Transportation最新文献

The study aims to assess the impact of information and communication technology (ICT) on travel and out-of-home activity by analyzing primary data collected from ride-hailing app users in Yogyakarta Province, Indonesia. The data was analyzed using a random effect ordered logistic panel model. The results indicate a complementary relationship between food delivery and eating out. Individuals who frequently use food delivery services are more likely to also engage in eating out activities, and vice versa. Furthermore, the COVID-19 pandemic had a significant impact on these behaviors. Eating out experienced a substantial decrease during the pandemic, while food delivery remained stable. The findings highlight that food delivery cannot easily replace the experience of eating out, even under lockdown conditions. Overall, this study provides insights into the dynamics between food delivery and eating out, emphasizing the complexities and limitations of substituting one with the other.

Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

This paper explores the influencing factors of commuters’ willingness to use ridesplitting services in the post-COVID-19 era – including promotional strategies – and the possible differences of these factors among commuters with different home-to-work commuting distances. A survey developed for this study was conducted among 1600 commuters from Shanghai, China between September and November 2021. A correlated random parameters ordered probit model is used to estimate the impact of various factors on the willingness to use ridesplitting services for individuals with different trip distances. The model results indicate that the delay compensation strategy has the potential to offer the largest increase in the likelihood of using ridesplitting services, as compared to other promotional strategies (i.e., discount, credit, and priority service strategies), particularly for medium- and long-distance home-to-work commuters. At the same time, the likelihood of using ridesplitting services may vary across specific types of commuters, such as residence owners, commuters using automobile-based transportation modes, travelers with flexible work schedules, and commuters who frequently work overtime. The paper’s insights may be used by ridesplitting service providers to assist in designing effective strategies to promote ridesplitting services.

While urban regions continue to grow, much of the urbanization that is occurring is better described as suburbanization. This is generating and will continue to generate immense pressure on our social and environmental systems. To address these challenges and exploit specific suburban opportunities, cities globally require a complete understanding of the complexity of how human and environmental systems are uniquely intertwined within suburban contexts. The Suburban Mobilities (SuMo) cluster at the University of Toronto Scarborough (UTSC) aims to address these academic and policy challenges, generating transformative, interdisciplinary, partnered research about suburban contexts that will allow communities to solve transportation challenges facing the suburbanized world in the twenty-first century. Among the multiple projects developed within the SuMo cluster, one highlight is the design of a multidimensional survey in Scarborough, an eastern suburb of Toronto, Canada. Multiple transportation, land use, pricing and census data sources have allowed us to characterize this area to date, and we wondered what information would be helpful to collect in a survey to fill data gaps that will enable a better and deeper characterization of transportation’s impacts on quality of life of people living in Scarborough. This article details the particularities of the Scarborough context, as well as the design process, sampling strategy, representativeness, main descriptive results, and ongoing work using the survey. Finally, reduced and aggregated survey data is available for the general public with respective documentation for ease of use.

The emergence and development of shared E-bicycles (SEB) has resulted in complex connection and substitution relationships with public transportation (PT) in urban areas, impacting the social benefits of integrated transportation systems and the return on investment of PT subsidies. Accurately identifying how SEB complement and/or compete with PT is still a crucial challenge. Therefore, this research proposes a new discrimination method based on multi-source data fusion to address this issue. The method’s discrimination accuracy is evaluated based on both telephone interviews data and transaction data from SEB users in Shenyang City, China. The results indicate that SEB are primarily used for single-mode trips (75%). When SEB are used to combine with other modes, they mainly serve as connecting modes to rail transit (22.1%). On the other hand, shared bicycles (SB) and buses are the two modes most replaced by SEB, followed by private cars and walking. 63% of SEB rides can be accurately classified by the proposed method in terms of their connection and substitution relationship with PT. Incorporating anonymized socioeconomic attribute information from big data holds promise for further enhancing the method’s performance. This research provides key insights for assessing the societal benefits of SEBs on PT, offering valuable theoretical and methodological support for transportation planning.

The significance of public transit in curbing greenhouse gas (GHG) emissions and reducing vehicle miles traveled (VMT) goes beyond its users. Investments in transit infrastructure, coupled with service enhancements and their consequential impacts on urban development (termed as indirect effects), have the potential to foster location efficiency. This concept encompasses the advantageous proximity of vital destinations such as workplaces and retail establishments to the residences that necessitate access. In this context, investments made in public transit systems exhibit a multiplier effect, commonly quantified as the reduction in VMT per each passenger mile of transit usage. While this topic has gained attention over the past few decades, an agreement regarding the size of the multiplier effect has yet to be reached among researchers. This study employs a multilevel structural equation model and leverages a comprehensive database of household travel survey data from 31 diverse regions. By utilizing trip-level data, this study provides results that possess external validity and generalizability, overcoming limitations identified in earlier research. Additionally, this study aims to present a simplified formula that enables transit agencies nationwide to compute their unique multipliers. The findings suggest that regions with extensive transit systems exhibit higher transit multipliers compared to regions with limited transit access. Furthermore, the impact of transit within a community extends well beyond merely the reduction in private vehicle usage by transit passengers. Rather, the alterations in the built environment in transit-served communities lead to substantial VMT savings, surpassing the effects solely attributed to transit passenger usage.

