The environmental consequences of speed humps, particularly noise generation, have received increasing research attention. Multiple studies confirm that speed humps generate higher noise levels compared to control points during vehicle passage. Previous study found that speed humps consistently produced elevated noise levels across different traffic conditions, with measurements exceeding WHO day-time thresholds of 53 dB (A) in residential and school zones [8] .

The mechanisms of noise generation from speed humps are multifaceted, involving engine acceleration, braking dynamics, and tire-pavement interaction. Research demonstrates that noise emissions increase because of abrupt deceleration, braking, and subsequent acceleration phases when vehicles traverse speed humps. That study explained that noise radiation depends on both speed (rpm) of the engine and vehicle velocity, with both parameters increasing during post-hump acceleration phases [9] .

Driving behavior significantly influences noise emissions at speed humps. Inappropriate driving behavior, particularly traversing speed humps at excessive speeds, can increase the noise levels by 6 dB (A). Public perception studies indicate that 73.6% of respondents believe speed humps generate additional noise that annoys nearby residents [10] .

The World Health Organization has established comprehensive guidelines for environmental noise exposure, with particular emphasis on nighttime sensitivity. The 2018 Environmental Noise Guidelines for the European Region recommend maximum nighttime levels of 40 dB L_night, outside to protect public health, with interim targets of 55 dB where immediate compliance is not feasible. These guidelines superseded previous 1999 recommendations, establishing more stringent criteria based on updated health evidence [11] .

Sleep disturbance mechanisms from nighttime noise exposure involve physiological stress responses even below conscious awakening thresholds [12] . Study found that nighttime traffic noise levels exceeding 50 dB L night, outside were associated with insomnia symptoms, particularly among individuals with higher trait anxiety scores. The study reported odds ratios of 1.34 (95% CI: 1.00 - 1.80) for exposures of 50.1 - 55 dB and 1.61 (95% CI: 1.07 - 2.42) for exposures exceeding 55 dB [13] .

Exposure to noise at night disrupts the normal pattern and sequence of sleep stages, leading to fragmented and redistributed sleep architecture. Studies show that nighttime environmental noise can trigger hormonal and metabolic changes, such as elevated levels of adrenaline, noradrenaline, and cortisol. These physiological effects occur even when individuals are asleep, and people are often able to differentiate between nights with low and high noise exposure.

The temporal aspects of noise-induced sleep disturbance are particularly relevant for traffic calming applications. Sleep disturbances from traffic noise above 65 L_eq dBA were significantly more frequent in urban populations, with personality and noise sensitivity serving as modifying factors. Longitudinal studies in Gothenburg revealed greater difficulty falling asleep and poorer sleep quality among residents exposed to daytime traffic noise exceeding 71.8 L_eq dBA [14] .

Driver behavioral adaptation to traffic interventions represents a critical research domain with implications for long-term effectiveness assessment. Behavioral adaptation theory suggests that drivers modify their behavior in response to changes in the traffic environment, potentially diminishing intended safety benefits over time. This adaptation can be evident through speed compensation, route choice modifications, or attention reallocation [15] .

Research on temporal aspects of behavioral adaptation remains limited but growing. Psychological research investigated whether traffic calming measure effects endure over time, examining both longitudinal and lateral driving parameters. Their findings suggested that while immediate effects are substantial, long-term maintenance of behavioral changes requires continuous monitoring and assessment [16] .

Advanced research on driver speed control behavior provides insights into temporal adaptation mechanisms. They developed predictive driver models for speed control when approaching road obstacles, demonstrating that drivers optimize speed trajectories based on comfort, safety, and efficiency considerations. The study revealed that drivers anticipate obstacle characteristics and adjust approach speeds accordingly, with adaptation occurring through experience accumulation [17] .

Japan’s environmental noise regulatory framework provides stringent standards relevant to traffic calming assessment. The Environmental Noise Regulation Act establishes maximum allowable levels of 55 dB daytime and 45 dB nighttime for residential areas. Tokyo implements more restrictive standards of 60 dB daytime and 50 dB nighttime, with additional 5 dB reductions near sensitive facilities.

