ABSTRACT
This paper aims to explore the impact of residents'personal attributes, household attributes, travel characteristics, pandemic perception attributes, behavioral intentions, choice attitudes and other factors on travel mode choices in different stages of the COVID-19 pandemic. A mixed Logit model is constructed based on the travel data from the sampling survey of Beijing residents under three stages of the pandemic, the initial phase, the outbreak phase, and the stabilization phase. The results show that travel distance is positively correlated with travel mode choice in all three stages of the pandemic. The maximum predicted marginal values of each stage and their corresponding variables are 3.299 (5~10 km), 2.983 (>10 km), and 3.148 (5~10 km), respectively, and long-distance travel has the greatest impact on the travel mode choice. The perceived attributes of the pandemic and travel distance have obvious moderating effects on the travel mode choice. During the pandemic outbreak period, the perceived pandemic attributes, behavioral intentions, and choice attitude variables have a significant negative correlation with the travel mode choice, and residents'psychological concern of being cross-infected during travel is obvious;only 18.8% of travelers choose to travel by bus or subway, and the travel structure changes significantly. During the stable period of the pandemic, the choice of attitude variable has a positive and significant impact on the choice of transportation mode, and the degree of influence becomes larger, indicating that travelers demand a higher rate of population contact and strictness of pandemic prevention measures for transportation mode. The research results can provide a reference basis for travel decisions of travelers under public health events and the prevention and control of the pandemic by relevant management departments. © 2022 Journal Northern Jiaotong University. All rights reserved.
ABSTRACT
This paper aims to explore the impact of residents'personal attributes, household attributes, travel characteristics, pandemic perception attributes, behavioral intentions, choice attitudes and other factors on travel mode choices in different stages of the COVID-19 pandemic. A mixed Logit model is constructed based on the travel data from the sampling survey of Beijing residents under three stages of the pandemic, the initial phase, the outbreak phase, and the stabilization phase. The results show that travel distance is positively correlated with travel mode choice in all three stages of the pandemic. The maximum predicted marginal values of each stage and their corresponding variables are 3.299 (5~10 km), 2.983 (>10 km), and 3.148 (5~10 km), respectively, and long-distance travel has the greatest impact on the travel mode choice. The perceived attributes of the pandemic and travel distance have obvious moderating effects on the travel mode choice. During the pandemic outbreak period, the perceived pandemic attributes, behavioral intentions, and choice attitude variables have a significant negative correlation with the travel mode choice, and residents'psychological concern of being cross-infected during travel is obvious;only 18.8% of travelers choose to travel by bus or subway, and the travel structure changes significantly. During the stable period of the pandemic, the choice of attitude variable has a positive and significant impact on the choice of transportation mode, and the degree of influence becomes larger, indicating that travelers demand a higher rate of population contact and strictness of pandemic prevention measures for transportation mode. The research results can provide a reference basis for travel decisions of travelers under public health events and the prevention and control of the pandemic by relevant management departments. © 2022 Journal Northern Jiaotong University. All rights reserved.
ABSTRACT
This study aims to evaluate to what level bicycles can replace motorized vehicles during the outbreak. The survey respondents were asked to choose between a bicycle and existing motorized vehicles for seven choice scenarios based on traffic congestion and bicycle infrastructure. This study integrated a multiple-indicator, multiple-cause (MIMIC) and a mixed logit model to examine the attitudes change caused by the outbreak and the social influences on the preference for bicycles. The results indicated that developing bicycle infrastructure encourages the adoption of bicycles, although most users tend to come from low-income and less-educated people. Based on the MIMIC model results, this study found significant differences in attitudes change and social influences across respondents' socioeconomic characteristics, as male respondents were more likely to be easily influenced by their friends with respect to cycling than females. Attitudes change related to global warming and environmental consciousness also affected the preference for bicycles.
