IEEE Computational Intelligence Magazine - May 2022 - 99

Electron., vol. 62, no. 12, pp. 7883-7891, Dec. 2015, doi:
10.1109/TIE.2015.2418314.
[16] A. Ullah, X. Yao, S. Shaheen, and H. Ning, " Advances
in position based routing towards its enabled
fog-oriented VANET: A survey, " IEEE Trans. Intell.
Transp. Syst., vol. 21, no. 2, pp. 828-840, Feb. 2020, doi:
10.1109/TITS.2019.2893067.
[17] M. H. Eiza, T. Owens, Q. Ni, and Q. Shi, " Situation-aware
QoS routing algorithm for vehicular ad
hoc networks, " IEEE Trans. Veh. Technol., vol. 64, no.
12, pp. 5520-5535, Dec. 2015, doi: 10.1109/TVT.2015.
2485305.
[18] G. Li, L. Boukhatem, and J. Wu, " Adaptive qualityof-service-based
routing for vehicular ad hoc networks
with ant colony optimization, " IEEE Trans. Veh. Technol.,
vol. 66, no. 4, pp. 3249-3264, Apr. 2017, doi: 10.1109/
TVT.2016.2586382.
[19] G. Sun, Y. Zhang, D. Liao, H. Yu, X. Du, and M.
Guizani, " Bus-trajectory-based street-centric routing for
message delivery in urban vehicular ad hoc networks, "
IEEE Trans. Veh. Technol., vol. 67, no. 8, pp. 7550-7563,
Aug. 2018, doi: 10.1109/TVT.2018.2828651.
[20] G. Sun, Y. Zhang, H. Yu, X. Du, and M. Guizani,
" Intersection fog-based distributed routing for V2V communication
in urban vehicular ad hoc networks, " IEEE
Trans. Intell. Transp. Syst., vol. 21, no. 6, pp. 2409-2426,
Jun. 2020, doi: 10.1109/TITS.2019.2918255.
[21] M. H. Eiza, T. Owens, and Q. Ni, " Secure and robust
multi-constrained QoS aware routing algorithm
for VANETs, " IEEE Trans. Dependable Secure Comput.,
vol. 13, no. 1, pp. 32-45, Jan./Feb. 2016, doi: 10.1109/
TDSC.2014.2382602.
[22] S. Safavat and D. B. Rawat, " On the elliptic
curve cryptography for privacy-aware secure ACOAODV
routing in intent-based internet of vehicles
for smart cities, " IEEE Trans. Intell. Transp. Syst., vol.
22, no. 8, pp. 5050-5059, Aug. 2021, doi: 10.1109/
TITS.2020.3008361.
[23] L. T. Tan, R. Q. Hu, and L. Hanzo, " Twin-timescale
artificial intelligence aided mobility-aware edge caching
and computing in vehicular networks, " IEEE Trans. Veh.
Technol., vol. 68, no. 4, pp. 3086-3099, Apr. 2019, doi:
10.1109/TVT.2019.2893898.
[24] J. Chen et al., " Service-oriented dynamic connection
management for software-defined internet of vehicles, "
IEEE Trans. Intell. Transp. Syst., vol. 18, no. 10, pp. 2826-
2837, Oct. 2017, doi: 10.1109/TITS.2017.2705978.
[25] J. Feng, Z. Liu, C. Wu, and Y. Ji, " AVE: Autonomous
vehicular edge computing framework with ACO-based
scheduling, " IEEE Trans. Veh. Technol., vol. 66, no. 12,
pp. 10,660-10,675, Dec. 2017.
[26] F. Sun et al., " Cooperative task scheduling for computation
offloading in vehicular cloud, " IEEE Trans. Veh.
Technol., vol. 67, no. 11, pp. 11,049-11,061, Nov. 2018.
[27] A. A. Khan, M. Abolhasan, W. Ni, J. Lipman,
and A. Jamalipour, " A hybrid-fuzzy logic guided genetic
algorithm (H-FLGA) approach for resource optimization
in 5G VANETs, " IEEE Trans. Veh. Technol.,
vol. 68, no. 7, pp. 6964-6974, Jul. 2019, doi: 10.1109/
TVT.2019.2915194.
