PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Analysis of vehicle horn use and factors at intersectionsin an urban area of Taiwan
Masayuki TAKADA1; Shoki TSUNEKAWA2; Kazuma HASHIMOTO3; Tamaki INADA4
Yoshinao OEDA5; Katsuya YAMAUCHI6; Ki-Hong KIM7; Shin-ichiro IWAMIYA8
1,4,6
2,3

Graduate school of Design, Kyushu University, Japan
5

7

Faculty of Design, Kyushu University, Japan

Faculty of Engineering, Kyushu University, Japan

Faculty of Media and Information Resources, Surugadai University, Japan
8

College of Art, Nihon University, Japan

ABSTRACTIn urban areas in many countries, noise pollution is generated by the frequent use of car and motorcycle horns.
The final goal of the present study is to clarify the causes of horn use and to propose a transportation system
to suppress horn use. To this end, we have investigated relationships between horn use and factors such as
driver awareness, behavior, and vehicular environment. Our previous study investigated causal relationships
between horn use and the vehicular environment in Taiwan, and found that horn use was more frequent whengridlock occurred at intersections. The present study analyzed details of gridlock situations at threeintersections in Taipei during which vehicle horns were used. We found many cases where honking began
after vehicles entered the intersection to turn left during the green-light phase. In particular, drivers often usedtheir horns when they had to wait for more than 4 seconds for the vehicles in front of them to start moving
after a green light allowed left turns. Logistic regression analysis revealed that the probability of a driverusing a vehicle horn was significantly affected by the duration between the signal turning green and thevehicle in front of them starting to move.
Keywords: Horn use, Intersection, Traffic environment

1. INTRODUCTION
Horn sounds are acoustically designed to have high sound pressure levels so that vehicle drivers
can be alerted to danger in a noisy environment. Therefore, frequent horn use is expected to contributeto noise problems near roads and streets. In fact, several countries face noise problems relating tofrequent honking. Phan et al. reported that frequent horn honking is a major factor of road traffic noise
in urban areas of Vietnam (1). Countermeasures for these problems, such as the reduction of horn use,
are urgently needed.
To control driver horn use, it is necessary to clarify the causes of such use. Various intrinsic andextrinsic factors may affect horn use. Shinar regarded horn use as an indicator of driver aggression andinvestigated relationships between driver horn use and several factors including the duration that adriver has to wait for the vehicle in front to start moving, the level of traffic congestio n, and theduration of green-light signals (2). Moreover, according to Fujii’s socio-psychological model of1

takada@design.kyushu-u.ac.jp
shoki.tsunekawa@outlook.jp
3k.hashi.10563@gmail.com
4t.inada.media@gmail.com
5
oeda@doc.kyushu-u.ac.jp
6yamauchi@design.kyushu-u.ac.jp
7
kim.kihong@surugadai.ac.jp
8
iwamiya@kag.bbiq.jp
27153

drivers' traffic behaviors (3), certain behaviors are induced by behavioral intention, which are linked
with psychological factors such as personal attitude (defined as preference), personal norms, perceived
behavioral control (i.e., expectation concerning the ease of initiating certain traffic behaviors), andmoral obligation. These results suggest that driver horn use, which is regarded as a traffic behavior, isaffected by intrinsic (psychological) factors. A questionnaire survey conducted in South Korea (4)reported a relationship between drivers’ psychological factors and the manner of horn use. Results
revealed that drivers who did not usually use their horn were reluctant to do so, and that they generally
briefly honked to alert another driver to danger or to gain their attention out of necessity.
Questionnaire surveys in Japan suggested that the traffic environment influences driver horn use,
and found a relationship between the place and honking pattern of horn use, and the traffic volume andhonking pattern (5). Driver horn use may be affected by extrinsic factors surrounding drivers.
Accordingly, we propose a similar behavioral model of driver horn use, and suggest that intrinsic andextrinsic factors affect behavioral intention and evoke certain traffic behaviors including horn use, as
shown in Figure 1. To solve noise problems relating to vehicle horn use, it is necessary tocomprehensively understand the intrinsic and extrinsic factors leading to driver horn usage.
The present study focuses on the effects of extrinsic factors on driver horn use, which involve thetransportation system and traffic environment such as traffic volume, road size, and traffic signals. We
previously measured and recorded horn use and traffic circumstances (e.g., the traffic volume andtraffic signals) at intersections in an urban area of Taiwan (6). Results revealed strong relationships
between the traffic volume of standard-sized vehicles and the frequency of horn use. Furthermore, ananalysis of the temporal variation of horn use during traffic signal cycles indicated that horn use
increased when gridlock occurred. A further analysis was required to extract quantitative parameters
related to the transportation system and traffic environment that affected horn use. In the present study,
therefore, we conducted a detailed analysis to clarify causal reasons for driver horn use at the sameintersections in the urban area of Taiwan that we previously studied.
Road structure (㐨㊰ᵓ㐀)
Traffic volume (஺㏻㔞)Attitude (ែᗘ)
Personal norms (ಶேつ⠊)
Perceived behavioral control(▱ぬ⾜ືไᚚᛶ)Moral obligation (㐨ᚨព㆑)䠖 intrinsic factors

