Too Loud to Hear the Train! Noise Assessment,Implications,and Mitigation Strategies on Light Rail Platforms

Abstract

Transit planners often place light rail station platforms in the middle of freeways to avoid land acquisition costs and neighborhood opposition. However, waiting for the rail at these platforms is unpleasant and may present a health risk. The study measures, compares, and contrasts noise levels at such “freeway stations” with noise levels encountered at non-freeway stations. It identifies the factors contributing to noise in both types of stations and presents recommendations for quieter light rail station platforms.

Keywords

LRT platforms noise freeway stations

Introduction

This article focuses on one understudied setting of everyday life—the middle-of-the freeway light rail platform. Increasingly, light rail transit (LRT) systems are being planned and constructed in the middle of freeways to avoid the challenges of right-of-way acquisition, increase train speeds, and avoid the hazards of at-grade crossings. In Los Angeles County, such “freeway stations” exist along segments of the Green and Gold light rail lines. The siting of LRT platforms on a freeway right-of-way may make good economic sense, allowing for faster operation, less modal conflicts, and lower construction costs. It may even attract less opposition from nearby residents and businesses. On the other hand, such stations are not ideal for transit riders who are exposed to an unpleasant and possibly unhealthy environment while waiting for the train. Passengers on these platforms are exposed to higher than average levels of air pollution from vehicles traveling on the freeway and passing by the station (Kam et al. 2011). The most immediately noticeable problem, however, is that transit riders at these freeway stations are subjected to elevated noise levels generated by the passing vehicles.

Prolonged exposure to high levels of noise has negative effects in the short and long term. Long-term effects may include damage to human health. Researchers have shown a conclusive link between hearing loss and exposure to high ambient noise levels. Other studies have linked cardiovascular problems, particularly hypertension, to long-term exposure to high noise levels (Passchier-Vermeer and Passchier 2000; Chepesiuk 2005). Increased risk of ischemic heart disease has also been found in those who are exposed to elevated noise levels (Babisch 2005). While research has so far not specifically studied health problems in transit passengers, it is possible that passengers who use freeway stations may experience some long-term damage to their health.

In the short term, the high levels of noise on station platforms create an unpleasant environment for passengers waiting for their train. Transit riders have difficulty holding conversations with fellow passengers or on their phones. Research into annoyance caused by noise shows that people exposed to high noise levels have difficulty concentrating, making even silent activities, such as reading, problematic (García 2001). Additionally, the high noise levels prevent the effective use of loudspeakers in the station to provide information to riders, which is important in emergency situations. Some passengers may find the environment unpleasant enough that they choose not to use these stations or ride the train at all.

Little scholarly research has examined noise pollution on LRT stations along freeways and major arterials. To our knowledge, no other scholarly planning study has examined the environmental characteristics and problematic nature of middle-of-the freeway rail platforms even though such platforms are being constructed repeatedly in a number of U.S. cities. This study addresses this gap by measuring comprehensively (all elevated platforms on Los Angeles transit network) and accurately (taking multiple measurements, at different times, and at different locations on the platforms) the noise levels at light rail platforms. In addition to measuring noise, we seek to understand and explain how different locational, topographic, and design characteristics of LRT platforms affect noise levels and offer recommendations for planners on how to mitigate noise.

From December 2011 to April 2012, we conducted a series of noise measurements on eleven freeway and twenty non-freeway LRT platforms in Los Angeles County. In addition to comparing the noise levels at different stations, we also sought to determine station design elements that may affect noise on station platforms. Within stations, noise levels vary depending on where on the station platform a passenger stands. Elements such as canopies that reflect sound from passing vehicles back onto the platforms, large vertical objects such as walls that obstruct some vehicle noise, and the distance between the roadway and the station platform may account for some of this variation. Identifying design features that reduce or increase noise may help to either retrofit existing stations or improve the design of new stations located in freeway medians.

In the sections that follow, we first give a literature overview of the health problems associated with noise exposure, focusing particularly on studies that examine passenger noise exposure. We also review the literature to identify factors affecting noise levels on highways. Turning to our empirical study, we present the findings from our fieldwork observations and measurements at 31 station platforms, followed by a discussion of possible noise mitigation strategies for quieter LRT platforms.

Impact of Noise on Health

A great deal of research has documented the physical and psychological damage that excessive noise causes to humans. Studies focus either on long-term exposure to elevated ambient noise levels (e.g., Passchier-Vermeer and Passchier 2000) or assess the effects of exposure to brief, extremely loud sounds such a gunshots, known as “impulsive” or “impact” noise (Muhr and Rosenhall 2011). This research has identified hearing loss, hypertension, cardiovascular disease, sleep deprivation, immune system disorders, and birth defects among the effects of noise exposure (Passchier-Vermeer and Passchier 2000; Chepesiuk 2005).

