and analyse transport related social media content: The potential and the challenges. Transportation Research Part C: Emerging Technologies, 77, 275–291. https://doi.org/10.1016/j.trc.2017.02.003
   Web of Science ®Google Scholar
• Kumar, N., Rahman, S. S., & Dhakad, N. (2021). Fuzzy inference enabled deep reinforcement learning-based traffic light control for intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 22(8), 4919–4928. https://doi.org/10.1109/TITS.2020.2984033
   Web of Science ®Google Scholar
• Kumarage, S. P., Rajapaksha, R., De Silva, D., Bandara, J. (2017). Traffic flow estimation for urban roads based on crowdsourced data and machine learning principles [Paper presentation]. First International Conference on Intelligent Transport Systems (pp. 263–273).
   Google Scholar
• Kurczewski, N. (n.d.). U.S.News BEST CARS. https://cars.usnews.com/cars-trucks/cars-that-are-almost-self-driving.
   Google Scholar
• Kuzminykh Ievgeniia, K. S. (2015). Development of smart infocommunication networks for intellectual municipal services. Second International Scientific-Practical Conference.
   Google Scholar
• Landry, D. M., & Morin, M. R. (2013). Estimating parking spot occupancy [Paper presentation]. Conference on Stochastic Processes.
   Google Scholar
• Leclercq, L., Parzani, C., Knoop, V. L., Amourette, J., & Hoogendoorn, S. P. (2015). Macroscopic traffic dynamics with heterogeneous route patterns. Transportation Research Procedia, 7, 631–650. https://doi.org/10.1016/j.trpro.2015.06.033
   Google Scholar
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
   Web of Science ®Google Scholar
• Lee, Y., Cho, H., & Kim, S. (2019). Smart water dispenser for companion animals. In Advances in computer communication and computational sciences (pp. 385–393). Springer.
   Google Scholar
• Lefebvre, N., Chen, X., Beauseroy, P., & Zhu, M. (2017). Traffic flow estimation using acoustic signal. Engineering Applications of Artificial Intelligence, 64, 164–171. https://doi.org/10.1016/j.engappai.2017.05.019
   Web of Science ®Google Scholar
• Leonid, A., & Toby, W. (2012). Finding optimal paths in multi-modal public transportation networks using hub nodes and TRANSIT algorithm. Artificial Intelligence and Logistics, 7, 1–7.
   Google Scholar
• Letaifa, S. B. (2015). How to strategize smart cities: Revealing the SMART model. Journal of Business Research, 68(7), 1414–1419.
   Web of Science ®Google Scholar
• Levin, M. W., & Boyles, S. D. (2016). A multiclass cell transmission model for shared human and autonomous vehicle roads. Transportation Research Part C: Emerging Technologies, 62, 103–116. https://doi.org/10.1016/j.trc.2015.10.005
   Web of Science ®Google Scholar
• Li, J. (2002). Fuzzy evolutionary approaches for bus and rail driver scheduling [Unpublished doctoral dissertation]. University of Leeds.
   Google Scholar
• Li, X., & Hsiao, J. (2018). Big data oriented intelligent traffic evacuation path fuzzy control system. Journal of Intelligent & Fuzzy Systems, 35(4), 4205–4213. https://doi.org/10.3233/JIFS-169741
   Web of Science ®Google Scholar
• Li, Y., & Voege, T. (2017). Mobility as a service (MaaS): Challenges of implementation and policy required. Journal of Transportation Technologies, 07(02), 95–106. (https://doi.org/10.4236/jtts.2017.72007
   Google Scholar
• LILIUM home page. (n.d.). https://lilium.com/.
   Google Scholar
• Lin, T., Rivano, H., & Le Mouël, F. (2017). A survey of smart parking solutions. IEEE Transactions on Intelligent Transportation Systems, 18(12), 3229–3253. https://doi.org/10.1109/TITS.2017.2685143
   Web of Science ®Google Scholar
• Liu, J., Li, J., Zhang, L., Dai, F., Zhang, Y., Meng, X., & Shen, J. (2018). Secure intelligent traffic light control using fog computing. Future Generation Computer Systems, 78, 817–824. https://doi.org/10.1016/j.future.2017.02.017
   Web of Science ®Google Scholar
• Liu, Z., Pu, L., Meng, Z., Yang, X., Zhu, K., & Zhang, L. (2015). Pofs: A novel pedestrian-oriented forewarning system for vulnerable pedestrian safety [Paper presentation].2015 International Conference on Connected Vehicles and Expo (ICCVE) (pp. 100–105). https://doi.org/10.1109/ICCVE.2015.63
   Google Scholar
• Łukasik, S., Żak, S. (2009). Firefly algorithm for continuous constrained optimization tasks [Paper presentation]. International Conference on Computational Collective Intelligence (pp. 97–106).
