Gridlock in the Greater Toronto Area: Its geography and intensity during key periods

Sweet, Matthias N.; Harrison, C.; Kanaroglou, Pavlos

doi:10.1016/j.apgeog.2015.01.011

Cited by 16 publications

(3 citation statements)

References 24 publications

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“…Moya-Gómez and García-Palomares, 2015;, Inrix (e.g. Sweet et al, 2015), websites like Google Maps (e.g. García-Albertos et al, 2019), and the public availability of generalized transit feed specification (GTFS) data and automatic vehicle location (AVL) data from transit authorities (e.g.…”

Section: New Trends Of Accessibility Indicators Developmentmentioning

confidence: 99%

A literature review on accessibility using bibliometric analysis techniques

Shi

Blainey

Sun

et al. 2020

Journal of Transport Geography

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Section: New Trends Of Accessibility Indicators Developmentmentioning

confidence: 99%

A literature review on accessibility using bibliometric analysis techniques

Shi

Blainey

Sun

et al. 2020

Journal of Transport Geography

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“…The best-known of these companies is Google, although Inrix and TomTom also provide this type of data. The works published using this type of data sources include particularly those from Toronto by Sweet et al (2014Sweet et al ( , 2015, who studied the impact of congestion on accessibility and its consequences on company localisation using historic speed data for motorways and major arterial networks provided by Inrix. Elsewhere, Moya-Gómez and García-Palomares (2015) applied data supplied by the TomTom navigation company -specifically the "Speed Profiles" product-to create dynamic maps that reveal the impact of congestion on daily accessibility in the metropolitan area of Madrid.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

et al. 2017

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Accessibility is essentially a dynamic concept. However, most studies on urban accessibility take a static approach, overlooking the fact that accessibility conditions change dramatically throughout the day. Due to their high spatial and temporal resolution, the new data sources (Big Data) offer new possibilities for the study of accessibility. The aim of this paper is to analyse urban accessibility considering its two components -the performance of the transport network and the attractiveness of the destinations-using a dynamic approach using data from TomTom and Twitter respectively. This allows us to obtain profiles that highlight the daily variations in accessibility in the city of Madrid, and identify the influence of congestion and the changes in location of the population. These profiles reveal significant variations according to transport zones. Each transport zone has its own accessibility profile, and thus its own specific problems, which require solutions that are also specific.

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“…Therefore, travel time uncertainty concern should be one of essential factors in the accessibility modeling (Handley et al, 2019). Today's new big data sources open up a growing field of research on temporal variations of travel time in both private and public transportation accessibility assessment, including dynamic data from companies that offer daily speed profiles with a high spatial and temporal resolution, such as TomTom (e.g., Moya‐Gómez et al, 2018; Moya‐Gómez & García‐Palomares, 2015), Inrix (e.g., Sweet et al, 2015), Google Maps (e.g., García‐Albertos et al, 2019) and more, and the public availability of generalized transit feed specification (GTFS) data and automatic vehicle location (AVL) data from transit authorities (e.g., Boisjoly & El‐Geneidy, 2016; Farber et al, 2014; Farber & Fu, 2017; Fayyaz et al, 2017; Fransen et al, 2015; Järv et al, 2018; Wessel & Farber, 2019).…”

Section: Introductionmentioning

confidence: 99%

A spatiotemporal analysis of metro‐stop based accessibility to bus services in Shanghai, China

Shi,

Yan

2023

Transactions in GIS

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Ground‐level bus offers flexible services with relatively inexpensive fares and high‐level accessibility, thus is the dominant feeder mode for metro in the majority of metropolitan areas in China. Intermodal transfer between the metro and bus networks is therefore a crucial element in the successful operation of an integrated transit system. In this circumstance, a key challenge is lack of appropriate methodologies to evaluate the spatial–temporal disparities of intermodal transfers between metro and bus. To address this issue, this research aims to promote an existing two‐step floating catchment area (2SFCA) model by incorporating the temporal variability of service supply, demand, and travel time to provide a more realistic estimation of accessibility using smart card data and automatic vehicle location data. The proposed methodology was applied in a case study of the metropolitan area of Shanghai in the specific context of metro‐stop based accessibility to bus services. The results of the case study show that the daily fluctuations in output metro‐to‐bus transfer accessibility values are highly sensitive to temporal dynamics of transfer time, service supply, and demand. It is hoped the results output here could give planners and policymakers greater insight into spatiotemporal dynamics on transfer accessibility, and help to establish an effective and efficient integrated transit system.

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Gridlock in the Greater Toronto Area: Its geography and intensity during key periods

Cited by 16 publications

References 24 publications

A literature review on accessibility using bibliometric analysis techniques

A literature review on accessibility using bibliometric analysis techniques

Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

A spatiotemporal analysis of metro‐stop based accessibility to bus services in Shanghai, China

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