Behavioral
intention㻔⾜ືពᅗ㻕

Horn use㻔ᜩᇳ̅ဇ㻕

Target (౑⏝ᑐ㇟)Regulation (ἲつ)

䠖 extrinsic factors

Figure 1 – Behavioral model of horn use adapted from a socio-psychological model (3). Various intrinsic andextrinsic factors influence driver behavioral intention and evoke horn use.

2. PROCEDURE
2.1 Survey sites and equipment
Our survey was carried out at three intersections (hereafter, Intersections 1 to 3) in Taipei. Noise
measurements and noise recordings were continuously made over 12 hours (from 7:00 to 19:00) on one
weekday and on one weekend day at each intersection. Figure 2 illustrates the surveyed intersections.
Data from 10-minute spans in every hour (e.g., 7:20-7:30, 8:20-8:30, etc.) were extracted for analysis.
Table 1 presents features of each intersection such as the number of lanes in each direction and theminimum and maximum total traffic volumes during the measurement periods for analysis (i.e., 10
minutes per hour).
To record the overall view of an intersection, including all vehicles passing through the intersection,
two video cameras (SONY HDR-CX560V and SONY HDR-CX370) were set on a pedestrian bridgeover or near an intersection. To measure the noise level of road traffic noise and to record the roadtraffic noise itself, a sound level meter (RION NL-52) was installed at a height of 1.5 m near the
middle of the pedestrian bridge.
We analyzed traffic volume and road traffic noise recorded over 10-minute spans in every hour. To
measure traffic volume, vehicles were categorized as motorcycles, standard -sized vehicles, and7154

large-sized vehicles. Large-sized vehicles comprised specific vehicle types, such as buses and mixer
trucks. Other vehicles, such as minibuses and light trucks, were classified as standard -sized vehicles.
The time at which drivers used their horn and the honking pattern of horn use were obtained
through an analysis of road traffic noise using data management software (RION AS-60). Vehicle horn
use and traffic volume data were analyzed in 10-minute periods.
Table 1 – Intersection features
The number of lanes at an entrance (lanes

