Travel Behaviour and Society最新文献

The COVID-19 pandemic has caused major disruptions to people’s daily life and travel. This paper aims to reveal the impact of the COVID-19 pandemic on people’s travel in New South Wales (NSW), Australia, and to explore potential measures to recover public transport patronage in the new normal. Research data is collected from a survey of 1,045 residents in NSW, Australia between October 2021 and May 2022. Results show that travel behaviors are significantly different during the pandemic compared to the pre-COVID and the new normal periods. Multiple key factors affecting travelers’ choices in terms of travel mode, travel purpose and their acceptance of emerging mobilities like on-demand transport, autonomous vehicles and drones are identified, including age group, residential area, household status (e.g., couple family with children), household income, need for travel assistance, and travel-related attitude towards health and safety. The research findings suggest that emerging mobilities could provide potential solutions to transport services in a pandemic scenario.

This paper models the ridesourcing market with an explicit consideration of driver order cancellation, and examines the impacts of driver order cancellation on the market. The operation strategy (service pricing, fleet sizing, subsidy to drivers) of the ridesourcing platform has been examined in the presence of driver order cancellation, where the operator maximizes platform profit or social welfare. It is found that the maximum platform profit and/or rider demand after considering driver order cancellation will be smaller than those when order cancellation from drivers is not considered (baseline scenario), i.e., ignoring driver order cancellation will overestimate profit and social welfare. Our results also show that subsidy to drivers to avoid driver order cancellation should be properly set, while compensating the drivers for the whole pickup distance may indeed reduce platform profit when demand is excessive or supply is insufficient.

As urban expansion accelerates, travel distance and vehicle miles traveled continue to grow. Simultaneously, agricultural land is gradually giving way to urban construction, and ancient communities are transforming into urban villages as a result of being surrounded by urban land. Given the widespread distribution of urban villages in Chinese cities, which accommodate a large number of low-income groups, paying attention to the travel behavior of these groups is beneficial for promoting the fair development ofsociety. Therefore, this study utilized resident survey data from urban villages and commercial housing communities of Zhuhai in 2018 and built a structural equation modeling analysis framework to investigate the differences in the influence of the built environment (BE) on the travel behavior of these two types of housing. The results showed that the BE of urban villages had a significantly different impact on travel behavior compared to commercial housing. This discovery can help authorities better understand the impact of the BE of urban villages on travel distance, transit choices, and walking/cycling decisions. Moreover, this research is conducive to proposing targeted measures for sustainable transportation development in urban villages.

Fully autonomous vehicles (AVs) allow users to engage in multitasking behavior while traveling, potentially inducing longer travel because multitasking in AVs would generate a positive utility. Eventually, this may further induce residential relocation, as positive utility virtually reduces the value of time. Such influence may vary depending on whether the AV is used individually or with others (i.e., ride-sharing), as well as the type and amount of multitasking activities carried out in the vehicle. This study examines the influence of the type of AV (ride-sharing or individually used) and the type and amount of in-vehicle multitasking activities on residential location choice behavior through a pivoted stated preference survey. Residential location choice behavior is represented by a panel binary mixed logit model. The model estimation results indicate that the willingness to pay for monthly rent to shorten commuting time is significantly lower when individually used AVs are introduced, compared to non-AVs (i.e., existing automobiles) and ride-shared AVs. Hence, further urban sprawl could occur if individually used AVs become prevalent. Such negative impacts on urban form, however, would be substantially small when AV is introduced under the ride-sharing scheme. It was also found that individuals who can engage in more multitasking behavior in an AV will accept longer travel regardless of the type of AV (ride-sharing or individually used), while individuals who can hardly perform in-car activities tend to resist additional commuting travel time. Moreover, the impact of automated driving at the city scale was examined by running simulations of residential choice in Hiroshima City as a case study. The results suggest that multitasking behaviors in AVs would have modest impacts on urban structure.

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have so far proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.

To examine one element of this issue, we analyzed whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. We used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. We observed notable shifts in the timing of post-pandemic PM peak travel, so we examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).

