How Does the Weather Influence Travel Activity? Evidence from Michigan
Submitted for review for the 2005 Annual TTRA Conference
by
Charles Shih and Sarah Nicholls
Department of Community, Agriculture, Recreation & Resource Studies
Michigan State University
131 Natural Resources Building
East Lansing, MI 48824-1222
Phone: (517) 353 5190
Email
Charles Shih: shrjuany@msu.edu
Sarah Nicholls: nicho210@msu.edu
Submitted for review as an oral presentation only (option a)
Session topic “travel research methods, approaches or uses”
How Does the Weather Influence Travel Activity? Evidence from Michigan
Given the heavy dependence of a large number of tourism activities on the appropriate
weather conditions, either to make them possible, e.g., adequate snow for skiing, or, at least,
enjoyable, e.g., warm enough conditions for beach activities, the lack of published research on
the relationships between participation and the weather is surprising. In light of increasing
evidence regarding climate change, most notably the global trends towards rising temperatures
and sea levels (IPCC, 2001), this lack of knowledge should be of special concern to tourism
providers worldwide. Despite the call by Butler (2001) for the industry to pay much closer
attention to this issue, climate change remains largely absent from the tourism literature.
Rising temperatures have the potential to negatively impact both winter and summer
destinations, by reducing the length of seasons for winter activities such as skiing while leaving
some summer destinations such as those in southern Europe simply too hot for human comfort.
The impacts of projected climate change will vary spatially, however, and many regions which
currently experience somewhat marginal weather may in the future experience more pleasant
conditions, thereby increasing their attractiveness as seasonal or year-round tourism destinations
(Agnew and Viner, 2001). The outlook for the tourism industry is, therefore, mixed, and further
research is crucial so as to best equip the industry for successful adaptation to future conditions.
Analysis of the likely impacts of projected climate change on tourism activities and
patterns first requires understanding of current relationships between participation and the
weather, and it is this dynamic that forms the basis of this study. Specifically, the purpose of the
paper is to ascertain the major influences and, hence, the relative significance of weather
variations, on tourism traffic in the state of Michigan. The somewhat generic measure of “tourism
traffic,” rather than levels of participation in individual activities such as skiing or camping, was
chosen to represent the overall level of tourism activity in the state, and was considered
appropriate since the majority of Michigan travelers use cars and other recreational vehicles as
their primary mode of transportation (as noted by, e.g., Rink, 2004).
Methods
Daily highway traffic counts were obtained from the Michigan Department of
Transportation for a 10-year period (1991-2000) for 18 “class counters.” Such counters record the
number of axles on each vehicle, thereby allowing the removal of large vehicles (four or more
axles), likely to be traveling for commercial rather than tourism purposes, from the dataset.
Whereas previous research has focused mainly on the influence of income and prices on
aggregated consumption data such as annual visits (e.g., Crouch, 1994; Lim, 1999), the use of
daily data enables the investigation of much shorter-term variations in the explanatory variables.
The following independent variables were available for inclusion in the analyses: daily
maximum temperature, daily precipitation, economic conditions (as suggested by the monthly
Consumer Confidence Index published by The Conference Board), average weekly gas prices (as
recorded by the American Automobile Association), time of week (specifically, MondayThursday, Friday or Sunday, or Saturday), public holidays (plus the traditional travel days
immediately before and after such holidays), and year (to account for time series issues). Weather
data were acquired from the recording station closest to each traffic counter.
Separate regressions were run for each season, namely winter (Dec-Feb), spring (MarMay), summer (Jun-Aug) and fall (Sep-Nov). Experimentation with various forms indicated that
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a logarithmic transformation of the dependent variable produced the most useful models. The
regressions each followed the general form: log (traffic) = f (temperature, precipitation, consumer
confidence, gas price, day of week, public holidays, year).
Findings
Model performance (as measured by R2) varied substantially with the type of route on
which the traffic counter was located, classified by the authors as either urban, rural, or tourist.
The lowest levels of performance (R2 below 0.4) were discovered for the four counters on rural
roads, routes along which the majority of traffic is likely to be local, e.g., M-95 in the Upper
Peninsula. In urban areas, e.g., along M-10 in Detroit, performance was somewhat improved,
ranging from 0.4 to 0.6 across the six counters. However, much of this traffic is also likely to be
local, business, and/or commuter. The highest levels of performance (R2 above 0.6) were
recorded for the seven counters on roads likely to receive rather large proportions of tourist
traffic, e.g., US-127 in Clare, one of the most heavily utilized routes between the lower half of
Michigan and the northern parts. Since the focus of the study was primarily on building models
of tourism-related movement, the following observations refer only to the seven counters located
along corridors dominated by tourist traffic. Table 1 demonstrates a sample set of results.
Table 1. Sample Results for Class Counter on US-127 at Clare, Michigan
Season
Independent Variables
Constant
Maximum temperature
T-T* (spline term)
Precipitation
Consumer confidence
Gas prices
Fri/Sun (non-holiday)
Saturday (non-holiday)
Holiday weekend (begin/end)
Holiday weekend (middle)
Year
Model R-square
Spring
β
Sig.
-84.46
0.00
0.02
0.00
0.00
0.08
0.00
0.20
0.19
0.04
0.76
0.00
0.27
0.00
1.10
0.00
0.66
0.00
0.05
0.00
0.83
Summer
β
Sig.