ABSTRACT
In this stated preferences study, we describe for the first time French citizens' preferences for various epidemic control measures, to inform longer-term strategies and future epidemics. We used a discrete choice experiment in a representative sample of 908 adults in November 2020 (before vaccination was available) to quantify the trade-off they were willing to make between restrictions on the social, cultural, and economic life, school closing, targeted lockdown of high-incidence areas, constraints to directly protect vulnerable persons (e.g., self-isolation), and measures to overcome the risk of hospital overload. The estimation of mixed logit models with correlated random effects shows that some trade-offs exist to avoid overload of hospitals and intensive care units, at the expense of stricter control measures with the potential to reduce individuals' welfare. The willingness to accept restrictions was shared to a large extent across subgroups according to age, gender, education, vulnerability to the COVID-19 epidemic, and other socio-demographic or economic variables. However, individuals who felt at greater risk from COVID-19, and individuals expressing high confidence in the governmental management of the health and economic crisis, more easily accepted all these restrictions. Finally, we compared the welfare impact of alternative strategies combining different epidemic control measures. Our results suggest that policies close to a targeted lockdown or with medically prescribed self-isolation were those satisfying the largest share of the population and achieving high gain in average welfare, while average welfare was maximized by the combination of all highly restrictive measures. This illustrates the difficulty in making preference-based decisions on restrictions.
ABSTRACT
Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively;thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19. Copyright © 2022 Zhang, Shao, Wang, Huang, Mi and Zhuang.
ABSTRACT
As the private sector is under heavy pressure to serve the ever-growing e-commerce market, the potential of implementing new disruptive mobility/logistics services for increasing the level of the current last-mile delivery (LMD) services, is emerging. Vehicle automation technology, characterized by high-capacity utilization and asset intensity, appears to be a prominent response to easing this pressure, while contributing to mitigation of the adverse effects associated with the deployment of LMD activities. This research studied the perceptions of Greek end-users/consumers, regarding the introduction of autonomous/automated/driverless vehicles (AVs) in innovative delivery services. To achieve this, a mixed logit model was developed, based on a Stated Preferences (SP) experiment, designed to capture the demand of alternative last-mile delivery modes/services, such as drones, pods, and autonomous vans, compared to traditional delivery services. The results show that the traditional delivery, i.e., having a dedicated delivery person who picks up the parcels at a consolidation point and delivers them directly to the recipients while driving a non-autonomous vehicle—conventional van, bike, e-bike, e-scooter—remains the most acceptable delivery method. Moreover, the analysis indicated that there is no interest yet in deploying home deliveries with drones or AVs, and that participants are unwilling to pay extra charges for having access to more advanced last-mile delivery modes/services. Thus, it is important to promote the benefits of innovative modes and services for LMD, in order to increase public awareness and receptivity in Greece.
ABSTRACT
To contain the sudden spread of SARS-CoV-2, many governments encouraged people to work from home, generating an unprecedented diffusion of this activity. Furthermore, Covid-19 has induced drastic changes in everyday life and travel habits, which might persist in the future. This paper aims to understand and estimate the potential long-term impacts of telework on the environment due to the pandemic, by analyzing factors affecting the frequency of telecommuting, the mode choice for traveling to work, and pollutant emissions generated by these trips. Data from a mobility survey administered in Padova (Italy) was used. Results indicate that Covid-19 could cause a rebound effect reversing the positive impacts of working from home, since, even if the number of trips could be reduced, many shifts towards non-sustainable travel modes could occur. The promotion of telework should be combined with measures fostering sustainable travel habits to pave the way towards a future green mobility.
ABSTRACT
In order to explore the choice behavior of residents' travel mode in the post-COVID-19 era, a choice behavior experiment was conducted. A mixed Logit model and a latent class conditional Logit model of travel mode choice were constructed based on the data obtained from questionnaire surveys. Stata software was used to calibrate the model parameters, and the main factors influencing residents' travel mode choices were obtained. The results show that both models reflect the heterogeneity of individual travel mode choices. Compared with the mixed Logit model, the latent class conditional Logit model has an improvement of 13% in the goodness of fit and an increase of 3.03% in the prediction accuracy, which provides an effective tool for analyzing individual heterogeneity of travel behavior under public health emergencies. The latent class conditional Logit model divides residents into four and five groups according to the two scenarios of low and medium risk areas. From the perspective of travel mode attributes, the waiting time and the traveling time have become the most important influencing factors for residents to choose the travel modes. From the perspective of personal socio-economic attributes, women with higher incomes are more inclined to choose private cars to travel. The older are more sensitive to travel costs, and men are more willing to choose bus and subway travel. Copyright © 2022 by Science Press.