Public Transportation (PT) is a universal service in most countries, and it is acknowledged for its social and environmental role in enhancing accessibility and promoting a sustainable transport system. However, when disruptions alter the service, the level of service (LoS) can be massively affected. Consequently, the perceived quality can be influenced, and users can be encouraged (or forced) to modify their subsequent modal choice, in accordance with the users’ socioeconomic profile. A survival analysis, namely a Cox proportional hazards model, was tested in Bologna, Italy, using real data provided by TPER S.p.A, specifically Automatic Vehicle Location (AVL) and Automatic Passenger Counter (APC). This analysis aimed to assess the variations in demand over time taking into account variables related to the socioeconomic characteristics of the demand and several service attributes. The results contribute to the literature in several ways. Firstly, they confirm the predominant role of PT in the modal alternative spectrum of disadvantaged users. Secondly, they provide insights into the perception of quality service among different user categories, including commuters and non-frequent users.

Home-to-Work (H2W) commuting travel, together with urbanization and transport networks, have seen significant development since the end of the 19th century in both workers’ lives and transport network traffic. The attraction of metropolitan areas and the ease of transport have even triggered long-distance commuting, sometimes in conjunction with overnighting practices that extend the commuting tours (or cycles) beyond the single day level. Based on the French nationwide household travel survey of 2019, this article gives a comprehensive description of commuting practices across origin-destination distances and over time, as experienced by individual workers at the monthly level. Commuting rhythms are characterized in terms of commuting cycle length in days and monthly frequency. Four typical patterns are identified: namely bi-daily, full daily, mono-daily and overnighting. Their respective shares are measured in the statistical populations of (i) workers, (ii) days of life or just at work, (iii) distances travelled on modal networks. Elementary discrete-choice models of H2W rhythm choice are designed and estimated, revealing the influence of travel impedance in terms of time and distance at the level of one-way trips for short-range H2W commuting and at the level of monthly budgets for long-range H2W commuting. Lastly, the shares of commuting trips in modal traffic on road and rail networks are measured, highlighting the significant impact of long-distance commuting on transport-related GHG emissions.

In ride-sharing, fixing the roles of commuters with vehicles could come at an opportunity of missed balance between riders and drivers. The decisions made by commuters with vehicles about roles are related to both to-work travel and return-home travel. Commuters may choose not to drive when they have a confirmed ride back home. This study designs a stable ride-sharing model with role flexibility for the commuting trip chain. The preferences of commuters over matching partners in trip chains are formulated. A stable matching algorithm based on commuting trip chain is designed. To assess the performance of the proposed model and algorithm, real-life data from a resident trip survey in Dalian are used to design experimental cases. The results indicate that stable matching based on the trip chain is not a consequence of the repetition of successive one-period stable matching. Compared with matching in two phases, the matching rate of commuters increases by an average of 10%. When the number of commuters with role flexibility increases by four times, the matching rate of commuters increases by an average of 5.6%.

Network criticality assessment aims to identify important nodes or links within a network that that are critical to its overall performance. Existing studies often focus on factors such as traffic volume and sociodemographic attributes, giving preference to heavily traveled highways in densely populated areas. Rural highways, particularly those with low traffic volumes, tend to be undervalued, despite their potential significance in sustaining network connectivity and facilitating access to services and opportunities within local communities. This paper addresses this concern by developing a criticality framework grounded in the egalitarian principle. The framework comprises two complimentary measures: normalized betweenness centrality and detour importance. The former measures a road’s relative importance in efficiently linking the local network by quantifying the percentage of origin-destination pairs within the neighborhood that utilize it. The latter, detour importance, reflects the aggregated percentage of trips rerouted to the road during disruptions to other critical roads, thereby highlighting its role in providing the needed redundancy of the network. Given the fundamental role of finding realistic shortest paths in this analysis, a large-scale GPS-based probe vehicle dataset is integrated to provide real-world travel time information. The results from the Kentucky case study validate the effectiveness of the developed framework. The framework utilizes efficient algorithms and readily available data, making it practically feasible for large-scale applications.