Income, Weather, and Transit Dependence: Examining Public Transportation Ridership in the Chicago Metropolitan Area

McCormack | Page 1

Income, weather, and transit dependence: Examining public transportation ridership in the Chicago Metropolitan Area

Kristen McCormack April 23, 2015 Pomona College, Claremont, CA

I. Introduction

Weather and Public Transit In the United States, there is an ongoing debate about the future of public transit systems. Many factors affect the popularity of public transit, including the quality, price, and convenience of services, the characteristics of alternatives to services, demographic factors, and cultural norms. The effects on transit demand of many of these factors, including the price of public transit, the price of gasoline, service quality and frequency, income, car ownership, and attitudes towards transit have been studied extensively. The relationship between weather and public transit ridership, however, has only recently been explored. Weather is expected to cause changes in transit ridership by affecting the quality of service, travel time, and experience travelling to and waiting for transit service. The effect of weather on transit decisions can be characterized by two key behavioral responses. First, people may substitute one form of transportation for another. For example, if heavy snow increases traffic congestion or causes roads to seem less safe, people may decide to use public transit instead of travelling by car. Second, weather may affect ridership by changing the type, frequency, and timing of discretionary trips. For example, pleasant weather may lead to more frequent trips to the park while rain may cause cancellations in sporting events or outdoor public gatherings. People may plan more flexible errands around weather; for example, people may delay trips to the grocery store if, on a given day, they expect the experience of accessing public transit to be particularly undesirable. The same type of trip may be more discretionary for some people than it is for others. For example, some people may have flexible work hours or the option of working from home while others may face more severe consequences for failing to appear at their workplace at a designated time. Previous studies have focused on the relationship between weather and system-wide public transit ridership. Generally, these studies have observed that adverse weather is correlated with lower ridership. One study of the relationship between changes in weather and changes in daily bus and rail ridership in the Chicago area found that warmer temperatures and the existence of fog led to higher ridership while wind, rain, and snow led to a decrease in ridership; extreme weather had no significant effect. As expected, the study also found that bus ridership was more sensitive to weather variation than was rail ridership (Guo et al., 2007). The positive relationship between temperature and ridership and negative relationship between wind, rain and ridership was also observed in a study of buses in Gipuzkoa, Spain (Arana et al., 2014). In a study of bus ridership in Pierce County, Washington, Stover et al. (2012) similarly found that rain, cold temperatures in winter, snow in fall and winter, and wind in all seasons except summer were associated with a lower bus ridership. A study of transit choice in Bergen, Norway, found that wind was the most important variable in explaining travel mode. Generally, however, weather McCormack | Page 2 was observed to have little effect on decisions to switch between methods of public and private transportation (Aaheim and Hauge, 2005). While considerable progress in this area has been made in recent years, there remain significant gaps in existing literature. Some studies have considered the relationship between absolute weather conditions and ridership and some have studied the relationship between relative weather conditions, or changes in conditions from the previous day, and ridership; however, no studies have considered the effects of both absolute and relative conditions in a single model. This is concerning because if relative and absolute weather effects are correlated, models that fail to consider both relative and absolute weather may suffer from omitted-variable bias. In addition, no studies have compared the effects on ridership of relative and absolute weather conditions.

Transit Dependence The effect of transit dependence on the relationship between weather and transit ridership also remains unexplored. It has been widely observed that the demographic characteristics of a population affects how that populations uses public transit. In particular, significant differences have been observed between how people of different levels of income use public transit systems. Glaeser et al. (2008) suggested that the poor tend to live closer to city centers in order to have better access to public transit. In the United States, people with low incomes tend to travel less and take shorter trips than people with higher incomes. While a large proportion of both low- and high-income populations travels by car, low-income persons are more likely to walk or bike and are more likely to use bus or rail services than are high-income persons. They are also more likely to be regular users of public transit (Guiliano, 2005). Many of these differences may be attributed to lower rates of automobile ownership among low-income populations. Low-income households are less likely to own a car (Guiliano, 2005) and more likely to share a car among multiple drivers than are high-income households (Waller, 2005). Lacking access to a car causes lower-income individuals to be more transit dependent. Their demand for transit is therefore expected to be less responsive to changes in price or other factors. Indeed, the price elasticity for car owners (-0.41) is more than four times as large as the price elasticity of non-car owners (-0.10) (Gillen, 1994). Despite the well-studied differences in transit use by people of different income levels, no studies have explicitly considered how income may affect the relationship between weather and transit ridership. Questions concerning transit dependence may be especially important to ask given the trend over the past century, noted by Garrett & Taylor (1999), in which public transit ridership has increasingly been comprised of people who are transit dependent due to age, income, or physical limitations. Over time, public transit systems have been losing market share to private vehicles, and during this decline, public transit systems have extended services to wealthier areas rather than improving service for the poor (Garrett & Taylor, 1999). Transit authorities have received heavy criticism in response to these policies.

Changing Climate Understanding the relationship between weather and public transit ridership, including how this relationship is affected by the income of riders and potential riders, is increasingly important in light of current and future climactic change. Climate change is expected to increase global mean surface temperatures by 0.3˚C to 4.8˚C by the end of the 21st century (IPCC WG1 McCormack | Page 3

AR5, 2014).1 However, this change is not expected to be uniform across regions or across time. In addition, climate change will likely increase the variability of weather and frequency and severity of extreme weather events (Meehl et al., 2000; Greenough et al. 2001; Easterling et al., 2000). There will likely be fewer days characterized by extreme cold and more days characterized by extreme heat. In addition, heat waves are expected to occur higher frequently and to last longer. Precipitation events will also likely occur more frequently and be more intense. (IPCC WG1 AR5, 2014). Since weather appears to affect ridership and this effect is likely different for populations of different incomes, gaining a better understanding of how extreme weather and increased variability in weather will affect ridership may provide insight into future public transit use.

