scholarly journals Rationalizing Urban Transportation using Smart Card Data

2019 ◽  
Vol 8 (11S) ◽  
pp. 507-515
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Population ◽  
Transport Systems ◽  
Living Standard ◽  
Transit System ◽  
Advantages And Disadvantages ◽  
Smart Card Data ◽  
The Impact

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport

scholarly journals Equity in public transport — a case of Bangalore’s city bus transport

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Deepak Baindur ◽  
Pooja Rao
Keyword(s):  
Low Income ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Areas ◽  
Survey Study ◽  
Transport Systems ◽  
City Bus ◽  
Main Challenge ◽  
The Cost ◽  
The Impact

In most urban areas, buses are the most heavily used form of public transportation[1] and more so in Indian cities where buses make up for over 90% of public transport ridership[2]. In the selected Indian metro cities, where formal bus based PT systems are operated by public agencies, they are over-reliant on state support to sustain operations as fare box collections are inadequate in spite of having relatively high ridership. The main challenge for all this is to achieve long term financial sustainability of public transport systems while providing good quality and affordable bus services.This paper investigates internal and external factors that led to the steep and recurrent fare increases in the Bangalore city bus services in the period from 2012–2014 which are operated by Bangalore Metropolitan Transport Corporation. In order to estimate the impact of the recent bus fare increases that have had on the economically weaker sections of the society dependent on these services, the paper presents the results of a random sampling survey study carried out in a central locality in the city that has a large slum area.The key findings throw light on the various ways in which the low income bus users have adapted to reduce their travel costs through changes in travel behavior, travel pattern and modal shifts. The cost of the behavioral changes through lost opportunities and the cost of the modal shifts of the persons earlier favoring public transportation draw attention to the significance of public transport fare policies. Furthermore, the management and operations of the BMTC agency show scope for improvement which can translate into better revenue generation and consequent reduction in fares.

scholarly journals Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data

2020 ◽  
Vol 12 (12) ◽  
pp. 5010
Author(s):  
Pengfei Lin ◽  
Jiancheng Weng ◽  
Dimitrios Alivanistos ◽  
Siyong Ma ◽  
Baocai Yin
Keyword(s):  
Survey Data ◽  
Smart Card ◽  
Public Transport ◽  
Travel Behavior ◽  
Transport Systems ◽  
Behavior Patterns ◽  
Light Gradient ◽  
Smart Card Data ◽  
Departure Time ◽  
Commuting Patterns

Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to their departure time using the latent Dirichlet allocation model. To enhance the interpretation of the behavior patterns in each cluster, we investigated the relationship between the socioeconomic characteristics of the residence locations and commuter cluster distributions. Approximately 3.1 million cardholders were identified as commuters, accounting for 67.39% of daily passenger volume. Their commuting routes indicated the existence of job–house imbalance and excess commuting in Beijing. We further segmented commuters into six clusters with different temporal patterns, including two-peak, staggered shifts, flexible departure time, and single-peak. The residences of commuters are mainly concentrated in the low housing price and high or medium population density areas; subway facilities will promote people to commute using public transport. This study will help stakeholders optimize the public transport networks, scheduling scheme, and policy accordingly, thus ameliorating commuting within cities.

scholarly journals Demand management of congested public transport systems: a conceptual framework and application using smart card data

2019 ◽  
Vol 47 (5) ◽  
pp. 2337-2365 ◽  
Author(s):  
Anne Halvorsen ◽  
Haris N. Koutsopoulos ◽  
Zhenliang Ma ◽  
Jinhua Zhao
Keyword(s):  
Conceptual Framework ◽  
Smart Card ◽  
Public Transport ◽  
Demand Management ◽  
Transport Systems ◽  
Smart Card Data

scholarly journals A GIS-Based Methodology for Evaluating the Increase in Multimodal Transport between Bicycle and Rail Transport Systems. A Case Study in Palermo

2021 ◽  
Vol 10 (5) ◽  
pp. 321
Author(s):  
Alessandro Emilio Capodici ◽  
Gabriele D’Orso ◽  
Marco Migliore
Keyword(s):  
High School ◽  
Public Transport ◽  
Choice Model ◽  
Rail Transport ◽  
Transport Systems ◽  
Sustainable Mobility ◽  
Transit System ◽  
Modal Choice ◽  
Transport Services ◽  
Multimodal Transport

Background: In a world where every municipality is pursuing the goals of more sustainable mobility, bicycles play a fundamental role in getting rid of private cars and travelling by an eco-friendly mode of transport. Additionally, private and shared bikes can be used as a feeder transit system, solving the problem of the first- and last-mile trips. Thanks to GIS (Geographic Information System) software, it is possible to evaluate the effectiveness of such a sustainable means of transport in future users’ modal choice. Methods: Running an accessibility analysis of cycling and rail transport services, the potential mobility demand attracted by these services and the possible multimodality between bicycle and rail transport systems can be assessed. Moreover, thanks to a modal choice model calibrated for high school students, it could be verified if students will be really motivated to adopt this solution for their home-to-school trips. Results: The GIS-based analysis showed that almost half of the active population in the study area might potentially abandon the use of their private car in favour of a bike and its combination with public transport systems; furthermore, the percentage of the students of one high school of Palermo, the Einstein High School, sharply increases from 1.5% up to 10.1%, thanks also to the combination with the rail transport service. Conclusions: The GIS-based methodology shows that multimodal transport can be an effective way to pursue a more sustainable mobility in cities and efficiently connect suburbs with low-frequent public transport services to the main public transport nodes.

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

scholarly journals Behavior of Traffic Congestion and Public Transport in Eight Large Cities in Latin America during the COVID-19 Pandemic

2021 ◽  
Vol 11 (10) ◽  
pp. 4703
Author(s):  
Renato Andara ◽  
Jesús Ortego-Osa ◽  
Melva Inés Gómez-Caicedo ◽  
Rodrigo Ramírez-Pisco ◽  
Luis Manuel Navas-Gracia ◽  
...  
Keyword(s):  
Latin American ◽  
Public Transport ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Urban Transport ◽  
Control Measures ◽  
Johns Hopkins Hospital ◽  
Large Cities ◽  
Public Data ◽  
The Impact

This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities.

scholarly journals Exploring Equity in Public Transportation Planning Using Smart Card Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3039
Author(s):  
Kiarash Ghasemlou ◽  
Murat Ergun ◽  
Nima Dadashzadeh
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Transportation Planning ◽  
Demand Characteristic ◽  
Monthly Data ◽  
Smart Card Data ◽  
Planning Methods ◽  
Public Transportation Planning ◽  
Daily Travel ◽  
A Share

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

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
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