Hearing loss is the most obvious and well-understood effect of prolonged exposure to high noise levels. Referred to as noise-induced hearing loss (NIHL), this hearing damage can occur immediately in those exposed to extremely high noise levels (140 dB and above), but at lower decibel levels it takes daily exposure over the course of many years for damage to occur (WHO 1999). At 75 dB, it would take 40 years of exposure over 8 hours each day for the average person to suffer hearing loss. Although noise levels at transit stations in highway medians are usually higher than 75 dB (Wolf 1996; ATS 2009), these levels may not be high enough to cause hearing damage even in those who use the stations daily over the course of many years because passengers spend relatively short periods of time on the platforms.

While the effects of noise on hearing loss are well established, other effects are not as well studied or proven. Chang et al. (2009), de Kluizenaar et al. (2007), and Ndrepepa and Twardella (2011) find a relationship between high noise levels and hypertension, but other studies do not find a strong enough connection to directly connect elevated blood pressure to noise exposure. Researchers in Switzerland, for example, found a correlation between hypertension and exposure to noise from trains, but no correlation with exposure to highway noise, except in diabetics (Dratva et al. 2011). Researchers have also examined the relationship between noise annoyance and ischemic heart disease. Studies by Babisch et al. (2005) and Belojevic and Saric-Tanaskovic (2002) show a positive correlation between high noise levels (>70 dB) and heart disease in men but not women. Others, however, argue that the correlation, while positive, is not statistically significant (Ndrepepa and Twardella 2011). At this stage, the research, while generally pointing toward an association between noise and cardiovascular problems, is not sufficiently advanced to make a direct connection between the two.

Annoyance, the feeling of discomfort or displeasure that noise causes when it interrupts an activity, is a major effect of exposure to elevated noise levels. For passengers on noisy transit platforms, annoyance is likely a common effect of noise exposure. Although noise annoyance studies depend on subjective responses from those surveyed, researchers have been able to make generalizations about the levels of sound at which people become annoyed. The level of annoyance is determined in part by the context in which respondents are exposed to this noise: workers in an office may report annoyance at levels as low as 55 dB, while factory workers may not report annoyance until 85 dB (Passchier-Vermeer and Passchier 2000). Schultz (1978) states that about 50 percent of respondents report being highly annoyed when exposed to sound at 80 dB, while 30–40 percent become highly annoyed at 70 db. Other researchers have noted that the source of noise (highway, airplane, or train) changes the levels at which people begin to experience annoyance (Miedema and Vos 1998; Fidell 2003; Kryter 2007). These studies find that respondents generally rank airplane noise as the most annoying, highway noise second most, and train noise the least annoying.

A common thread in these studies is that research focuses on long-term exposure to noise, usually eight hours per day or more at noise levels between 55 and 75 dB. There are no studies accurately estimating health impacts to passengers waiting for relatively short periods on freeway-centered transit platforms with high noise levels. However, the feeling of annoyance from noise exposure, which is significant at levels as low as 70 dB, is sufficiently well established and warrants the study of noise on transit platforms.

Measuring Noise in Transit Environments

Research on transportation noise focuses on the exposure of people living, working, or going to school near noise sources such as highways, airports, and rail lines. Guidelines have been issued by the Environmental Protection Agency and the Occupational Safety and Health Administration for exposure to noise in residential and occupational settings (EPA 1974; NIOSH 1998).¹ Much less research or guidance, however, is available on the acceptable noise levels for transit riders, whether they are exposed to noise while riding a transit vehicle or waiting at a noisy platform. A handful of studies have examined passengers’ exposure to noise at heavy rail stations and on rail vehicles. Gershon et al. (2006) measured noise at subway stations in New York City, as well as on the subways and at bus stops on major streets. They found an average of 86 dB, although on some subway cars and platforms the maximum sound level exceeded 100 dB, and a maximum reading of 89 dB was taken at a curbside bus stop. Although the study measured noise in enclosed spaces (the subway platforms and cars), and at curbside stops on city streets rather than on a freeway median, it does provide a comparison point for our study.

In a similar study, Dinno, King, and Powell (2011) measured the sound levels found on BART trains in the San Francisco Bay Area. The authors found that the levels were elevated enough (40–48 percent of the maximum daily noise exposure levels set by the EPA) to potentially have deleterious effects for the health of riders who frequently use the trains for long commutes. Two other studies have measured noise problems for passengers on transit platforms, although not those located in freeway medians. Koushki (2002) examined passengers’ exposure to high noise levels in Kuwaiti bus terminals. Chang and Herman (1974) examined noise effects of heavy-rail trains on passengers in Chicago and determined that despite noise levels that occasionally reached 115dB and frequently impeded conversation, the likelihood of hearing loss was remote.