Policy Relevance Studying how income affects the relationship between weather and ridership, especially in a changing climate, is highly relevant to current social, political, and economic debates. A better understanding of the relationship between weather and transit use may enable improved transportation system design and superior service provision by transportation authorities. It may also have significant urban planning and public health implications. For example, understanding how travel behavior changes in response to extreme weather may inform more targeted interventions, such as the opening of shelters and public, temperature-controlled buildings in times of extreme heat or cold. In addition, understanding how different populations may respond to extreme heat may enable more directed public health warning systems. Therefore, gaining a better understanding of how extreme weather and increased variability in weather will affect ridership may enable improved public policy and business practices.

II. Case Study of the Chicago Transit Authority

Overview This study hopes to addresses the gaps in the weather-transit literature by considering how income affects the relationship between ridership and absolute and relative weather conditions. To explore these relationships, the paper focuses on ridership within the Chicago Transit Authority (CTA) rail system from 2001 to 2013.

Climate of Chicago The metropolitan area of Chicago is characterized by a continental climate. It has a significant urban heat island effect that causes the city of Chicago to be warmer than surrounding areas by, on average, 2°F. The climate of Chicago is also influenced by its proximity to Lake Michigan, which moderates climate and contributes to high winter precipitation. Weather in Chicago is highly variable, and Chicago’s location under the polar jet stream causes the area to experience low-pressure storm systems (Angel, 2009). Several forms of extreme weather, including heat waves, cold fronts, blizzards, extreme wind, floods, and tornados affect the Chicago area. Chicago’s most notorious extreme weather event occurred in the summer of 1995, when a heat wave resulted in 700 heat-related deaths.

1 Temperature projections vary significantly based on future emissions scenarios. Ranges presented in the IPCC report include the 5% to 95% model ranges. The range represented here includes the ranges from all emission scenarios. McCormack | Page 4

People who were isolated and lacked access to transportation were at most risk during this time (Semanza et al., 1996). The 2012 heat wave, while severe, had much smaller mortality effects.

People, Income, and Transit In 2013, Chicago was home to 2.7 million people earning a median household income of $47,270. Nearly a quarter (22.6%) of those people lived below the poverty line. While income inequality in Chicago ranks it the 8th most unequal city out of the 50 largest cities in the United States (Berube, 2014), Chicago is not highly segregated by income. Out of the 30 largest metropolitan areas, Chicago has the 19th highest residential income segregation (Fry and Taylor, 2012). In 2012, 27.7 percent of households in Chicago do not own a car, ranking Chicago 7th out of the 30 largest cities in the United States. This percent has also been growing; there was a 2.3 percent decrease in car ownership from 2007 to 2012 (Sivak, 2014).

Chicago Transit Authority The Chicago Transit Authority (CTA) operates the second largest public transit system in the United States. The rail system, known as the Chicago ‘L,’ serves 146 stations and the CTA bus system has over 128 routes (CTA, 2014). Both the rail and the bus systems are the third largest in the United States by ridership. The prominence of CTA as a method of transit, the quality of available transit data, and the variability of weather in Chicago motivated the selection of the CTA for the focus of this study. In 2007, Guo et al. used system-wide CTA data to observe the effects of changes in weather on changes in CTA rail and bus ridership. This paper considers both relative and absolute weather conditions and studies CTA transit system data at the station level, which allows for the consideration of income differences in the weather-ridership relationship.

III. Data

Sources Daily station entries for 145 stations were obtained online from the CTA publically available ridership database.2 Data include daily ‘L’ system ridership by station from January 1, 2001 to December 31, 2013. A review of the ridership data revealed that a noticeable number of stations were recorded to have 0 riders on a particular day (1.4% of observations). Because this study does not attempt to capture station closure, these observations were omitted. Observations with fewer than 20 daily riders were also omitted because the high frequency of these observations did not appear to reflect true daily ridership (0.3% of observations). Historic fare data were obtained from the CTA. There were three increases in the base rail fare over the study period: in 2004 from $1.50 to $1.75, in 2006 to $2.00 and in 2009 to $2.25. Ultimately, fare information was not used in favor of year fixed effects. Weather data for the same period from over one hundred stations in the Chicago area were obtained from the National Oceanic and Atmospheric Administration’s National Climactic Data Center’s Global Historical Climatology Network (ncdc.noaa.gov). Data include daily totals for precipitation, snowfall, snow depth, maximum temperature, minimum temperature, average wind speed, maximum 2-minute and 5-minute wind speeds, among other weather variables. A dummy variable for fog was calculated from this dataset by combining the data set dummy

2 https://data.cityofchicago.org/Transportation/CTA-Ridership-L-Station-Entries-Daily-Totals McCormack | Page 5 variables for WT01 (fog, ice fog, or freezing fog), WT02 (heavy fog or heavy freezing fog), WT21 (ground fog), and WT22 (ice fog or freezing fog). Demographic data were obtained from the Current Population Survey (CPS) and from the US Census Bureau. CPS, which is sponsored by the US Census Bureau and the US Bureau of Labor Statistics, provides nation-wide data related to employment and earnings. For this study, CPS 5-year data from 2009 to 2013 were used to determine median household income, unemployment rate, and labor force participation for each census tract in the Chicago area. Income and employment data were also obtained from 2000 long form Census data on the level of census tracts. These data were used to linearly interpolate income and employment estimates for years between 2001 and 2008. Income data were adjusted to 2013 dollars using annual CPI values for the Chicago-Gary-Kenosha, Ill-Ind-Wisc area (US Department of Labor, Bureau of Labor Statistics). Historical gasoline prices were obtained from the U.S. Energy Information Administration. This dataset includes weekly retail prices for all grades and formulation of gasoline from 2001 to 2013. Daily values were linearly interpolated from these data. All price data were adjusted to 2013 dollars using annual CPI values for the Chicago area.

