The economic and environmental impacts of transportation demand in urban areas have drawn considerable attention from policymakers and researchers, especially as cities confront rising fuel prices and environmental challenges. Among the factors influencing sustainable development, reducing travel demand, particularly vehicle miles travel (VMT), is considered a key strategy for sustainability. ( Wang & Chen, 2014 ). Hence, gasoline prices directly impact vehicle travel’s frequency and distance. Studies on price elasticity within the transportation industry highlight that consumer responses to changes in fuel prices are complex and depend on numerous variables, including geographic location, income levels, and travel needs ( Almansour, 2022 ; Fridstrøm & Østli, 2021 ). Seasonal effect particularly affects gasoline prices in countries with diverse seasonal changes ( Donna, 2021 ; Goetzke & Vance, 2021 ; Wardman, 2022 ).

Therefore, understanding the seasonal variability in travel demand elasticity is crucial for transportation planners aiming to anticipate and manage changes in urban travel behavior. Seasonal factors such as temperature, school schedules, and holiday periods influence travel patterns, often fluctuating demand elasticity ( Martínez et al., 2020 ; McKercher et al., 2024 ). For instance, summer months may see an increase in discretionary travel as more people undertake vacations. In contrast, winter months might yield lower elasticity due to adverse weather conditions and reduced leisure travel. These seasonal variations not only affect the volume of VMT but also influence the sensitivity of drivers to fuel price changes, which has implications for infrastructure planning and fuel tax policy ( Afanasyev et al., 2021 ). Given the growing policy emphasis on sustainable urban mobility and reducing greenhouse gas emissions, the need for a nuanced understanding of these dynamics has become more urgent. However, a notable gap remains in understanding how seasonal variations affect travel demand elasticity in response to gasoline price changes, particularly within metropolitan settings.

Accordingly, this study addresses the research question: how does the elasticity of travel demand in response to gasoline price changes vary across different seasons? Thus, this study addresses this question by analyzing the seasonal variations in gasoline price elasticity within the Washington, D.C., metropolitan area. It aims to make both empirical and theoretical contributions to the field of transportation economics. Empirically, analyzing seasonal travel demand elasticity contributes to a detailed understanding of short-term versus long-term price responses in urban travel. At the same time, theoretically, it expands existing elasticity models to account for seasonal variables. This research ultimately seeks to inform urban transportation policies, particularly fuel taxes and public transportation investments, by providing evidence on the temporal aspects of gasoline price sensitivity in metropolitan areas, which has been underexplored in elasticity literature. The findings have implications for urban resilience strategies, especially as cities adapt to the dual pressures of fluctuating fuel costs and the need for sustainable transportation solutions.

The remainder of the study is organized as follows: Section 2 reviews the literature on the relations between gasoline prices and VMT while considering seasonal variations, and Section 3 explains the methodology. Section 4 presents results and discussions, and the research implications and Section 5 has the conclusions, study limitations, and future research directions.

The elasticity of travel demand regarding gasoline price changes is crucial in transportation economics, offering insights into consumer behavior and policy implications. Typically, gasoline price elasticity indicates the change in travel demand corresponding to changes in fuel prices, with studies observing low elasticity in the short term as travelers have limited alternatives ( Donna, 2021 ; Goetzke & Vance, 2021 ; Wardman, 2022 ). However, as the temporal dimension extends, demand often becomes more elastic as individuals have time to adapt by shifting to alternative modes of transport, consolidating trips, or altering work arrangements ( Hensher et al., 2024 ; Wu et al., 2023 ). Seasonal variabilities add further complexity as gasoline price responsiveness changes across different times of the year, influenced by environmental, seasonal travel demands, and fuel price volatility.

Studies on the seasonal variability of travel demand elasticity have revealed that seasonal climate changes significantly affect the elasticity of gasoline demand. Gourley (2021) observed that cold weather often yields lower price elasticity as consumers have limited alternatives for travel in adverse weather conditions. This is more apparent in areas where cycling or walking is impractical in winter. However, warmer seasons bring higher elasticity as consumers engage in more discretionary travel such as vacations, making gasoline demand more sensitive to price changes ( Goyal et al., 2021 ; Vickerman, 2024 ). These seasonal variations stipulate the essence of context in assessing fuel price sensitivity, as regions with distinct seasonal changes may have different responsiveness to fuel price changes.

