Transportation最新文献

This study employed Structural Equation Modeling to examine how different aspects of quality in metro rail transport impact user satisfaction. Additionally, it investigated whether satisfaction patterns remain consistent over the years and between different genders, using the Multiple-Group Analysis (MGA) methodology. Data were collected through a survey of metro rail transport satisfaction in Porto Alegre, Brazil, in the years 2019, 2020, 2021, and 2022. The findings highlighted the relationships between latent factors perceived by users and general satisfaction, emphasizing the significant influence of terminal characteristics, especially infrastructure. Additionally, an influence of service characteristics was identified, particularly regarding crowding, air conditioning, and train interval. The results of the multi-group analysis indicated non-uniformity in some essential groups outlined in the study, showing non-invariance between some year and gender groups, suggesting that some groups may present a distinct satisfaction pattern. These results underscore the importance of considering different aspects of metro rail service to enhance user satisfaction, as they provide valuable insights that can assist public transport planners in devising effective strategies to deliver high-quality services, aiming to retain and attract new passengers.

Investment in urban public transportation involves strategic decisions regarding the organization of transit lines in the urban space and the choice of their technology in addition to the size of both fleet and vehicles of each line, and their spacing (lines density). The parametric city model (PCM) of Fielbaum et al. (Transp Res Part B Methodol 94:298–338; Netw Spat Econ 17:343–365) was created to conceive and decide a structural design - the first step of the strategic design - in real size problems, representing the city through zones and centers and evaluating predetermined lines-structures considering a single technology. In this paper the scope and capabilities of the PCM are expanded to help the structural design of urban transit systems, by opening the search for the type of lines-structures studied and considering two technologies (modes), noting that the simplified representation constitutes a useful tool for transport policy that departs from the usual “black-box” procedures. The approach is applied to a PCM version of Santiago, Chile, showing that schemes different from feeder-trunk might be convenient. The results support the options presently taken in Santiago based upon subway expansions but changing the role of the bus system.

In many cities, shared micromobility services (SMMS) have become popular. These services contribute to the popularity of car-alternative mobility by promoting the use of micro-vehicles. Bike-sharing and e-scooter-sharing systems are examples of these services. Despite their potential, the share of SMMS is still marginal. To unlock their full potential, policymakers and service providers need to comprehend the wider implications of their strategies on the adoption, use, and profitability of these services. This paper investigates the implications on travel demand, use patterns, and business profit of two strategic decisions: the size of the fleet and the pricing of shared bikes and e-scooters. This research is based on an agent-based transport simulation framework and trip records of shared bike and e-scooter users from the city of Lyon, France. The results show that despite their actual marginal share, SMMS have a non-negligible growth potential in Lyon. This potential is unfulfilled due to sub-optimal pricing and fleet size strategies. In the paper, more optimal strategies are discussed from the point of view of service providers and customers. These findings can be generalized to other cities and shared micromobility services. They can also be of interest to policymakers and service providers in the design and operation of successful and efficient SMMS.

Micro-mobility is an emerging mode of urban transportation, particularly for its potential to reduce private car use and build sustainable cities. Bikes and electric bikes (e-bikes) are two common types of micro-mobility. Previous studies mainly analyzed the two types separately, failing to provide a comparative and systematic investigation of shared dockless bike/e-bike systems in different cities. To fill this gap, this paper presents a multiscale geospatial network framework to analyze the differences between bike and e-bike usage patterns, taking five cities with different urban contexts in Zhejiang Province (China) as examples. We first organize the shared micro-mobility data into bike networks and e-bike networks. Then, we analyze these networks from the macro, meso, and micro scales by applying complex network analysis and data mining techniques. A series of visual graphs are designed to help understand latent patterns. The similarities and differences among the five cities in statistical information, visitation hotspots, micro-mobility network indicators, and community structures are clearly demonstrated. The results can enrich our understanding of the system usage patterns, and help micro-mobility operators and transportation planners make evidence-based policies to promote sustainable urban development.

Reducing transportation carbon emissions in cities is crucial for sustainable development. Although polycentric urban morphology is viewed as a potential strategy for enhancing transportation carbon emission efficiency, its role remains contentious, and the effects of various dimensions of polycentricity have not been fully examined. This study investigates the spatiotemporal effects of polycentric urban morphology on transportation carbon emission efficiency. Analyzing per capita transportation carbon emissions (PTCEs) in 285 Chinese cities from 2000 to 2020, we quantified urban polycentricity across four dimensions: number of urban centers, distance between centers, sub-center importance, and compactness. We employed a geographically and temporally weighted regression model to assess how these dimensions influenced PTCEs over time and space. Our results reveal an overall increase in both PTCEs and polycentricity, with significant regional variations. Initially, the number of urban centers was negatively correlated with PTCEs, but this effect weakened over time. In contrast, greater distances between centers and higher compactness were associated with increased PTCEs, with these effects intensifying over time. The influence of sub-center importance on PTCEs followed an inverted U-shaped trend. Spatially, the number of centers correlated negatively with PTCEs, while sub-center importance correlated positively. The benefits of reduced distance between centers diminished from northeast to southwest China, and the impact of compactness on PTCEs varied, being positive in the east and negative in the west. These findings highlight the need for region-specific spatial policies to effectively enhance urban transportation carbon emission efficiency and develop low-carbon urban transportation systems.

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.