Three studies have examined noise in Los Angeles transit environments. In a study conducted shortly after the Green Line was built, Wolf (1996) investigated noise at its stations, located in the median of the I-105 freeway. He also estimated the noise reduction capability of on-platform shelters, sound absorptive materials on the canopies and other overhead structures on the platforms, and sound barriers between the highway and the platform. Taking measurements at only two stations (Crenshaw and Lakewood stations), Wolf found that noise levels ranged from 80 to 88 dB at locations in different parts of the platforms.

A more recent Los Angeles study examined noise levels at the platform of the 37th Street Station of the Harbor Transitway, located in the center of the I-110 Freeway, finding that noise levels ranged from 78 to 87 dB (ATS 2009). The authors argued that these levels are not high enough to cause hearing damage even with long-term exposure but are sufficiently high to impede most conversation and cause annoyance. Annoyance and displeasure was also confirmed by Banerjee (2005), who surveyed transit riders waiting on highway medians in Los Angeles. A similar study of transit stations along SR-520, a highway near Seattle, also found that passengers waiting for the bus on platforms along the side of the highway cited noise as a problem (Transportation Issues, Inc. 2005). None of the latter two studies, however, measured noise levels.

Factors Affecting Traffic Noise

Passengers waiting on the platforms of freeway transit stations are affected primarily by traffic noise—the noise of cars driving by. A train entering or leaving the station also adds to this noise. Traffic noise is affected by the mix of vehicles on the freeway. Since a heavy truck is about 10 dB louder than a passenger car, a freeway with significant truck traffic will be much louder than one with very few trucks (FHWA 2007a). The speed of passing traffic is also an important factor affecting traffic noise. Additionally, traffic volume also affects noise but not significantly. Using a modified decibel scale (the so-called A-weighting) that assigns less weight to noises with very low or high frequencies, since the human ear does not typically recognize them, researchers have found that a doubling of cars will result in only 3 dBA increase of noise,² assuming the same speeds and traffic mix (FHWA 2007a; Seto et al. 2007).

For passenger vehicles traveling at speeds greater than 30 mph, the greatest source of noise is the sound of tires rolling on the pavement. The sound of the revolving tire striking the pavement and the air that escapes from the space between the roadway and the grooves of the tire contribute to this noise. When measured in close proximity, the sound of a tire traveling at high speed can exceed 90 dB (FHWA 2007a).

Finally, temperature affects traffic noise levels. Researchers have found that in lower temperatures both the pavement and tires become stiffer because of the cold, and noise from the pavement increases by as much a 1 dB for each 10 degree decline in temperature (Bendtsen, Lu, and Kohler 2009a; Rasmussen 2011).

The relationship of the roadway and station platform—whether they are at the same level or at different elevations—is likely to affect noise levels on the platform. However, we have not encountered studies examining levels of traffic noise at platforms of different elevations. Additionally, different locations on the platform may have different noise levels because of the presence or absence of design elements such as walls, canopies, etc. To examine the noise levels at different stations, and how these are influenced by the aforementioned factors, we turn to our case study.

Measuring Noise Exposure on Los Angeles LRT Platforms

The Context

The stations in this study are located across a wide area of Los Angeles County (Figure 1). The Green Line, running for the most part along the I-105 Freeway, stretches from Norwalk to Redondo Beach. The line has eight stations in the middle of the freeway: Lakewood, Long Beach, Imperial/Wilmington, Avalon, Harbor Freeway, Vermont, Crenshaw, and Hawthorne. Three stations along the Gold Line, which travels from Pasadena to downtown Los Angeles and turns east to East Los Angeles, are in the middle of Interstate 210. These three stations—Lake, Allen, and Sierra Madre Villa—are all located in Pasadena. In addition to these eleven “freeway” stations, we also took detailed noise measurements at the platforms of twenty other stations on the Green and Gold lines. These non-freeway stations represented eight different typologies, in terms of their topographic characteristics, being above, below, or at street level (Table 1).³ The freeway stations, on the other hand, have the same basic design: a single platform located in the freeway median at the same level as the adjacent travel lanes. Two of the stations (Vermont and Lakewood) are located below street level, however, so that the roadway above covers nearly the entire station (Figure 2).

Figure 1.

Gold and Green Lines in Los Angeles County.

Table 1.

Typology of Non-Freeway Stations.

Type	Station Name	Relationship to Surface Street Level	Platform Location
1	Little Tokyo (Gold Line)	Street level	One side of street
	Indiana (Gold Line)
2	Pico-Aliso (Gold Line)	Street level	Middle of the street
	Maravilla (Gold Line)
	East LA Civic Center (Gold Line)
	Atlantic (Gold Line)
	Highland Park (Gold Line)
3	Heritage Square (Gold Line)Mission (Gold Line)	Street level	Platforms on both sides of the street
3	Heritage Square (Gold Line)Mission (Gold Line)		Platforms on both sides of the street
4	Del Mar (Gold Line)	Street level	Platform between two streets (rail tracks on separate right-of-way; buildings on both sides of rail right-of-way separate platform from streets)
4	Fillmore (Gold Line)
5	Chinatown (Gold Line)	Above street level	Center of street
	El Segundo (Green Line)
	Douglas (Green Line)
	Redondo Beach (Green Line)
6	Lincoln Heights (Green Line)	Above street level	Perpendicular to street
	Aviation (Green Line)
7	Mariposa (Green Line)	Above street level	One side of street
8	Southwest Museum (Gold Line)	Below street level	One side of street
	Memorial Park (Gold Line)

Figure 2.