   Google Scholar
• Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873.
   Web of Science ®Google Scholar
• Ma, H., Zhao, D., & Yuan, P. (2014). Opportunities in mobile crowd sensing. IEEE Communications Magazine, 52(8), 29–35. https://doi.org/10.1109/MCOM.2014.6871666
   Web of Science ®Google Scholar
• Margaritoff, M. (n.d.). Watch the Ehang 184 passenger drone successfully taxi someone around. http://www.thedrive.com/aerial/18261/watch-the-ehang-184-passenger-drone-successfully-taxi-someone-around.
   Google Scholar
• Markoff, J. (n.d.). Google cars drive themselves, in traffic. http://www.nytimes.com/2010/10/10/science/10google.html.
   Google Scholar
• Mathew, T. V. I. B. (2014). Cell transmission models. Transportation System Engineering, 47, 18.1–18.15.
   Google Scholar
• Melnyk, P., Djahel, S., & Nait-Abdesselam, F. (2019). Towards a smart parking management system for smart cities [Paper presentation]. 2019 IEEE International Smart Cities Conference (ISC2) (pp. 542–546).
   Google Scholar
• Menouar, H., Guvenc, I., Akkaya, K., Uluagac, A. S., Kadri, A., & Tuncer, A. (2017). UAV-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Communications Magazine, 55(3), 22–28. https://doi.org/10.1109/MCOM.2017.1600238CM
   Web of Science ®Google Scholar
• Merriam-Webster Dictionary. (n.d.). https://www.merriam-webster.com/dictionary/intermodal.
   Google Scholar
• Michel, S., & Chidlovskii, B. (2016). Stochastic optimization of public transport schedules to reduce transfer waiting times [Paper presentation]. Smart Cities Conference (ISC2), 2016 IEEE International (pp. 1–6).
   Google Scholar
• Mitchell, H. B. (2007). Multi-sensor data fusion: An introduction. Springer Science & Business Media.
   Google Scholar
• Modi, P., Papanikolopoulos, N. P., & Morellas, V. (2011). Counting empty parking spots at truck stops using computer vision (Tech. Rep.). Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota.
   Google Scholar
• Montori, F., Jayaraman, P. P., Yavari, A., Hassani, A., & Georgakopoulos, D. (2018). The Curse of Sensing: Survey of techniques and challenges to cope with sparse and dense data in mobile crowd sensing for Internet of Things. Pervasive and Mobile Computing, 49, 111–125. https://doi.org/10.1016/j.pmcj.2018.06.009
   Web of Science ®Google Scholar
• Montoya, A., Guéret, C., Mendoza, J. E., & Villegas, J. G. (2017). The electric vehicle routing problem with nonlinear charging function. Transportation Research Part B: Methodological, 103, 87–110. http://www.sciencedirect.com/science/article/pii/S0191261516304556 (Green Urban Transportation)
   Web of Science ®Google Scholar
• Moura, R. L., Sussner, P. (2018). A fuzzy approach towards parking space occupancy detection using low-quality magnetic sensors [Paper presentation]. North American Fuzzy Information Processing Society Annual Conference (pp. 374–384).
   Google Scholar
• Moustaka, V., Vakali, A., & Anthopoulos, L. G. (2019). A systematic review for smart city data analytics. ACM Computing Surveys, 51(5), 1–41. https://doi.org/10.1145/3239566
   Web of Science ®Google Scholar
• Moustaka, V., Vakali, A., Anthopoulos, L. G. (2017). CityDNA: Smart city dimensions’ correlations for identifying urban profile [Paper presentation]. Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1167–1172). https://doi.org/10.1145/3041021.3054714
   Google Scholar
• Nawaz, S., Efstratiou, C., Mascolo, C. (2013). Parksense: A smartphone based sensing system for on-street parking [Paper presentation]. Proceedings of the 19th Annual International Conference on Mobile Computing & Networking (pp. 75–86). https://doi.org/10.1145/2500423.2500438
   Google Scholar
• Ning, Z., Xia, F., Ullah, N., Kong, X., & Hu, X. (2017). Vehicular social networks: Enabling smart mobility. IEEE Communications Magazine, 55(5), 16–55. https://doi.org/10.1109/MCOM.2017.1600263
   Web of Science ®Google Scholar
• Okuda, T., Hirasawa, S., Matsukuma, N., Fukumoto, T., & Shimura, A. (2012). Smart mobility for smart cities. Hitachi Review, 61(3), 141–146.
