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THERMAL COMFORT AND CROWDING ON PUBLIC TRANSIT,
AND THEIR EFFECTS ON COMMUTER MODE CHOICE IN BEIJING, CHINA
Diwen Shen (Corresponding Author)
Master of City Planning Student
Department of City and Regional Planning
University of Pennsylvania School of Design
210 S. 34th St., G-29 Meyerson Hall
Philadelphia, PA, 19104
+1 (510) 610-5132, diwen@design.upenn.edu
Zheng Zhang
Master of Environmental Studies, Tohoku University, Japan
Enterprise Risk Services, Deloitte China
Tower W2, Beijing Oriental Plaza, 1 East Chang An Avenue
Beijing, China, 100738
+86 138-1181-5926, zhaaeeng@gmail.com
Yurong Yu
International Development Studies Student
College of Humanities and Development Studies
China Agricultural University
17 Qinghuadonglu Rd.
Beijing, China, 100083
+86 156-5293-6070, sherryyuon@cau.edu.cn
Submitted for consideration for presentation to:
2016 Transportation Research Board Annual Meeting
Nov 15, 2015
Words: 7,240, Tables: 3, Figures: 3
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ABSTRACT
The 2007 Beijing Public Transit Fare Reform was accompanied by high levels of crowding and
poor air-conditioning provision on public transit. We found high dissatisfaction regarding
thermal comfort and crowding among Beijing’s commuters during summer time. Through an
intercept survey of 813 regular Beijing residents using a stated-preference (SP) approach, we
found that high temperatures and crowding levels on buses or subways have statistically
significant negative effects on the probability of choosing that mode. In general, respondents
were more sensitive to high crowding levels compared to high temperatures, agreeing with
literature. Poor thermal comfort and high crowding levels potentially have a higher negative
impact on transit ridership than doubling Beijing’s 2014 transit fare prices. Nevertheless, transit
service quality is highly subjective and their effects are context specific. The case of Beijing
suggests that under extreme conditions, service quality attributes that are often overlooked such
as onboard temepratures and crowding will have notable impacts on commuter mode choice.
KEY WORDS: Public Transit, Thermal Comfort, Crowding, Mode Choice
WORD COUNT: 7,240 (5,740 + 6*250) words, including 3 table and 3 figures
INTRODUCTION
Beijing, China is a rapidly developing mega-city home to 21.5 million permanent residents as of
2014, with a per capita GDP of US$16,278 (7.5% annual growth) (1), approaching “high-income
economy” status as defined by the World Bank (2). In January, 2007, facing worsening traffic
congestion, Beijing enacted the 2007 “Public Transit Fare Reform”, eliminating competition
among bus operators and switching to flat fares of ¥0.4 per ride (US$0.07) for almost all cityarea bus routes. Particularly, fares for air-conditioned buses were reduced by up to 80%. Subway
fares were also cut from ¥2-3 (US$0.3-0.5), with additional charges each transfer, to a flat fare of
¥2 (US$0.3). These fares were mandated by the government, which covered deficits. The 2007
fare cuts led to higher ridership especially for air-conditioned bus routes and subway lines.
From 2006 to 2012, the mode share for general travel purposes rose from 24.4% to 27.2%
for bus, and from 5.8% to 16.8% for subway as new lines opened. Private car use increased only
slightly from 31.6% to 32.6% (4). Bicycle use dropped significantly during this period from
27.7% to 13.9%, contributing to most transit ridership gains and contradicting the initial goal of
reducing auto use. Whether the 2007 reform was successful in alleviating traffic congestion
remains debatable for the share of auto use from 2006 to 2012 almost remained the same (4).
While our study focuses on the 2007 reform, interestingly, fares were raised in January, 2015 due
to high transit budget deficits and crowding through the 2015 “Public Transit Fare Adjustment”.
Historic fare structures and air-conditioning options are illustrated in Table 1.
