scholarly journals Do transportation network companies decrease or increase congestion?

2019 ◽  
Vol 5 (5) ◽  
pp. eaau2670 ◽  
Author(s):  
Gregory D. Erhardt ◽  
Sneha Roy ◽  
Drew Cooper ◽  
Bhargava Sana ◽  
Mei Chen ◽  
...  
Keyword(s):  
San Francisco ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Time Data ◽  
Travel Time Data ◽  
Application Programming ◽  
Using Data ◽  
Programming Interfaces ◽  
Insight Into ◽  
Transportation Network Companies

This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.

scholarly journals Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

2021 ◽  
Author(s):  
Gregory D. Erhardt ◽  
Richard Alexander Mucci ◽  
Drew Cooper ◽  
Bhargava Sana ◽  
Mei Chen ◽  
...  
Keyword(s):  
San Francisco ◽  
Transportation Network ◽  
Transportation Systems ◽  
Light Rail ◽  
Transit Ridership ◽  
Application Programming ◽  
Transit Agencies ◽  
Using Data ◽  
Transit Use ◽  
Transportation Network Companies

AbstractTransportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.

Impacts on Vehicle Occupancy and Airport Curb Congestion of Transportation Network Companies at Airports

2018 ◽  
Vol 2672 (23) ◽  
pp. 52-58 ◽  
Author(s):  
Karina Hermawan ◽  
Amelia C. Regan
Keyword(s):  
Los Angeles ◽  
San Francisco ◽  
Public Transit ◽  
Transportation Network ◽  
Light Rail ◽  
International Airport ◽  
Using Data ◽  
Standard Service ◽  
Transportation Network Companies

How does the growth of transportation network companies (TNCs) at airports affect the use of shared modes and congestion? Using data from the 2015 passenger survey from Los Angeles International Airport (LAX), San Francisco International Airport (SFO), and Oakland International Airport (OAK), this research analyzes TNCs’ relationship with shared modes (modes that typically have higher vehicle-occupancy and include public transit such as buses and light rail, shared vans or shuttles) and the demand for their shared vs. standard service at the airport. Because TNCs both replace shared rides and make them possible, the research also measured the net effects at these airports. The results suggest that in 2015, TNCs caused 215,000 and 25,000 passengers to switch from shared to private modes at SFO and OAK, respectively. By 2020, the increase is expected to be about 840,000 and 107,000 passengers per year, respectively.

scholarly journals European Loans and Visits System (ELViS) as a Use Case for a Collection Descriptions Standard

2020 ◽  
Vol 4 ◽  
Author(s):  
Sharif Islam
Keyword(s):  
Data Model ◽  
User Requirements ◽  
Use Case ◽  
Current Development ◽  
Heterogeneous Datasets ◽  
Application Programming ◽  
Data Elements ◽  
Programming Interfaces ◽  
Improve Access ◽  
Insight Into

The European Loans and Visits System (ELViS) is an e-service in development designed to improve access to natural history collections across Europe. Bringing together heterogeneous datasets about institutions, people, collections and specimens, ELViS will provide an e-service (with application programming interfaces (APIs) and portal) that handles various stages of collections-based research. One of the main functionalities of ELViS is to facilitate loan and visit requests related to collections. To facilitate activities such as searching for collections, requesting loans, generating reports on collection usage, and ensuring interoperability with existing and new systems and services, ELViS must use a standard way of describing collections. In this talk, I show how ELViS can use the Collection Descriptions (CD) standard currently being developed by the CD Task Group at TDWG. I will provide a brief introduction to ELViS, summarise the current development efforts, and show how the Collection Description standard can support specific user requirements (gathered via an extensive set of user stories). I will also provide insight into the data elements within ELViS (see Fig. 1) and how they relate to the Collection Description data model.

scholarly journals Novel and Practical Method to Quantify the Quality of Mobility: Mobility Energy Productivity Metric

2019 ◽  
Vol 2673 (10) ◽  
pp. 141-152 ◽  
Author(s):  
Yi Hou ◽  
Venu Garikapati ◽  
Ambarish Nag ◽  
Stanley E. Young ◽  
Tom Grushka
Keyword(s):  
Practical Method ◽  
Time Data ◽  
Energy Productivity ◽  
Technology Infrastructure ◽  
Web Based ◽  
Trip Purpose ◽  
Technology Innovations ◽  
Travel Time Data ◽  
Application Programming

Recent technology innovations are enabling fundamental improvements in mobility systems, including options for new travel modes, methods, and opportunities to connect people with goods, services, and employment. A desire to quantify and compare both existing and emerging transportation options motivated development of the mobility energy productivity (MEP) metric described here. The MEP metric fundamentally measures the potential of a city’s transportation system to connect a person to a variety of services and activities that define a high quality of life, relative to the convenience, cost, and energy needed to provide these connections. Fundamentally derived from accessibility theory, the MEP advances practice by using readily available travel time data (either from web-based application programming interfaces or outputs from an urban transportation model) combined with established parameters that reflect the energy intensity and cost of various travel modes, and relative frequency of activity engagement. The construction of the MEP metric allows for aggregation and disaggregation to the appropriate spatial, modal, and trip purpose resolution, as analysis needs dictate. The MEP could be used to compare alternative futures related to technology, infrastructure investment, or policy, providing a much-needed tool for planners, researchers, and analysts.