   Google Scholar
• On-Demand Public Transportation Changing Human Mobility in Cities. (n.d.). http://openroad-project.com/.
   Google Scholar
• Osaba, E., Del Ser, J., Bilbao, M. N., Lopez-Garcia, P., Nebro, A. J. (2018). Multi-objective design of time-constrained bike routes using bio-inspired meta-heuristics [Paper presentation]. International Conference on Bioinspired Methods and Their Applications (pp. 197–210).
   Google Scholar
• Pala, Z., & Inanc, N. (2007). Smart parking applications using RFID technology [Paper presentation]. Rfid Eurasia, 2007 1st Annual (pp. 1–3).
   Google Scholar
• Papa, E., & Lauwers, D. (2015). Smart mobility: Opportunity or threat to innovate places and cities [Paper presentation]. 20th International Conference on Urban Planning and Regional Development in the Information Society (Real Corp 2015) (pp. 543–550).
   Google Scholar
• Patel, M., Ukani, V. (2012). Optimized handoff process in IEEE802. 11p based VANET [Paper presentation]. 2012 1st International Conference on Emerging Technology Trends in Electronics, Communication & Networking (pp. 1–5).
   Google Scholar
• Pellicer, S., Santa, G., Bleda, A. L., Maestre, R., Jara, A. J., & Skarmeta, A. G. (2013). A global perspective of smart cities: A survey [Paper presentation]. In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2013 Seventh International Conference on (pp. 439–444).
   Google Scholar
• Peng, H., Du, B., Liu, M., Liu, M., Ji, S., Wang, S., Zhang, X., & He, L. (2021). Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Information Sciences, 578, 401–416. https://doi.org/10.1016/j.ins.2021.07.007
   Web of Science ®Google Scholar
• Petrolo, R., Loscri, V., & Mitton, N. (2014). Towards a cloud of things smart city. IEEE COMSOC MMTC E-Letter, 9(5), 44–48.
   Google Scholar
• Pilloni, V. (2018). How data will transform industrial processes: Crowdsensing, crowdsourcing and big data as pillars of industry 4.0. Future Internet, 10(3), 24. https://doi.org/10.3390/fi10030024
   Web of Science ®Google Scholar
• Pratelli, A., Petri, M., Ierpi, M., Di Matteo, M. (2018). Integration of Bluetooth, vehicle count data and transport model results by means of Datamining techniques [Paper presentation]. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1–6).
   Google Scholar
• Quinn, J. (2008). System and method for predicting parking spot availability. Google Patents (US Patent App. 11/849,493)
   Google Scholar
• Rafique, S., Gul, S., Jan, K., & Khan, G. M. (2023). Optimized real-time parking management framework using deep learning. Expert Systems with Applications, 220, 119686. https://doi.org/10.1016/j.eswa.2023.119686
   Web of Science ®Google Scholar
• REMON. (n.d.). http://remon-hanoi.net/en/results.
   Google Scholar
• Resat, H. G., & Turkay, M. (2015). Design and operation of intermodal transportation network in the Marmara region of Turkey. Transportation Research Part E: Logistics and Transportation Review, 83, 16–33. https://doi.org/10.1016/j.tre.2015.08.006
   Web of Science ®Google Scholar
• Roman, C., Liao, R., Ball, P., Ou, S., & de Heaver, M. (2018). Detecting on-street parking spaces in smart cities: Performance evaluation of fixed and mobile sensing systems. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2234–2245. https://doi.org/10.1109/TITS.2018.2804169
   Web of Science ®Google Scholar
• Rottondi, C., Neglia, G., & Verticale, G. (2016). Complexity analysis of optimal recharge scheduling for electric vehicles. IEEE Transactions on Vehicular Technology, 65(6), 4106–4117. https://doi.org/10.1109/TVT.2015.2441635
   Web of Science ®Google Scholar
• Rouphail, N. M., Park, B. B., & Sacks, J. (2000). Direct signal timing optimization: Strategy development and results [Paper presentation]. XI Pan American Conference in Traffic and Transportation Engineering.