Moreover, the elasticity of gasoline demand also varies seasonally based on geographic and socioeconomic variabilities, shaping the types of travel alternatives available to different demographics. For instance, densely populated urban areas, such as Beijing and Texas, with public transit alternatives show high elasticity in warmer seasons when alternative modes like biking and transit are accessible ( Chun et al., 2024 ; Wu & Liao, 2020 ). However, suburban and rural areas, where car dependence is high, demonstrate more consistent demand regardless of season ( Molamphy, 2024 ). Additionally, to an asymmetrical response, consumers adjust more to fuel price increases than decreases, particularly in peak travel seasons like summer ( Kamyabi & Chidmi, 2022 ; Kumar et al., 2020 ). This asymmetry indicates that the budgetary impact of gasoline prices on consumers is intensified during high-travel seasons, with direct effects on their travel demand elasticity.

Despite significant findings on the relationship between gasoline prices and travel demand, there are no studies on how this elasticity shifts across seasons in urban settings with granular geographic and socioeconomic data. This study aims to fill that gap by analyzing seasonal variations in travel demand elasticity in the Washington, D.C., metropolitan area, focusing on how fuel price responsiveness differs across census tracts and seasons. By integrating a seasonal framework into elasticity modeling, this study contributes to a refined understanding of travel demand patterns, enabling transportation policymakers to craft more effective, seasonally adjusted interventions for congestion and environmental impact mitigation.

The econometric analysis of travel demand elasticity highlights key relationships between the dependent variable, VMT, and various predictors, including gasoline prices, population density, and road infrastructure. In Table 3 , the constant term 2.02, representing the baseline VMT when all predictors are set to zero, was not statistically significant (p = .193), suggesting that the model’s intercept does not provide a meaningful standalone interpretation. This result does not undermine the model’s utility; instead, it highlights real-world travel behaviors, where VMTs are driven by dynamic predictors like gas prices, population density, and road infrastructure. Thus, the significance of these predictors validates the model’s accuracy and capacity to inform practical, data-driven transportation policies and planning strategies. Hence, the coefficient for gasoline price (Gasoline Price) was statistically significant (p < .001), indicating that a 1% increase in gas price corresponds to a .44-unit increase in VMT. This result underscores the influence of fuel costs on travel behavior, aligning with prior studies in transportation economics ( Garcia-Sierra et al., 2015 ; Yang & Timmermans, 2013 ) ( Table 3 ).

Among the control variables, population density demonstrated a significant negative effect on VMT, with a coefficient of −.18 (p < .001). This suggests that higher population density is associated with reduced vehicular travel, likely due to greater access to alternative transportation modes or reduced need for personal vehicle use in denser areas like Shanghai and New York ( Boulange et al., 2017 ; Saghapour et al., 2016 ). Road infrastructure, measured by the road count, exhibited a positive and highly significant relationship with VMT, with a coefficient of .14 (p < .001), indicating that increased road availability encourages more significant vehicle usage ( Yu & Zhao, 2021 ). However, household income did not exhibit a statistically significant effect (p = .218), suggesting that income variations within the study region do not meaningfully influence travel demand. This challenges the traditional assumption that income levels heavily dictate travel demand ( Magriço et al., 2023 ). This could result from the narrow income range in the study region, widespread access to alternative transportation options, and fixed travel needs.

Seasonal variations further revealed significant patterns in travel demand elasticity. Compared with the reference season (summer), average VMT decreased by .1 units in spring (p = .018), .07 units in fall (p = .043), and .17 units in winter (p = .001). These findings align with a study that stipulates there is reduced travel activity during colder months, particularly in winter, when adverse weather conditions likely discourage driving ( Gössling et al., 2023 ). Seasonal impacts may also reflect shifts in discretionary travel, as summer often coincides with vacations and leisure trips, while colder seasons see fewer opportunities for outdoor activities. Additionally, winter’s pronounced reduction in VMT could be attributed to an ice-shedding risk, reduced daylight hours, and a general decline in non-essential travel ( Hudde, 2023 ; Matejicka & Georgakis, 2022 ). In contrast, spring and fall show more moderate decreases, likely due to transitional weather conditions that permit limited discretionary travel. These seasonal patterns have practical implications for transportation planning as they suggest that policy interventions, such as promoting public transit or implementing congestion pricing, should account for the temporal variability in travel demand to maximize year-round efficiency and effectiveness.