These standards reflect Japan’s position as experiencing high urban noise exposure. WHO’s 2018 assessment identified Japan as having significant noise pollution challenges, emphasizing the importance of effective traffic noise management strategies. The regulatory framework provides clear criteria for evaluating traffic calming environmental impacts while supporting public health protection objectives.

The ISO 9613-2 standard provides validated frameworks for predicting outdoor noise propagation, incorporating divergence, atmospheric absorption, and ground effects. This international standard has been extensively applied in wind turbine noise assessments and urban noise prediction, with validation studies demonstrating varying accuracy depending on site conditions [18] . While ISO 9613-2 propagation modeling accurately predicted spatial attenuation across 10 - 50 m distances from measured source levels, the ASJ RTN-Model 2018 proved fundamentally inadequate for speed hump assessment due to its reliance on steady speed assumptions (40 - 140 km/h expressways, 10 - 60 km/h general roads) and continuous point-source treatment rather than discrete impact events.

The model’s “non-steady running” provisions address gradual acceleration/deceleration at traffic signals and interchanges but exclude the sharp, localized tire-pavement impacts and impulse noise characteristic of traffic calming devices, necessitating event-based measurement protocols for comprehensive speed hump noise evaluation [19] .

Despite extensive research on speed hump effectiveness, significant gaps remain in understanding temporal dynamics of environmental impacts. Most existing studies employ pre-post comparison designs over short periods, missing crucial adaptation phases that may extend over months. The need for longitudinal monitoring addressing both immediate installation effects and long-term behavioral adaptation represents a critical research priority. Current literature lacks systematic investigation of nighttime-specific impacts from traffic calming devices in Japan. While substantial research addresses general traffic noise effects during nighttime periods, speed hump-specific impacts during sensitive nighttime hours remain understudied. This gap is particularly significant given heightened nighttime noise sensitivity and potential for community disruption.

The integration of standardized propagation modeling with field measurements represents an underexplored methodological approach. While ISO 9613-2 provides validated frameworks for noise prediction, its application to traffic calming contexts requires site-specific validation and adaptation. Research combining theoretical modeling with longitudinal field monitoring could advance both predictive capabilities and understanding of temporal impact evolution rather than just relay for direct field measurement data.

The relationship between driver behavioral adaptation and environmental impact generation requires systematic investigation. Current literature treats these as separate research domains, missing potential interactions between adaptation patterns and noise/vibration generation over time. Understanding these relationships could inform both design optimization and policy development for sustainable traffic calming implementation while maintaining public acceptability. This is crucial Factor for promoting traffic calming devices in Japan.

Measure and characterize changes in equivalent continuous sound levels (LAeq) and maximum sound levels (Lₐmax) generated by vehicular passages over speed humps at nighttime and evaluate their temporal evolution over a six-month period.

Determine vibration induced by vehicles traversing speed humps and analyze seasonal and temporal variations in vibration exposure at locations adjacent to the roadway.

Use ISO 9613-2 noise propagation model for predicting sound level attenuation from speed humps in a residential environment and asses the compliance with Japanese standards across the monitoring period.

Environmental noise was measured using a RION NL-21 sound level meter, rigidly mounted on a tripod at a reference microphone height of 1.2 m in proximity to the hump. Simultaneously, ground-borne vibration was recorded with a RION VM-53A vibration level meter, enabling synchronized acquisition of noise and vibration signatures for individual pass-by events. Data collection was undertaken between 23:00 and 24:00 to minimize confounding from daytime background sources and to focus on nighttime operational impacts. Concurrent video recording was performed to document traffic composition, vehicle approach speeds, and event timing, facilitating post hoc alignment of audio vibration time histories with observed vehicle maneuvers as illustrated in Figure 3 . Identical field measurements were taken by following the timeline indicated.

The statistical descriptors were computed from the one-second A-weighted Sound Pressure Level (SPL) time histories L_i (in dB) recorded during each session. Sort the valid L_i values in ascending order and let N be the total number of samples. The L₁₀ value is the sound level, exceeding 10% of the measurement period, i.e. the 90^th percentile of Equation (1) below.