   Google Scholar
• Saka, A. A., Ndiritu, S., Agboh, D. K., & Glassco, R. (2001). Integrated parking system. Journal of Transportation Engineering, 127(4), 327–333. https://doi.org/10.1061/(ASCE)0733-947X(2001)127:4(327)
   Google Scholar
• Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2018). Software platforms for smart cities: Concepts, requirements, challenges, and a unified reference architecture. ACM Computing Surveys, 50(6), 1–37. https://doi.org/10.1145/3124391
   Web of Science ®Google Scholar
• Sasaki, Y., Emaru, T., & Ravankar, A. A. (2021). SVM based pedestrian detection system for sidewalk snow removing machines [Paper presentation]. 2021 IEEE/SICE International Symposium on System Integration (SII) (pp. 700–701). https://doi.org/10.1109/IEEECONF49454.2021.9382618
   Google Scholar
• Sheehan, B., Murphy, F., Mullins, M., & Ryan, C. (2019). Connected and autonomous vehicles: A cyber-risk classification framework. Transportation Research Part A: Policy and Practice, 124, 523–536. http://www.sciencedirect.com/science/article/pii/S096585641830555X
   Web of Science ®Google Scholar
• Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190–200. https://doi.org/10.1080/15472450.2017.1336053
   Web of Science ®Google Scholar
• Shoup, D. C. (2006). Cruising for parking. Transport Policy, 13(6), 479–486. https://doi.org/10.1016/j.tranpol.2006.05.005
   Web of Science ®Google Scholar
• Siegel, J. E., Erb, D. C., & Sarma, S. E. (2018). A survey of the connected vehicle landscape architectures, enabling technologies, applications, and development areas. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2391–2406. https://doi.org/10.1109/TITS.2017.2749459
   Web of Science ®Google Scholar
• Someswar, G. M., Dayananda, R., Anupama, S., Priyadarshini, J., & Shariff, A. A. (2017). Design & development of an autonomic integrated car parking system. Compusoft, 6(3), 2309–2312.
   Google Scholar
• Staricco, L. (2013). Smart mobility: Opportunità e condizioni. Tema. Journal of Land Use, Mobility and Environment, 6(3), 342–354.
   Google Scholar
• Suske, D., Tcheumadjeu, T., Calvin, L., Sohr, A., Xiaoxu, B. (2016). Traffic information systems for smart mobility as part of smart cities [Paper presentation]. International Symposium on Smart Mobility for Smarter Cities (SmartCity 2016).
   Google Scholar
• Tafidis, P., Teixeira, J., Bahmankhah, B., Macedo, E., Coelho, M. C., Bandeira, J. (2017). Exploring crowdsourcing information to predict traffic-related impacts [Paper presentation]. Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 2017 IEEE International Conference on (pp. 1–6).
   Google Scholar
• Tcheumadjeu, L. C. T., Luber, A., Brockfeld, E., Gurczik, G., Sohr, A., & Sauerländer, A. (2017). Integration of mobile wireless RF sensors into a traffic information system. Transportation Research Procedia, 25, 1865–1883. https://doi.org/10.1016/j.trpro.2017.05.168
   Google Scholar
• Topics, T. (n.d.). The first true autonomous cars won’t be for sale. https://www.ttnews.com/articles/first-true-autonomous-cars-wont-be-sale.
   Google Scholar
• Uber Elevate. (n.d.). https://www.uber.com/us/en/elevate/.
   Google Scholar
• Vlahogianni, E. I., Kepaptsoglou, K., Tsetsos, V., & Karlaftis, M. G. (2016). A real-time parking prediction system for smart cities. Journal of Intelligent Transportation Systems, 20(2), 192–204. https://doi.org/10.1080/15472450.2015.1037955
   Web of Science ®Google Scholar
• VOLOCOPTER home page. (n.d.). https://www.volocopter.com.
   Google Scholar
• von Kursell, G. (n.d.). CityAirbus demonstrator passes major propulsion testing milestone. https://www.airbus.com/newsroom/press-releases/en/2017/10/cityairbus-demonstrator-passes-major-propulsion-testing-mileston.html.
