Smart Transportation Solutions Using AI to Optimize Traffic Flow and Reduce Urban Congestion for Improved Urban Mobility
Downloads
Background: The fast growth of cities has led to worsen traffic problems and longer wait times and increased pollution which requires smart transportation solutions to address these issues. Smart transportation systems which use Artificial Intelligence (AI) provide an effective solution to enhance traffic management and protect road users while achieving better urban transportation system performance. Methods: The research applied a quantitative approach to study smart transportation systems which use AI technology affect various aspects. Data from 195 participants who included transportation engineers and urban planners. Descriptive statistics together with percentage analysis and correlation techniques to study traffic patterns and their effects on reducing delays and boosting safety and improving fuel consumption and public transportation contentment. Results: AI-based transportation systems positively because 91.3% of respondents observed better traffic movement and 88.7% experienced shorter wait times and 85.1% reported improved safety and 83.6% achieved better fuel efficiency and 87.2% expressed higher satisfaction levels. Correlation analysis demonstrates strong positive connections between essential variables which show that traffic flow relates to delay reduction through r = 0.78 and safety through r = 0.71 and satisfaction through r = 0.74. The data shows that fuel consumption levels create negative relationships with efficiency-related variables because users need less energy to operate these systems. Conclusion: AI-based smart transportation networks deliver better urban mobility results which improve safety performance and generate sustainable advantages. Multiple operational difficulties because it requires expensive equipment and users struggle to protect their data privacy while lacking sufficient expertise.
S. Paiva, M. Ahad, G. Tripathi, N. Feroz, and G. Casalino, “Enabling Technologies for urban smart mobility: recent trends, opportunities and challenges,” Sensors, vol. 21, no. 6, p. 2143, 2021, doi: 10.3390/s21062143.
P. a. M. S. A. Munhoz et al., “Smart mobility: The main drivers for increasing the intelligence of urban mobility,” Sustainability, vol. 12, no. 24, p. 10675, 2020, doi: 10.3390/su122410675.
A. Nikitas, K. Michalakopoulou, E. T. Njoya, and D. Karampatzakis, “Artificial intelligence, transport and the smart City: Definitions and dimensions of a new mobility era,” Sustainability, vol. 12, no. 7, p. 2789, 2020, doi: 10.3390/su12072789.
Z. Allam and A. Sharifi, “Research structure and trends of smart urban mobility,” Smart Cities, vol. 5, no. 2, pp. 539–561, 2022, doi: 10.3390/smartcities5020029.
S. F. Smith, “Smart infrastructure for future urban mobility,” AI Mag., vol. 41, no. 1, pp. 5–18, 2020, doi: 10.1609/aimag.v41i1.5290.
Y. Chen, A. Ardila-Gomez, and G. Frame, “Achieving energy savings by intelligent transportation systems investments in the context of smart cities,” Transp. Res. D Transp. Environ., vol. 54, pp. 381–396, 2017, doi: 10.1016/j.trd.2017.06.008.
R. Mangiaracina, A. Perego, G. Salvadori, and A. Tumino, “A comprehensive view of intelligent transport systems for urban smart mobility,” International Journal of Logistics Research and Applications, vol. 20, no. 1, pp. 39–52, 2016, doi: 10.1080/13675567.2016.1241220.
R. Abduljabbar, H. Dia, S. Liyanage, and S. A. Bagloee, “Applications of Artificial Intelligence in Transport: An Overview,” Sustainability, vol. 11, no. 1, p. 189, 2019, doi: 10.3390/su11010189.
Z. Karami and R. Kashef, “Smart transportation planning: Data, models, and algorithms,” Transportation Engineering, vol. 2, p. 100013, 2020, doi: 10.1016/j.treng.2020.100013.
D. R. Aleko and S. Djahel, “An efficient adaptive traffic light control system for urban road traffic congestion reduction in smart cities,” Information, vol. 11, no. 2, p. 119, 2020, doi: 10.3390/info11020119.
P. Masek et al., “A Harmonized Perspective on Transportation management in Smart Cities: The Novel IoT-Driven Environment for Road Traffic Modeling,” Sensors, vol. 16, no. 11, p. 1872, 2016, doi: 10.3390/s16111872.
U. Hasan, A. Whyte, and H. A. Jassmi, “A review of the transformation of road transport systems: Are we ready for the next step in artificially intelligent sustainable transport?,” Applied System Innovation, vol. 3, no. 1, p. 1, 2019, doi: 10.3390/asi3010001.
E. Macioszek, A. Granà, P. Fernandes, and M. C. Coelho, “New Perspectives and Challenges in traffic and Transportation Engineering Supporting energy saving in Smart Cities—A multidisciplinary approach to a global problem,” Energies (Basel)., vol. 15, no. 12, p. 4191, 2022, doi: 10.3390/en15124191.