Time Period
Before 2007
2007 - 2014
Since 2015
None
“Public Transit
Fare Reform”
“Public Transit
Fare Adjustment”
Policy Measure
Air-Conditioned
No
Yes
No
Yes
Yes
Approximate Fare for 8km Single Journey Trip:
Bus
¥ 1.0
(US$0.2)
¥ 1.6
(US$0.3)
¥ 0.4
(US$0.07)
¥ 1.0
(US$0.2)
Shen, D., Z. Zhang, Y. Yu 2016 TRB Annual Meeting
Subway
¥ 4.0
(US$0.6)
¥ 2.0
(US$0.3)
2
¥ 4.0
(US$0.6)
¥ 16
¥ 18
¥ 20
¥ 24.5
n/a
(US$2.6)
(US$2.9)
(US$3.2)
(US$4.0)
Note: n/a = not applicable. Almost all taxis were equipped with air-conditioning by 2007. Meanwhile, a small
number of buses remain unequipped with air-conditioning by 2015.
Taxi
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TABLE 1 HISTORIC TRANSIT AND TAXI FARES IN BEIJING
Problems of high crowding levels and insufficient air-conditioning onboard transit were
prominent following the reform. In 2012, peak hour subway passenger loads for 8 out of
Beijing’s 13 subway lines surpassed design capacity, with the Changping Line having a load
factor of up to 150% (4). Crowding and high onboard temperatures during the summer have been
consistent problems for BPT’s bus routes in Beijing even before the reform. For Bashi’s routes,
however, they were new problems. The 2007 fare cut greatly increased deficits for Beijing’s
transit sector, with an annual deficit of ¥9,744 million (US$1,624 million) where revenues only
covered about 53.4% of operating costs in 2011 (5). Most government transit subsidies were
allocated to cover the deficit of bus operations due to stagnating fare revenues and high operating
costs. Such costs were mainly associated with significantly higher fuel prices, which rose 49.9%
from 2006 to 2012, and likely led to aggressive budget cutting measures such as reducing the
provision of air-conditioning.
Also in the same period, China aggressively implemented nationwide “energy
conservation and emissions reduction” measures. In June, 2007, China’s Central Government
announced the “Notice Regarding Strict Enforcement of Air-conditioning Temperature Setting
Standard in Public Buildings” (6) which mandated that all public building spaces must not set
indoor air-conditioning temperatures below 26 °C (78.8 °F) in the summer, with little regard of
the activity, clothing level, and density of people within those spaces. Buses and subways mainly
followed this mandate and stirred complaints, with China’s official Xinhua News Agency citing
an article titled “Air-conditioned Buses Do Not Operate Air-conditioning, Beijing’s Low Bus
Fares Mean Bearing High Temperatures?” just eight days after the mandate was issued (7).
Beijing’s 2007 reform led to an interesting tradeoff between fare prices and passenger
comfort. Through this study, we would like to use this opportunity to understand the levels of
thermal comfort and crowding preferred (or accepted) by commuters given lower fares, as well
as how they affect commuter mode choice across bus, subway, car/taxi, and bike.
LITERATURE REVIEW
Overview
Beijing’s 2010 Comprehensive Transportation Survey found that bus and subway ridership were
not sensitive to fare price changes, likely because fares were already low, but were sensitive to
travel time (8). Other than directly altering travel costs and durations, pleasant riding conditions
are needed to make public transit less stressful and indirectly reduce perceived costs and travel
times. Personal comfort factors include seat and ride comfort (seat size, padding, leg room,
acceleration, braking, vehicle sway, odors, and noise), appropriate climate control for local
conditions (heating, air conditioning) (9), service deliveries (management of crew and rolling
stock, safety, and infrastructure capacity) (10) and additional services (meal coupons, free wifi
etc.) (11). Their effects vary between “sticky” and “discretionary” travelers (12). On the other
hand, transit comfort improvements might not necessarily lead to higher ridership even though
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they are highly praised by passengers (13). More important for transit is to provide basic levels
of access, reliability and competitive costs that are already offered by the auto, and only after
these are achieved should other “context-specific, perceived” service quality attributes be
emphasized. Many studies on travel mode choice do not account for thermal comfort and
crowding likely because they are not “context specific”, which might not be the case in Beijing.