Lakewood Station.

Methodology/Data Collection

To measure noise levels on the thirty-one transit platforms, we used a Quest NoisePro DLX dosimeter. The dosimeter’s microphone clips onto the shirt collar, just below the ear, so that sound picked up by the device approximates the same sound levels that the ear receives. The dosimeter settings were as follows: 3 dB exchange rate, 70 dB threshold, 40–110 or 70–140 dB range, and slow response rate.⁴ After every third measurement session, we used a Quest QC-10 calibrator to ensure the dosimeter’s accuracy. All readings in this study were taken using the A-weighted scale (that measures sound in dBA), which is the usual standard when measuring environmental noise’s effect on people because it more closely captures the actual sound levels that the human ear registers (Peterson and Gross 1967 ).

In order to capture the expected variations in sound levels at different times of day and days of the week, we took readings at four different times at each station: one during the morning rush hour period (7 a.m. to 9 a.m.), one at evening rush hour (5 p.m. to 7 p.m.), one in the middle portion of the day (10 a.m. to 3 p.m.) on weekday, and one during the middle of the day (10 a.m. to 3 p.m.) on a weekend day. The rush hour times roughly approximate when traffic is heaviest and traffic speeds are lowest on freeways.

To take account of variations in the noise levels within each station platform, we took measurements at six to eight different locations on each platform. The following station design elements determined the locations on the platforms where we took readings:

Locations with no obstruction in the line of sight to the adjacent roadway

Locations next to walls, large map boxes, or other tall, solid structures that block line of sight to the roadway

Locations at the ends of platforms, as well as in the middle portions

Locations under canopies or other structures above the platform that may reflect noise from the roadway back onto the platform.

At each platform location of the thirty-one stations, we took readings with the dosimeter for two minutes, recording the average ambient sound level as well as the maximum and minimum sound levels during that period.⁵ While measuring the noise levels on each platform, we also noted (1) the time measurements began; (2) the approximate speed of vehicles traveling in each direction on the adjacent freeway or street; (3) the approximate percentage of trucks or buses (as opposed to passenger cars) on the freeway or street; and (4) the approximate temperature.

Additionally, we obtained from Caltrans the average daily traffic volumes for the freeway segments adjacent to each station (Caltrans 2011a, 2011b). For non-freeway stations, we obtained traffic counts for the street(s) adjacent to the station.⁶ These traffic levels may serve as a proxy for automobile traffic at each station. Finally, we used the measurement tool on Google Earth to calculate the approximate distance from the platform to the nearest travel lane of the freeway or street.

The study has several limitations. It was impossible to estimate the effect of different speeds on the noise levels at the platform because our readings did not capture a full range of variation in traffic speeds at most stations, and the visual estimates of traffic speed and percentage of trucks on the road may not be accurate. Additionally, the yearly average of daily traffic volumes was used as a proxy but may have not been similar to the actual traffic volume when the readings were taken. We also noted the presence or absence of canopies and walls on the platform but we did not account for their various lengths and designs. Despite these limitations, with at least four different readings taken at each of six to eight different locations on each station platform, and varying traffic speeds at several locations, the measurements capture enough variation to allow several comparisons of noise levels both among stations and at different locations within the same stations.

Findings

Tables 2 and 3 show our measurements for freeway and non-freeway stations. The average noise level for all freeway stations ranged from 81.7 to 88.1 dBA, while on some platforms the maximum measured noise exceeded 90 dBA. In contrast, the average noise levels for non-freeway station platforms ranged from 63.6 to 72.4 dBA. The noise difference between freeway and non-freeway stations is very significant. The decibel scale is logarithmic, meaning that a 10-point increase in decibels represents a doubling of sound intensity—80 dB is twice as loud as 70 dB. Thus, the Imperial/Wilmington station at the middle of the I-105 freeway (Figure 3), with an average noise level of 86.1 dBA, is four times louder than the Del Mar station with an average noise level of 66.2 dBA, which is not on a freeway and has buildings on both sides of the rail right-of-way that separate the platform from streets (Figure 4)

Table 2.

Noise Levels (in dBA) at All Freeway Stations.

