Extending public transit accessibility models to recognise transfer location

Chia, Jason; Lee, Jinwoo

doi:10.1016/j.jtrangeo.2019.102618

Cited by 9 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TOD should not be viewed simply as a way to increase the demand for transit services by placing more residents within the station or stop catchment area. Rather, it is most effective as an approach that uses the accessibility provided by key nodes on integrated public transport systems to create centers that reduce reliance on vehicles (Chia & Lee, 2020;Mees, 2014). Integrating the merits of TOD and shared mobility can upscale the multimodal mobility service to address the diverse transport needs in cities with complex transport supply and land-use characteristics (DeMaio, 2009).…”

Section: 5mentioning

confidence: 99%

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

Chi,

Lee

2024

JTLU

View full text Add to dashboard Cite

The impacts of transit-oriented development (TOD) on travel behavior have been extensively studied, with a predominant focus on cross-sectional analyses that provide a static evaluation at a specific point in time by comparing TODs and non-TODs. Longitudinal assessments that capture changes in behavior over time remain relatively uncommon, and the literature tends to overlook differences in evaluating TOD effects across cross-sectional and longitudinal analyses. Additionally, the role of trip purpose as a significant but unexplored variable influencing the degree of TOD effects is often disregarded. To address these gaps, this systematic review examines 48 quantitative studies, comparing the effects of TOD on travel behavior from cross-sectional and longitudinal perspectives, restructuring indicators of effects into transit use, non-motorized travel, vehicle dependence, and vehicle ownership, and differentiating the effects by trip purpose. A metric has been introduced to quantitatively assess the impact of TOD on travel behavior. The pooled results indicate that private vehicle usage remains high in TOD areas, particularly for non-commuting trips, and that the longitudinal effects of TOD are limited and potentially influenced by individual travel attitudes, residential self-selection, and long-term travel habit change. Furthermore, the methodological differences between cross-sectional and longitudinal studies may lead to divergent conclusions regarding the effects of TOD on travel behavior. Our analysis sheds light on the importance of carefully selecting an appropriate method for a given research question to maximize the accuracy and relevance of the findings. Combining TOD and shared mobility can create a more efficient multi-model transport network that meets the diverse needs of city residents and improves accessibility for all. Overall, this review provides new insights into the impacts of TOD on travel behavior and supports the potential for a paradigm shift toward multimodal transport through the integration of TOD and shared mobility.

show abstract

Section: 5mentioning

confidence: 99%

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

Chi,

Lee

2024

JTLU

View full text Add to dashboard Cite

show abstract

“…Assessing the status of public transport is covered by a vast number of related studies in different study cases (urban rail, bus, metro, or even multimodal) worldwide, where accessibility, comfort, security, and other factors are considered relevant to improve the network [32][33][34][35]. For this purpose, researchers and operators recurrently perform inquiries, inference (e.g., gravity model) or datadriven analysis (e.g., data mining on smart card data) to extract users' behaviour.…”

Section: Transfer and Trip Status Indicatorsmentioning

confidence: 99%

“…Besides, the identified studies so far, analyse complementary indicators individually. Chia et al [35] analyze travel time indicator only and show that most inner-city suburbs in Brisbane, Australia, has high accessibility, although by including transfer needs the outcome reveals opposite results. In other words, an integrative exploration of both indicators allows to discover non-trivial knowledge about network status.…”

Section: Transfer and Trip Status Indicatorsmentioning

confidence: 99%

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

Cerqueira

Arsénio

Henriques

2022

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Background The provision of seamless public transport supply requires a complete understanding of the real traffic dynamics, comprising origin-to-destination multimodal mobility patterns along the transport network. However, most current solutions are centred on the volumetric analysis of passengers’ flows, generally neglecting transfer, walking, and waiting needs, as well as the changes in the mobility patterns with the calendar and user profile. These challenges prevent a comprehensive assessment of the routing and scheduling vulnerabilities of (multimodal) public transport networks. Research aims/questions The research presented in this paper aims at addressing the above challenges by proposing a novel approach that extends dynamic Origin-Destination (OD) matrix inference to dynamic OD matrix inference with aggregated statistics, highlighting vulnerabilities and multimodal mobility patterns from individual trip record data. Methodology Given specific spatial and temporal criteria, the proposed methodology extends dynamic Origin-Destination (OD) matrices with aggregated statistics, using smart-card validations gathered from (multimodal) public transport networks. More specifically, three major contributions are tackled; i) the data enrichment in the OD matrices with statistical information besides trip volume (e.g., transfer and trip features); ii) the detection of vulnerabilities on the network pertaining to walking distances and trip durations in a user-centric way and iii) the decomposition of traffic flows in accordance with calendrical rules and user (passenger) profiles. The set of contributions are validated on the bus-and-metro public transport network in the city of Lisbon. Results The proposed approach for inferring OD matrices yields four unique contributions. First, we allow inference to consider multimodal commuting patterns, detecting individual trips undertaken along with different operators. Second, we support dynamic matrices’ OD inference along with parameterizable time intervals and calendrical rules, and further support the decomposition of traffic flows according to the user profile. Third, we allow parameterization of the desirable spatial granularity and visualisation preferences. Fourth, our solution efficiently computes several statistics that support OD matrix analysis, helping with the detection of vulnerabilities throughout the transport network. More specifically, statistical indicators related to travellers’ functional mobility needs (commuters for working purposes, etc.), walking distances and trip durations are supported. The inferred dynamic OD matrices are the outcome of a developed software with strict guarantees of usability. Results from the case study using data gathered from the two main public transport operators (Bus and Metro) in the city of Lisbon show that 77.3% of alighting stops can be estimated with a high confidence degree from bus smart-card data. The inferred OD matrices (Bus and Metro) in the city of Lisbon reveal vulnerabilities along specific OD pairs, offering the bus public operators in Lisbon new knowledge and a means to better understand dynamics and validate OD assumptions.

show abstract

“…Binary Logistic Modeling. This study adopts the binary logistic model to explore the influencing factors of shared mobility choice behavior near metro stations under various conditions (65,66). The method has been broadly utilized for mode choice decision modeling because of its simplicity of translating probability.…”

Section: ¤ Preprocessing Task Ii-variable Extractionmentioning

confidence: 99%

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Zhang

2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

show abstract

Extending public transit accessibility models to recognise transfer location

Cited by 9 publications

References 43 publications

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Contact Info

Product

Resources

About