Defining Extreme Weather To study the effects of weather on ridership, it is necessary to establish definitions for extreme weather events. Guo et al. (2007) established thresholds to define extreme wind, temperature, and heavy rain. While a justification for the definition of extreme wind was provided (“25mph…can blow dust and paper from the ground”), the thresholds provided for temperature and heavy rain lacked sufficient links to health or safety. The thresholds for temperature (increase or decrease ≥ 12°F from previous day) appear to be entirely arbitrary. The threshold for rain similarly lacks support; the threshold was set to the 80th percentile of rainy days, which corresponds to ≥ 0.6 inches per day, but was not justified further. In addition, an extreme snow dummy was not included. Many definitions of extreme heat, rain, snow, and wind have been used in the meteorological and health literature. To establish the definitions of extreme weather for this paper, national definitions and local climate deviations were considered. In addition, robustness checks were performed to test the sensitivity of the model to different definitions of extreme weather. Definitions of extreme heat often reference a maximum temperature exceeding a threshold between 90°F and 100°F (Easterling, 2000; CMAP, 2013). To define extreme heat in this paper, the 97.5th percentile of maximum temperature over the summer months (June to August) was determined. This threshold method, when applied to a series of days, has been used to define a heat wave (Meehl and Tebaldi, 2004). From 2001 to 2013, the 97.5th percentile of maximum temperatures was 95°F. The National Weather Service typically issues warnings for extreme heat based on the Heat Index, which is determined both by temperature and by relative humidity. Excessive heat warnings are issued if a heat index of 105°F persists for more than three hours or if a heat index of 115°F is ever observed. For all relative humidity levels within a normal range, a 95°F temperature corresponds to warnings for at least extreme caution, and potentially danger or extreme danger of heat disorders with “prolonged exposure or strenuous activity.” A temperature of 100°F, which was used to test the sensitivity of this threshold, corresponds to danger or extreme danger for all levels of relative humidity. McCormack | Page 6

Extreme cold is defined by the city of Chicago as days where the minimum temperature falls below 32°F. Because most travel takes place during the day, the definition provided by Easterling (2000) was considered. By this definition, days characterized by extreme cold are those in which the maximum temperature fell below freezing. In Chicago’s winter months (December to February), however, more than 40% of days fit this definition. Therefore, this study took an approach similar to that of Medina-Ramon et al. (2006) and tested the model with definitions extreme cold as the 1st (10°F) and 5th (17°F) percentiles of maximum temperature in the winter months. The initial test was conducted with extreme cold defined as 15°F, which falls between these definitions. Since plowing roads and diverting water can greatly affect the impact of precipitation on a population, precipitation may be considered to be more dependent on city-specific infrastructure than is extreme temperature. Therefore, several definitions of extreme rain and snow were also used. Days with heavy rain were defined as those with greater than 0.6in (81st percentile of rainy days). Extreme rain was not found to be significant in the model for a 0.6in cutoff or for a 2in cutoff (99th percentile of rainy days). Days with heavy snow were defined as those with snowfall greater than 2in (76th percentile of snowy days). To test the sensitivity of the model to this threshold, a 3in (88th percentile of snowy days) threshold was also considered. Days with extreme wind were defined as those with a greater than 25mph maximum 2- minute wind speed (Guo et al., 2007).

Geographic Analysis For this analysis, median household income, labor force participation, and the unemployment rate were estimated for the population entering each transit station. People may walk, bike, or take other forms of public transit to a transit station. Ideally, it would be possible to capture both those who walk to the metro and those who take a combination of bus and transit to their destination, but the current data availability would make such an analysis unfeasible. Therefore, estimates of the demographics of those entering each station were calculated based on the characteristics of the surrounding community, which was defined as those living within a ring of a defined radius around each station. Several studies have estimated the distance that people are willing to walk to access public transit. Estimates for how far the average person is willing to walk to public transit typically fall close to 0.4 km (Zhao et al., 2007). In a study of the Toronto Transit Commission, Alshalalfah and Shalaby (2007) found that 60% of transit users lived within 0.3km airline distance of a transit station. O’Sullivan and Morrall (1996) found that in Calgary, Canada, people typically walk farther to train stations than to bus stops and the average walking distance to stations was 0.6km in the suburbs and 0.3km in the city. The 75th percentile walking distance to stations was 0.8km in the suburbs and 0.4km in the city. For this study, yearly census tract data and 0.4km and 1km buffer zones were used to estimate the demographic characteristics of each station. Using ArcGIS, intersections of these buffer zones and census tracts were created. Labor force participation, unemployment rate, and median household income of each buffer zone were then determined assuming uniform population density across each census tract. Illustrations of how these buffer zones were used to represent different income levels and different levels of transit ridership is provided in the appendix.

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Summary Statistics The following variables were used in this analysis. Summary statistics for per-station daily ridership, median household income, the unemployment rate, and the size of the labor force were calculated by determining the average characteristics of the population living within 0.4km of each station for every day in the study period. Summary statistics for weather and prices are calculated over the time period of the study.