   Google Scholar
• Wang, J., Jeong, C.-H., Zimmerman, N., Healy, R., Wang, D., Ke, F., & Evans, G. J. (2015). Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters. Atmospheric Measurement Techniques, 8(8), 3263–3275. https://doi.org/10.5194/amt-8-3263-2015
   Web of Science ®Google Scholar
• Wang, S., Djahel, S., Zhang, Z., & McManis, J. (2016). Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 17(10), 2888–2899.
   Web of Science ®Google Scholar
• Weiss, A. A. (1984). ARMA models with arch errors. Journal of Time Series Analysis, 5(2), 129–143. https://doi.org/10.1111/j.1467-9892.1984.tb00382.x
   Google Scholar
• Wemegah, T. D., Zhu, S., & Atombo, C. (2018). Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis. European Transport Research Review, 10(2), 1–14. https://doi.org/10.1186/s12544-018-0313-9
   Web of Science ®Google Scholar
• Wielinski, G., Trépanier, M., & Morency, C. (2017). Electric and hybrid car use in a free-floating carsharing system. International Journal of Sustainable Transportation, 11(3), 161–169. https://doi.org/10.1080/15568318.2016.1220653
   Web of Science ®Google Scholar
• Windfeld, E. S., & Brooks, M. S.-L. (2015). Medical waste management–A review. Journal of Environmental Management, 163, 98–108. https://doi.org/10.1016/j.jenvman.2015.08.013
   PubMed Web of Science ®Google Scholar
• Woodard, D., Nogin, G., Koch, P., Racz, D., Goldszmidt, M., & Horvitz, E. (2017). Predicting travel time reliability using mobile phone GPS data. Transportation Research Part C: Emerging Technologies, 75, 30–44. https://doi.org/10.1016/j.trc.2016.10.011
   Web of Science ®Google Scholar
• WORKHORSE home page. (n.d.). https://workhorse.com/.
   Google Scholar
• Yan, X., & Zhu, Z. (2021). Quantifying the impact of covid-19 on e-bike safety in china via multi-output and clustering-based regression models. PloS One, 16(8), e0256610. https://doi.org/10.1371/journal.pone.0256610
   PubMed Web of Science ®Google Scholar
• Yang, Z., & Pun-Cheng, L. S. (2018). Vehicle detection in intelligent transportation systems and its applications under varying environments: A review. Image and Vision Computing, 69, 143–154. https://doi.org/10.1016/j.imavis.2017.09.008
   Web of Science ®Google Scholar
• Yaqoob, I., Khan, L. U., Kazmi, S. A., Imran, M., Guizani, N., & Hong, C. S. (2020). Autonomous driving cars in smart cities: Recent advances, requirements, and challenges. IEEE Network. 34(1), 174–181. https://doi.org/10.1109/MNET.2019.1900120
   Web of Science ®Google Scholar
• Ye, M., Chen, J., Li, X., Ma, K., & Liu, Y. (2021). Energy management strategy of a hybrid power system based on v2x vehicle speed prediction. Sensors, 21(16), 5370. https://doi.org/10.3390/s21165370
   PubMed Web of Science ®Google Scholar
• Yperman, I., Logghe, S., Immers, B. (2005). The Link Transmission Model: An efficient implementation of the kinematic wave theory in traffic networks. Proceedings of the 10th EWGT Meeting (pp. 122–127.)
   Google Scholar
• Yu, J. J. Q., Yu, W., & Gu, J. (2019). Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3806–3817.
   Web of Science ®Google Scholar
• Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020). A survey of autonomous driving: Common practices and emerging technologies. IEEE Access. 8, 58443–58469. https://doi.org/10.1109/ACCESS.2020.2983149
   Web of Science ®Google Scholar
• Zavodjančík, T., Kasanický, G., & Demčáková, L. (2021). Reduction of pedestrian accidents–automated road vehicles. Transportation Research Procedia, 55, 1605–1612. https://doi.org/10.1016/j.trpro.2021.07.150
   Google Scholar
• Zhou, H., Xu, W., Chen, J., & Wang, W. (2020). Evolutionary v2x technologies toward the internet of vehicles: Challenges and opportunities. Proceedings of the IEEE, 108(2), 308–323. https://doi.org/10.1109/JPROC.2019.2961937
   Web of Science ®Google Scholar