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Public transport resilience during emergency: A simulated case in Torino
Published in Gianluca Dell’Acqua, Fred Wegman, Transport Infrastructure and Systems, 2017
F. Deflorio, H.D. Gonzalez Zapata, M. Diana
All the transit data used to perform the analysis are generally available through the General Transit Feed Specification (GTFS), an open standard widely adopted to publish PT data on internet. This information includes the stops and routes, frequency of service, vehicle classification, etc. (More information can be found on: https://developers.google.com/transit/gtfs/)
Pillar
Published in Andrew Braham, Sadie Casillas, Fundamentals of Sustainability in Civil Engineering, 2020
As researchers have begun to qualify social aspects of sustainability in civil engineering, more focus has turned to identifying ways in which engineers can incorporate enhancing social equity and accessibility into transportation planning. Guthrie et al. sought to utilize general transit feed specification (GTFS) data to understand accessibility impacts of proposed transit projects in early planning stages to maximize social benefits associated with these large public investments (2017). GTFS is a data specification which allows public transit agencies to publish transit data in a more generally accessible format that can be utilized by a wide variety of software applications. Based on current GTFS data and proposed 2040 transit improvements for the Twin Cities region of Minneapolis-Saint Paul, Minnesota, Guthrie et al. developed a hypothetical transit network to explore impacts on access to job vacancies in historically disadvantaged areas. They found that quantifying accessibility during planning stages paints a comprehensive picture of the benefits offered by proposed improvements , allowing for more informed decisions to be made. This type of analysis also allows for more effective communication and debate surrounding planning decisions and provides a methodology for incorporating social sustainability into transit planning. Kuzio also conducted a research with a planning-focused approach, exploring how 20 different metropolitan planning organizations (MPO) prepare for emerging technologies and consider the implications on equity (2019). Results indicated that MPOs are making social equity an increasing priority; however, more consideration throughout the entire planning process is needed, including considering the implications of emerging technology on equity. By surveying approaches taken by MPOs across the country and of varying planning environments, this research sought to provide a starting point for planners and policymakers to understand how others are evolving policy and planning strategies to more effectively incorporate social sustainability.
Integrating data-driven and simulation models to predict traffic state affected by road incidents
Published in Transportation Letters, 2022
Sajjad Shafiei, Adriana-Simona Mihăiţă, Hoang Nguyen, Chen Cai
This study evaluates the proposed framework models for one of the major subnetworks in Sydney, stretching alongside the Victoria Road corridor from CBD to western city (see Figure 2). The subnetwork includes 1,310 links and 428 nodes. The General Transit Feed Specification (GTFS) data is used to import public transport information such as bus time schedules, lines, and bus stop data. There are 81 signalized intersections with the adaptive SCATS control system running. The link traffic counts obtained from the SCATS detectors are aggregated in 15-min time intervals. Most of the SCATS signals are located throughout the main corridor and near the Sydney CBD. The simulation is conducted for 4-hour morning peak hours from 6:00 to 10:00 AM using AIMSUN microscopic simulation model. AIMSUN is a discrete-event simulation tool established based on car-following and lane-changing models (Aimsun 2013). Therefore, detailed traffic phenomena such as congestion propagation and dissipation of queues are simulated over time. We used a modified multinomial logit model as an advanced stochastic route choice model (Aimsun 2013). Maximum five shortest paths are calculated using Dijkstra’s label-setting algorithm. The probability of choosing a path k is then calculated according to the utility function of each path.
Using data and technology to integrate mobility modes in low-income cities
Published in Transport Reviews, 2021
Dana Yanocha, Jacob Mason, Jonas Hagen
Coupled with data transparency, the development of data standards makes data more useable by establishing consistency in formatting and release. The most notable transportation-related data standard is the General Transit Feed Specification (GTFS), which was developed in 2005 and uses standardised language to track transit stops, routes, and schedules. GTFS exists both in static (based on transit schedules) and real-time (based on GPS or other sensor tracking of vehicle locations) feeds. Real-time GTFS is more beneficial for users interacting with trip-planning and other integrative platforms; however, it is more expensive and complex for cities to generate than static GTFS. Overall, the simplistic structure of GTFS has led to widespread use by hundreds of public transit agencies worldwide (Eros et al., 2014).
Assessment of transport equity to Central Business District (CBD) in Sydney, Australia
Published in Transportation Letters, 2020
Zeyuan Qi, Samsung Lim, Taha Hossein Rashidi
Three main data sources were used in the study. The first is the General Transit Feed Specification (GTFS) data, which provides information on public transport facilities including bus routes, rail routes, and ferry routes and their station locations. This dataset was used in ArcGIS Desktop, a system for analyzing and mapping geographic data. More and more researchers gain attention about GTFS (Farber, Morang, and Widener 2014; Owen and Levinson 2015; Fransen et al. 2015). This study constructed a fully multimodal public transit network based on the January 2018 GTFS data. The second dataset is a travel time matrix which was automatically collected in May 2017 by calling the Google Maps Distance Matrix API via Python script. A trip calculated by Google Maps API can involve more than one public transport mode if necessary. In other words, all types of travel such as travel by single public transport mode without/with transfers and combination of multiple transport modes can be taken into account. Therefore, all possible components of the total travel time for one trip are included in the calculation given in Equation (1).