To ensure the robustness of the models, several tests were conducted. The ANOVA test for the regression model in Table 3 yielded an F-statistic of 34.14 (p < .001), confirming the model’s statistical significance and ability to explain travel demand variations. Additionally, Table 4 shows the results of four statistical tests to assess whether the data follows a normal distribution. A high p-value (greater than .05) suggests that the data do not significantly deviate from normality. All four tests indicate that the data do not deviate significantly from the normal distribution. This means that different statistical methods that assume the normality of the data could be used. Therefore, a closer look at the quantile-quantile (Q-Q) plot is always a good idea. The Q-Q plot in Figure 4 evaluates the normality of the residuals by comparing their quantiles to those of a theoretical normal distribution. Most points closely align with the diagonal reference line, indicating that the residuals are approximately normally distributed. While slight deviations appear at the tails, these are minor and fall within the confidence bands, suggesting the normality assumption holds sufficiently for valid model inference. Hence, these results are accurate and emphasize the critical influence of gasoline prices, road count, and seasonal factors on vehicular travel in the Washington, D.C., metropolitan area.

95% confidence interval for coefficients and ANOVA test of F-statistic of 34.14 (p < .001).

This study comprehensively analyzes the seasonal variations in travel demand elasticity in response to gasoline price changes, focusing on the Washington, D.C., metropolitan area. Integrating seasonal, socioeconomic, and infrastructure-related variables into an econometric framework offers a nuanced understanding of the dynamic factors influencing VMT. The results illustrate the critical roles of gasoline prices, population density, road infrastructure, and seasonal effects in shaping travel demand patterns. Specifically, the significant impact of gasoline prices highlights the potential of fuel pricing policies as an effective lever to manage vehicular travel and promote sustainable urban mobility. Incorporating seasonal variations, such as the notable reduction in VMT during winter months, adds an essential temporal dimension to existing elasticity models, allowing policymakers to tailor interventions to specific times of the year.

The results possess significant practical and theoretical ramifications. The study emphasizes the effectiveness of gasoline taxes, road infrastructure design, and transit-oriented development to alleviate congestion and minimize environmental effects. The study indicates that elevated gasoline prices significantly decrease VMT, underscoring the efficacy of dynamic or targeted fuel taxation in urban settings. Likewise, the inverse correlation between population density and VMT underscores the necessity of creating compact, multimodal urban environments. This study theoretically enhances transportation research by elucidating seasonal impacts on travel demand, addressing a notable deficiency in the elasticity literature. The study illustrates that travel habits are influenced not only by fuel prices and infrastructure availability but also by temporal factors, including weather and discretionary travel options, which were frequently neglected in earlier models.

This study possesses certain limitations that provide opportunities for future directions. First, the study is confined to the Washington, D.C., metropolitan area, perhaps limiting the applicability of the findings to other places with distinct socioeconomic or geographic attributes. Second, the study depends on aggregate data, which may obscure individual-level variances in travel behavior and fuel price sensitivity influenced by demographic or lifestyle factors. Third, although the study encompasses seasonal variations in travel demand, it fails to consider extreme weather phenomena, such as intense storms or heat waves, which may also impact travel patterns.

Therefore, future research should address these limitations by expanding the geographic scope to include metropolitan areas with differing climates, levels of urbanization, and socioeconomic contexts. Comparative analyses across regions can provide deeper insights into the generalizability of seasonal travel demand elasticity. Additionally, incorporating disaggregated data at the household or individual level could offer a more granular understanding of how demographic factors, such as age, gender, or occupation, interact with fuel price sensitivity and seasonal effects.

Future studies could examine the impact of alternative fuel adoption and electric vehicle penetration on seasonal travel demand elasticity. This would provide critical insights into how transitioning to sustainable transportation modes affects traditional elasticity models. Moreover, integrating extreme weather conditions into the analysis would enhance the robustness of the findings, particularly as climate change increases the frequency and intensity of such events. Also, exploring dynamic models that account for lagged responses to fuel price changes could yield a more accurate understanding of long-term travel demand adjustments, complementing the short-term elasticity insights provided by this study.