[28] X. Feng, X. Ling, H. Zheng, Z. Chen, and Y. Xu,
" Adaptive multi-kernel SVM with spatial-temporal correlation
for short-term traffic flow prediction, " IEEE
Trans. Intell. Transp. Syst., vol. 20, no. 6, pp. 2001-2013,
Jun. 2019, doi: 10.1109/TITS.2018.2854913.
[29] D. Chen, " Research on traffic flow prediction in the
big data environment based on the improved RBF neural
network, " IEEE Trans. Ind. Informat., vol. 13, no. 4, pp.
2000-2008, Aug. 2017, doi: 10.1109/TII.2017.2682855.
[30] J. J. Sanchez-Medina, M. J. Galan-Moreno, and E.
Rubio-Royo, " Traffic signal optimization in 'La Almozara'
district in Saragossa under congestion conditions, using
genetic algorithms, traffic microsimulation, and cluster
computing, " IEEE Trans. Intell. Transp. Syst., vol. 11,
no. 1, pp. 132-141, Mar. 2010, doi: 10.1109/TITS.2009.
2034383.
[31] Z. Li, M. Shahidehpour, S. Bahramirad, and A. Khodaei,
" Optimising traffic signal settings in smart cities, "
IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2382-2393, Sep.
2017, doi: 10.1109/TSG.2016.2526032.
[32] J. García-Nieto, A. C. Olivera, and E. Alba, " Optimal
cycle program of traffic lights with particle swarm optimization, "
IEEE Trans. Evol. Comput., vol. 17, no. 6, pp.
823-839, Dec. 2013, doi: 10.1109/TEVC.2013.2260755.
[33] J. Ferrer, M. López-Ibáñez, and E. Alba, " Reliable
simulation-optimization of traffic lights in a real-world
city, " Appl. Soft Comput., vol. 78, pp. 697-711, May 2019,
doi: 10.1016/j.asoc.2019.03.016.
[34] Y. Bi, D. Srinivasan, X. Lu, Z. Sun, and W. Zeng,
" Type-2 fuzzy multi-intersection traffic signal control
with differential evolution optimization, " Expert Syst.
Appl., vol. 41, no. 16, pp. 7338-7349, Nov. 2014, doi:
10.1016/j.eswa.2014.06.022.
[35] Y. Bie, X. Xiong, Y. Yan, and X. Qu, " Dynamic
headway control for high-frequency bus line based on
speed guidance and intersection signal adjustment, "
Comput. Aided Civ. Infrastruct. Eng., vol. 35 pp. 4-25, Jan.
2020, doi: 10.1111/mice.12446.
[36] Y. Zhang, K. Gao, Y. Zhang, and R. Su, " Traffic
light scheduling for pedestrian-vehicle mixed-flow networks, "
IEEE Trans. Intell. Transp. Syst., vol. 20, no. 4, pp.
1468-1483, Apr. 2019, doi: 10.1109/TITS.2018.2852646.
[37] K. Gao, Y. Zhang, Y. Zhang, R. Su, and P. N. Suganthan,
" Meta-heuristics for bi-objective urban traffic
light scheduling problems, " IEEE Trans. Intell. Transp.
Syst., vol. 20, no. 7, pp. 2618-2629, Jul. 2019, doi:
10.1109/TITS.2018.2868728.
[38] N. Shaukat et al., " A survey on electric vehicle transportation
within smart grid system, " Renew. Sustain. Energy
Rev., vol. 81, no. part 1, pp. 1329-1349, Jan. 2018,
doi: 10.1016/j.rser.2017.05.092.
[39] H. Zhang, Z. Hu, Z. Xu, and Y. Song, " An integrated
planning framework for different types of PEV
charging facilities in urban area, " IEEE Trans. Smart Grid,
vol. 7, no. 5, pp. 2273-2284, Sep. 2016, doi: 10.1109/
TSG.2015.2436069.
[40] B. Zeng, J. Feng, N. Liu, and Y. Liu, " Co-optimized
parking lot placement and incentive design for promoting
PEV integration considering decision-dependent uncertainties, "
IEEE Trans. Ind. Informat., vol. 17, no. 3, pp.
1863-1872, Mar. 2021, doi: 10.1109/TII.2020.2993815.
