scholarly journals Big Data de Transporte Público na Análise da Variabilidade de Indicadores da Acessibilidade às Oportunidades de Trabalho e Educação

2019 ◽  
Author(s):  
Carlos Kauê Vieira Braga
Keyword(s):  
Big Data ◽  
Travel Time ◽  
Smart Card ◽  
Public Transport ◽  
Central Area ◽  
Dispersion Measure ◽  
Travel Times ◽  
Time Data ◽  
Strong Concentration ◽  
Smart Card Data

Accessibility, defined as the potential of opportunities for interaction, is a key concept in urban planning. Traditionally, the measurement of accessibility relied on methods that used simplified information on transit networks. Recent developments of public transport structured data through Global Transit Feed Specification (GTFS) fostered the creation and standardization of many tools to estimate travel times between origin-destination pairs, feeding the calculation of accessibility indicators. However, researchers are beginning to better understand to what extent differences between scheduled GTFS data and real services can affect accessibility estimates. The aim ofthis dissertation is to analyze the variability of accessibility indicators by public transport to work and education opportunities using location data and smart card data in a large transportation system. This variability of accessibility is investigated through three main hypotheses: 1) there is a difference between measuring it with Scheduled GTFS versus Empiric GTFS; 2) there is a difference between measuring it with Empiric GTFS based on median observed travel times versus based on a dispersion measure of observed travel times on the network; 3) there is a difference between measuring it to different departure times within peak hour. Initially, the proposed method consolidates and integrates one month of big data database, preparing them to the GTFS reconstruction approach. Using geoprocessing techniques, it starts by transforming raw GPS points to a structured transit timetable. The timetable times are then aggregated by 15 minutes intervals and segments between stops, determining the median and 85 percentile travel time to produce two empiric GTFS files. These files, alongside the Scheduled GTFS, are then used to estimate travel time between origin-destination pairs, making possible to estimate accessibility indicators based on scheduled and real-time data at different departure times within peak hour. Smart card data is used to estimate time thresholds to calculate cumulative indicators. The results corroborate the three hypotheses of variability on the spatial distribution of accessibility indicator by work and education activities. This variability is higher for access to jobs opportunities given its strong concentration on the central area of Fortaleza. These results suggest that decision makers take this variability into consideration when they assess the accessibility impacts of future interventions.

Examining public transport usage by older adults with smart card data: A longitudinal study in Japan

2021 ◽  
Vol 93 ◽  
pp. 103046
Author(s):  
Shasha Liu ◽  
Toshiyuki Yamamoto ◽  
Enjian Yao ◽  
Toshiyuki Nakamura
Keyword(s):  
Older Adults ◽  
Longitudinal Study ◽  
Smart Card ◽  
Public Transport ◽  
Smart Card Data

Detection of Activities of Public Transport Users by Analyzing Smart Card Data

2012 ◽  
Vol 2276 (1) ◽  
pp. 48-55 ◽  
Author(s):  
Flavio Devillaine ◽  
Marcela Munizaga ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Smart Card Data

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

scholarly journals Analysis of public transport connections along the railway line No. 406 on the section Szczecin Główny – Police

2018 ◽  
Vol 2018 (2) ◽  
pp. 22-31
Author(s):  
Karol F. Abramek ◽  
Paweł Regulski
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Travel Times ◽  
Railway Stations ◽  
Journey Time ◽  
Railway Line ◽  
General Manner

The article presents an analysis of selected public transport lines running along the railway line Szczecin Główny – Police. Examined journey time by public transport between the railway stations and stops. Compared to the travel time by train and passenger public transport vehicles. In addition, a comparison of planned and actual travel times of public transport vehicles. In a general manner specified number of passenger public transport.

Seismic wave travel times from nuclear explosions

1958 ◽  
Vol 48 (4) ◽  
pp. 377-398
Author(s):  
Dean S. Carder ◽  
Leslie F. Bailey
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Strong Variation ◽  
Time Data ◽  
Owens Valley ◽  
Central California ◽  
Nuclear Explosions ◽  
Travel Time Data ◽  
The Pacific ◽  
Wave Travel

Abstract A large number of seismograph records from nuclear explosions in the Nevada and Pacific Island proving grounds have been collected and analyzed. The Nevada explosions were well recorded to distances of 6°.5 (450 mi.) and weakly recorded as far as 17°.5, and under favorable circumstances as far as 34°. The Pacific explosions had world-wide recording except that regional data were necessarily meager. The Nevada data confirm that the crustal thickness in the area is about 35 km., with associations of 6.1 km/sec. speeds in the crust and 8.0 to 8.2 km/sec. speeds beneath it. They indicate that there is no uniform layering in the crust, and that if higher-speed media do exist, they are not consistent; also, that the crust between the proving grounds and central California shows a thickening probably as high as 70 or 75 km., and that this thickened portion may extend beneath the Owens Valley. The data also point to a discontinuity at postulated depths of 160 to 185 km. Pacific travel times out to 14° are from 4 to 8 sec. earlier than similar continental data partly because of a thinner crust, 17 km. or less, under the atolls and partly because speeds in the top of the mantle are more nearly 8.15 km/sec. than 8.0 km/sec. More distant points, at 17°.5 and 18°.5, indicate slower travel times—about 8.1 km/sec. A fairly sharp discontinuity at 19° in the travel-time data is indicated. Travel times from Pacific sources to North America follow closely Jeffreys-Bullen 1948 and Gutenberg 1953 travel-time curves for surface foci except they are about 2 sec. earlier on the continent, and Arctic and Pacific basin data are about 2 sec. still earlier. The core reflection PcP shows a strong variation in amplitude with slight changes in distance at two points where sufficient data were available.

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