Advances in information and communication technologies (ICT) have dramatically changed the nature of shopping and the way people travel. As this technology becomes deeply rooted in people’s lives, understanding the interplay between this way and personal travel is becoming increasingly important for planners. Using travel diary data from the 2017 National Household Travel Survey (NHTS) data for structural equation modeling (SEM) analysis, it revealed the interaction between e-shopping and shopping trips and the factors that affect this bidirectional relationship. Results show that e-shopping motivates shopping trips, and in-store shopping inhibits online shopping. It can be obtained that the increase of one standard deviation of e-shopping will increase the shopping trip by 0.17 standard deviation. When shopping trips increase by one standard deviation, e-shopping behavior also decreases by 0.12 standard deviation. The results also demonstrated that e-shopping and shopping travel behavior is heterogeneous across a variety of exogenous factors such as personal attributes, household characteristics, geography, travel distance/duration, and travel mode. Identifying the interaction may help formulate better transportation policies and lay the foundation for travel demand management strategies to reduce the stress on the transportation system and meet individual travel needs.

Analyzing the spatial-temporal distribution of travel carbon emissions will help government departments to develop effective policies and strategies for carbon emission management. This research proposes a trajectory-based analysis method to identify sources of high travel carbon emissions and the relationship between car use and travel carbon emissions. The vehicle-specific power model (VSP), which considers the effect of the vehicle operating speed on emissions, was used to estimate emissions from the travel origin to the destination. The research area was divided into grids according to the population distribution, and the grid carbon emissions (GCE) and grid average carbon emissions (GACE) were calculated. This article used several spatial measurement models to investigate the spatial-temporal trend and influencing factors of travel emissions. A case study using one month of Ningbo taxi data showed the following. 1) The concentration of emission sources was significantly reduced during the evening peak, but the proportion of the contribution was relatively high. 2) Many areas in the suburbs had a high proportion of high emitters throughout the day and not only during the commuting period. 3) Population density and car use ratio were used to explain the quantitative relationship between car use and travel emission sources. This study can guide travel carbon emission monitoring and local carbon emission reduction strategies.

The evaluation of accessibility to healthcare greatly influences public policy. However, existing approaches mainly rely on network topology technology to estimate the driving paths available to healthcare users and the spatial impedances that they may encounter; these approaches do not suffice to represent the real life travel scenarios of urban traffic. This paper proposes a data-driven method to measure healthcare accessibility. The travel records related to visiting a health facility via taxi (VHT) were identified based on the travel destination, and the travel time for each trip was accurately recorded. The spatial interpolation model converts discrete taxi trajectory data into continuous data surfaces, and an improved cumulative accessibility measure method is applied to determine the accessibility to healthcare. A case study using four months of actual Ningbo taxi data shows that the mean absolute percentage error (MAPE) is 7.1%, and the root mean square error (RMSE) is approximately 2.57min. The case study results highlight that changes in travel speed over time have a significant impact on accessibility to healthcare facilities. This method measures the spatial impedance encountered by residents visiting medical facilities by capturing real travel scenarios and presenting the methodological implications of evaluating healthcare accessibility.

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Reducing car dependence is the key to achieving the goal of green and sustainable development. Compared with the existing studies, which mainly focus on administrative areas, this study takes residential areas as the research unit. Four spatial regression models were used to investigate the effect on car dependence of six factors of the built environment (land use mix, population density, jobs–housing balance, bus stop density, metro station density, and road network density). Various test results show that the geography-weighted regression (GWR) model has more substantial explanatory power and that the estimated coefficients of built environment characteristics vary positively or negatively in diverse residential communities. The findings demonstrate that the impact of built environment characteristics on car dependence is significantly spatially heterogeneous. These results are conducive to better comprehending how built environment factors affect car dependence and help establish policies and strategies to promote sustainable transportation.