The following sections will specifically look at various studies that document the effects of
crowding and thermal comfort, with a final summary pertaining to the context of Beijing.
Crowding
The effects of crowding have been well documented. Crowding onboard transit leads to longer
boarding times, longer waiting times, bunching, and increased unreliability (14). A study in
Mumbai found that the perceived length of train rides increases as crowding increases (4). Like
most studies, SP approaches were used, and crowding levels were measured by the density of
standing passengers/m2 and were graphically presented to respondents. Measuring seat
availability is also important. One study found that “the proportion of users sitting”, which
affects the probability of getting a seat, to more properly account for the disutility of crowding,
compared to “the number of users standing” (14). Another study suggested that standing
allowance should be included when measuring passenger capacity for short journeys, but for
long journeys only seat availability should be used (15).
The disutilities from crowding also include stress and anxiety, although subjective
opinions vary largely between individuals (20, 24). A study in Dublin found that respondents did
not show much agreement with to verbal statements pertaining to commuting stress, but
commuting stress correlated significantly with commute features such as crowding (16).
Interestingly, in terms of stress, reductions in crowding for bus and rail were found to be more
beneficial than improvements in reliability, and that rail users would derive greater benefits from
crowding reduction. Another study showed that the density of seated passengers immediately
proximate to the passenger significantly affected stress, while overall passenger density was
mostly inconsequential (17). Crowding also can result in symptoms such as headaches and
sleeplessness, privacy invasion, loss of productivity when riding; crowding also increases
passengers’ willingness to pay for reduced travel times (14). Accompanying these results, a
study of subway trains in Beijing found that air quality was significantly poorer during rush
hours compared to regular hours (18).
The main takeaway is that perceptions of crowding are highly subjective and context
dependent. None of the above studies addressed thermal comfort, likely because onboard
temperatures were not problematic, and that finding a seat was the larger concern in the
European studies.
Thermal Comfort
The International Organization for Standardization (ISO)’s ISO 7730 is a standard for the
ergonomics of the thermal environment. It provides optimal indoor temperatures using
calculations of PMV (predicted mean vote), PPD (predicted percentage of dissatisfied) and local
thermal comfort customs (19). Optimal temperatures depend on outdoor temperatures, thermal
radiation, humidity, air speed, and personal factors such as activity and clothing. A study of
passengers of the East Japan Railway Company suggested a “comfort range’ of 11-27 °C (51.880.6 °F) in the station concourse and platform (20), using an SP approach in addition to
recordings of passenger clothing and live temperature measurements. The optimal temperature
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inside vehicles are likely to be lower, since passenger density and occupancy times at stations are
typically lower than they are onboard. Acceptable indoor temperatures also very between airconditioned spaces and naturally ventilated spaces due to the adaptability of individuals’ body,
expectations and behavior (activity and clothing levels). For summer indoors with light activity
levels, the mean suggested temperature is around 23.5 °C (74.3 °F) for air-conditioned spaces
and 25.5 °C (77.9 °F) for non-air-conditioned spaces. Exact values depend on outdoor
temperatures. Thus, if air-conditioning is not operating, passenger discomfort will be more
pronounced on vehicles designed to be air-conditioned, compared to vehicles with no airconditioning equipment (21).
Western studies generally show that passengers are not very sensitive to air-conditioning.