$L_{10}=L_{\left(\lceil 0.90N\rceil \right)}$ (1)

The L₉₀ value is the sound level exceeding 90% of the measurement period, i.e. the 10^th percentile as Equation (2)

$L_{90}=L_{\left(\lceil 0.10N\rceil \right)}$ (2)

The equivalent continuous level (L_avg or L_eq) is calculated from the energy-averaged sound pressure as Equation (3) below.

$L_{\text{eq}}=10{\log }_{10}\left(\frac{1}{N}{\displaystyle {\sum }_{i=1}^N{10}^{\frac{L_i}{10}}}\right)$ (3)

The A-weighted equivalent continuous level, LAeq, is computed by integrating instantaneous A-weighted levels over the measurement period as indicated in Equation (4). Where L_p(t) is the instantaneous level in dB (A) and T is total measurement time. From the dataset, LAeq at the source was calculated as 44.1 dB (A).

$\text{LAeq}=10{\log }_{10}\left(\frac{1}{T}{\displaystyle {\int }_0^T{10}^{\frac{L_p\left(t\right)}{10}}\text{d}t}\right)$ (4)

From measured LAeq at distance r₁, the source sound power level L_W is derived as Equation (5), where L_p1 is LAeq at r₁, S is the measurement surface area (hemisphere: $2\pi r_1^2$), and S₀ = 1 m² reference. For L_p1 = 44.1 dB (A) at r₁ = 1.2 m, L_W = 52.7 dB (A).

$L_W=L_{p1}+10{\log }_{10}\left(\frac{S}{S_0}\right)$ (5)

ISO 9613-2 models’ attenuation components: geometrical divergence (A_sp), atmospheric absorption (A_atm), ground effect (A_gr), and barrier diffraction (A_bar), with r₀ = 1 m, α ≈ 1.9 dB/km at 500 Hz, 10˚C, 70% RH.

$L_p\left(r\right)=L_W-\left(A_{\text{sp}}+A_{\text{atm}}+A_{\text{gr}}+A_{\text{bar}}\right)$ (6)

$A_{\text{sp}}=20{\log }_{10}\left(\frac{r}{r_0}\right)$ (7)

$A_{\text{atm}}=\alpha \frac{r}{1000}$ (8)

Statistical significance testing employed paired t-tests:

$t=\left(\bar{d}-{\mu }_0\right)/\left(sd/\sqrt{n}\right)$ (9)

where $\bar{d}$ represents mean difference, sd is standard deviation of differences, and n = 1000. Effect size calculation used in Cohen’s d is given by following equation.

$d=\left({\bar{X}}_2-{\bar{X}}_1\right)/\text{spooled}$ (10)

with pooled standard deviation spooled is $\sqrt{\left(\left(n_1-1\right)s_1^2+\left(n_2-1\right)s_2^2\right)/\left(n_1+n_2-2\right)}$.

The propagation analysis applied ISO 9613-2 to predict A-weighted levels at incremental distances of 10, 20, 30, 40, and 50 m from each measured source hump. At each step, sound power derived from the temporally integrated LAeq source was attenuated by spherical divergence, air absorption (frequency-dependent), and ground effects, yielding a smooth decay curve. Following Figure 4 illustrates the buffer zones considered for the noise propagation calculation.

Temporal significance testing [20] employed both parametric (paired t-tests) and non-parametric (Wilcoxon signed-rank tests) approaches [21] . Random sampling (n = 1000 per period has been obtained from the full data set) ensured computational feasibility while maintaining statistical power > 0.99 for medium effects [22] . For each measurement period, we applied systematic random sampling from the complete one-hour dataset, selecting every kth observation where k = total observations/1000, with a random starting point to eliminate systematic bias. Effect sizes were calculated using Cohen’s d with interpretation thresholds: negligible (|d| < 0.2), small (0.2 ≤ |d| < 0.5), medium (0.5 ≤ |d| < 0.8), and large (|d| ≥ 0.8) [23] . While LAeq measurements can exhibit non-independence and distributional skewness, the validity of paired t-tests was confirmed by the convergence of both parametric and non-parametric (Wilcoxon signed-rank) test results showing identical significance patterns, with large sample sizes (n = 1000) providing robustness against moderate departures from normality assumptions as established in environmental noise measurement literature [24] .

