scholarly journals Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

2018 ◽  
Vol 10 (11) ◽  
pp. 4178 ◽  
Author(s):  
Yadi Zhu ◽  
Feng Chen ◽  
Ming Li ◽  
Zijia Wang
Keyword(s):  
Smart Card ◽  
House Prices ◽  
House Price ◽  
User Profile ◽  
Urban Rail Transit ◽  
Feature Identification ◽  
Middle Income ◽  
Profile Information ◽  
Individual Mobility ◽  
Smart Card Data

Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information.

scholarly journals Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data

2021 ◽  
Vol 18 (5) ◽  
pp. 2711
Author(s):  
Xintao Liu ◽  
Ziwei Lin ◽  
Jianwei Huang ◽  
He Gao ◽  
Wenzhong Shi
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Urban Areas ◽  
Medical Service ◽  
Potential Model ◽  
House Price ◽  
Spillover Effect ◽  
The Public ◽  
Service Accessibility ◽  
Smart Card Data

The measurement of medical service accessibility is typically based on driving or Euclidean distance. However, in most non-emergency cases, public transport is the travel mode used by the public to access medical services. Yet, there has been little evaluation of the public transport system-based inequality of medical service accessibility. This work uses massive real smart card data (SCD) and an improved potential model to estimate the public transport-based medical service accessibility in Beijing, China. These real SCD data are used to calculate travel costs in terms of time and distance, and medical service accessibility is estimated using an improved potential model. The spatiotemporal variations and patterns of medical service accessibility are explored, and the results show that it is unevenly spatiotemporally distributed across the study area. For example, medical service accessibility in urban areas is higher than that in suburban areas, accessibility during peak periods is higher than that during off-peak periods, and accessibility on weekends is generally higher than that on weekdays. To explore the association of medical service accessibility with socio-economic factors, the relationship between accessibility and house price is investigated via a spatial econometric analysis. The results show that, at a global level, house price is positively correlated with medical service accessibility. In particular, the medical service accessibility of a higher-priced spatial housing unit is lower than that of its neighboring spatial units, owing to the positive spatial spillover effect of house price. This work sheds new light on the inequality of medical service accessibility from the perspective of public transport, which may benefit urban policymakers and planners.

scholarly journals DO INFLATION, INTEREST RATE AND COST OF RENTING AFFECT THE PRICE OF TERRACE HOUSES IN PENANG?

2020 ◽  
Vol 18 (13) ◽  
Author(s):  
Zaemah Zainuddin ◽  
Rosylin Mohd Yusof
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Multivariate Regression ◽  
House Prices ◽  
House Price ◽  
Multivariate Regression Analysis ◽  
Middle Income ◽  
Rental Rate ◽  
Mortgage Rates ◽  
The Cost

In Malaysia, the housing ownership is reported to decrease from 85% in 1999 to 72.5% in 2010. This is due to the outstripped increase of house price over the income level and the unstable economic situation which creates unaffordability to own a house for many people. Therefore, the main objective of this study is to examine whether the price of terrace houses in Penang is being affected with fundamental factors such as inflation, interest rates and the cost of renting. This study uses multivariate regression analysis with quarterly data of terrace house prices (HPI terrace house in Penang), inflation (CPI) and interest rate (mortgage rates) from 2009: Q1 to 2016: Q4. Evidently, the cost of renting terrace houses in Penang does not have any impact on the price of terrace houses and the stable movement of cost of renting indicates that the growth of rental rate is at acceptable price for middle income earners.

scholarly journals Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao Fu ◽  
Yu Gu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Rapid Development ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Mobility Patterns ◽  
Passenger Flow ◽  
Smart Card Data ◽  
The Impact ◽  
Metro Network

Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. The appearance of new metro lines may significantly affect passenger flow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger flow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). The impact of the new metro line on passenger flow distribution and travel time in the metro network is first analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. The results show that the new metro line influences passenger flow, travel time, and travel time reliability in the metro network and has different impacts on different categories of commuters.

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 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.

scholarly journals The role and significance of planning in the determination of house prices in Australia: Recent policy debates

2021 ◽  
pp. 0308518X2198894
Author(s):  
Peter Phibbs ◽  
Nicole Gurran
Keyword(s):  
Land Use ◽  
Housing Market ◽  
Land Use Planning ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Evidence Base ◽  
Urban Housing ◽  
Price Inflation ◽  
Depth Analysis

On the world stage, Australian cities have been punching above their weight in global indexes of housing prices, sparking heated debates about the causes of and remedies for, sustained house price inflation. This paper examines the evidence base underpinning such debates, and the policy claims made by key commentators and stakeholders. With reference to the wider context of Australia’s housing market over a 20 year period, as well as an in depth analysis of a research paper by Australia’s central Reserve Bank, we show how economic theories commonly position land use planning as a primary driver of new supply constraints but overlook other explanations for housing market behavior. In doing so, we offer an alternative understanding of urban housing markets and land use planning interventions as a basis for more effective policy intervention in Australian and other world cities.

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