Table 1: Summary Statistics (0.4km, N=664,615) Variable Mean Std. Dev Min Max Daily riders per station 3,155.985 3,003.337 20 36,017

Median household income (2013 dollars) 57,991.61 25,228.52 13,234.58 128,292.4 Unemployment rate (%) 10.685 7.824 1.149 63.664 Size of labor force 1,914.66 1,419.825 155.436 8,010.312

Maximum temperature (°F) 59.544 21.138 -0.94 102.92 Daily precipitation (inches) 0.104 0.325 0 6.858 Daily snowfall (inches) 0.102 0.616 0 13.583 Fastest 2-minute wind speed (mph) 19.542 5.864 6.935 55.029

Retail price of gasoline (2013 dollars/gallon) 2.944 0.816 1.390 4.588

IV. Theoretical Framework

As noted by Guo et al. (2007), ridership can be modeled using two main methods. First, it may be modeled in absolute terms as the number of riders who entered the system during a given day. Alternatively, it may be measured as the number of riders who entered the system relative to ridership from the previous day or from previous months. The second method, which has been favored by Guo et al. (2007) allows the model to capture short-term behavioral changes associated with weather but does not capture the impact of absolute weather conditions. However, the method of measuring absolute weather conditions is problematic because it may be influenced by seasonal factors unrelated to weather. In addition, other variables influencing ridership must be included in absolute models because they may study changes over period of time in which changes in factors, such as price and population, may affect ridership. The challenge of using absolute models can be mitigated somewhat by creating separate models for each season (Stover & McCormack, 2012). In this paper, I present a hybrid approach, which measures absolute ridership as a function of absolute weather and changes in weather from the previous day. This model therefore captures both the effect of absolute weather on ridership and the effect of relative weather on ridership. An individual may make a transit decision based on absolute weather (“It is cold outside”), previous weather (“It is warmer than it has been), and future weather forecasts (“It is expected to be warmer tomorrow”). Because expectations of future weather are more difficult to determine, only absolute weather and changes in weather conditions from the previous day were included in this analysis. To study the effect of income on the relationship between weather and ridership, separate models were also created for income brackets divided along several different income levels. McCormack | Page 8

Different thresholds, including the 20th, 50th, and 70th percentiles of income were used to test the sensitivity of these divisions. This analysis attempts to consider the behavioral changes of people who are dependent on transit because of income, so a division between those with very low income, defined as those with income lower than the bottom 10th percentile of income, was also included in this analysis. To prevent bias in the coefficients, other variables unrelated to weather that affect ridership were included (Stover & McCormack, 2012). These variables capture the price of alternatives to transit (price of gasoline), number of workers (size of labor force, unemployment), as well as time and place (day of the week, station/route). Month fixed effects were included to account for seasonal variation in ridership. In favor of including a variable for the price of transit (fare), year fixed effects were also included. These fixed effects, which entirely capture fare changes, also control for changes in unobserved factors affecting transit over time.

The theoretical model presented in this paper is as follows (i=station-day):

푟𝑖푑푒푟푠푖 = 훽0 + (훽1푔푎푠푝푟𝑖푐푒 + 훽2푓푎푟푒) + (훽3푙푎푏표푟퐹표푟푐푒 + 훽4푢푛푒푚푝푅푎푡푒) + (훽푖푤푒푎푡ℎ푒푟 + 훽푗Δ푤푒푎푡ℎ푒푟 + 훽푘푒푥푡푟푒푚푒푊푒푎푡ℎ푒푟) + (훽푙푑푎푦푇푦푝푒 + 훽푚푚표푛푡ℎ퐷푢푚푚𝑖푒푠 + 훽푚푦푒푎푟퐷푢푚푚𝑖푒푠 + 훽푚푠푡푎푡𝑖표푛퐷푢푚푚𝑖푒푠) + 휀푛

V. Methodological Approach

To develop and evaluate a model of transit ridership, the hybrid model proposed in this paper was tested. Initially, the model was designed to include only non-weather variables. Weather variables and change in weather variables were added incrementally to this basic, non- weather model, so that the effect of the addition of each set of variables could be observed. Absolute weather was included, followed by extreme weather and finally changes in weather from the previous day. After this testing, the model was then estimated for populations of different income levels. The difference in the value and significance of coefficients between these groups was observed.

VI. Fitting the Model

Model Specifications Table 2 depicts various model specifications. In the original model (Model 1), non- weather, non-income explanatory variables were included. In Models 2, 3, and 4, basic absolute weather, extreme weather, and relative weather were added. In Model 5, extreme wind and rain were excluded. Because 0.4km buffer zones have the greatest theoretical basis, they were used in specifying the model. Once the final model was selected (Model 5), model coefficients were estimated with 1km data. There were very few differences between the 0.4km and 1km models. In addition, the same variables that were not significant at the 1% level when tested with 0.4km data were also not significant when tested with the 1km data. Due to the results of a Breush- Pagen/Cook-Weisberg test, which suggested evidence of heteroskedasticity, robust standard errors were used in all model estimations. The selected model (Model 5) reveals that higher ridership is associated with a higher price of gas and a larger labor force. The relationship between the unemployment rate and ridership was not significant. However, because only a few fare changes occurred during the McCormack | Page 9 study period, this coefficient estimate may have little meaning. Robustness checks for the thresholds of extreme weather were conducted and are included in the appendix.