We found a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. We also found that afternoon/evening trip-making in each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). We found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

Shared bicycle travel has become an important travel mode for urban residents, and bike-sharing platforms are also booming in major cities worldwide. The bike-sharing platform provides users with systematic services: members refer to annual bike-sharing service subscribers and casual users refer to holders of a day pass or single ride ticket. Even though casual users account for a large share of ridership and revenue at bike-share systems in New York City (NYC), very little is known about the characteristics and preferences of casual users and how they compare to members. Based on the open-docked bike-sharing dataset from Citi Bike, we analyze the bike usage patterns of members and casual users in NYC, and how these patterns change in the face of the COVID-19 pandemic on a typical day level. We find that the COVID-19 pandemic has negatively influenced members' bike trip counts on weekdays; bike travel time increases for members during the pandemic and decreases for casual users after the pandemic. To make a profound study concerning spatial heterogeneities, we employ Gaussian Mixture Model (GMM) to cluster the spatiotemporal changes of the station-level bike usage and obtain four clusters for each user type. Combined with the Points of Interest (POI) information, we find that member-related cluster with commuting POIs is significantly affected by the pandemic, while leisure trips are the most severely affected for casual users. Compared with the central area, peripheral clusters with residential and religious POIs are less affected by the pandemic. According to our findings, new operational strategies such as flexible subscriptions can be developed to attract more users, maintain their stickiness, and improve the bike-sharing level of services.

Dockless bike-sharing (DBS) systems have emerged as a popular mode of transportation in urban areas. While existing literature has explored the potential effects of DBS on urban systems, there is limited research on its impact on housing markets. This study addresses this gap by investigating the heterogeneous effects of DBS usage intensity on house prices at various distances from subway stations in Shanghai. Utilizing Mobike trip data and a dataset of 50,837 second-hand houses sold between May 2016 and December 2018, the analysis reveals that DBS usage intensity positively impacts house prices in areas outside 800 m and within 3000 m from subway stations, resulting in a 1.4 % increase in house prices for every 1,000 DBS rides within a 500 m radius. The study also finds that the marginal effect of DBS usage intensity on house prices is contingent on the distance from subway stations. For distances shorter than 2.33 km, the marginal effect rises with increasing distance. Conversely, for distances exceeding 2.33 km, the marginal effect declines and turns insignificant beyond 3 km. These findings imply that the positive influence of DBS on house prices is more pronounced in areas that are neither too close nor too far from subway stations, where people are more likely to use DBS to connect to subway networks. The findings of this study contribute to a better understanding of the complex relationship between DBS and real estate markets.

One of the key travel behavioural assumptions in the 15-min City concept is that if daily necessities are nearby, residents would be encouraged to use slower but more sustainable modes, such as walking, cycling and public transit to reach these destinations, thereby reducing car dependence. This research explores non-work car use associated with the 15-min City concept in the City of Toronto, Canada. We first calculate transportation accessibility, or what we refer to as access intensity, to five categories of daily necessities (food, commercial, health, recreation, and education establishments) using walking, cycling, public transit, and driving. Next, these results are analyzed using a set of minimum access criteria to particular amenities within the different destination types to determine a set of binary access or sufficiency scores. Spatial patterns of accessibilities by mode show expected pockets of high and low access. However, further analysis of non-work trip rates using travel survey data suggests that increases in 15-min accessibilities by walking, cycling, and transit are associated with decreases in the use of driving for 15-min trips. In particular, driving rates decrease as sufficient walking, cycling, and transit access improves with the largest decrease associated with sufficient walking access to all five categories of necessities. This work offers important implications for sustainable transportation and land use planning as it appears that residents do use alternative and more sustainable modes when they are associated with sufficient accessibility to all categories of destinations.

Many cities around the world have developed Transit-oriented development (TOD) based on subway systems, expecting to alleviate problems such as carbon emissions and pollution in the transportation sector. Although previous studies have proved the contributions to mitigate congestion and carbon emission pressure, there is a lack of evaluation on whether TOD can promote green mode choice and effectively reduce carbon emissions of first/last-mile trips in existing studies. This study aims to verify that subway stations with high TOD levels have a positive effect on reducing the carbon emissions of first/last-mile trips. We evaluate subway stations in Hangzhou and classify them into two categories based on their TOD levels. Then the carbon emission of ten typical subway stations is calculated based on survey data of first/last-mile trips and empirical formula. The nested logit model (NL) is used to analyze the correlation between the choice of first/last-mile mode and personal and environmental factors. The case results show that subway stations with a higher TOD level usually exhibit higher daily passenger flows (increased by approximately two-fold) and lower per capita carbon emissions (reduced by approximately 70 %). These findings proved that the high TOD level has a positive impact on carbon emissions of subway first/last mile trips, which could provide data support and insights for TOD development, especially in developing countries.

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data.

We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%.

While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of $\approx$ 92 k trips from 235 users over a period of roughly one and a half years.

With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.