A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles
Open accessAbstract
Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook.Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas.Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention.Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users.Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data.The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
According to studies, AVs in public transportation have improved traffic flow and reduced congestion.
“From a social and economic perspective, according to studies [29][30][31], AVs in public transportation have improved traffic flow and reduced congestion.”
From Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature (2024) · cites this paper as [30]
Section 4.2 reports shorter travel times and lower traffic volumes with autonomous mobility-on-demand ridesharing, with further improvements when congestion control is added. But Section 4 identifies these as simulation results, not observed improvements in public transportation generally; Section 4.2 also says serious rush-hour congestion persisted with ridesharing alone. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAssociated environmental benefits include improved air quality with an average reduction of nitrogen dioxide concentrations up to 4%.
“Associated environmental benefits include improved air quality with average reduction of nitrogen dioxide concentrations up to -4% [30,40,41].”
From Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature (2024) · cites this paper as [30]
The Introduction discusses reducing carbon emissions, but does not report nitrogen dioxide concentrations. Section 4.2 reports changes in EV traffic volumes and travel times, not air quality. Searches for nitrogen dioxide, NO₂, air pollution, and the claimed 4% reduction found no passage addressing that result. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 41
- S. Çolak, A. Lima, M.C. González, Understanding congested travel in urban areas, Nat. Commun. 7 (2016), 1-8. · DOI
- C. Menelaou, S. Timotheou, P. Kolios, C.G. Panayiotou, M.M. Polycarpou, Minimizing traffic congestion through continuous- time route reservations with travel time predictions, IEEE Trans. Intell. Veh. 4 (2019), 141-153. · DOI
- A. Ahmadian, B. Mohammadi-Ivatloo, A. Elkamel, A review on plug-in electric vehicles: introduction, current status, and load modeling techniques, J. Mod. Power Syst. Clean Energy. 8 (2020), 412-425. · DOI
- F. Ahmad, M.S. Alam, I.S. Alsaidan, S.M. Shariff, Battery swap- ping station for electric vehicles: opportunities and challenges, IET Smart Grid. 3 (2020), 280-286. · DOI
- V. Cirimele, M. Diana, F. Bellotti, R. Berta, N. El Sayed, A. Kobeissi, et al., The fabric ICT platform for managing wire- less dynamic charging road lanes, IEEE Trans. Veh. Technol. 69 (2020), 2501-2512. · DOI
- A. Artuñedo, R.M. Del Toro, R.E. Haber, Consensus-based coop- erative control based on pollution sensing and traffic information for urban traffic networks, Sensors. 17 (2017), 953. · DOI
- J. Godoy, J. Pérez, E. Onieva, J. Villagrá, V. Milanés, R. Haber, A driverless vehicle demonstration on motorways and in urban environments, Transport. 30 (2015), 253-263. · DOI
- F. Castaño, G. Beruvides, A. Villalonga, R.E. Haber, Self-tuning method for increased obstacle detection reliability based on internet of things LiDAR sensor models, Sensors. 18 (2018), 1508. · DOI
- T. Chu, J. Wang, L. Codecà, Z. Li, Multi-agent deep reinforcement learning for large-scale traffic signal control, IEEE Trans. Intell. Transp. Syst. 21 (2020), 1086-1095. · DOI · Paper page
- Z. Yao, L. Shen, R. Liu, Y. Jiang, X. Yang, A dynamic predictive traffic signal control framework in a cross-sectional vehicle infras- tructure integration environment, IEEE Trans. Intell. Transp. Syst. 21 (2020), 1455-1466. · DOI
- Z. Li, H. Yu, G. Zhang, S. Dong, C.Z. Xu, Network-wide traffic signal control optimization using a multi-agent deep reinforce- ment learning, Transp. Res. Part C Emerg. Technol. 125 (2021), 103059. · DOI
- A. Boukerche, D. Zhong, P. Sun, A novel reinforcement learning- based cooperative traffic signal system through max-pressure control, IEEE Trans. Veh. Technol. (2021). · DOI
- J. Mou, Intersection traffic control based on multi-objective opti- mization, IEEE Access. 8 (2020), 61615-61620. · DOI
- H. Liu, C. Claudel, R. Machemehl, K.A. Perrine, A robust traffic control model considering uncertainties in turning ratios, IEEE Trans. Intell. Transp. Syst. (2021). · DOI
- X. Li, P. Ye, J. Jin, F. Zhu, F.Y. Wang, Data augmented deep behav- ioral cloning for urban traffic control operations under a parallel learning framework, IEEE Trans. Intell. Transp. Syst. (2021). · DOI
- C. You, J. Lu, D. Filev, P. Tsiotras, Autonomous planning and con- trol for intelligent vehicles in traffic, IEEE Trans. Intell. Transp. Syst. 21 (2020), 2339-2349. · DOI
- Y. Du, W. ShangGuan, L. Chai, A coupled vehicle-signal control method at signalized intersections in mixed traffic environment, IEEE Trans. Veh. Technol. 70 (2021), 2089-2100. · DOI
- S. Feng, Z. Song, Z. Li, Y. Zhang, L. Li, Robust platoon control in mixed traffic flow based on tube model predictive control, IEEE Trans. Intell. Veh. (2021). · DOI
- S. Bahrami, M.J. Roorda, Optimal traffic management policies for mixed human and automated traffic flows, Transp. Res. Part A Policy Pract. 135 (2020), 130-143. · DOI · Paper page
