Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis)investments over time

Abstract

We put forward a new data object called the public transit travel time cube and demonstrate how the cube can be used in the analysis of transit travel time changes over space and time. The travel time cube contains the shortest path transit travel time between sets of origins and destinations in the city, at all times of day. Once computed, a wide range of investigations become readily available to the transit planner or transportation researcher. We conduct three demonstrative analyses using travel time cubes for the Wasatch Front, Utah and the Portland region in Oregon. Our studies investigate how travel times were impacted by service cuts and expansions in the two regions respectively and the impact this had on jobs accessibility. We also use the travel time cube to study the last mile problem, and compute the travel time savings and the stability gained by solving the last mile problem with bicycling. The paper concludes with an expanded discussion on the merits of the travel time cube and outlines four avenues for continued research.

Social Sustainability in Metropolitan Areas: Accessibility and Equity in the Case of the Metropolitan Area of Valencia (Spain)

“…In their complexity, metropolitan areas present imbalances that can, on occasion, be particularly striking. To attempt to reduce these exclusions to the minimum and achieve a fairer spatial distribution of public resources, some location models include criteria such as public utility, i.e., the number of people using the service, travelling costs and means of transport [11,[49][50][51][52][53][54][55].…”

Multi‐temporal transport network models for accessibility studies

“…The methods by which each study obtains travel time vary. For public transport modes, recent studies have relied on general transit feed specification (GTFS) data (Boisjoly & El‐Geneidy, ; Dewulf et al, ; El‐Geneidy et al, ; Farber & Fu, ; Fayyaz et al, ; Kujala et al, ; Lee & Miller, ; Widener et al, ). GTFS data include several types of text files in comma‐separated values (CSV) containing information on: stops, routes, itineraries, service schedules, fares, and travel times for all routes.…”

“…Recently, accessibility studies have explored the temporal variation, mostly based on public transport mode (Boisjoly & El‐Geneidy, ; Djurhuus, Sten Hansen, Aadahl, & Glümer, ; El‐Geneidy et al, ; Farber & Fu, ; Fayyaz, Liu, & Zhang, ; Kujala, Weckström, Mladenović, & Saramäki, ; Lee & Miller, ; Widener, Farber, Neutens, & Horner, ), a few on private mode (García‐Albertos, Picornell, Salas‐Olmedo, & Gutiérrez, in press; Moya‐Gómez & García‐Palomares, ; Páez, Moniruzzaman, Bourbonnais, & Morency, ), and some on both private and public modes (Benenson, Martens, Rofé, & Kwartler, ; Dewulf et al, ; Owen & Levinson, ; Salonen & Toivonen, ; Tenkanen, Saarsalmi, Järv, Salonen, & Toivonen, ). The transit emphasis may be related to data restrictions as well.…”

Big Data y nuevas geografías: la huella digital de las actividades humanas

“…Varios trabajos han calculado la variación temporal en la accesibilidad en transporte público a los servicios utilizando la API de Google Maps o directamente los ficheros GTFS (Farber et al, 2014;Stępniak y Goliszek, 2017). Modificando los ficheros GTFS se pueden realizar simulaciones sobre cambios en los tiempos de viaje en transporte público producidos por supresión de líneas o modificación de las frecuencias de servicio (Farber y Fu, 2017). También es posible analizar dinámicamente los tiempos de viaje en transporte privado.…”