Minimum total traffic volume

exclusive to left turns) / lanes at an exit

~ Maximum total traffic volume

North

South

West

East

Weekday

Weekend

Intersection 1

5(1)/45(2)/44(2)/33(0)/31020~1648

531~1135Intersection 2

5(1)/4

3(1)/22(0)/22(1)/2986~1839

621~1480

Intersection 3

4(1)/4

6(1)/4

3(1)/3

3(1)/3

747~1541

263~944

Intersection 3

Intersection 2
Intersection 1

Figure 2 – Map of surveyed sites

3. ANALYSIS AND RESULTS
3.1 Horn honking during traffic signal cycles
Detailed situations when drivers used their horn during traffic signal cycles (i.e., from the onset of
the green-light phase, indicated by “phase I”, to the onset of the next cycle) were analyzed for eachintersection. The traffic signal cycles were composed of four phases a s shown in Figure 3. For instance,in Intersection 2, Roman numerals I to IV represent the green-light phase for straight travel in the east–west direction and turning right to the north (I), straight travel in the direction from west to east andturning left to the north (II), entry into the intersection from the north only (III), and entry into theintersection from the south only (IV). Table 2 gives green light durations in each phase and traffic
signal cycle durations at each intersection. Such durations varied depending on measurement days andtime zones.
Table 3 presents frequencies of driver horn use, which were counted during green -light phases in allmeasurement periods of every hour (i.e., the total duration of green light phases within the 10-minuteperiods for each of the 12 hours measured). The frequencies of honking on weekdays were generally
higher than those on weekends. Many instances of horn use were found during phases I and II at
Intersection 1, and during phases other than phase II at Intersection 2. At Intersection 3, thefrequencies of horn use during phases II and III were higher than those during other phases.
As shown in Table 2, the green light duration was different among different phases, measurement7155

days, and intersections. Therefore, the horn counting durations were equalized to 10 minute periods,
and the frequencies of honking per 10-minute spans were extrapolated based on honking frequency
within 1-second span measures in each of four green-light phases, which were averaged in allmeasurement periods and days. Table 4 shows the estimated honking frequencies in cases when eachgreen-light phase was 10 minutes in duration. Results revealed that horn use was more frequent duringphases I and II at Intersection 1, and during phases other than phase I at Intersection 2. At Intersection
3, horn use during phases I and IV seemed to be relatively large, although overall horn usage was lower
compared with other intersections.
In green-light phases in which horn use was frequently recorded, such as phase II in Intersections 1
and 2, and phase IV in Intersection 3, drivers were allowed to turn left as well as going straight (e.g.,in phase II in Intersections 1 and 2), as shown in Figure 3. According to our video analysis, there weremany cases where honking began after vehicles entered the intersection to turn left during thegreen-light phase. Therefore, we next investigated the causes of driver horn use in situations whengreen-light signals allowed left turns.

(1) Intersection 1(2) Intersection 2(3) Intersection 3
Figure 3 – Diagram of traffic flow in traffic signal cycles, which were composed of four green -lightphases (I to IV) for straight travel or travel turning right or le ft

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Table 2 – Green light duration during each phase and traffic signal cycle duration
Green light duration on weekdays [seconds] /Traffic signal cycle
on weekends [seconds]

duration [seconds]

Phase I

Phase II

Phase III

Phase IV

Weekday

Weekend

Intersection 1

35~45/36~45

41~46/29~37

72/41~47

42~47/30~38

200

150

Intersection 270~75/74~84

11~15/15

43~48/40

62~69/61~71

200

200

Intersection 3

15/15

65~85/45~55

70~85/50~60

30~35/25~30

200

150

Table 3 – Honking frequencies during green-light phases in all measurement periods of every hour

Intersection 1

Intersection 2

Intersection 3

Phase I

Phase II

Phase III

Phase IV

Weekday

48

56

37

35

Weekend

47

39

19

28

Total

95

95

56

63

Weekday

64

16

74

77

Weekend

50

26

66

131

Total

114

42

140

208

Weekday

10

47

36

28

Weekend

13

29

31

16

Total

23

76

67

44

Table 4 – Estimated honking frequencies in cases when each green-light phase was 10 minutes induration

3.2

Phase I

Phase II

Phase III

Phase IVIntersection 1

17

19

7

12

Intersection 2

13

25

27

25

Intersection 3

11

9

7

11

Analysis of instances of honking during the green-light phase allowing left turns andcauses of driver horn use
We used video data to analyze instances of honking during the green-light phase allowing left turns(i.e., phase II at Intersections 1 and 2, and phase IV at Intersection 3). The scenarios when vehicle
horns were used were first categorized into four types of situations including “breaking into a line”,“slowness”, “sudden stops” and “stopping state”. In the situation of “breaking into a line”, a driver ofa car that breaks into a line to turn left is honked at by a driver of another car. In the situation o f“slowness”, a driver of a slowly moving car was honked at by another driver. The situation of “sudden
stops” means that a driver used a horn when he/she had to stop suddenly due to a vehicle in front
stopping. In the situation of “stopping state”, a car at a stop line failed to start even though agreen-light signal allowed left turns, leading to a driver of another car to honk. Table 5 gives
frequencies of each situation when horns were used during the above -mentioned phases from every
measurement period. Results indicated that horn uses in “stopping state” situations were frequently
found at each intersection, although there were more cases in which detailed situations were unclear.
We next investigated the duration from the onset of the green-light phase to the timing when a car7157