VI. Results

Model of Ridership for All Income Levels Ridership is expected to be higher on warmer days with less rain, snow, and wind. An increase in temperature of 10°F, for example, would, on average, be correlated with an increase in per station daily ridership of about 50 riders. An increase in rain and snow by 1 inch is correlated with, on average, a decrease in per station ridership of 65 and 44 riders respectively. An increase in 2-minute maximum wind speeds of 5mph is associated with 21 fewer riders. The absolute effects of non-extreme weather on ridership, while not insignificant, do appear to be small. Given an average daily per-station ridership of 3,156 each of these effects would account for approximately a 1 to 2 percent change in ridership. The relationship between fog and ridership is not significant. Extreme heat and cold are associated with lower ridership, but extreme snow is associated with higher ridership. Days with extreme heat and cold are associated with about 139 and 169 fewer riders, on average. Days with extreme snow are associated with 146 more riders, on average. These results should be interpreted as a non-continuous shift in the linearly modeled relationship between absolute weather conditions and ridership. For example, a day where the maximum temperature is 10°F would be expected to have, on average, (20 × 5) + 169 = 269 fewer riders than a day when the maximum temperature reaches 30°F. The interpretation of the relative weather coefficients is less intuitive than is the interpretation of absolute and extreme weather effects. Ridership is expected to be higher when the current day is cooler, snowier, windier, and foggier than the previous day. The relationship between ridership and changes in rain from the previous day was not significant.

Model of Ridership for People of Different Incomes To determine whether the ridership behavior of stations of different income levels follow the same or different models, a Chow test was conducted for several income divisions using Model 5. Once again, 0.4km data were used. Stations were divided into two groups based on the 10th ($27,804), 20th ($32,390), 50th ($54,222), and 70th ($73,330) percentiles of household income. In all of these divisions, the null hypothesis was rejected at the 1% level indicating that there is strong evidence that there is a different relationship between ridership and the explanatory variables for low- and high-income stations. To examine the differences between the two groups, Model 5 was estimated for low- and high- income groups of stations. The coefficient estimates for these different groups appear in Table 3. Without exception, when differences between higher income and lower income models are significant at the 1% level, ridership at lower-income stations is less sensitive to the explanatory variable than is ridership at higher-income stations. Higher income station ridership appears to be more sensitive to both non-weather related and weather-related factors. Across income divisions, higher income station ridership is consistently more responsive to the day of the week. In three of four divisions, higher income stations were more responsive to the unemployment rate. Across income divisions, higher income station ridership is consistently more sensitive to temperature. In three out of four divisions, higher income stations appear to be more responsive McCormack | Page 10 to extreme heat and extreme cold. In half of the income divisions, higher-income stations appear to more sensitive to a change in temperature from the previous day. When income was divided at the 20th percentile, ridership at higher-income stations appeared to additionally be more responsive than lower-income stations to rain, wind, and extreme snow. When income was divided at the 70th percentile, ridership at higher-income stations was also more responsive to changes in snow and fog.

Model of Ridership for People of Different Incomes: Accounting for Size There is a noticeable difference in the number of people per day that enter low- and high- income stations. This difference is illustrated in the two maps of income and ridership that are included in the Appendix. Stations closer to the city center tend to have a greater number of riders and tend to be in areas with higher median income. It is important to note, therefore, that while the coefficient differences illustrated in Table 3 demonstrate that high-income station ridership is more responsive to explanatory variables than is the station ridership of low income stations, it does not necessarily follow that high-income individuals are more responsive to these factors than are low-income individuals. Indeed, when coefficients are normalized by average per-day station ridership, as seen in Table 5, the rich appear to be more responsive than the poor only to the day of the week and, in half of the income divisions, to the unemployment rate. By contrast, the poor appear to be more responsive than the rich in several ways, all of which only appear for the 10th and 20th percentile divisions. In these two divisions, the poor appear to be more responsive to the price of gas and to the size of the labor force than are the rich. In the 10th percentile division, they also appear to be more responsive to the unemployment rate. The poor are also more responsive to temperature, rain, and wind in these divisions. In the 10th percentile division, they are also more responsive to snow. Possible interpretations of these results follow in the conclusion.

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Table 2: Comparison of Regressions (0.4km)

variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 5 (1km) gasprice 35.757 24.984 22.592 19.554 20.004 20.170 laborForce 0.376 0.376 0.376 0.376 0.376 0.153 unempRate -1461.823 -1459.592 -1459.734 -1459.617 -1459.523 -2135.903 sunday -2243.704 -2246.861 -2247.462 -2246.647 -2246.585 -2245.178 saturday -1590.710 -1589.218 -1589.591 -1588.811 -1588.589 -1587.155 maxTemp - 4.828 4.648 4.970 4.957 4.949 rain - -64.947 -56.458 -49.185 -65.453 -65.613 snow - -2.268 xx -29.164 -44.726 -44.185 -44.171 wind - -3.145 -2.405 -3.890 -4.155 -4.144 fog - -2.879 xx -5.436 xx 3.392 xx - - extHeat - - -143.928 -139.449 -139.363 -139.355 extCold - - -163.452 -166.787 -168.744 -168.847 extRain - - -18.369 xx -17.593 xx - - extSnow - - 145.986 143.650 146.110 145.794 extWind - - -9.137 xx -7.833 xx - - ΔmaxTemp - - - -1.527 -1.566 -1.561 Δrain - - - -9.361 xx - - Δsnow - - - 19.282 18.476 18.516 Δwind - - - 1.454 1.349 1.346 Δfog - - - -15.217 -14.419 -14.396 Constant (Jan, 2001) 2520.096 2450.616 2458.545 2479.431 2485.796 1585.988 Adjusted R2 0.8553 0.8556 0.8557 0.8557 0.8557 0.8564 all unmarked coefficients are significant at the 1% level xx indicates lack of significance at the 1% level

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Table 3: Estimation of Model 5 for Different Income Groups (0.4km)