A study for an Australian rail company (12, 22) shows that passengers’ willingness to pay for
improvements in “heating and air-conditioning” were lower than for layout and design,
cleanliness, ease of boarding, quietness, train outside appearance, and announcements. On the
other hand, the perceived cost of crowding is relatively high. Compared to a “crowded seat”, the
perceived extra cost of “crush standing 20 min or longer” is nearly 8 times as high (12, 22). One
study in Sydney and Melbourne found that many passengers reported onboard temperatures to be
too hot, but overall passengers would prefer no air conditioning if a higher fare is required. Their
sample, however, had a very high proportion of riders who “had a seat all the way”, whereas
Beijing has much higher baseline levels of crowding and temperatures than in western cities (6,
23). Similarly, other studies found air-conditioning improvements were little valued measured by
price-equivalent benefits and patronage effects (24, 25).
Finally, thermal comfort is a subjective concept as are other “soft” service quality
attributes. Passengers’ judgments of temperatures and crowding levels have high variability and
are based more “personal tastes” rather than the functioning of the air-conditioning system (26).
Discussion
In summary, transit ridership seems to be sensitive to changes in crowding but not significantly
for thermal comfort. Such results usually assume fairly acceptable temperature and crowding
levels even before improvements are made. The current study will show how this was not the
case in post-2007 Beijing, as almost none of the above studies measure thermal comfort and
crowding in such contexts. In addition, none study the interaction between crowding and thermal
comfort as well as how they affect ridership for alternative travel modes. We bridge this gap
using survey data collected in Beijing.
METHODOLOGY
An intercept survey was conducted in Beijing in the summer of 2014 that targeted adult regular
Beijing residents. The following data categories were collected in the order of appearance in the
questionnaire: 1) Travel characteristics for their evening commute in both 2014 and 2006, 2)
preferred and perceived thermal comfort and crowding on their transit options, 3) socioeconomic
data, and 4) a set of SP games that included 4 travel mode options, with varying levels of airconditioning and crowding levels for bus and subway. To limit the number of questions, we only
collected data on respondents’ evening commute (presumably from work back to their residence).
We expected respondents to have more flexible travel times and mode choices in their afternoon
commute compared to their evening commute. We were interested in measuring whether
respondents would make different mode choice decisions if they had such freedom (which is not
always the case, of course). Using this data, we first summarize temperature preferences and
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compare them with perceived thermal comfort and crowding levels, and then used a multinomial
logit (MNL) model to analyze mode choices using the SP data.
WANGJING
ZHONGGUANCUN
XIDAN
SHIJINGSHAN
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GUOMAO
FIGURE 1 Survey Sites. Map Source: Beijing Urban Master Plan (2004-2020) (27).
= Main Surveyed Sites
To conduct the survey, survey workers were recruited from universities in Beijing.
Surveys were conducted at major commercial districts in Beijing, including Zhongguancun,
Xidan, Shijingshan, Wangjing, and Guomao, illustrated in Figure 1. About 1 in every 2 to 3
potential respondents were approached, and among them, around 1 in every 2 to 3 individuals
agreed to complete the questionnaire. Survey workers read questions and recorded responses,
and each survey took around 15 minutes. The total sample size was 813, including a small
number of online surveys that were emailed to potential respondents. Survey workers were given
“red bags” containing perks of ¥5 (US$0.8) in cash to offer respondents after obtaining consent.
However, almost all respondents declined to be compensated. The survey was conducted during
Beijing’s hottest summer days between Jul 16th and Aug 15th in 2014. The average daily peak
temperature was 31.8°C (89.2°F), which necessitated the provision of air-conditioning onboard
transit and in other crowded public spaces, and provided an opportunity to study respondents
travel preferences under such conditions (27).
