scholarly journals How does the Built Environment Influence Public Transit Choice in Urban Villages in China?

2018 ◽  
Vol 11 (1) ◽  
pp. 148 ◽  
Author(s):  
Le Yu ◽  
Binglei Xie ◽  
Edwin Chan
Keyword(s):  
Built Environment ◽  
Low Income ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Public Transit ◽  
Multinomial Logistic Regression ◽  
Large Population ◽  
Travel Behaviour ◽  
New Perspective ◽  
Urban Villages

With growing traffic congestion and environmental issues, the interactions between travel behaviour and the built environment have drawn attention from researchers and policymakers to take effective measures to encourage more sustainable travel modes and to curb car trips, especially in urbanising areas where travel demand is very complicated. This paper presents how built environmental factors affect public transit choice behaviour in urban villages in China, where a large population of low-income workers are accommodated. This location had a high demand for public transit and special built environmental characteristics. Multinomial logistic regression was employed to examine both the determinants and magnitude of their influence. The results indicate that the impacts of built environments apply particularly in urban villages compared to those in formal residences. In particular, mixed land use generates an adverse effect on public transit choice, a surprising outcome which is contrary to previous common conclusions. This study contributes by addressing a special type of neighbourhood in order to narrow down the research gap in this domain. The findings help to suggest effective measures to satisfy public transit demand efficiently and also provide a new perspective for urban regeneration.

scholarly journals Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning Approach

2021 ◽  
Author(s):  
Long Chen ◽  
Piyushimita Vonu Thakuriah ◽  
Konstantinos Ampountolas
Keyword(s):  
New York ◽  
Deep Learning ◽  
Built Environment ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Service Providers ◽  
Road Traffic ◽  
Travel Behaviour ◽  
Demand Prediction ◽  
Research Areas

AbstractAs ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet’s prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services.

scholarly journals What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

2021 ◽  
Vol 13 (16) ◽  
pp. 9324
Author(s):  
Sujae Kim ◽  
Sangho Choo ◽  
Sungtaek Choi ◽  
Hyangsook Lee
Keyword(s):  
Traffic Congestion ◽  
Latent Class ◽  
Travel Demand ◽  
Public Transit ◽  
Latent Class Model ◽  
Influencing Factor ◽  
Demand Management ◽  
Related Factors ◽  
Travel Demand Management ◽  
Trip Frequency

Mobility as a Service (MaaS), which integrates public and shared transportation into a single service, is drawing attention as a travel demand management strategy aimed at reducing automobile dependency and encouraging public transit. In particular, there have been few studies that recognize traffic congestion during peak hours and identify related factors for practical application. The purpose of this study is to explore what factors affect Seoul commuters’ mode choice including MaaS. A web-based survey that 161 commuters participated in was conducted to collect information about personal, household, and travel attributes, together with their mode preference for MaaS. A latent class model was developed to classify unobserved latent groups based on trip frequency by means and to identify factors influencing mode-specific utilities (in particular, MaaS service) for each class. The result shows that latent classes are divided into two groups (public transit-oriented commuters and balanced mode commuters). Most variables have significant impacts on choice for MaaS. The coefficient of MaaS choice of Class 1 and Class 2 were different. These findings suggest there is a difference between the classes according to trip frequency by means as an influencing factor in MaaS choice.

scholarly journals The future of emerging technologies in public transit in Greater Toronto Area

2021 ◽  
Author(s):  
Zaiem Haider
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Emerging Technologies ◽  
Public Transit ◽  
Elderly Person ◽  
Advanced Technology ◽  
Greater Toronto Area ◽  
The World ◽  
Transit Agencies

In communities throughout the world, strong and convenient public transportation makes valuable contributions to economic development, increased safety, energy conservation, a cleaner environment, less traffic congestion, and an improved quality of life. Whether it's a disabled person on her way to a doctor appointment, a child on the way to the library, or an elderly person going to buy groceries, rails, buses and vans connect people to their community. While transit serves many purposes, one of the most important of which is to provide critical access and mobility for transit-dependent and lower-income residents country wide, it also reduces the pressure on critical commute corridors by offering a convenient alternative to driving alone. People who are dependent on public transit, the young or the old, the disabled or the low-income, deserve a first-class system. A survey was conducted by City Pulse Toronto (CP 24) and the question they put to the viewers was "Would improved public transit convinces you to give up your car?" The result was amazing that 96% of the people using cars opted for Public transit. In the last decade statistics depict that the cities that have adopted emerging technologies in public transit are reaping the benefits of their increased rider ship by almost three fold. It is disappointing to see that the transit-using trend in Greater Toronto Area (GTA) has decreased in the past five years except in the regions where transit agencies are updating their systems. Throughout the North America and other parts of the world, transit agencies are deploying automatic vehicle location and control fleet management systems, electronic and interactive customer information systems, and contact/contactless fare collection systems to save costs, improve operations and management efficiency and provide better service to customers. In this project an effort is made to depict the extent of adoption of advanced technology in the provision of public transportation service in Greater Toronto Area. The focus is on some of the most innovative or comprehensive implementations, categorized under two types of services/technologies, Automatic Passenger Counting and Electronic Fare Payment. Another objective of this study is to assemble the knowledge on successful applications of advanced technologies, the issues in their implementation, the goals and benefits of Intelligent Transportation System's integration. The study focuses on institutional, operational and technical barriers with the expectation that this will lead to more widespread adoption of ITS systems and techniques.

scholarly journals Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260969
Author(s):  
Shahram Heydari ◽  
Garyfallos Konstantinoudis ◽  
Abdul Wahid Behsoodi
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Urban Areas ◽  
Travel Demand ◽  
Public Transit ◽  
Temporal Correlation ◽  
Travel Behaviour ◽  
Time Series Model ◽  
The World ◽  
Bike Sharing