10th percentile division 20th percentile division 50th percentile division 70th percentile division variable ≤$27,804 >$27,804 ≤$32,390 >$32,390 ≤$54,222 >$54,222 ≤$73,330 >$73,330 gasprice 47.593 16.231 26.952 19.069 12.512 27.921 12.222 36.385 laborForce -0.273* 0.366* -0.227* 0.379* -0.004 0.65 xx 0.007 0.633 xx unempRate -1695.01 -1497.073 -1336.222* -2804.722*R -588.396* -6064.737*R -756.544* -5060.253*R sunday -989.898* -2390.989*R -956.15* -2547.969*R -1565.207* -2889.258*R -1668.321* -3533.529*R saturday -623.488* -1699.573*R -608.208* -1818.43*R -1056.286* -2091.798*R -1149.224* -2567.374*R maxTemp 3.62* 5.12*R 2.957* 5.491*R 4.066* 5.819*R 4.063* 7.026*R rain -61.682 -65.241 -48.933* -69.229*R -59.226 -72.151 -57.957 -82.969 snow -42.028 -43.461 -33.439 -46.406 -42.328 -44.449 -37.306 -59.964 wind -4.091 -4.208 -2.989* -4.444*R -3.705 -4.492 -3.502 -5.508 extHeat -87.178 -143.889 -77.352* -157.585*R -91.46* -186.789*R -105.14* -231.187*R extCold -127.096 -168.534 -99.632* -187.635*R -129.746* -206.567*R -133.173* -252.411*R extSnow 80.266 151.922 82.622* 161.977*R 113.413 172.803 130.6 189.679 ΔmaxTemp -0.815 -1.672 -0.586* -1.836*R -1.129 -2.047 -1.167* -2.507*R Δsnow 19.187 17.873 11.459 19.734 15.742 21.078 11.954* 32.662*R Δwind 1.324 1.351 0.924 1.428 1.206 1.441 1.107 1.85 Δfog -12.064 -14.38 -7.975 -15.563 -10.862 -17.332 -8.863* -26.003*R constant 2212.447* 2598.98* 2005.437* 2853.182* 2403.969* 2719.751* 2486.351* 3420.151* Adjusted R2 0.8725 0.8555 0.8832 0.8524 0.8842 0.846 0.8812 0.8365 *indicates that the 95% confidence intervals for coefficient estimates do not overlap R indicates more responsive group (if signs are equal) xx indicates lack of significance of the variable at the 1% level McCormack | Page 13

Table 4: Estimation of Model 5 for Different Income Groups by Percent of Average Ridership (0.4km)

10th percentile division 20th percentile division 50th percentile division 70th percentile division variable ≤$27,804 >$27,804 ≤$32,390 >$32,390 ≤$54,222 >$54,222 ≤$73,330 >$73,330 gasprice 47.593* R 16.231* 26.952* R 19.069* 12.512 27.921 12.222 36.385 laborForce -0.273* R 0.366* -0.227* R 0.379* -0.004 0.65 xx 0.007 0.633 xx unempRate -1695.01* R -1497.073* -1336.222 -2804.722 -588.396* -6064.737*R -756.544* -5060.253*R sunday -989.898* -2390.989*R -956.15* -2547.969*R -1565.207* -2889.258*R -1668.321* -3533.529*R saturday -623.488* -1699.573*R -608.208* -1818.43*R -1056.286* -2091.798*R -1149.224* -2567.374*R maxTemp 3.62* R 5.12 2.957* R 5.491* 4.066 5.819 4.063 7.026 rain -61.682* R -65.241 -48.933* R -69.229* -59.226 -72.151 -57.957 -82.969 snow -42.028* R -43.461 -33.439 -46.406 -42.328 -44.449 -37.306 -59.964 wind -4.091* R -4.208 -2.989* R -4.444* -3.705 -4.492 -3.502 -5.508 extHeat -87.178 -143.889 -77.352 -157.585 -91.46 -186.789 -105.14 -231.187 extCold -127.096 -168.534 -99.632 -187.635 -129.746 -206.567 -133.173 -252.411 extSnow 80.266 151.922 82.622 161.977 113.413 172.803 130.6 189.679 ΔmaxTemp -0.815 -1.672 -0.586 -1.836 -1.129 -2.047 -1.167 -2.507 Δsnow 19.187 17.873 11.459 19.734 15.742 21.078 11.954 32.662 Δwind 1.324 1.351 0.924 1.428 1.206 1.441 1.107 1.85 Δfog -12.064 -14.38 -7.975 -15.563 -10.862 -17.332 -8.863 -26.003 constant 2212.447* 2598.98* 2005.437* 2853.182* 2403.969* 2719.751* 2486.351 3420.151 avg riders 1705.861 3317.96 1545.986 3557.747 2370.929 3936.327 2434.915 4833.531 Adjusted R2 0.8725 0.8555 0.8832 0.8524 0.8842 0.846 0.8812 0.8365 *indicates that the 95% confidence intervals for coefficient estimates do not overlap R indicates more responsive group (if signs are equal) xx indicates lack of significance of the variable at the 1% level McCormack | Page 14

VIII. Conclusion

Relationship between Ridership and Weather The general model presented in this paper reveals results consistent with the majority of literature. As temperature increases, there is an increase in ridership, but this effect is very small. Days with more rain, snow, fog, and wind are associated with a decrease in ridership. Extreme heat and cold are associated with a decrease in ridership while extreme snow was associated with an increase in ridership. This may be because extreme snow makes driving conditions unsafe. When incorporated into a model that also examines absolute weather, the effects of relative weather on ridership change from those observed by Guo et al. (2007). While Guo found that an increase in ridership was associated with an increase in temperature from the previous day and a decrease in precipitation and wind, this model finds that an increase in ridership is associated with a decrease in temperature and rain and an increase in snow and wind.