Station	Average	Maximum	Minimum	Roadway above Platform
Avalon	88.1	92.0	83.8	N
Lakewood	88.0	92.9	83.1	Y
Vermont	87.7	91.9	83.7	Y
Crenshaw	87.1	90.8	82.4	N
Harbor	86.7	90.4	82.0	N
Imperial/Wilmington	86.1	90.6	81.3	N
Allen	85.7	90.5	81.2	N
Hawthorne	85.5	89.8	81.6	N
Long Beach	83.5	88.5	78.8	N
Sierra Madre Villa	83.1	87.4	78.7	N
Lake	81.7	87.3	78.1	N

Table 3.

Noise Levels (in dBA) at All Non-Freeway Stations.

Station	Type	Average	Maximum	Minimum	Near Freeway
Redondo Beach	5	72.4	79.6	68.8	Y
Lincoln Heights	6	71.8	80.0	67.6	Y
Little Tokyo	1	71.6	81.0	59.5	N
Aviation	6	71.6	79.4	67.0	Y
El Segundo	5	69.7	78.9	61.3	N
ELA Civic Center	2	69.6	82.0	56.9	N
Chinatown	5	69.4	79.6	62.2	N
Memorial	8	69.1	81.5	54.1	N
Maravilla	2	68.7	78.7	61.3	N
Pico-Aliso	2	68.3	80.3	56.7	N
Atlantic	2	67.4	76.1	60.2	N
Mariposa	7	67.3	76.0	61.3	N
Indiana	1	67.2	78.3	55.3	N
Heritage Square	3	66.8	76.9	60.3	N
Del Mar	4	66.2	79.4	55.5	N
Southwest Museum	8	66.0	76.0	57.5	N
Mission	3	65.8	80.1	53.0	N
Fillmore	4	65.7	79.0	54.2	N
Douglas	5	65.5	75.3	60.2	N
Highland Park	2	63.6	75.7	52.2	N

Figure 3.

Imperial and Wilmington freeway station.

Figure 4.

Del Mar (non-freeway) station.

The 6.4 dBA difference between the loudest and quietest freeway stations and the 8.8 dBA difference between the loudest and quietest non-freeway stations are statistically significant. Several factors may explain the difference in the noise levels among stations of the same kind. For example, the two freeway stations of Lakewood and Vermont that have roadways passing directly above the station platform are among the loudest stations. The sound from vehicles on the freeway reflects off of the bottom of the roadway back onto the platform rather than moving up and away from the station, and so increases the amount of noise that reaches passengers. If non-freeway stations are in proximity to a freeway, they are bound to be noisier. Three out of the four noisiest non-freeway stations (Redondo Beach, Lincoln Heights, and Aviation) are in proximity to a freeway. Although an overhead roadway (for freeway stations) or proximity to a freeway (for non-freeway stations) will increase the average decibel level, these are not the only factors contributing to noise at a station. The loudest freeway station, Avalon, has no roadway above it. Additional factors, such as station design features, level and mix of freeway/roadway traffic (automobiles and trucks), likely contribute to high noise levels on platforms.

Statistical Models

To test the impact and significance of different variables on platform noise levels, we used ordinary least squares (OLS) multiple linear regression modeling. We constructed two different models, one for freeway stations, the other for non-freeway stations, using our noise measurements on station platforms as dependent variables. In constructing these models, we kept variables that the literature suggests may affect noise levels. We tested all variables for multicollinearity and, as will be explained below, excluded one variable that was found highly collinear with another. Table 4 shows the means of some of the independent variables for freeway and non-freeway stations.

Table 4.

Means of Independent Variables for Freeway and Nonfreeway Stations.

	Mean (for Freeway Stations)	Mean (for Non-Freeway Stations)
AADT	519,619 cars	19,356
Speed	55 mph (westbound)	33 mph (primary road)
Speed	62 mph (eastbound)	37 mph (secondary road)
%Trucks	8.0% (westbound)	6.0% (primary road)
%Trucks	7.0% (eastbound)	6.0% (secondary road)
Distance (from closest road)	32 ft	99 ft

Note: AADT = average annual daily throughput.

