Full cost accessibility

Cui, Mengying; Levinson, David

doi:10.5198/jtlu.2018.1042

Cited by 39 publications

(31 citation statements)

References 39 publications

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“…In studies of transit specifically, this "ease" has typically been defined as a function of travel time only, though other cost functions have been developed. For example El-Geneidy, Levinson, et al (2016) included the monetary fare price to account for the experiences of low income transit users -and Cui and Levinson (2018) propose a general (not specific to transit) measure of accessibility that incorporates external costs of travel such as insurance, pollution, and public subsidy of infrastructure. Nassir et al (2016) propose a utility-based accessibility function, attempting to measure the subjective experience of using transit.…”

Section: Transit Accessibilitymentioning

confidence: 99%

“…Many major transit agencies around the world struggle with crowding to some degree. One could also choose to incorporate fares into an accessibility measure (as Cui and Levinson, 2018;Nassir et al, 2016) and look 7 Conceptually, accessibility can be thought of as measuring the utility of the transport-landuse system as an integrated whole (See Geurs and Van Wee, 2004) so it makes little sense prima facie to disregard half of the equation. However the separate consideration of modes in much accessibility analysis substantially complicates the picture especially when the use of different modes (e.g.…”

Section: Perspective and Selection Biasmentioning

confidence: 99%

“…For examples of other metrics applied to accessibility, see El-Geneidy,Levinson, et al (2016) andCui and Levinson (2018).…”

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confidence: 99%

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Accessibility Beyond the Schedule

Wessel¹

2019

Preprint

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The study of accessibility - the ease of reaching destinations - by public transport has made huge advances thanks to the availability of standardized, routable transit schedule data.The General Transit Feed Specification (GTFS) has provided researchers with a vast trove of machine-readable data allowing for highly detailed spatio-temporal modelling of scheduled transit operations.Yet it is well established that in the real-world schedules are imperfect - vehicles often run late, get bunched, miss transfers, arrive too full for anyone to board, and otherwise behave in predictably unpredictable ways. Schedule data alone cannot possibly account for this distinctly stochastic component of much transit service, which to date has been considered separate from accessibility analysis under the umbrella of ``reliability''. This dissertation takes the perspective that transit service is reliably unreliable and will continue to be so until humans are taken out of the equation. Detailed observations of actual service can be used to construct more realistic models for estimating travel times and thus accessibility via transit.Chapter 2 introduces a novel method of converting a detailed GPS record of transit fleet locations into a retrospective GTFS package. This backward-looking "schedule'' format allows the same tools developed for schedule-based GTFS analysis to be applied in Chapter 3 to a more accurate depiction of actual transit accessibility. The findings indicate that models of transit accessibility based on schedule data alone tend to produce substantial overestimates of accessibility and systematic spatial errors by failing to account for normal irregularities in service provision. Chapter 4 points toward a way of better suiting available GTFS analysis tools to actual transit service by addressing the problem of imperfect information in modelled route choice. The travel time implications for a large minority of trips are shown to be substantial. Transit accessibility research has come a long way in the last decade and has a long way yet to go. Models based on schedule data alone should give way in many cases to models based on service as actually provided, acknowledging that schedules may guide but rarely constrain the transit services that passengers actually use every day.

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Section: Transit Accessibilitymentioning

confidence: 99%

Section: Perspective and Selection Biasmentioning

confidence: 99%

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Accessibility Beyond the Schedule

Wessel¹

2019

Preprint

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“…In studies of transit specifically, this "ease" has typically been defined as a function of travel time only, though other cost functions have been developed. For example El-Geneidy et al (2016) included the monetary fare price to account for the experiences of low income transit users -and Cui and Levinson (2018) propose a general (not specific to transit) measure of accessibility that incorporates external costs of travel such as insurance, pollution, and public subsidy of infrastructure. Nassir et al (2016) propose a utility-based accessibility function, attempting to measure the subjective experience of using transit.…”

Section: Transit Accessibilitymentioning

confidence: 99%

The Effect of Route-choice Strategy on Transit Travel Time Estimates

Wessel¹,

Farber²

2019

Preprint

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Estimates of travel time by public transit often rely on the calculation of a shortest-path between two points for a given departure time. Such shortest-paths are time-dependent and not always stable from one moment to the next. Given that actual transit passengers necessarily have imperfect information about the system, their route selection strategies are heuristic and cannot be expected to achieve optimal travel times for all possible departures. Thus an algorithm that returns optimal travel times at all moments will tend to underestimate real travel times all else being equal. While several researchers have noted this issue none have yet measured the extent of the problem. This study observes and measures this effect by contrasting two alternative heuristic routing strategies to a standard shortest-path calculation. The Toronto Transit Commission is used as a case study and we model actual transit operations for the agency over the course of a normal week with archived AVL data transformed into a retrospective GTFS dataset. Travel times are estimated using two alternative route-choice assumptions: 1) habitual selection of the itinerary with the best average travel time and 2) dynamic choice of the next-departing route in a predefined choice set. It is shown that most trips present passengers with a complex choice among competing itineraries and that the choice of itinerary at any given moment of departure may entail substantial travel time risk relative to the optimal outcome. In the context of accessibility modelling, where travel times are typically considered as a distribution, the optimal path method is observed in aggregate to underestimate travel time by about 3-4 minutes at the median and 6-7 minutes at the \nth{90} percentile for a typical trip.

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“…Ahora bien, en el marco de la agenda de equidad, establecida por los Objetivos de Desarrollo Sostenible (ODS) de las Naciones Unidas, se hace uso del concepto accesibilidad, pues a través de éste se respaldan las diferentes medidas de equidad (Weiss et al, 2018). Otras definiciones del término plantean la interacción entre formas básicas de actividad humana (Geurs y Van Bee, 2004), analizadas desde diferentes enfoques como: desarrollo orientado al tránsito (Conesa, 2018), estructura urbana (Boarnet y Wang, 2019), usos del suelo, inequidad (Bocarejo, 2012), planeación del transporte (Geurs, y Van Bee, 2004;Escobar et al, 2015b), influencia en el costo del transporte (Cui y Levinson, 2018), sostenibilidad (Vega, 2011;Escobar et al, 2015a), acceso a servicios u oportunidades (Wachs y Kumagai, 1973, Sahebgharani et al, 2019, comercio (Zuluaga y Escobar, 2016;Montoya et al, 2017), Salud (Sarmiento et al, 2020), entre otros.…”

Section: Introductionunclassified