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected—partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March–December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

scholarly journals The future of emerging technologies in public transit in Greater Toronto Area

2021 ◽  
Author(s):  
Zaiem Haider
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Emerging Technologies ◽  
Public Transit ◽  
Elderly Person ◽  
Advanced Technology ◽  
Greater Toronto Area ◽  
The World ◽  
Transit Agencies

In communities throughout the world, strong and convenient public transportation makes valuable contributions to economic development, increased safety, energy conservation, a cleaner environment, less traffic congestion, and an improved quality of life. Whether it's a disabled person on her way to a doctor appointment, a child on the way to the library, or an elderly person going to buy groceries, rails, buses and vans connect people to their community. While transit serves many purposes, one of the most important of which is to provide critical access and mobility for transit-dependent and lower-income residents country wide, it also reduces the pressure on critical commute corridors by offering a convenient alternative to driving alone. People who are dependent on public transit, the young or the old, the disabled or the low-income, deserve a first-class system. A survey was conducted by City Pulse Toronto (CP 24) and the question they put to the viewers was "Would improved public transit convinces you to give up your car?" The result was amazing that 96% of the people using cars opted for Public transit. In the last decade statistics depict that the cities that have adopted emerging technologies in public transit are reaping the benefits of their increased rider ship by almost three fold. It is disappointing to see that the transit-using trend in Greater Toronto Area (GTA) has decreased in the past five years except in the regions where transit agencies are updating their systems. Throughout the North America and other parts of the world, transit agencies are deploying automatic vehicle location and control fleet management systems, electronic and interactive customer information systems, and contact/contactless fare collection systems to save costs, improve operations and management efficiency and provide better service to customers. In this project an effort is made to depict the extent of adoption of advanced technology in the provision of public transportation service in Greater Toronto Area. The focus is on some of the most innovative or comprehensive implementations, categorized under two types of services/technologies, Automatic Passenger Counting and Electronic Fare Payment. Another objective of this study is to assemble the knowledge on successful applications of advanced technologies, the issues in their implementation, the goals and benefits of Intelligent Transportation System's integration. The study focuses on institutional, operational and technical barriers with the expectation that this will lead to more widespread adoption of ITS systems and techniques.

The Drive to Work

2016 ◽  
Vol 37 (1) ◽  
pp. 66-82 ◽  
Author(s):  
Evelyn Blumenberg ◽  
Gregory Pierce
Keyword(s):  
Low Income ◽  
Public Transit ◽  
Multinomial Logistic Regression ◽  
Employment Outcomes ◽  
Welfare To Work ◽  
Housing Assistance ◽  
Longitudinal Survey Data ◽  
Strong Positive Relationship ◽  
The Relationship

Transportation enables low-income individuals to find and travel to employment. This article analyzes the relationship between access to automobiles and public transit and employment outcomes of low-income households. We use longitudinal survey data from participants in the Welfare to Work Voucher Program, which was conducted in five US metropolitan areas between 1999 and 2005. Multinomial logistic regression shows that baseline access to automobiles has a strong positive relationship to follow-up employment but public transit access and receipt of housing assistance do not. Our findings suggest that enhancing car access will notably improve employment outcomes among very-low-income adults, but other assistance will have, at best, marginal effects.

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

scholarly journals Understanding Users’ Satisfaction Towards Public Transit System in India: A Case-Study of Mumbai

2021 ◽  
Vol 10 (3) ◽  
pp. 155
Author(s):  
Rahul Das
Keyword(s):  
Service Quality ◽  
Public Transport ◽  
Travel Demand ◽  
Resource Constraints ◽  
Public Transit ◽  
Sampling Bias ◽  
Relevant Information ◽  
Transit System ◽  
Transport Services ◽  
Developing Cities

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score.

scholarly journals Correlates of Transitions in Food Insecurity Status during the Early Stages of the COVID-19 Pandemic among Ethnically Diverse Households in Central Texas

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2597
Author(s):  
Kathryn M. Janda ◽  
Nalini Ranjit ◽  
Deborah Salvo ◽  
Aida Nielsen ◽  
Pablo Lemoine ◽  
...  
Keyword(s):  
Food Insecurity ◽  
Food Availability ◽  
Low Income ◽  
Food Access ◽  
Multinomial Logistic Regression ◽  
Related Factors ◽  
Logistic Regression Models ◽  
Early Stages ◽  
Central Texas ◽  
June July

Food insecurity increased substantially in the USA during the early stages of the 2020 COVID-19 pandemic. The purpose of this study was to identify potential sociodemographic and food access-related factors that were associated with continuing or transitioning into food insecurity in a diverse population. An electronic survey was completed by 367 households living in low-income communities in Central Texas during June–July 2020. Multinomial logistic regression models were developed to examine the associations among food insecurity transitions during COVID-19 and various sociodemographic and food access-related factors, including race/ethnicity, children in the household, loss of employment/wages, language, and issues with food availability, accessibility, affordability, and stability during the pandemic. Sociodemographic and food access-related factors associated with staying or becoming newly food insecure were similar but not identical. Having children in the household, changes in employment/wages, changing shopping location due to food availability, accessibility and/or affordability issues, issues with food availability, and stability of food supply were associated with becoming newly food insecure and staying food insecure during the pandemic. Identifying as Latino and/or Black was associated with staying food insecure during COVID-19. These findings suggest that the COVID-19 pandemic did not create new food insecurity disparities. Rather, the pandemic exacerbated pre-existing disparities.

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