Tables 5 and 6 indicate the dependent and independent variables for the two regression models. For both models, the dependent variable was the average ambient sound level at each platform location for each unique station observation. We used three categories of independent variables: (1) variables relating to the locational/topographical characteristics of the platform (distance of platform from adjacent freeway or roadways; elevated platform); (2) variables relating to traffic characteristics of adjacent freeways and roadways (traffic speed of cars on adjacent freeway/roadway; average annual daily throughput [AADT] of cars and trucks on the freeway/roadway sections adjacent to the platform used as a proxy for the traffic of automobiles and trucks during observations); (3) variables relating to platform design elements such as the presence of canopies, walls, map boxes, or other obstructions that may block the sound from passing vehicles. While all freeway stations were on a freeway median that was at a roughly similar elevation as the freeway, some non-freeway stations had platforms elevated over the roadway. Thus, a dummy variable was used for the non-freeway station model to indicate the presence or absence of an elevated platform. While the temperature during the noise measurements at the freeway stations was roughly similar (about 60^oF), there were significant temperature variations during the measurements at non-freeway stations (ranging from 49 to 85^oF). For this reason, we included temperature as an independent variable in the regression model for the non-freeway stations. None of the non-freeway station platforms had walls; therefore, we did not include this independent variable in the regression model for non-freeway stations. In some cases, non-freeway stations were close to two different roadways or close to a freeway; thus, the non-freeway station regression model has two distance variables (the busiest roadway was deemed the primary), and a dummy variable indicating the presence or absence of an adjacent freeway. Lastly, during our noise measurements at the non-freeway stations, we took notice if a train was approaching or exiting the station. In the much noisier freeway stations, the arrival of a train (light rail) did not seem to increase the noise significantly. For this reason, the independent variable denoting presence of absence of a train at the station was included only for the non-freeway stations.

Table 5.

Variables in the Regression Model for Freeway Stations.

Variables	Unit of Measurement	Description
Dependent variable
AvgdBA	dBA	Average ambient sound level at each platform location for each unique station observation
Independent variables
AADT	Number of vehicles	Average annual daily automobile traffic on freeway segment adjacent to platform
Speed (Westbound)	mph	Traffic speed of vehicles traveling west on the freeway
Speed (Eastbound)	mph	Traffic speed of vehicles traveling east on the freeway
%Trucks (Westbound)	Number of trucks	Average daily percentage of tracks traveling west and passing in front of platform
%Trucks (Eastbound)	Number of trucks	Average daily percentage of tracks traveling east and passing in front of platform
Distance	Feet	Distance of platform from roadway
Canopy	Dummy variable	Presence or absence of platform canopy
Wall	Dummy variable	Presence or absence of platform wall
Map Box	Dummy variable	Presence or absence of platform map box

Note: AADT = average annual daily throughput.

Table 6.

Variables in the Regression Model for Nonfreeway Stations.

Variables	Unit of Measurement	Description
Dependent variable
AvgdBA	dBA	Average ambient sound level at each platform location for each unique station observation
Independent variables
AADT	Number of vehicles	Average annual daily traffic on roadway segment adjacent to platform
Speed (Westbound)	mph	Traffic speed of vehicles traveling west on the roadway
Speed (Eastbound)	mph	Traffic speed of vehicles traveling east on the roadway
%Trucks (Westbound)	Number of trucks	Average daily percentage of tracks traveling west and passing in front of platform
%Trucks (Eastbound)	Number of trucks	Average daily percentage of tracks traveling east and passing in front of platform
Distance1	Feet	Distance of platform from primary roadway
Distance2⁷	Feet	Distance of platform from secondary roadway
Canopy	Dummy variable	Presence or absence of platform canopy
Map Box	Dummy variable	Presence or absence of map box on platform
Near Freeway	Dummy variable	Presence or absence of freeway within 500 ft of platform
Above Ground	Dummy variable	Presence or absence of elevated (above roadway) platform
Temperature	Degrees Fahrenheit	Weather temperature during observation
Train in Station	Dummy variable	Presence or absence of train in station

Note: AADT = average annual daily throughput.

The results of the regression for the freeway stations appear in Table 7. We can see that the AADT of the freeway segment adjacent to the platform has a significant adverse impact on noise. Since we know from other studies that traffic volume has only a small positive impact on noise if other variables such as speed and traffic mix remain constant (FHWA 2007a), it is probable that the significant adverse relationship between AADT and platform noise exists because the high AADT signifies high levels of traffic congestion and thus low freeway speeds, which in turn translates into lower levels of noise. Indeed, we find that the speed of the freeway traffic has a significant positive relationship with noise at the platforms. Stations along a freeway with higher daily truck traffic were associated with higher noise levels. The presence of canopies seems to create a marked increase in noise levels at the platform, possibly because canopies can reflect noise from passing vehicles back onto the station platform (Figure 5). In contrast, the existence of a number of map boxes on platforms (metal and Plexiglas surfaces 6.5 ft tall, 3 ft wide, 6 in. thick) seems to have a small positive effect in blocking noise from the freeway traffic.

Table 7.

Ordinary Least Squares Regression Model for Average Ambient Sound Level on Freeway-Station Platforms.

	Standard Coefficients	t	Significance
AADT	−0.479	−9.743**	0.000
Speed (westbound)	0.128	3.095*	0.002
Speed (eastbound)	0.320	7.724**	0.000
%Trucks (westbound)	0.257	2.178*	0.030
%Trucks (eastbound)	0.550	4.706**	0.000
Distance	0.005	0.102	0.919
Canopy	0.251	6.049**	0.000
Wall	−0.044	−1.058	0.291
Map box	0.102	2.526*	0.012
N	554
Residual standard error	2.213
Adjusted R²	0.429
F	31.363

Note: AADT = average annual daily throughput.

p < 0.05 (two-tailed); **p < 0.01 (two-tailed).