ABSTRACT
To analyze the impact of COVID-19 on the travel mode choice behavior with diverse shared mobility services, this study designed the stated preference (SP) questionnaire for the multi-modal transportation system which include conventional travel modes, ride hailing, ride sharing, car sharing, and bike sharing. The mixed Logit models with panel data were proposed to investigate the travel mode choices before and during COVID-19. The influence differences of explanatory variables are compared, and the joint effects of perceived pandemic severity and mode choice inertia are examined. Based on the elasticity analysis, the mode choice preferences are predicted corresponding to different management policies under COVID-19 pandemic. The results indicate that the perception to pandemic severity has significant impacts on the ridership of ride sharing and car sharing, and the mode choice inertia obviously affects the usage of ride hailing, car sharing, and bike sharing. When the perceived pandemic severity reduces to 30%~50%, the strategy of increasing parking charge to 1.6~3.0 times would reduce the usage of private car to pre-pandemic condition, and the car sharing with lower close contact risk could become a main substitute. When the perceived pandemic severity is higher than 60%, the strategy of increasing the travel safety of ride sharing to 1.4~3.6 times would improve the ridership. Copyright © 2022 by Science Press.
ABSTRACT
The need to recognise and account for the influence of working from home on commuting activity has never been so real as a result of the COVID-19 pandemic. Not only does this change the performance of the transport network, it also means that the way in which transport modellers and planners use models estimated on a typical weekday of travel and expand it up to the week and the year must be questioned and appropriately revised to adjust for the quantum of working from home. Although teleworking is not a new phenomenon, what is new is the ferocity by which it has been imposed on individuals throughout the world, and the expectation that working from home is no longer a temporary phenomenon but one that is likely to continue to some non-marginal extent given its acceptance and revealed preferences from both many employees and employ where working from home makes good sense. This paper formalises the relationship between working from home and commuting by day of the week and time of day for two large metropolitan areas in Australia, Brisbane and Sydney, using a mixed logit choice model, identifying the influences on such choices together with a mapping model between the probability of working from home and socioeconomic and other contextual influences that are commonly used in strategic transport models to predict demand for various modes by location. The findings, based on Wave 3 (approximately 6 months from the initial outbreak of the pandemic) of an ongoing data collection exercise, provide the first formal evidence for Australia in enabling transport planners to adjust their predicted modal shares and overall modal travel activity for the presence of working from home.
ABSTRACT
Purpose The objective is to determine if consumer preferences for grocery purchasing are impacted by the severity of the COVID-19 pandemic and whether these preferences differ by demographic and psychographic characteristics. Design/methodology/approach The authors conduct an online discrete choice experiment (DCE) with 900 U.S. consumers to assess grocery shopping preferences under various scenarios of the COVID-19 pandemic (i.e. decreasing, constant and increasing cases). The attribute of interest is the purchasing method (i.e. in-store purchase, in-store pickup, curbside pickup and home delivery) with minimum order requirements, time windows and fees as secondary attributes. Heterogeneity in individual-level willingness-to-pay (WTP) estimates for the main attribute is analyzed by means of mixed logit and quantile regression techniques. Findings The mixed logit model reveals heterogeneity in WTP estimates for grocery purchasing methods across participants. According to estimates from quantile regressions, the heterogeneity is partly explained by the severity of the COVID-19 pandemic. For example, the home delivery purchasing method is less preferred when the number of cases is decreasing. The results also show that consumer preferences for grocery shopping methods are affected more by psychographic characteristics than demographic characteristics. Consumers who comply with COVID-19 directives (e.g. wear face coverings) have stronger preferences for curbside pickup and home delivery, particularly at the tails of the WTP distributions. Originality/value Although there is much data on food consumer behavior during the COVID-19 pandemic at the aggregate level, there are few analyses of grocery shopping preferences at the individual level. The study represents a first attempt to relate individuals' demographic and psychographic characteristics to their grocery shopping preferences during the COVID-19 pandemic, thus yielding numerous recommendations in terms of consumer segmentation.