[41] S. Alegre, J. V. Míguez, and J. Carpio, " Modelling of
electric and parallel-hybrid electric vehicle using Matlab/
Simulink environment and planning of charging stations
through a geographic information system and genetic algorithms, "
Renew. Sustain. Energy Rev., vol. 74, pp. 1020-
1027, Jul. 2017, doi: 10.1016/j.rser.2017.03.041.
[42] P. Sadeghi-Barzani, A. Rajabi-Ghahnavieh, and H.
Kazemi-Karegar, " Optimal fast charging station placing
and sizing, " Appl. Energy, vol. 125, pp. 289-299, Jul.
2014, doi: 10.1016/j.apenergy.2014.03.077.
[43] Y. D. Ko and Y. J. Jang, " The optimal system design
of the online electric vehicle utilizing wireless power
transmission technology, " IEEE Trans. Intell. Transp.
Syst., vol. 14, no. 3, pp. 1255-1265, Sep. 2013, doi:
10.1109/TITS.2013.2259159.
[44] Z. Moghaddam, I. Ahmad, D. Habibi, and Q. V.
Phung, " Smart charging strategy for electric vehicle charging
stations, " IEEE Trans. Transport. Electrific., vol. 4, no.
1, pp. 76-88, Mar. 2018, doi: 10.1109/TTE.2017.2753403.
[45] W. Liu, Y. Gong, W. Chen, Z. Liu, H. Wang,
and J. Zhang, " Coordinated charging scheduling of
electric vehicles: A mixed-variable differential evolution
approach, " IEEE Trans. Intell. Transp. Syst., vol.
21, no. 12, pp. 5094-5109, Dec. 2020, doi: 10.1109/
TITS.2019.2948596.
[46] Y. Zheng, Z. Y. Dong, Y. Xu, K. Meng, J. H. Zhao,
and J. Qiu, " Electric vehicle battery charging/swap stations
in distribution systems: Comparison study and
optimal planning, " IEEE Trans. Power Syst., vol. 29, no.
1, pp. 221-229, Jan. 2014, doi: 10.1109/TPWRS.2013.
2278852.
[47] Q. Kang, J. Wang, M. Zhou, and A. C. Ammari,
" Centralized charging strategy and scheduling algorithm
for electric vehicles under a battery swapping scenario, "
IEEE Trans. Intell. Transp. Syst., vol. 17, no. 3, pp. 659-
669, Mar. 2016, doi: 10.1109/TITS.2015.2487323.
[48] H. Wu, G. K. H. Pang, K. L. Choy, and H. Y.
Lam, " An optimization model for electric vehicle battery
charging at a battery swapping station, " IEEE Trans.
Veh. Technol., vol. 67, no. 2, pp. 881-895, Feb. 2018, doi:
10.1109/TVT.2017.2758404.
[49] S. Yang, M. Wu, X. Yao, and J. Jiang, " Load modeling
and identification based on ant colony algorithms
for EV charging stations, " IEEE Trans. Power Syst.,
vol. 30, no. 4, pp. 1997-2003, Jul. 2015, doi: 10.1109/
TPWRS.2014.2352263.
[50] M. H. Amini, M. P. Moghaddam, and O. Karabasoglu,
" Simultaneous allocation of electric vehicles'
parking lots and distributed renewable resources in smart
power distribution networks, " Sustain. Cities Soc., vol.
28, pp. 332-342, Jan. 2017, doi: 10.1016/j.scs.2016.10.
006.
[51] M. Rahmani-Andebili, H. Shen, and M. FotuhiFiruzabad,
" Planning and operation of parking lots considering
system, traffic, and drivers behavioral model, "
IEEE Trans. Syst., Man, Cybern. Syst., vol. 49, no. 9, pp.
1879-1892, Sep. 2019, doi: 10.1109/TSMC.2018.2824122.
[52] J. Zhao, F. Wen, Z. Y. Dong, Y. Xue, and K. P.
Wong, " Optimal dispatch of electric vehicles and wind
power using enhanced particle swarm optimization, "
IEEE Trans. Ind. Informat., vol. 8, no. 4, pp. 889-899,
Nov. 2012, doi: 10.1109/TII.2012.2205398.