In late 2015, follow-up interviews of bus and taxi drivers were conducted to understand
the evolution of air-conditioning availability, the incentives affecting the level of airconditioning provided, and how they potentially affect passengers. BPT bus drivers indicated
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that current (2013-2015) company-wide policies mandate drivers to operate air-conditioning
when onboard temperatures exceed 26 °C (78.8°F), and drivers are allowed to use temperature
settings between 24 °C (75.2 °F) and 26 °C (78.8 °F). However, unsatisfactory thermal comfort
might be caused by three factors. First, temperature preferences vary between people and it is
impossible to satisfy everyone even if air-conditioning is appropriately operating. Second, fuelsaving rewards are given to drivers with efficient fuel usage, incentivizing some drivers to cut
air-conditioning provision to preserve fuel. This is most common during summer days that are
“not too hot”; during the hottest days, air-conditioning usually operates fully, both for the drivers’
own comfort and to prevent passengers from filing complaints, which are taken very seriously by
drivers. Third, most drivers prefer not to (or have yet become accustomed to) be exposed directly
to air-conditioning, for their own physical comfort and health. This is associated with the fact
that air-conditioning was only introduced to most buses and trains at around 2007-2008. These
results are similar for taxi drivers, except that most drivers indicated that their decision to operate
air-conditioning is not affected by its extra fuel costs, which are relatively low compared to taxi
licensing fees.
RESULTS
Table 2 provides a data summary. Among the sample of Beijing residents, 72.5% were full-time
employed, compared to a citywide level of 53.3%; 22.7% were full-time students, compared to a
citywide level of 17.2% (5). On average, the sample had a higher proportion of females, higher
household incomes, and was younger, more educated than the citywide average. 26.7% used bus
and 44.1% used subway as their primary travel mode for commuting, which are higher than
citywide levels of 22.8% and 14.8% as of 2002 (4). On average, commute distances were longer
than the citywide average, whereas travel times were similar to average, likely indicating that
respondents had better access to public transit (especially subway) compared to the average
Beijing resident. It is worth noting that official census data covers rural areas that are not of
interest to this paper.
Between 2006 and 2014, respondents saw a significant increase in car ownership from
16.7% to 59.3%, and around 40% purchased their household’s first car during this period. Many
households also added a second or third car. On the other hand, the proportion that drove to
commute only increased slightly from 11.5 to 12.6%. In comparison, subway ridership saw
significant growth from 31.6% to 44.1%, and bus ridership saw a slight decrease. The majority
of respondents reported increased satisfaction towards their public options. Through an openended question, the most prevalent “soft” transit service quality factors that potentially affect
mode choice, as indicated by respondents, include air-conditioning, crowding, we well as
cleanliness, noise, odor, quality of driving, public manners of other passengers, and “private
space”. Among all respondents, only 238 maintained the same residential and work addresses
between 2006 and 2014. This is because many respondents switched from student status to
working status during the 8-year-long period. Among those who did not change addresses, only 9
changed their primary travel mode. Due to the small amount of “mode switchers”, we cannot
model whether thermal comfort or crowding had an effect on their mode choice. Should any
effect on mode choice exist, we would expect it to be relatively small.
TABLE 2 Data Summary (4, 5, 8)
2014
Mean
2006
St. Dev.
Mean
St. Dev.
Difference
Comparison:
City-Wide Averages
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Occupation
Full-Time Employed
0.725
0.446
0.458
0.499
0.267***
0.533
Full-Time Student
0.227
0.419
0.236
0.425
-0.009
0.172
Evening Commute Characteristics
Workdays per Month
21.521
5.574
21.396
5.574
0.126
N/A
Workplace Ringroad
3.374
1.123
3.355
1.182
0.019
N/A
Hr of Day of Trip
17.568
1.901
17.517
1.821
0.051
17:00 (Peak)
Travel Distance (Km)
14.515
11.940
13.298
12.533
1.216*
10.600
Travel Time (Min)
45.482
29.784
43.694
33.241
1.788
47.000
Monthly Cost (¥)
221.394
508.842
184.283
488.269
30.219
227.167
Car
0.126
0.332
0.116
0.320
0.010
0.229
Taxi
0.021
0.144
0.025
0.156
-0.004
0.033
Bike
0.065
0.246
0.072
0.258
-0.007
0.139
Subway
0.441
0.497
0.317
0.465
0.124***
0.148
Bus
0.267
0.442
0.342
0.474
-0.075***
0.228
Walk
0.067
0.251
0.122
0.327
-0.054***
0.194
Gender (1=Female)
0.545
0.498
n/a
n/a
n/a
0.484
Age
29.517
10.171
n/a
n/a
n/a
37.700
Education (Yrs)
14.358
2.584
N/A
N/A
N/A
11.500
Household Income
(¥10,000s)
15.142
9.162
N/A
N/A
N/A
11.098
Household Population
3.172
1.097
N/A
N/A
N/A
2.700
Households With Cars
0.583
0.493
0.167
0.373
0.416***
0.420
0.760
0.812
0.192
0.459
0.568***
0.197
Primary Travel Mode
Socioeconomic Variables
# Cars in Household
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**
***
Note: p<0.1; p<0.05; p<0.01 using t-test. N/A = not available or not collected. n/a = not applicable. Citywide
averages for occupation and socioeconomic data are from 2012; commuter and travel mode data are from 2010;
monthly transportation cost data is from 2013.