S. Porru, F. E. Misso, F. E. Pani, and C. Repetto, “Smart mobility and public transport: Opportunities and challenges in rural and urban areas,” Journal of Traffic and Transportation Engineering (English Edition), vol. 7, no. 1, pp. 88–97, 2019, doi: 10.1016/j.jtte.2019.10.002.
A. Raza et al., “Evaluation of a Sustainable Urban Transportation System in terms of Traffic Congestion—A case study in Taxila, Pakistan,” Sustainability, vol. 14, no. 19, p. 12325, 2022, doi: 10.3390/su141912325.
K. L. Ang, J. K. P. Seng, E. Ngharamike, and G. K. Ijemaru, “Emerging technologies for smart cities’ transportation: Geo-Information, Data Analytics and Machine learning approaches,” ISPRS Int. J. Geoinf., vol. 11, no. 2, p. 85, 2022, doi: 10.3390/ijgi11020085.
F. Golbabaei, T. Yigitcanlar, and J. Bunker, “The role of shared autonomous vehicle systems in delivering smart urban mobility: A systematic review of the literature,” Int. J. Sustain. Transp., vol. 15, no. 10, pp. 731–748, 2020, doi: 10.1080/15568318.2020.1798571.
L. Guevara and F. A. Cheein, “The role of 5G Technologies: Challenges in smart cities and intelligent transportation systems,” Sustainability, vol. 12, no. 16, p. 6469, 2020, doi: 10.3390/su12166469.
C. a. R. L. Brennand, G. P. R. Filho, G. Maia, F. Cunha, D. L. Guidoni, and L. A. Villas, “Towards a FOG-Enabled intelligent transportation system to reduce traffic jam,” Sensors, vol. 19, no. 18, p. 3916, 2019, doi: 10.3390/s19183916.
J. L. Zambrano-Martinez et al., “A centralized Route-Management solution for autonomous vehicles in urban areas,” Electronics (Basel)., vol. 8, no. 7, p. 722, 2019, doi: 10.3390/electronics8070722.
M. Šoštarić, K. Vidović, M. Jakovljević, and O. Lale, “Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement,” Sustainability, vol. 13, no. 13, p. 7162, 2021, doi: 10.3390/su13137162.
H. Amer, N. Salman, M. Hawes, M. Chaqfeh, L. Mihaylova, and M. Mayfield, “An improved simulated annealing technique for enhanced mobility in smart cities,” Sensors, vol. 16, no. 7, p. 1013, 2016, doi: 10.3390/s16071013.
E. Zadobrischi, L. Cosovanu, and M. Dimian, “Traffic Flow Density Model and Dynamic Traffic Congestion Model Simulation Based on Practice Case with Vehicle Network and System Traffic Intelligent Communication,” Symmetry (Basel)., vol. 12, no. 7, p. 1172, 2020, doi: 10.3390/sym12071172.
E. Carter, P. Adam, D. Tsakis, S. Shaw, R. Watson, and P. Ryan, “Enhancing pedestrian mobility in Smart Cities using Big Data,” Journal of Management Analytics, vol. 7, no. 2, pp. 173–188, 2020, doi: 10.1080/23270012.2020.1741039.
K. Lu, B. Han, and X. Zhou, “Smart Urban Transit Systems: From integrated framework to interdisciplinary perspective,” Urban Rail Transit, vol. 4, no. 2, pp. 49–67, 2018, doi: 10.1007/s40864-018-0080-x.
O. Ghaffarpasand, M. Burke, L. K. Osei, H. Ursell, S. Chapman, and F. D. Pope, “Vehicle telematics for Safer, Cleaner and More Sustainable Urban Transport: a review,” Sustainability, vol. 14, no. 24, p. 16386, 2022, doi: 10.3390/su142416386.
M. Kane and J. Whitehead, “How to ride transport disruption –a sustainable framework for future urban mobility,” Australian Planner, vol. 54, no. 3, pp. 177–185, 2017, doi: 10.1080/07293682.2018.1424002.
R. C. Shit, “Crowd intelligence for sustainable futuristic intelligent transportation system: a review,” IET Intelligent Transport Systems, vol. 14, no. 6, pp. 480–494, 2020, doi: 10.1049/iet-its.2019.0321.
J. Malasek, “A set of tools for making urban transport more sustainable,” Transportation Research Procedia, vol. 14, pp. 876–885, 2016, doi: 10.1016/j.trpro.2016.05.059.
E. Walraven, M. T. Spaan, and B. Bakker, “Traffic flow optimization: A reinforcement learning approach,” Eng. Appl. Artif. Intell., vol. 52, pp. 203–212, 2016, doi: 10.1016/j.engappai.2016.01.001.
.png)