Figure 5.

Station canopy.

Examining the variables that affect platform noise at non-freeway stations (Table 8), we excluded the variable “distance to secondary roadway” which was found to be strongly correlated with elevated (above ground) stations. We found a significant adverse relationship between temperature and platform noise, which is consistent with findings from other studies (Bendtsen et al. 2009a; Rasmussen 2011). As confirmed by the regression results, a train entering or exiting non-freeway stations adds significantly to platform noise. The relationship between the AADT of the primary roadway and platform noise was not significant for non-freeway stations. However, the traffic mix and traffic speeds on the primary roadway had a significant impact on platform noise, with fast-moving vehicular traffic and significant presence of trucks resulting into noisier platforms. Similarly, presence of a freeway close to the platform and presence of an elevated platform had significant positive effects on platform noise. Interestingly, none of the platform design elements (canopies and map boxes) made a difference in the noise levels of non-freeway platforms. The negative relationship (significant only at the 0.05 level) between the speed of traffic on the secondary roadway and platform noise seems to be counterintuitive. However, if we take into account that secondary roadways had generally low traffic volumes, and maximum speed limits of 35 mph, a “high speed” on such roadways, may signify small numbers of cars rolling smoothly and with relatively low noise.

Table 8.

Ordinary Least Squares Regression Model for Average Ambient Sound Level on Non-Freeway Station Platforms.

	Standardized Coefficient	t	Significance
AADT	−0.087	−1.242	0.216
Speed (primary roadway)	0.278	3.042**	0.003
Speed (second roadway)	0.145	−2.212*	0.028
%Trucks (primary roadway)	0.241	3.074**	0.002
%Trucks (second roadway)	0.728	−0.242	0.809
Distance (from primary roadway)	−0.119	−1.592	0.113
Canopy	0.029	0.515	0.607
Map box	0.020	0.365	0.716
Near freeway	0.186	2.116*	0.036
Above ground	0.366	3.974**	0.000
Temperature	−0.297	−5.440**	0.000
Train in station	0.338	6.474**	0.000
N	585
Standard error of the estimate	3.4450
Adjusted R²	0.528
F	18.652

Note: AADT = average annual daily throughput.

p < 0.05 (two-tailed); **p < 0.01 (two-tailed).

Potential Noise Mitigation Strategies

The study found that noise levels at freeway stations are high enough to cause annoyance to transit passengers. Although siting stations in the median of freeways has the possible benefits of lowering land acquisition costs and avoiding community opposition, the high levels of noise on freeway platforms degrades the transit experience and possibly the health of passengers waiting for the train. Other than banning the practice of locating LRT platforms on freeway medians, what can city planners and/or transit operators do to effectively reduce noise at the platforms? In this section, we outline some recommendations that include the employment of new materials and technologies, design interventions, and regulation.

Employment of New Materials and Technologies

For automobiles traveling at speeds greater than 30 mph, the greatest source of noise is the sound of tires rolling on the pavement (FHWA 2007b). Researchers have been able to produce new types of asphalt and paving materials that can reduce the noise level by several decibels (FHWA 2007b; Ahammed and Tighe 2011). These new pavement materials are not inexpensive, however, and face some additional challenges. Their more porous form provides better sound absorption but tends to wear out more quickly than denser pavement types (FHWA 2007b). Additionally, the noise-reducing qualities of these pavements decline over time, as the pores in the pavement become clogged with dirt and other debris (Bendtsen, Lu, and Kohler 2009b). Despite the higher cost of maintaining and replacing such pavement, cities may want to consider their selective application at sections of the roadway next to transit stations on freeway medians.

An area where new technologies have already helped significantly to reduce automobile noise is in the development of quieter tires. The tire industry estimates that passenger cars are today less than 20 percent and trucks are less than 10 percent as noisy as those produced thirty years ago, but believes that “the noise reduction available from the tires alone may have reached the limits of the current state of the art” (Ashley 2013, 1–3). Despite this assessment, we cannot rule out that technological innovation will not lead to even quieter tires in the future. Local agencies, however, cannot enforce the installation of quiet tires on private automobiles. It is only a federal agency—the U.S. Department of Transportation or the National Highway Traffic Safety Administration (NHTSA)—that has the authority to instigate noise standards for tires. Such standards are in place in Europe, since 2004, when the Economic Commission for Europe (ECE) instigated noise standards for automobile tires (Ashley 2013).