Thermal Comfort & Crowding: Preferences and Actual Levels
We now present respondents’ preferred temperatures as well as self-reported thermal comfort
and crowding levels. Respondents were asked what their preferred temperatures were at home
and at work. The average preferred temperature was 25.1 °C (77.2 °F), agreeing with past results
(21) to be slightly lower than the 26 °C (78.8 °F) standard set by the Chinese government. Male
respondents had an average preferred temperature of 24.76 °C (76.57 °F), lower than female
respondents’ 25.37 °C (77.67 °F) and is statistically significant. On average, higher income and
lower income groups were more sensitive to temperature compared the middle-income
respondents, likely due to activity, work environment, and clothing differences.
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In terms of self-reported thermal comfort, 19.4% bus riders, 16.6% subway riders, and
12.4% car/taxi riders reported average onboard temperatures to be “very hot” for their primary
travel mode. In terms of crowding, 41.6% bus riders and 59.9% subway riders reported their
average ride to be “very crowded”. This suggests that thermal comfort was poorest for bus, while
crowding was more severe for subway. Overall, crowding dissatisfaction was much higher than
air-conditioning, which agrees with our literature review that crowding is often a more
significant problem. Worth noting is that high dissatisfaction does not necessarily lead to mode
shift, as previously discussed (6, 23).
SP Approach
We now present results from an SP approach in order to better understand the potential effect of
thermal comfort and crowding not clearly depicted in the previous summaries, which traditional
revealed preference approaches would not provide (29, 30).
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FIGURE 2 Two Examples of the Six Game Cards Presented to Respondents
We asked respondents to choose their preferred mode for their evening commute in 6
games. Figure 2 shows 2 examples. Respondents were given the prompt: “Suppose travel
distance is 8km. Given the information provided below, choose your most preferred travel mode
for commuting from work/school back to your residence”. 4 mode options were given, including
bus, subway, car/taxi, and bike. Variables included onboard temperatures, crowding, as well as
distance, price and travel time for a single journey based on citywide averages (8).
Among the 6 games, there were 3 main scenarios based on onboard temperature and
crowding levels. The baseline scenario had comfortable levels of temperature, noted as “cool”,
and crowding, noted as “not crowded”. In alternative Scenario 1, bus and subway temperatures
were both changed to “very hot”, while crowding levels remained “not crowded”. In alternative
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Scenario 2, subway temperature was changed to “cool” from scenario 1, but in exchange,
crowding level was changed to “very crowded”. For each of these three main scenarios, we
included an additional scenario where fare prices were doubled from their original levels for bus
and subway, keeping other factors the same. This closely mimicked the fare hike in January 2015
through the “Public Transit Fare Adjustment” policy measure, 4 months after our survey was
completed, and allows the comparison between the effects of thermal comfort, crowding, and
fare price.
The alternative variant effects of onboard air-conditioning, crowding, and price levels on
ridership are estimated using a multinomial logit (MNL) model, shown in Table 3.
