The Effectiveness of Public Transit Subsidies on Ridership and the Environment: Evidence from Canada

2016 ◽  
Author(s):  
Nicholas Rivers ◽  
Bora Plumptre
Keyword(s):  
Public Transit

A Study on the Public Transit Transfer Behavior in New Rail Cities Based on Travelers’ Cognition

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Shenghui Zhao ◽  
Lishan Sun ◽  
Dewen Kong ◽  
Jinghan Cao ◽  
Yan Wang
Keyword(s):  
Public Transit ◽  
The Public ◽  
Transfer Behavior

Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems using a Graph Convolutional Network with Public Transit Usage Data

2021 ◽  
pp. 036119812110120
Author(s):  
Jung-Hoon Cho ◽  
Seung Woo Ham ◽  
Dong-Kyu Kim
Keyword(s):  
Adjacency Matrix ◽  
Public Transit ◽  
Demand Forecasting ◽  
The Other ◽  
Convolutional Network ◽  
Demand Pattern ◽  
Temporal Features ◽  
Unmet Demand ◽  
Bike Sharing ◽  
Main Component

With the growth of the bike-sharing system, the problem of demand forecasting has become important to the bike-sharing system. This study aims to develop a novel prediction model that enhances the accuracy of the peak hourly demand. A spatiotemporal graph convolutional network (STGCN) is constructed to consider both the spatial and temporal features. One of the model’s essential steps is determining the main component of the adjacency matrix and the node feature matrix. To achieve this, 131 days of data from the bike-sharing system in Seoul are used and experiments conducted on the models with various adjacency matrices and node feature matrices, including public transit usage. The results indicate that the STGCN models reflecting the previous demand pattern to the adjacency matrix show outstanding performance in predicting demand compared with the other models. The results also show that the model that includes bus boarding and alighting records is more accurate than the model that contains subway records, inferring that buses have a greater connection to bike-sharing than the subway. The proposed STGCN with public transit data contributes to the alleviation of unmet demand by enhancing the accuracy in predicting peak demand.

scholarly journals Public transit usage and air quality index during the COVID-19 lockdown

2021 ◽  
Vol 286 ◽  
pp. 112166
Author(s):  
Mohammad Ali Sahraei ◽  
Emre Kuşkapan ◽  
Muhammed Yasin Çodur
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Public Transit ◽  
Air Quality Index

Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform

2020 ◽  
Vol 54 (24) ◽  
pp. 15613-15621
Author(s):  
Derek V. Mallia ◽  
Logan E. Mitchell ◽  
Lewis Kunik ◽  
Ben Fasoli ◽  
Ryan Bares ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Public Transit ◽  
Light Rail

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.

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