Transit Dependence Without exception, ridership at high-income stations appears to be more responsive to model parameters than was ridership at lower-income stations. However, when results are adjusted by average per-station ridership of stations in each grouping, it appears that while station ridership in rich neighborhoods may be more responsive in absolute terms to the explanatory variables, a greater percent of potential high-income riders do not appear to be deterred from or attracted to subway ridership as a result of weather conditions. Indeed, when coefficient estimates are adjusted by the average per-station ridership of stations in each income group, it appears that the rich are, by and large not more responsive than the poor; the rich are only consistently more responsive than the poor to the fixed effect representing the weekends and holidays. Their ridership may decline more than the ridership of the poor during weekends because they may work less than the poor on weekends. Alternatively, stations in rich neighborhoods may correspond to stations in areas where many people of both low- and high-incomes work. The results of this analysis suggest that very low-income groups may actually be more responsive than higher-income groups to weather conditions. For the 10th and 20th percentile divisions, the poor appear to be more responsive to the price of gasoline, the size of the labor force, and, for the 10th percentile division, the unemployment rate. The poor also appear to be more responsive to temperature, rain, wind, and in the 10th percentile division, snow. This finding does not appear to be consistent with the notion of transit dependence. Since lower income groups have poorer access to alternative forms of transit, it may be expected that they will be less responsive to changes in weather. Several alternative explanations may shed light on this trend. First, the poor may walk greater distances or take more forms of transit to and from the metro than do the rich. They therefore may be exposed to weather conditions for a longer period of time than are the rich. The greater responsiveness of the poor to weather may also be related to the commonly observed trend that lower-income neighborhoods tend to have poorer infrastructure and public services than do higher-income neighborhoods (Inman & Rubinfeld, 1979). If the streets of rich neighborhoods are plowed before the streets of lower-income neighborhoods, high-income persons may be able to access the metro when doing so remains unfeasible for low-income persons. McCormack | Page 15

It is important to note that, while the correspondence of high-income stations with downtown business districts may explain the greater responsiveness of ridership at high-income stations to the day of the week, this trend is unlikely to explain the apparent greater responsiveness of ridership at very low-income stations to temperature, rain, snow and wind. The majority of stations in the downtown area, as illustrated by the map in the appendix, have a median household income greater than $100,000. If observed responsiveness were driven by this trend, significant differences in responsiveness would likely appear in the higher income divisions, such as the 50th percentile division, where the sample sizes of the two groups are more even and income divisions correspond more clearly to division between downtown and not- downtown locations. While neither the poor nor the rich appear to be more responsive to extreme weather, it is important to note that, due to the limited number of days with extreme weather, the confidence intervals around all coefficient estimates were large. For the two lowest percentile divisions, the poor did appear to be more responsive to these events than the rich, although not significantly so.

Policy Implications The general model presented in this paper provides evidence for the hypothesis that public transit ridership is affected by absolute, relative, and extreme weather. People respond most dramatically to extreme heat, cold, and snow and they have moderate responses to rain, snow, and temperature. Ridership appears to respond to a lesser extent to wind and changes in weather from the previous day. As the climate warms, we may expect to see a greater frequency of warm days and days characterized by extreme heat, and we may expect fewer days to be characterized by extreme cold. As more becomes known about the future of climate in the Chicago area and in other cities, models such as those presented in this paper may be used to enable better service provision and design of public transit systems. The comparison of high- and low-income stations reveals a surprising trend: the lowest- income populations, those who would be expected to be most transit-dependent, are more responsive to absolute weather than are high-income populations. This paper offers several potential explanations for this trend. Lower-income individuals may be more exposed to weather conditions when travelling to and from the metro system, and lower-income neighborhoods may receive inferior public services that make accessing the metro system difficult. If the most-transit dependent populations have trouble accessing transportation during adverse weather conditions, this may raise several concerns. First, lack of mobility has been widely understood to be a risk-factor in extreme heat events (Semanza et al., 1996). In addition, this trend is concerning because, if those persons who are most dependent on public transit are the least able to adapt to changes in weather, it may be possible that the lack of sufficient public services to low-income neighborhoods or the lack of sufficient transportation to metro stations is deterring low-income persons from make trips that are important for employment, health, or general quality of life. This trend therefore merits more extensive exploration.