TABLE 3 Probability of Choosing Bus, Subway, Bike Relative to Car/Taxi
Variable
Intercept
Scenario 1: (Dummy)
Bus Hot, Subway Hot
Scenario 2: (Dummy)
Bus Hot, Subway Crowded
Additional Scenario: (Dummy)
Transit Fares Doubled
Sensitive to Temperature (Dummy)
Poor Transit Satisfaction (Dummy)
Student (Dummy)
Gender (1=Female)
Age (Years)
Alternative
Odds
p-value
Bus
40.246***
p = 0.000
Subway
33.016***
p = 0.000
Bike
12.846***
p = 0.000
Bus
0.454***
p = 0.000
Subway
0.468***
p = 0.000
Bike
1.067
p = 0.723
Bus
0.899
p = 0.408
Subway
0.507***
p = 0.000
Bike
1.105
p = 0.596
Bus
1.115
p = 0.283
Subway
0.816**
p = 0.025
Bike
1.388**
p = 0.022
Bus
0.424***
p = 0.000
Subway
0.477***
p = 0.000
Bike
0.553***
p = 0.003
Bus
0.418***
p = 0.000
Subway
0.605***
p = 0.000
Bike
0.497***
p = 0.005
Bus
1.355**
p = 0.040
Subway
0.967
p = 0.802
Bike
1.804***
p = 0.003
Bus
1.090
p = 0.401
Subway
1.195*
p = 0.052
Bike
0.546***
p = 0.000
Bus
1.005
p = 0.366
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Subway
0.981***
p = 0.000
Bike
0.993
p = 0.408
Bus
0.893***
p = 0.000
Subway
0.989
p = 0.554
Bike
0.858***
p = 0.000
Bus
0.912***
p = 0.000
Subway
0.938***
p = 0.000
Bike
0.942***
p = 0.000
N
4,122
Log Likelihood
-4,687.40
LR Test
604.144*** (df = 24)
Note: *p<0.1; **p<0.05; ***p<0.01.
We find statistically significant reductions in the probability of choosing bus and subway
ridership in Scenario 1 (“Bus Hot, Subway Hot”) relative to car/taxi, as well as for subway
Scenario 2 (“Bus Hot, Subway Crowded”). These results are intuitive: deterioration of thermal
comfort as well as an increase in crowding will likely hurt transit ridership compared to other
modes, given equal travel times, costs for the mode chosen as well as all alternatives. In the
additional scenario where bus and subway fare prices are doubled, we observe a statistically
significant probability reduction for subway and an increase for bike; a slight increase in the
probability of choosing bus was observed but not statistically significant, likely associated with
the fact that bus fares were already very low.
We then conducted Wald Tests to compare the odds predicted by the model. Both
Scenario 1 and Scenario 2 have “hot, not crowded” for bus, while subway was “hot, not crowed”
in Scenario 1, and “crowded, but not hot” in Scenario 2. The odds for choosing bus is
significantly higher in Scenario 2, suggesting that bus ridership is higher when the subway is
“crowded” compared to when the subway is “hot”. This is in line with literature that suggests
crowding has more impact on ridership than thermal comfort. There is no statistically significant
difference in the odds for subway between the two scenarios, which might suggest that crowding
and high temperatures on subway create similar levels of disutility. This shows that although the
effect of thermal comfort is smaller than crowding, it still has a sizable influence. Comparisons
for subway and bike were not statistically significant.
For each scenario, we compare the odds for choosing bus and subway. In Scenario 1,
“bus hot, subway hot” yields a similar negative effect for buses and subways with no statistically
significant difference. In Scenario 2, “bus hot, subway crowded”, the impact on subway is found
to be significantly higher than for bus, again showing respondents being more sensitive to
crowding than to air-conditioning. Finally, in the final scenario where bus and subway fares were
doubled from their original levels, the odds for subway is more sensitive to the price increase
compared to for bus, given the same percentage price change. Since Beijing’s 2014 bus fares
were much lower than subway fares, it is reasonable that doubling fares will not impact bus
ridership as much as for subway.