Design Interventions

The most common design mitigation to protect households adjacent to freeways from freeway noise is the installation of sound walls. Cities may also wish to consider the installation of sound walls at freeway station platforms. An earlier study of noise mitigation at the 37th Street Station, located in the middle of the Harbor Transitway, found that the installation of sound walls on the platform could reduce the amount of noise by as much as 13 dB (ATS 2009). Clear materials such as Plexiglas can be used to build sound walls that do not have the effect of enclosing the station with dark material. One disadvantage of sound walls is their high cost, around $200,000 per station (ATS 2009). It should also be noted that stations with roadways overhead are unlikely to receive much benefit from sound walls since much of the noise reflects off the bottom of the roads above, making the walls less effective overall. For this reason, some of the loudest stations would not benefit from this treatment.

Another design intervention would be to provide enclosed waiting areas for passengers on platforms. The cost of this option would depend on the materials and size of the enclosed area, but a calculation of these costs is beyond the scope of this study. None of the stations in this study included such waiting areas, and thus we have not been able to measure firsthand the extent to which they mitigate freeway noise. Wolf (1996), however, estimates that enclosed waiting areas could reduce noise levels by 7 or 8 dB. For stations with roadways overhead, a passenger shelter/waiting area with four walls and a roof may be the best option for reducing noise levels on the platform. Although at the loudest stations this reduction is not enough to give passengers complete relief from high noise levels, it would make their wait more comfortable. If these shelters are constructed in combination with a sound wall, significant noise reduction of about 15 dB may be possible.

It is likely that the strategic use and siting of certain design elements on the station platform can help dampen or deflect freeway noise. Canopies are necessary at stations to protect passengers from rain or sun. However, our study seems to indicate a link between the presence of canopies at freeway stations and increased platform noise, but such link was not observed in the non-freeway stations. Additional studies are needed to investigate how the positioning, shape, size, height, and type of material of canopies and walls could help dampen, rather than increase, roadway noise.

Regulation

Cities may also consider setting maximum allowable levels of noise for railway platforms on freeway medians, and making the construction of such platforms contingent on transit operators providing noise mitigation measures to meet these levels. While we are not aware of any such precedent for railway platforms, highway construction agencies are required to reduce the nearby residents’ exposure to noise from new or rebuilt highways. Depending on the land uses that exist adjacent to a highway under construction, noise levels have to be reduced to 52 dB (measured inside sensitive uses such as hospitals), 67 dB (measured outside residential establishments), or 72 dB (measured outside offices, hotels, or restaurants) (Caltrans 2011). Thus, cities may possibly consider a noise ordinance for station platforms.

The measures outlined above are only indicative of the types of strategies that cities may have at their disposal if they wish to mitigate noise at freeway platforms. More studies are needed to assess the costs and benefits of each strategy and determine which strategy or combination of strategies makes better sense in a particular spatial context.

Conclusion

Increasingly, cities are using freeway medians to locate LRT platforms. But such actions have consequences. This study found a significant difference in the noise levels between freeway and non-freeway stations. While platform noise levels at the non-freeway stations were not high enough to raise concerns, the noise at the freeway station platforms was significant enough to cause passenger annoyance, and even possibly some health issues. We found that certain locational elements and traffic characteristics such as the number, speed and type of vehicles traveling on the adjacent freeway lanes play a large role in the overall noise level. Additionally, the level of noise may vary because of topographical characteristics of the site. As this study showed, when freeway stations are also located below surface streets, the platform noise is even worse. Indeed, the Lakewood and Vermont stations were among the loudest of all stations studied in large part because of the noise that reflected off of the bottom of the road back onto the station platforms. The study also gave some evidence that certain platform elements are likely to have some effect on noise levels; however, a more thorough study is necessary to assess their impact.

Strategies that involve design, regulation, and/or the employment of new materials and technologies have the potential to mitigate noise at LRT platforms and should be considered by planners. Why should planners care about noise on the platforms? In the last three decades, a significant number of U.S. cities have invested in light rail systems in an attempt to reduce traffic congestion and environmental pollution and offer “greener” transport alternatives to the private automobile. Transportation planners and urban designers have emphasized the importance of considering the “whole journey approach,” when planning transit systems—namely that transit riders need to feel comfortable and safe at all settings of their trip (Loukaitou-Sideris 2012). Rail platforms on freeway medians are highly unpleasant and possibly unhealthy environments. By making platforms on freeway medians quieter, planners can improve the quality of an urban setting, possibly attract more passengers to using transit, and certainly make the wait for the train less unpleasant and more comfortable.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was partly funded by a grant from the University of California Institute of Transportation Studies Multi-Campus Research Project Initiative on Sustainable Transportation.

Notes

Author Biographies

Anastasia Loukaitou-Sideris is a professor in the UCLA Department of Urban Planning and Associate Dean of the Luskin School of Public Affairs. Her research interests include urban design, land use and transportation planning, and community development.

Alexander Schaffer holds a master’s degree from the UCLA Department of Urban Planning. He works as a transportation planner at Affiliated Computer Services, Inc.

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