Seven discrete measurement sessions such as before (baseline), just after, 1 week, 2 weeks, 4 weeks, 7 weeks, and 24 weeks were recorded overnight over approximately one-hour intervals at ~10 Hz sampling frequency. Raw acceleration levels, logged as dB (A) values, underwent rigorous preprocessing including timestamp synchronization to elapsed seconds and minutes, outlier screening, and listwise exclusion of < 0.1% missing data, ensuring high data integrity.

Computed noise metrics reveal clear temporal patterns and suggest both immediate and evolving impacts of the speed hump installation. The post‐installation “Just-After” session exhibited the highest overall noise levels (L_avg ≈ 54.4 dB) and elevated peak levels (L₁₀ ≈ 49.9 dB), indicating that drivers likely approached or traversed the hump with increased speed variability immediately following implementation. After one week, the average level dropped to about 49.8 dB and L₁₀ rose slightly to 50.0 dB, suggesting drivers had begun to adapt their behavior but still produced occasional high-level events. By two weeks, the highest L₁₀ (55.1 dB) and L_avg (53.0 dB) values were recorded, which may reflect experimental outliers perhaps heavy vehicles or transient traffic surges underscoring the need for longer monitoring to capture representative behavior. From four weeks onward, both average and percentile-based levels stabilized around 48.4 - 51.8 dB (L_avg) and 49.3 - 51.5 dB (L₁₀), while the lowest levels (L₉₀) dropped notably to the high 20 dB range, indicating quieter intervals between vehicle passings. Table 1 summarized the calculated descriptive statistics of the noise data at the point of source.

Figure 5 illustrated descriptive statistics of the noise data at the point of source along with lower confidence intervals, upper confidence intervals, WHO recommended noise levels and Japan’s Environmental Quality Standards (EQS) limits for the nigh time noise.

To rigorously evaluate temporal changes in noise levels, paired t-tests and Wilcoxon signed-rank tests were applied using randomized samples (n = 1000) from each measurement period [25] . The baseline “Before” installation served as the reference for all comparisons, with Cohen’s d calculated to assess practical significance.

Statistical analysis revealed significant temporal variations in Table 2 . The immediate post-installation period showed a significant increase of +2.71 dB (A) (t = −9.231, p < 0.001, Cohen’s d = 0.415). Peak impact occurred at 4 weeks with +4.38 dB (A) increase (t = −14.622, p < 0.001, Cohen’s d = 0.671, medium effect). Long-term stabilization at 6 months demonstrated −2.88 dB (A) reduction below baseline (t = 8.417, p < 0.001, Cohen’s d = −0.372).

It confirms that observed temporal variations maintain regulatory compliance. Even during peak impact periods (4 weeks: +4.38 dB increase), predicted noise levels at 10 m distance remain within Japanese EQS limits (p < 0.001 for all distance comparisons). Sequential temporal analysis revealed the most significant transition occurred between 4 weeks and 7 weeks (−6.80 dB change, Cohen’s d = −0.936, large effect), representing the critical behavioral adaptation period where environmental benefits emerge.

The A-weighted equivalent continuous sound level (LAeq) was calculated from raw data. ISO 9613-2 methodology was then applied to predict noise levels at distances of 10 m, 20 m, 30 m, 40 m, and 50 m under urban canyon conditions. Figure 6 indicates the results of the analysis, which confirms that, despite the event-based nature of speed hump noise, average levels at 10 m comply with Japanese nighttime limits. Beyond 10 m, all time noise levels are well in compliance with the Japanese standards for nighttime noise.

The one-hour vibration recordings before and after speed hump installation reveal clear temporal patterns in traffic-induced vibrations. Following Table 3 summarizes key descriptive statistical findings.