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VIII. References

Aaheim, H. A., & Hauge, K. E. (2005). Impacts of climate change on travel habits: A national assessment based on individual choices. Retrieved from http://brage.bibsys.no/xmlui/handle/11250/191992 Alshalalfah, B. W., & Shalaby, A. S. (2007). Case study: Relationship of walk access distance to transit with service, travel, and personal characteristics. Journal of Urban Planning and Development, 133(2), 114-118. Angel, J. (2009). Climate of Chicago-Description and normals [Prairie Research Institute]. Retrieved February 14, 2015, from http://www.sws.uiuc.edu/atmos/statecli/General/chicago-climate-narrative.htm Arana, P., Cabezudo, S., & Peñalba, M. (2014). Influence of weather conditions on transit ridership: A statistical study using data from Smartcards. Transportation Research Part A: Policy and Practice, 59, 1–12. doi:10.1016/j.tra.2013.10.019 Berube, A. (2014). All cities are not created unequal. Brookings Institution, Metropolitan Opportunity Series, 51. Chicago Metropolitan Agency for Planning (2013). Appendix A: Primary impacts of climate change in the Chicago region. Chicago Transit Authority facts at a glance. (Spring 2014). Retrieved February 15, 2015, from http://www.transitchicago.com/about/facts.aspx Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., & Mearns, L. O. (2000). Climate extremes: observations, modeling, and impacts. Science, 289(5487), 2068- 2074. Garrett, M., & Taylor, B. (1999). Reconsidering social equity in public transit. Berkeley Planning Journal, 13(1). Retrieved from http://escholarship.org/uc/item/1mc9t108 Gillen, D. (1994). Peak pricing strategies in transporation, utilities, and tele-communications: Lessons for road pricing. Curbing Gridlock. Trnsportaiton Research Board: 115-151. Glaeser, E. L., Kahn, M. E., & Rappaport, J. (2008). Why do the poor live in cities? The role of public transportation. Journal of Urban Economics, 63(1), 1–24. doi:10.1016/j.jue.2006.12.004 Greenough, G., McGeehin, M., Bernard, S. M., Trtanj, J., Riad, J., & Engelberg, D. (2001). The potential impacts of climate variability and change on health impacts of extreme weather events in the United States. Environmental Health Perspectives, 109(Suppl 2), 191-198. Giuliano, G. (2005). Low income, public transit, and mobility. Transportation Research Record: Journal of the Transportation Research Board, 1927(1), 63-70. Guo, Z., Wilson, N., & Rahbee, A. (2007). Impact of weather on transit ridership in Chicago, Illinois. Transportation Research Record: Journal of the Transportation Research Board, 2034, 3–10. doi:10.3141/2034-01 Inman, R. & Rubinfeld, D. (1979). The judicial pursuit of local fiscal equity, Harvard Law Review, 92, 1662-1750. Intergovernmental Panel on Climate Change (IPCC) (2014), Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. Medina-Ramón, M., Zanobetti, A., Cavanagh, D. P., & Schwartz, J. (2006). Extreme temperatures and mortality: assessing effect modification by personal characteristics and McCormack | Page 17

specific cause of death in a multi-city case-only analysis. Environmental health perspectives, 1331-1336. Meehl, G. A., & Tebaldi, C. (2004). More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305(5686), 994-997. Meehl, G. A., Zwiers, F., Evans, J., Knutson, T., Mearns, L., & Whetton, P. (2000). Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bulletin of the American Meteorological Society, 81(3), 427–436. doi:10.1175/1520-0477(2000)081<0427:TIEWAC>2.3.CO;2 O'Sullivan, S., & Morrall, J. (1996). Walking distances to and from light-rail transit stations. Transportation Research Record: Journal of the Transportation Research Board, 1538(1), 19-26. Fry, R. & Taylor, P. (2012). The rise of residential segregation by income. Pew Research Center. Social and Demographic Trends. Semenza, J. C., Rubin, C. H., Falter, K. H., Selanikio, J. D., Flanders, W. D., Howe, H. L., & Wilhelm, J. L. (1996). Heat-related deaths during the July 1995 heat wave in Chicago. New England Journal of Medicine, 335(2), 84–90. doi:10.1056/NEJM199607113350203 Sivak, M. (2014). Has motorization in the US Peaked? Part 4: Households without a light-duty vehicle. University of Michigan Transportation Research Institute. No. UMTRI-2014-5. Stover, V. W., & McCormack, E. D. (2012). The impact of weather on bus ridership in Pierce County, Washington. Journal of Public Transportation, 15(1), 95–110. Waller, M. (2005). High cost or high opportunity cost? Transportation and family economic success. Brookings Institution, Center on Children and Families, 35. Zhao, F., Chow, L. F., Li, M. T., Ubaka, I., & Gan, A. (2003). Forecasting transit walk accessibility: regression model alternative to buffer method. Transportation Research Record: Journal of the Transportation Research Board, 1835(1), 34-41.

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IX. Appendix: L System Map

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L System Map of Income in 2013 (0.4km)

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L System Map of Ridership in 2013 (0.4km)

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Sensitivity Tests

variable Model 5 extCold is >100°F extSnow is >3in extCold is <10°F extCold is <17°F (v. >95°F) (v. >2in) (v. <15°F) (v. <15°F) gasprice 20.004 20.113 20.137 20.01697 19.767 laborForce 0.376 0.376 0.376 0.3756342 0.376 unempRate -1459.523 -1459.526 -1459.532 -1459.479 -1459.532 sunday -2246.585 -2246.266 -2246.198 -2246.262 -2245.871 saturday -1588.589 -1588.053 -1588.810 -1588.57 -1588.745 maxTemp 4.957 4.838 5.101 5.17971 4.887 rain -65.453 -64.824 -66.136 -65.43354 -65.523 snow -44.185 -44.573 -22.139 -41.39902 -44.324 wind -4.155 -4.211 -4.246 -4.129455 -4.092 extHeat -139.363 -344.043 -141.279 -143.1273 -138.531 extCold -168.744 -171.904 -164.299 -213.8635 -162.891 extSnow 146.110 145.539 54.434 135.2031 145.465 ΔmaxTemp -1.566 -1.543 -1.577 -1.511853 -1.579 Δsnow 18.476 18.782 14.183 18.20744 18.180 Δwind 1.349 1.319 1.347 1.278241 1.308 Δfog -14.419 -14.467 -15.566 -13.65385 -14.709 Constant (Jan, 2001) 2485.796 2490.365 2477.583 2472.419 2488.424 Adjusted R2 0.8557 0.856 0.856 0.8557 0.8557 all unmarked coefficients are significant at the 1% level xx indicates lack of significance at the 1% level