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

Goumiri, Soumia; Yahiaoui, Saïd; Djahel, Soufiene

doi:10.1080/15472450.2023.2245750

Cited by 5 publications

(4 citation statements)

References 128 publications

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“…This appendix contains tables showing some data about the test networks. 3 [4] 4 [3] 2.0 1600 50 9 [10] 3 [12] 12 [3] 5.0 5400 100 11 [12] 4 [5] 5 [4] 2.0 1600 50 13 [14] 4 [11] 11 [4] 5.0 1600 50 15 [16] 5 [6] 6 [5] 2.0 1600 50 17 [18] 5 [9] 9 [5] 2.5 3500 75 19 [20] 6 [8] 8 [6] 2.5 3500 75 21 [22] 7 [8] 8 [7] 3.0 3500 75 23 [24] 7 [18] 18 [7] 2.5 1600 50 25 [26] 8 [9] 9 [8] 2.0 3500 75 27 [28] 8 [16] 16 [8] 2.5 3500 75 29 [30] 9 [ [40] 11 [12] 12 [11] 2.0 3500 75 41 [42] 11 [14] 14 [11] 3.0 3500 75 43 [44] 12 [13] 13 [12] 6.0 5400 100 45 [46] 13 [24] 24 [13] 2.0 3500 75 47 [48] 14 …”

Section: Appendix Bmentioning

confidence: 99%

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Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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In the near future, fully autonomous vehicles may revolutionise mobility and contribute to the development of the smart city concept. In this work, we assume that vehicles are not only fully autonomous but also centrally controlled by a single operator, who can also define the traffic light control parameters at intersections. With the aim of optimising the system to achieve a global optimum, the operator can define both the routes of the fleet of vehicles and the traffic light control parameters. This paper proposes a model for the joint optimisation of traffic light control parameters and autonomous vehicle routes to achieve the system optimum. The model, which is solved using a gradient algorithm, is tested on networks of different sizes. The results obtained show the validity of the proposed approach and the advantages of centralised management of vehicles and intersection control parameters.

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Section: Appendix Bmentioning

confidence: 99%

“…Smart mobility [1][2][3], a fundamental component of the smart city, will receive a strong impulse with the spread of autonomous vehicles [4][5][6][7][8][9]. Advances in automation will lead to fully autonomous driving; the user will lose the role of driver and become a passenger.…”

Section: Introductionmentioning

confidence: 99%

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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“…To ensure that the clusters are updated and to avoid the retention of old data, a forgetting method based on the time of entry of the last GPS point is used to determine the loss of relevance as time elapses and is calculated via Equation (1).…”

Section: Step 3: Distance-based Clusteringmentioning

confidence: 99%

“…Research and analysis of Intelligent Transportation Systems in urban areas have become essential today due to the complexity of this problem and its profound impact on society. The constant growth of the population in urban areas as well as the increase in vehicular traffic are obvious factors that require careful attention [1].…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering

Reyes,

Tolozano-Benites,

Lanzarini

et al. 2023

Sustainability

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Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

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Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms

Diniz,

Samanta,

Jayapandian

2023

2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS)

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

Cited by 5 publications

References 128 publications

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering

Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms

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