Examining circuity of urban transit networks from an equity perspective

Dixit, Malvika; Chowdhury, Subeh; Cats, Oded; Brands, Ties; Oort, Niels van; Hoogendoorn, Serge P.

doi:10.1016/j.jtrangeo.2021.102980

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…However, SCAFC is collecting not only the revenue, but also the information that comes with each payment such as the date and time of each entry, boarding and exiting locations, transit route number, fare concession of the payment, etc. All those data are advancing the related analysis in transit, as well as creating new orientation in the field such as Origin-Destination (OD) estimation (Hussain et al 2021;Seaborn et al, 2009;Trépanier et al, 2007), route choice analysis (Arriagada et al, 2022;Lee et al, 2022;Liu et al, 2022), supply and demand modelling Wang et al, 2015), fare change impact analysis (Wang et al, 2015), and equity analysis (Dixit et al, 2021).…”

Section: Smart Card Data Utilization In Public Transportationmentioning

confidence: 99%

“…Moreover, smart card data could be used in other directions of public transportation research. Dixit et al (2021) examined the circuity of urban transit networks from an equity perspective. People who lived in high-income areas in Amsterdam, Netherlands made fewer detours in transit trips.…”

Section: Smart Card Data Utilization In Public Transportationmentioning

confidence: 99%

“…People who lived in high-income areas in Amsterdam, Netherlands made fewer detours in transit trips. Dixit et al (2021) suggested integrating equity analysis in network design to avoid inequitable fare payments. In Brisbane, Australia, Wei (2022) and Zahnow and Corcoran (2021) explored other topics in the use of transit smart card data.…”

Section: Smart Card Data Utilization In Public Transportationmentioning

confidence: 99%

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Toronto Transit Commission and COVID-19: Statistical and Spatial Analysis of Ridership Changes During the Pandemic

2024

Preprint

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<p>The COVID-19 pandemic is an unprecedented event that has shifted travel patterns. The Toronto Transit Commission witnessed a significant ridership drop and subsequent fluctuations during the pandemic. This exploratory research investigates ridership changes using statistical and spatial analysis over different intervals based on changes in government-imposed restrictions. Ridership increased with the lifting of restrictions, while mixed ridership trends were observed during the intermittent lockdown periods. On the spatial side, relatively high degrees of homogeneity in ridership changes were discovered for census tracts with subway service, and census tracts along subway lines showed heterogeneity. For trips that end downtown, the ridership was cut in half compared to pre-pandemic levels and demonstrated similar change patterns to the trips ending elsewhere. Lastly, some socioeconomic and demographic factors were found to be the driving factors with spatial inherency for positive ridership change using geographic weighted regression models.</p>

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Section: Smart Card Data Utilization In Public Transportationmentioning

confidence: 99%

Section: Smart Card Data Utilization In Public Transportationmentioning

confidence: 99%

Section: Smart Card Data Utilization In Public Transportationmentioning

confidence: 99%

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Toronto Transit Commission and COVID-19: Statistical and Spatial Analysis of Ridership Changes During the Pandemic

2024

Preprint

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“…The same authors have incorporated spacing of transit lines, frequencies, vehicle sizes, and routes in the design and the analysis of scale economies in transit systems, showing, among other results, that considering spatial spacing as a design variable in transit network can increase the effect of scale economies [54]. Other considerations of line structure design include integrating existing bus and metro networks into a single connected transit network to increase connectivity [55], equity outcomes of transit network design with distancebased fares [56], the effect of ride-pooling on transit network design [57], and the design of a bus transit network where there is an existing metro network that can balance the modal split between metro and bus [58].…”

Section: Network Designmentioning

confidence: 99%

Optimal Urban Transit Investment Model and Its Application

Sharav

Shiftan

2021

Sustainability

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We present a new urban transit investment model, integrating transport economic theory regarding optimal investment with transport modeling, planning, and network design. The model expands on the theory of optimal transit network planning and investment, accounting for the effects of the investment on accessibility, level of service, and speed. The model seeks long-term optimal transit investment and optimal road pricing simultaneously in an integrated, unified model. To illustrate the advantages of our approach, we applied our empirical model to two case studies, Tel Aviv and Toronto, integrating our theoretical contribution into practice. Our results demonstrate the model’s ability to indicate the optimal transit mode and investment on a corridor level and the total investment required for the city transit network. The model results were compared to the actual and planned transit networks of Tel Aviv and Toronto and showed the model’s capability to produce a good balance of strategic design and network details. The research concludes that applying the right toll with the applicable transit investment is crucial for obtaining an efficient network and performance. This research can direct planners and policymakers in planning urban transport and provide a comprehensive set of guidelines for optimizing the simultaneous investment in mass transit and the congestion toll toward more sustainable cities and transportation systems.

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