- F. Zheng, C. Liu, X. Liu, S.E. Jabari, L. Lu, Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow, Transp. Res. Part C Emerg. Technol. 112 (2020), 203-219. · DOI
- T. Li, F. Guo, R. Krishnan, A. Sivakumar, J. Polak, Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles, Transp. Res. Part C Emerg. Technol. 115 (2020), 102630. · DOI
- M.A.S. Kamal, T. Hayakawa, J.I. Imura, Development and evalu- ation of an adaptive traffic signal control scheme under a mixed- automated traffic scenario, IEEE Trans. Intell. Transp. Syst. 21 (2020), 590-602. · DOI
- H. Qi, R. Dai, Q. Tang, X. Hu, Coordinated intersection signal design for mixed traffic flow of human-driven and connected and autonomous vehicles, IEEE Access. 8 (2020), 26067-26084. · DOI
- Z. Yang, Y. Feng, H.X. Liu, A cooperative driving framework for urban arterials in mixed traffic conditions, Transp. Res. Part C Emerg. Technol. 124 (2021), 102918. · DOI
- A. Sharma, Z. Zheng, J. Kim, A. Bhaskar, M.M. Haque, Assess- ing traffic disturbance, efficiency, and safety of the mixed traffic flow of connected vehicles and traditional vehicles by considering human factors, Transp. Res. Part C Emerg. Technol. 124 (2021), 102934. · DOI
- L. Guo, Y. Jia, Anticipative and predictive control of automated vehicles in communication-constrained connected mixed traffic, IEEE Trans. Intell. Veh. (2021). · DOI
- N. Wu, D. Li, Y. Xi, Distributed integrated control of a mixed traf- fic network with urban and freeway networks, IEEE Trans. Con- trol Syst. Technol. (2021). · DOI
- D. Ngoduy, N.H. Hoang, H.L. Vu, D. Watling, Multiclass dynamic system optimum solution for mixed traffic of human-driven and automated vehicles considering physical queues, Transp. Res. Part B Methodol. 145 (2021), 56-79. · DOI
- J. Wang, L. Lu, S. Peeta, Z. He, Optimal toll design problems under mixed traffic flow of human-driven vehicles and connected and autonomous vehicles, Transp. Res. Part C Emerg. Technol. 125 (2021), 102952. · DOI
- J. Zhang, Y. Pei, J. Shen, L. Wang, T. Ding, S. Wang, Charging strategy unifying spatial-temporal coordination of electric vehi- cles, IEEE Access. 8 (2020), 74853-74863. · DOI
- M. Ammous, S. Belakaria, S. Sorour, A. Abdel-Rahim, Joint delay and cost optimization of in-route charging for on-demand electric vehicles, IEEE Trans. Intell. Veh. 5 (2020), 149-164. · DOI
- A. Moradipari, M. Alizadeh, Pricing and routing mechanisms for differentiated services in an electric vehicle public charging sta- tion network, IEEE Trans. Smart Grid. 11 (2020), 1489-1499. · DOI
- H. Mao, J. Shi, Y. Zhou, G. Zhang, The electric vehicle rout- ing problem with time windows and multiple recharging options, IEEE Access. 8 (2020), 114864-114875. · DOI
- T. Qian, C. Shao, X. Wang, M. Shahidehpour, Deep reinforcement learning for EV charging navigation by coordinating smart grid and intelligent transportation system, IEEE Trans. Smart Grid. 11 (2020), 1714-1723. · DOI
- R. Chen, X. Qian, L. Miao, S.V. Ukkusuri, Optimal charging facil- ity location and capacity for electric vehicles considering route choice and charging time equilibrium, Comput. Oper. Res. 113 (2020), 104776. · DOI
- R. Basso, B. Kulcsár, I. Sanchez-Diaz, Electric vehicle routing problem with machine learning for energy prediction, Transp. Res. Part B Methodol. 145 (2021), 24-55. · DOI
- N. Chakraborty, A. Mondal, S. Mondal, Intelligent charge scheduling and eco-routing mechanism for electric vehicles: a multi-objective heuristic approach, Sustain. Cities Soc. 69 (2021), 102820. · DOI
- U. Bac, M. Erdem, Optimization of electric vehicle recharge schedule and routing problem with time windows and partial recharge: a comparative study for an urban logistics fleet, Sustain. Cities Soc. 70 (2021), 102883. · DOI
- E.Y. Kim, MRF model based real-time traffic flow prediction with support vector regression, Electron. Lett. 53 (2017), 243-245. · DOI
- E.Q. Martins, M.M. Pascoal, A new implementation of Yen's rank- ing loopless paths algorithm, Q. J. Belgian French Italian Oper. Res. Soc. 1 (2003), 121-133. · DOI
- G.P. Rocha Filho, R.I. Meneguette, J.R.T. Neto, A. Valejo, L. Weigang, J. Ueyama, G. Pessin, L.A. Villas, Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities, Ad Hoc Netw. 107 (2020), 102265. · DOI
Cited by
Sorted by citations
Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature
Makahleh, H. Y., Ferranti, E. J. S., & Dissanayake, D. (2024) · Future Transportation · doi:10.3390/futuretransp4020017 · Cited by 55 · see citing works
- Authors
- Huang, C.-J., Hu, K.-W., Ho, H. Y., Xie, B. Z., Feng, C.-C., & Chuang, H.-W.
- Affiliations
- National Dong Hwa University (TW)
- Corresponding author
- Huang, Chenn-Jung
- Venue
- International Journal of Computational Intelligence Systems
- Published
- 2021
- Volume
- 14
- Issue
- 1
- Pages
- 1714
- ISSN
- 1875-6883 (ISSN-L)
- OpenAlex
- W3166537533
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 1
- FWCI
- 0.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- International Journal of Computational Intelligence Systems (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology, Taiwan (MOST109-2221-E259-008, MOST 109, MOST108-2221-E259-006, MOST 108-, MOST 109-2221)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- urban traffic congestion, congestion prevention, mixed traffic, autonomous electric vehicles, electric vehicle charging strategies, route planning, support vector regression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