[53] B. Qiao and J. Liu, " Multi-objective dynamic economic
emission dispatch based on electric vehicles and
wind power integrated system using differential evolution
algorithm, " Renew. Energy, vol. 154, pp. 316-336,
Jul. 2020, doi: 10.1016/j.renene.2020.03.012.
[54] R. Liu, S. Li, L. Yang, and J. Yin, " Energy-efficient
subway train scheduling design with time-dependent
demand based on an approximate dynamic programming
approach, " IEEE Trans. Syst., Man, Cybern. Syst.,
vol. 50, no. 7, pp. 2475-2490, Jul. 2020, doi: 10.1109/
TSMC.2018.2818263.
[55] J. Zhong, M. Shen, J. Zhang, H. S. Chung, Y. Shi,
and Y. Li, " A differential evolution algorithm with
dual populations for solving periodic railway timetable
scheduling problem, " IEEE Trans. Evol. Comput.,
vol. 17, no. 4, pp. 512-527, Aug. 2013, doi: 10.1109/
TEVC.2012.2206394.
[56] E. Hassannayebi, S. H. Zegordi, M. R. Amin-Naseri,
and M. Yahini, " Optimizing headways for urban
rail transit services using adaptive particle swarm algorithms, "
Public Transp., vol. 10, pp. 23-62, May 2018, doi:
10.1007/s12469-016-0147-6.
[57] K. Nitisiri, M. Gen, and H. Ohwada, " A parallel
multi-objective genetic algorithm with learning based mutation
for railway scheduling, " Comput. Ind. Eng., vol. 130,
pp. 381-394, Apr. 2019, doi: 10.1016/j.cie.2019.02.035.
[58] F. Lin, S. Liu, Z. Yang, Y. Zhao, Z. Yang, and H.
Sun, " Multi-train energy saving for maximum usage of
regenerative energy by dwell time optimization in urban
rail transit using genetic algorithm, " Energies, vol. 9, Mar.
2016, Art. no. 208, doi: 10.3390/en9030208.
[59] H. Liu, M. Zhou, X. Guo, Z. Zhang, B. Ning, and
T. Tang, " Timetable optimization for regenerative energy
utilization in subway systems, " IEEE Trans. Intell.
Transp. Syst., vol. 20, no. 9, pp. 3247-3257, Sep. 2019, doi:
10.1109/TITS.2018.2873145.
[60] A. Fernández-Rodríguez, A. Fernández-Cardador,
A. P. Cucala, M. Domínguez, and T. Gonsalves, " Design
of robust and energy-efficient ATO speed profiles of
metropolitan lines considering train load variations and
delays, " IEEE Trans. Intell. Transp. Syst., vol. 16, no. 4, pp.
2061-2071, Aug. 2015, doi: 10.1109/TITS.2015.2391831.
[61] Y. Cao, Z. Wang, F. Liu, P. Li, and G. Xie, " Bio-inspired
speed curve optimization and sliding mode tracking
control for subway trains, " IEEE Trans. Veh. Technol.,
vol. 68, no. 7, pp. 6331-6342, Jul. 2019, doi: 10.1109/
TVT.2019.2914936.
[62] J. Feng, Z. Ye, C. Wang, M. Xu, and S. Labi, " An
integrated optimization model for energy saving in
metro operations, " IEEE Trans. Intell. Transp. Syst., vol.
20, no. 8, pp. 3059-3069, Aug. 2019, doi: 10.1109/
TITS.2018.2871347.
[63] G. Kim, Y. Ong, C. K. Heng, P. S. Tan, and N. A.
Zhang, " City vehicle routing problem (city VRP): A review, "
IEEE Trans. Intell. Transp. Syst., vol. 16, no. 4, pp.
1654-1666, Aug. 2015, doi: 10.1109/TITS.2015.2395536.
[64] R. Cuda, G. Guastaroba, and M. G. Speranza, " A
survey on two-echelon routing problems, " Comput. Oper.
Res., vol. 55, pp. 185-199, Mar. 2015, doi: 10.1016/j.
cor.2014.06.008.
[65] X. Yan, H. Huang, Z. Hao, and J. Wang, " A graphbased
fuzzy evolutionary algorithm for solving twoechelon
vehicle routing problems, " IEEE Trans. Evol.
MAY 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 99

IEEE Computational Intelligence Magazine - May 2022

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