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In addition to the above analysis, we found that those with high sensitivity to temperature
and very poor attitudes (satisfaction) towards transit had lower odds of choosing all three non-car
modes, and this result is statistically significant. A potential limitation is reverse-causality, in
which respondents report poor attitudes in response to bad transit commute experiences.
Using the MNL model, we predict the probability of choosing each mode over annual
household income, for the baseline scenario and each of the alternative scenarios discussed
above. Shown in Figure 3, as household income increases, the probability of choosing bus,
subway decreases, while it increases for cars/taxi; bike usage remains flat. Ridership is sensitive
to income for car/taxi and bus but not as so for subway and bike. Intuitively, subway maintains
large advantages in speed and reliability compared to bus, and it is thus reasonable for subway
ridership to be less sensible to income levels. Most interestingly, in most cases, the impacts of
doubling bus and subway fares are not as large as those of certain “crowding and thermal
comfort” scenarios. A reduction of fares from the “fare prices doubled” scenario to the baseline
scenario would only result in a ridership drop of 5-10 percentage points, while improving airconditioning and crowding combined may result in ridership gains of up to 20 percentage points.
As previously discussed, this is likely due to the assumption that each alternative scenario has the
exact same access time, wait time, and travel time – if two buses arrive at the same time and their
only difference is temperature and crowding, the respondent will mostly likely choose the one
they deem to have better comfort – such perfect alternatives do not exist. We have shown,
however, that thermal and comfort will likely affect mode choice, especially if other service
improvements are very costly to implement. Agreeing with literature, we show that commuters
are generally more sensitive to crowding than to temperature, and these effects are potentially
comparable to the effect of doubling fare prices.
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FIGURE 3 Predicted Probability of Choosing Each Mode, by Scenario, Household Income
CONCLUSION & DISCUSSOIN
In 2007, the Beijing “Public Transit Fare Reform” drastically cut bus and subway fares as well as
removed differentiated service. While inducing transit demand, it also left passengers facing poor
thermal comfort and high levels of crowding, common in the developing world. Using this
opportunity, we conducted an intercept survey in Beijing and studied the effects of thermal
comfort and crowding on mode choice, and how the effects of thermal comfort, crowding, and
fare prices compare.
Among survey respondents, high dissatisfaction was observed towards thermal comfort
and crowding levels on bus and subway. We utilized a stated-preference (SP) approach and
asked respondents to choose their preferred mode choice given set travel times, costs for each
alternative, as well as varying levels of thermal comfort and crowding. We found that poor
thermal comfort and crowding conditions onboard bus or subway lowered the probability of
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choosing that mode, and that the effect of crowding was larger than the effect for temperature,
agreeing with literature. More on, in the context of Beijing, the effects of thermal comfort and
crowding were found to be potentially comparable to the effects of doubling fare prices; both of
these comfort factors have the potential to induce new or more loyal public transit patrons.
Transit agencies and policy makers should not ignore “soft” service quality issues such as airconditioning and crowding if they become serious and “context specific”.
Set to be an exploratory study, our stated-preference survey only included a limited
number of scenarios and choices, although it was a simple method that was efficient in both time
and budget. Further research should expand on the current study and obtain more accurate data to
measure the effects of thermal comfort and crowding in a variety of contexts, and utilize more
extensive survey methods such as in-person household surveys to enhance the quality of
respondents’ recall. Self-reported temperatures and crowding would preferably be changed to
actual field measurements, to improve data quality on this, evidently, very subjective topic.
ACKNOWLEDGEMENTS
This study was mentored by Professor Daniel Chatman and Dr. Calanit Kamala, funded by the
SURF/L&S fellowship, all at the University of California, Berkeley, thanks very much to them.
Also thanks to Yueyang Shen, Xunan Zhang, Zhonglian Sun, Jinqi Wang, Guo Liu, Jingyi Yuan,
Weishen Miao, Ran Xin, Zhaocheng Li, Xuchao Gao, Weike Sun for their superb research
assistance.
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