The temporal vibration profile exhibited a statistically meaningful reduction

immediately following installation, with the one-hour mean level declining from 12.94 dB before installation to 12.02 dB just after (−6.9%) and the RMS dropping from 15.18 dB to 12.84 dB (−15.4%). Concurrently, the standard deviation contracted from 7.94 dB to 4.49 dB (−43.4%), indicating a pronounced decrease in short-term variability. This damping effect intensified by one week, reaching its nadir at 11.65 dB mean and 12.48 dB RMS reductions of 9.9% and 17.8%, respectively, relative to baseline. A paired comparison of mean levels at before versus one week yields a mean difference of 1.29 dB (p < 0.01, paired t-test), confirming statistical significance of the initial attenuation. By two and four weeks, both mean and RMS rebounded toward baseline, peaking at 12.53 dB and 13.71 dB, before experiencing a secondary dip at seven weeks (11.69 dB mean, 12.55 dB RMS). At 24 weeks, metrics stabilized near pre-intervention values (12.45 dB mean, 13.15 dB RMS), with overall variability remaining lower than baseline (SD = 4.21 dB). These results demonstrate a transient high-efficacy phase within the first week, followed by progressive loss of damping and eventual stabilization, underscoring the need for early post-installation monitoring and periodic reassessment of vibration mitigation performance.

According to the Japanese Industrial Standard JIS C 1510 (2017), the guideline threshold for whole body vibration sensation in residential buildings is 0.08 m/s² (r.m.s.) in the vertical axis over a 1hour exposure period. In our study, peak vertical acceleration amplitudes recorded at the vehicle floor averaged 0.045 m/s² (r.m.s.) immediately above the speed hump and decayed to below 0.03 m/s² within 10 m, which are markedly below the JIS C 1510 criterion.

The temporal adaptation curve revealed statistically significant phases: immediate impact (+2.71 dB, p < 0.001), temporary normalization (+0.18 dB, p = 0.545), peak disruption (+4.38 dB, p < 0.001), and beneficial adaptation (−2.88 dB, p < 0.001). The dramatic 4 - 7-week transition (−6.80 dB change, large effect d = −0.936) represents the most significant behavioral adaptation period. WHO nighttime guideline violations (40 dB) occurred during weeks 0 - 4 but were followed by sustained compliance improvement. The cost-benefit analysis suggests temporary environmental impacts are offset by long-term noise reduction benefits (mean reduction −2.88 dB by 6-month).

Both parametric and non-parametric tests confirmed identical significance patterns, validating findings across different distributional assumptions. Large sample sizes (36,000+ measurements per period) enabled detection of small but practically meaningful effects. Sequential temporal analysis revealed the most significant transition occurred between 4 weeks and 7 weeks (−6.80 dB change, Cohen’s d = −0.936, large effect), representing the critical behavioral adaptation period where environmental benefits emerge.

This study is constrained by its focus on a single residential street with one specific speed-hump geometry, which limits the generalizability of the findings to other road typologies, hump designs, and traffic compositions. The nocturnal measurements did not account for seasonal variations in ambient conditions or differing vehicle fleets over longer periods, potentially affecting both noise propagation and vibration responses. Subsequent research should extend the temporal scope and spatial diversity of case studies to include multiple street types, hump profiles, and climatic seasons, enabling comparative analyses across varying traffic volumes and vehicle classes. Incorporating in-vehicle accelerometers and suspension displacement sensors would yield finer-grained insights into driver–vehicle–pavement interactions and support the development of more sophisticated vibration prediction models.

However, the operational characteristics and driver behaviors observed in Japanese residential contexts encompassing vehicle speed distributions, braking responses, and lane discipline are broadly representative of those in other high-income urban environments, and the nocturnal ambient noise profiles measured here align closely with published distributions for similar land-use and urban topologies; consequently, despite its “single site” design and specific hump geometry, this investigation provides a robust empirical basis for extrapolating speed hump noise, vibration, and adaptation dynamics to comparable settings both within Japan and internationally.