scholarly journals Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics

2021 ◽  
Author(s):  
Sarvani Duvvuri ◽  
Srinivas S. Pulugurtha
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Pearson Correlation ◽  
Time Data ◽  
Network Characteristics ◽  
Time Index ◽  
Time Performance ◽  
Travel Time Data ◽  
Day Of The Week

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.

scholarly journals Transportation Network Companies: Drivers’ Perceptions of Ride-Sharing Regarding Climate Change and Extreme Weather

Climate ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 131
Author(s):  
Sandra Olivia Brugger ◽  
Theresa Watts
Keyword(s):  
Climate Change ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Extreme Weather ◽  
Sustainable Transportation ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Transportation Sector ◽  
Reduce Emissions ◽  
Transportation Network Companies

The transportation sector is a major factor contributing to climate change. Transportation Network Companies (TNC) may become part of solutions to reduce emissions and their drivers play an important role in doing so. This study aims to understand TNC driver’s perceptions of climate change, to understand how climate change and extreme weather affects their business and how they see their role in contributing to or mitigating climate change. We conducted an in-person survey of TNC drivers in Nevada, USA, and analyzed the derived information with descriptive statistics and content analysis. Among the 75 TNC drivers, almost half believe climate change is happening and is caused by human activities. We found TNC drivers and their business are affected by extreme weather events. Currently the drivers do not see their role in mitigating climate change and lack the awareness of green initiatives already in place by TNCs’. We conclude that TNCs could increase their climate change responsibility by providing driver incentives for cars with reduced emissions or by geographically expanding customer incentives for using sustainable TNC options such as car-pooling. By doing so, TNC may play a role in reducing global greenhouse gas emissions and traffic congestion; thus, contributing to improved sustainable transportation practices.

scholarly journals An Integrative Drug Repurposing Pipeline Using KNIME and Programmatic Data Access: A Case Study on COVID-19 Data

2020 ◽  
Author(s):  
Alzbeta Tuerkova ◽  
Barbara Zdrazil
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Open Data ◽  
Drug Repurposing ◽  
Data Access ◽  
Additional Advantage ◽  
Application Programming ◽  
Using Data ◽  
Chemical Abstracts Service ◽  
Programming Interfaces

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces. The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage<br>of flexibility, re-usability, and transparency. Here, we present a strategy for performing in silico drug repurposing with the analytics platform KNIME, using data for 38 suggested COVID-19 drug targets as a timely use case. The workflow includes a targeted download of data through web services, data curation (including chemical structure standardization), detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited dataset of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of COVID-19 data are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

scholarly journals An Integrative Drug Repurposing Pipeline Using KNIME and Programmatic Data Access: A Case Study on COVID-19 Data

2020 ◽  
Author(s):  
Alzbeta Tuerkova ◽  
Barbara Zdrazil
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Open Data ◽  
Drug Repurposing ◽  
Data Access ◽  
Additional Advantage ◽  
Application Programming ◽  
Using Data ◽  
Chemical Abstracts Service ◽  
Programming Interfaces

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces. The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage<br>of flexibility, re-usability, and transparency. Here, we present a strategy for performing in silico drug repurposing with the analytics platform KNIME, using data for 38 suggested COVID-19 drug targets as a timely use case. The workflow includes a targeted download of data through web services, data curation (including chemical structure standardization), detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited dataset of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of COVID-19 data are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

Profiling Transport Network Company Activity using Big Data

2018 ◽  
Vol 2672 (42) ◽  
pp. 192-202 ◽  
Author(s):  
Drew Cooper ◽  
Joe Castiglione ◽  
Alan Mislove ◽  
Christo Wilson
Keyword(s):  
San Francisco ◽  
Transportation Network ◽  
Time Of Day ◽  
Transport Network ◽  
Objective Data ◽  
Policy Decisions ◽  
Transit Ridership ◽  
Level Data ◽  
Application Programming ◽  
San Francisco County

Transportation network companies (TNCs) provide vehicle-for-hire services. They are distinguished from taxis primarily by the presumption that vehicles are privately owned by drivers. Unlike taxis, which must hold one of approximately 1,800 medallions licensed by the San Francisco Municipal Transportation Agency (SFMTA) to operate in San Francisco, there is no regulatory limit on the supply of TNCs. TNCs have an increasingly visible presence in San Francisco. However, there has been little or no objective data available on TNCs to allow planners to understand the number of trips they provide, the amount of vehicle miles traveled they generate, or their effects on congestion, transit ridership, transit operations, or safety. Without this type of data it is difficult to make informed planning and policy decisions. Discussions with Uber, Lyft, and the California Public Utilities Commission, which collects trip-level data from TNCs in California, requesting information on TNC trips have not resulted in any data being shared. Under increasing pressure from policymakers for objective data to inform policy decisions, the San Francisco County Transportation Authority (SFCTA) partnered with researchers from Northeastern University who developed a methodology for collecting data through Uber’s and Lyft’s application programming interfaces (APIs) with high spatial and temporal resolution. This paper provides a brief literature review on transport network company (TNC) data, and goes one to describe the methodology used to collect data, summarizes the process for converting the raw data into estimated TNC trips, and presents an analysis of the results of the TNC trip estimates. This study determined that TNCs serve a substantial number of trips in San Francisco, over 170,000 on a typical weekday, that these trips follow traditional time of day distributions, and that they tend to take place in the busiest parts of the City.

Sign in / Sign up

Export Citation Format

Share Document