at the front of a stop line started moving (i.e., the start delay time) using video data. Afterward, therelationship between the start delay time and horn use was investigated during the green -light phaseallowing left turns. There were two lanes for turning left in Intersection 1. In this intersection,
therefore, a similar analysis was conducted in each lane. Table 6 shows the difference of the mean start
delay time between situations with and without honking during the green -light phases allowing left
turns at each intersection. Mean start delay times when a horn was used were longer than 4 seconds,
although those when a horn was not used were around 2 seconds. T-tests were performed to determine
the significance of differences in the mean start delay times. Results revealed that the differences in themean start delay time between situations with and without honking were statistically significant. This
result suggests that one of the major reasons that drivers used horns at intersections is the start delay of
a vehicle in front of them.
Table 5 – Situation frequencies when horns were or were not used during green-light phases allowing leftturns
Types of situations when horns were used
Breaking
into a line

Slowness

Sudden stop

Stopping

Unclearstate (delay)

Without
honking

Intersection 1

10

4

3

27

55

117

Intersection 2

2

3

1

24

14

34

Intersection 3

2

6

0

6

29

95

Table 6 – Mean start delay time with and without honking during green-light phases allowing left turns(**: p<0.01)
Mean start delay time
Number of situations[seconds]
Intersection 1
Intersection 2
Intersection 3
All intersections

With honking

27

4.6

Without honking

117

2.2

With honking

24

6.2

Without honking

34

2.0

With honking

6

4.2

Without honking

95

2.1

With honking

57

5.2

Without honking

246

2.2

} **
} **
} **
} **

3.3
Analysis of the relationship between driver horn use and the traffic environment
To clarify the main factors related to the traffic environment that affect driver horn use, logistic
regression analysis was conducted using horn use as the dependent variable. Independent variables
included the start delay time, traffic volumes of three types of vehicles (i.e., motorcycles,
standard-sized vehicles, and large-sized vehicles), the duration of green-light signal allowing left turns,
the number of left turn lanes, and the number of exit lanes. The start delay time and the traffic volumeof standard-sized vehicles were statistically significant, suggesting that the probability of driver horn
use increases as the start timing or the volume of standard -sized vehicle increases (Table 7).
Furthermore, the probability of driver horn use may also increase as the duration of green-light signals
allowing left turns decreases because this variable approached significance (p=0.063).

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Table 7 – Logistic regression analysis of horn use (**: p<0.01, n.s.: not significant)Regression
Independent variables
SignificanceOdds ratio
coefficientStart delay time

0.514

**

1.672

Traffic volume of motorcycles

0.052

n.s.

1.054

Traffic volume of standard-sized vehicles

0.114

**

1.120

Traffic volume of large-sized vehicles−0.028

n.s.

0.973

Duration of green-light signals for left turns−0.094

n.s.

0.910

The number of lanes allowing left turns−0.449

n.s.

0.638

The number of exiting lanes

0.237

n.s.

1.268

3.4 Effect of honking on the sonic environment around intersections
To clarify the effect of honking on the sonic environment around intersections, the A -weighted
equivalent noise level (L Aeq,10min ) and the maximum noise level value (L Amax) of road traffic noise were
investigated when vehicle horns were and were not used at intersect ions. L Aeq,10min and L Amax whenhorns were not used were calculated using data management software (RION AS -60). Noise level data
corresponding to honking were deleted from the initial measures, and then values of L Aeq,10min andL Amax were recalculated. Figure 4 shows temporal variations of traffic volume and L Aeq,10min at eachmeasured hour of two measurement days at Intersection 1. Figure 5 shows results for L Amax.
Relationships between LAeq,10min and traffic volume were statistically significant in allmeasurements other than one weekday at Intersection 1 (Intersection 1: r weekday=−0.05, n.s.;r weekend=0.81, p<0.01; Intersection 2: r weekday=0.87, p<0.01; r weekend =0.76, p<0.01; Intersection 3:
r weekday=0.62, p<0.05; r weekend=0.85, p<0.01). Values of L Aeq,10min when horns were not used weresimilar to those when horns were used. In temporal variations of L Amax, values sometimes decreased
when vehicle horns were not used, and the maximum reduction in L Amax was about 8 dB in situations
when values of L Aeq,10min were relatively low (Figure 5).
78