-71.89
0.00
0.01
0.00
-0.07
0.05
0.00
0.45
0.00
0.09
-0.24
0.00
0.88
0.00
0.40
0.00
1.17
0.00
0.62
0.00
0.04
0.00
0.83
Fall
β
-85.27
0.02
0.00
0.00
-0.12
0.85
0.29
1.11
0.55
0.05
0.83
Sig.
0.00
0.00
0.00
0.03
0.28
0.00
0.00
0.00
0.00
0.00
Winter
B
Sig.
-80.29
0.00
0.00
0.45
-0.01
0.00
0.00
0.35
-0.12
0.17
0.80
0.00
0.26
0.00
0.71
0.00
0.44
0.00
0.04
0.00
0.74
Time appeared to have the most substantial impact on tourist traffic across all locations
and all four seasons. In almost all cases, the variables representing weekends (Friday-Sunday)
and holidays were statistically significant at the 5% level or better. On non-holiday weekends,
Fridays and Sundays exhibited substantially increased levels of traffic, while the increase on
Saturdays was less dramatic though still statistically greater than during the week. As expected,
holidays also tended to be associated with substantial increases in traffic, with the greatest rises
on the traditional travel days immediately preceding and following these days. The year variable,
representing an annual time trend, consistently indicated that traffic along the routes from which
data were available steadily increased by about 4% per annum over the time span studied.
Of the two weather variables available, maximum temperature appeared to have the most
substantial impact on tourist traffic. Temperature had a highly significant positive impact on
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traffic at all counter locations in spring, summer, and fall, but not in winter. In general, traffic
was found to increase by 1 to 2% with a 1ºC rise in temperature. Further analysis utilizing a
spline term, however, a technique that enables identification of shifts in a coefficient’s magnitude
at some particular level (Greene, 2002), suggested that a threshold temperature does indeed exist,
with summer traffic starting to decrease when temperature rises above 32ºC or 90ºF. Precipitation
appeared to have a lesser impact on tourist traffic than maximum temperature; its effect was
found to be significant in the fall and winter seasons only, for five of the seven counters.
Of special interest given current economic and political circumstances, gas prices were
found to have little to no impact on tourist traffic. Similarly, the variable representing consumer
confidence rarely reached statistical significance, again suggesting that economic factors may
have less influence on tourist traffic than other factors such as time and temperature.
Application of Results
The results suggest that the weather does indeed have a statistically significant impact on
tourism traffic in Michigan. Since previous analyses of these relationships have been rare, results
such as these, which more clearly articulate their magnitudes and directions, should be of great
utility to a multitude of tourism providers, for both their short and long term planning and
management decisions. This is especially the case for those offering traditional winter and
summer activities, such as skiing, camping and golfing, which are particularly weatherdependent. The models also demonstrate the relative impact of temporal and economic factors,
with the former appearing to exert significantly more influence on tourism travel in Michigan
than the latter, a finding that appears to contradict some surveys as well as popular opinion
regarding the deleterious impact of higher gas prices on automobile traffic (e.g., Jones, 2000).
Further, the models constructed would form the basis of any analysis of how future
climate variability and change might impact the outdoor recreation and tourism sectors. For
example, examination of traffic levels at one, two and three standard deviations above the
historical mean maximum temperature would reveal how travel activity might respond to the
warmer conditions suggested by most climate scientists. Similarly, potential deviations in other
weather variables, as well as in other economic, social or demographic conditions, could be
incorporated, providing the industry with a wide range of future scenarios to consider and plan
for. Acquisition of daily participation data for specific activities or from individual sites would
allow more targeted analyses than the somewhat generic, traffic-based models presented here.
Conclusions
Given the highly intuitive nature of the relationship between weather conditions and
tourism activity, the lack of research on this topic is surprising. While historical weather and
future climate data are plentiful, perhaps the greatest challenges facing tourism-related research
in this area are the raising of awareness of the issue, and the acquisition of historical participation
data upon which to conduct analyses of past and future trends. Once such data have been
acquired, our research suggests that it is indeed possible to construct rigorous models that can
then be adapted into highly meaningful decision support tools for the industry. Future research
will focus on integrating the models of tourist traffic with various climate change scenarios, as
well as on the construction of participation models for specific sectors of the Michigan tourism
industry such as skiing, camping and golf.
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References
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Tourism and Hospitality Research, 3(1), 37-61.
Butler, R. (2001). Editorial. Tourism and Hospitality Research, 3(1), 5-6.
Crouch, G. I. (1994). The study of international tourism demand: A review of findings. Journal
of Travel Research, 33(1), 12-23.
Greene, W. H. (2002). Econometric analysis. (6th. ed). New Jersey: Prentice Hall.
IPCC. (2001). Climate Change 2001: The ScientificBasis. Contribution of Working Group I to
the Third Assessment Report of the Intergovernmental Panel on Climate Change
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Jones, J.M. (2000). High gas prices affecting many Americans' driving and travel plans.
Retrieved January 13, 2005, from http://www.gallup.com/poll/content/login.aspx?ci=2806
Lim, C. (1999). A meta-analytic review of international tourism demand. Journal of Travel
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Rink, J. (2004). 1.1 Million to travel during Labor Day, says AAA Michigan. Retrieved January
14, 2005, from http://www.autoclubgroup.com/michigan/about_us/press_releases.asp?
articleID=1433&view=archive
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