The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview

Figures & data

Figure 1. Articles distribution over the years.

Table 1. Details of studies reviewed where AI was used in the prediction of traffic noise.

S/N	Author	Study area	Temporal scale	AI technique	Input variables
1	Cammarata et al., 1995	Italy	10 min	ANN	C, MC, HV, RW, H
2	Parbat & Nagarnaik, 2007	India	1 h	ANN	C, MC, HV, H, RW
3	Parbat & Nagarnaik, 2008	India	1 h	ANN	B/HV, C, TC, MC, W, D
4	Genaro et al., 2010	Spain	1 h	ANN	Tm, En, CW, St, Q, Cantecedent, Cdescendant, MCantecedent, MCdescendant, HVantecedent, HVdescendant, Vsiren, traffic condition, V, RS, LAS, LDS, PV, ST, D
5	Givargis & Karimi, 2010	Iran	1 h	ANN	Q, P, G, V, a (165–180)
6	Cirianni & Leonardi, 2011	Italy	1 h	ANFIS	Q, HV, V, and D
7	Cirianni & Leonardi, 2012	Italy	1 h	GRNN	Q, HV, MV, C, B, MC, V, 0.5RW
8	Al-Mutairi et al., 2012	Kuwait	5 min	ANN	V, equivalent C, P, RW, and H
9	Arora & Mosahari, 2012	India	1 h	ANN	Q, V, HV
10	Kumar et al., 2014	India	1 h	ANN	Q (Log Q), V (Log V), P
11	Nedic et al., 2014	Serbia	1 h	ANN	C, MV HV, HV, B, V
12	Žilionienė et al., 2014	Italy	1 h	ANN	Operational V (V85), DSS, Q
13	Sharma et al., 2014	India	1 h	ANFIS	Q, V, HK
14	Cirianni & Leonardi, 2015	Italy	1 h	ANN	P, Q, V, W
15	Garg et al., 2015	India	15–60 min	ANN	MC, TC, C, B, HV, V
16	Khouban et al., 2015	Iran	1 h	ANN	Q, G, RW, V, H
17	Torija & Ruiz, 2015	Spain	5 min	GPR, MLP, SMO
18	Garg et al., 2016	India	15–60	ANN	Lday and Lnight
19	Singh et al., 2016	India	1 h	ANN	Q, P, V
20	Codur et al., 2017	Turkey	1 h	ANN, ANFIS	Q, HV, V
21	Hamad et al., 2017	UAE	15 min	ANN	Q, V, D, T
22	Huang et al., 2017	China	1 h	ANN	HV, MV, C, V, D, angle, building height
23	Freitas et al., 2018	Portugal	1 h	ANN	PV, TX, V, and PD
24	Kumar et al., 2018	India	1 h	ANN	Q, P, V
25	Mansourkhaki et al., 2018	Iran	15 min	ANN	Q, V, C, MV, HV, MC, G, DBF, BRF
26	Tomić et al., 2018	Serbia	15 min	ANN	C, MV, HV, B, MC, D
27	Sharma et al., 2018	India	1 h	ANFIS	Equivalent Q, equivalent V, HK
28	Khalil et al., 2019	UAE	15 min	ANN	D, C, T, V, HV
29	Ahmed & Pradhan, 2019	Malaysia	15 min	ANN	MC, Q, C/Q, P, HWD, C, DSM
30	Bravo-Moncayo et al., 2019	Ecuador	1 h	ANN	Q, PV, V
31	Chen et al., 2020	Europe	1 h	ANN	Q, V, G
32	Nourani et al., 2020a	Cyprus	15 min	Ensemble,	C, B, HV, MC, P, V
33	Nourani et al., 2020b	Cyprus	15 min	Emotional ANN	C, B, HV, MC, P, V
34	Yin et al., 2020	USA	1 h	ANN, XGB, RF,	Q, Road network features, meteorological condition, LU
35	Ahmed et al., 2021	Malaysia	15 min	ANN, DNN	MC, Q, C/Q, P, HWD, C, DSM
36	Ahmed et al., 2021	Malaysia	15 min	ANN, RF	MC, Q, C/Q, P, HWD, C, DSM, time, T, Hm
37	AlKheder & Almutairi, 2021	Kuwait	15 min	ANFIS	LV, HV, V, RW, H, PC, Troad, Tair
30	Gilani & Mir, 2021	India	1 h	GTA	V, Q, RW, HV, HK
39	Chen et al., 2021	China	20 min	ANN	Q, P, V, G
40	Kim et al., 2021	China	1 h	ANN	MC, C, C, MV, HV& B, V
41	Singh et al., 2021	India	1 h	ANFIS	Q, V, P
42	Ranpise et al., 2021	India	15 min	ANN	Q, V, RW
43	Zhang et al., 2021	Czech	9 h	RNN	Vehicle ID, type, location, speed, accc/dec
44	Debnath et al., 2022	India	1 h	ANN	Q, V, P, G, RW
45	Fallah-Shorshani et al., 2022	USA	1 h	XGB	Q, Road network features, meteorological condition, LU
46	Singh et al., 2022	India	1 h		Q, P, V
47	Umar et al., 2022	Cyprus	15 min	SVR, GPR, ANN	C, B, HV, MV, MC, P, V, HK
48	Umar et al., 2023	Cyprus	15 min	Hybrid	C, B, HV, MV, MC, P, V, HK
49	Wang et al., 2023	China		Deep learning
50	Zhang et al., 2023	China	1 h	RF, K-NN, DT, GB, AR, SVR	Traffic composition, street form variables, LU variables, meteorological parameters

a: angle of view; Q: traffic volume; HV: heavy vehicles; C: cars; MC: motorcycles; P: percentage of HV; V: average speed; RW: road width; T: temperature; MV: medium vehicles; B: buses; G: road gradient; H: average height of the buildings facing the road; PV: pavement type; TX: road texture; BRF: building reflection factor; BF: building façade; DSS: distance of the vehicle from the sound source; HK: honking; D: distance of the receiver from the road edge; L: number of lanes; TC: tricycle; PD: pavement distress; HWD: highway density; DSM: digital surface model; RS: road slope; ST: street type; Tm: time of the day; En: environment; Hm: humidity; LU: land use.

Figure 2. Number of published papers concerning the countries where the study areas are located.

Figure 3. Data observation time.

Figure 4. Variables affecting traffic noise (Kumar et al., 2014).

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Data availability statement

No data has been generated during the study.