1500

76

1000

74

500

72

70

0
7

8

9

Traffic volume

2000

with honking

without honking

Intersection 1/weekend

78

1500

76

1000

74

500

72

70

0

10 11 12 13 14 15 16 17 18

LAeq,10min [dB]

without honking

Intersection 1/weekday

Traffic volume

Traffic volume

with honking

LAeq,10min [dB]

Traffic volume

2000

7

8

9

10 11 12 13 14 15 16 17 18

Time zone

Time zone

Figure 4 – Temporal variations of traffic volume and LAeq,10min at each measured hour at Intersection 1

Intersection 1/weekday

1500

Traffic volume

100
95
90

1000

85

500

80

0

75
7

8

9

10 11 12 13 14 15 16 17 18

Time zone

2000

with honking

without honking

Intersection 1/weekend

1500

100
95
90

1000

85

500

80

LAmax [dB]

without honking

Traffic volume

Traffic volume

with honking

LAmax [dB]

Traffic volume

2000

75

0
7

8

9

10 11 12 13 14 15 16 17 18

Time zone

Figure 5 – Temporal variations of traffic volume and L Amax at each measured hour at Intersection 1

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4. DISCUSSION AND CONCLUSIONS
Our analysis of vehicle horn use at intersections indicated that horns were frequently used duringgreen-light signals allowing left turns, and horns were frequently used in situations of “stopping states”
at each intersection. Our results suggest that, when a vehicle a t the stop line did not start moving assoon as the green-light signaled for left turns, other drivers used horns to request movement. These
start delays were major causes of driver horn use. According to statistical tests of the differences of themean start delay time between situations with and without honking during green -light phases allowing
left turns, mean start delays when horns were used were significantly longer (Table 6).
Logistic regression analysis revealed that the start delay time and the traffic volume ofstandard-sized vehicles significantly affected driver horn use. Tsunekawa et al. also reported that thefrequency of horn use generally increased as the traffic volume of standard -sized vehicles increased(6). Furthermore, the present results revealed that the start delay time was a major factor influencing
driver horn use. Drivers sometimes missed the initial green -light signal and started driving late. In
such situations, horn use from drivers in following cars was frequently detected. According tobehavioral testing, driver horn reaction time was approximately 2 to 4 seconds when drivers were
blocked by other cars after the green-light phase began during rush hours or weekends (2). Driver horn
use is expected to decrease when other drivers quickly respond to traffic signals, and shorter
green-light signal duration allowing left turns tends to increase driver horn use. Optimizing theduration of traffic signals may help to reduce horn use.
Results on temporal variations of noise level suggest that L Amax sometimes decrease with reduced
horn use in situations when values of L Aeq,10min were relatively low. Although sonic environment nearroads and streets will be improved as electric cars and hybrid vehicles become popular, horn uses
supposedly make it worse.
According to the simulation when vehicle horns were not used, L Aeq,10min did not change. The
relationship between the number of honks per minute and the increase in road traffic noise (L Aeq)
indicates that honking more than twice per minute can affect L Aeq (7). Results reported by Tsunekawa
et al. revealed up to thirty-three honks within each 10-minute measurement period (Intersection 1: 23,
Intersection 2: 33, Intersection 3: 17) (6). In the present study, therefore, results suggest that there are
no attenuations in L Aeq,10min under situations without honking.

ACKNOWLEDGEMENTS
The authors thank E. Keebaugh, PhD from Edanz Group (www.edanzediting.com/ac) for editing a
draft of this manuscript. This research was supported by JSPS KAKENHI Grant Number JP17H00812.

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