Transportation 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.
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.
Percent change in roadway speed in San Francisco between 2010 and 2016 attributable to each of five factors.
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Traffic congestion has worsened noticeably in San Francisco and other ...major cities over the past few years. This change could reasonably be explained by strong economic growth or other standard factors such as road and transit network changes. However, the worsening congestion also corresponds to the emergence of Transportation Network Companies (TNCs), such as Uber and Lyft, raising the question of whether the two trends may be related. Our research decomposes the contributors to increased congestion in San Francisco between 2010 and 2016, considering contributions from five incremental effects: road and transit network changes, population growth, employment growth, TNC volumes, and the effect of TNC pick-ups and drop-offs. We do so through a series of controlled travel demand model runs, supplemented with observed TNC data collected from the Application Programming Interfaces (APIs) of Uber and Lyft. Our results show that road and transit network changes over this period have only a small effect on congestion, population and employment growth each contribute about a quarter of the congestion increase, and that TNCs are the biggest contributor to growing congestion over this period, contributing about half of the increase in vehicle hours of delay, and adding to worsening travel time reliability. This research contradicts several studies that suggest TNCs may reduce congestion, and adds evidence in support of other recent empirical analyses showing that their net effect is to increase congestion. It is more data rich and spatially detailed than past studies, providing a better understanding of where and when TNCs add to congestion. This research gives transportation planners a better understanding of the causes of growing congestion, allowing them to more effectively craft strategies to mitigate or adapt to it.
A new open-source, web-based, configurable data visualization platform is presented that is specifically designed to support large-scale transportation simulations including MATSim and ActivitySim. ...It produces a wide array of interactive charts, maps, animations and analysis dashboards that are generally useful in the transportation domain. Interactive visualizations can be created and viewed locally on an analyst's laptop, or public web-based dashboards can be published for viewing on the open Internet. The details of software design are provided along with several examples of implementation at public agencies. User feedback shows the platform is found to be very flexible, while the straightforward configuration approach enables efficient development and deployment of web-based interactive visualizations. While it is not intended to replace geographic information systems or commercial software packages, the smaller curated set of capabilities is found by users to warrant its current adoption at several public agencies. Further work is needed to add more useful features, improve the platform's quality and user experience, and extend documentation.
Transportation network companies (TNCs) such as Uber and Lyft have grown tremendously over the last decade, particularly in the San Francisco Bay Area. Nonetheless, relatively little publicly ...available data exist about the users of these services, their travel behaviors, volume of use, the times and locations of TNC trips, and how TNC services are affecting transportation system performance overall. This paper describes the methods and descriptive results of the first large-scale smartphone-based TNC user survey conducted in the California Bay Area in the fall 2018 and spring of 2019.
Probe data that provide roadway speeds and travel times are increasingly being used for a variety of purposes in the transportation domain. A key use of these datasets has been roadway performance ...monitoring by state and local transportation agencies that are mandated to measure and report performance of their transportation networks. The San Francisco County Transportation Authority (SFCTA) monitors roadway performance as a part of the biennial Congestion Management Program (CMP) and primarily uses probe-based speed data for that purpose. Despite considerable savings in time and effort for data collection, integrating and processing the probe data still required a significant amount of manual work. This study highlights these challenges and proposes a data processing pipeline which includes an automated network conflation process, an efficient large data processing framework, and an interactive web-based visualization. In addition, all the scripts and code developed were made open source and are readily accessible from a public repository on GitHub. The value of the pipeline is demonstrated through the development of web-based interactive maps to monitor both long-term and short-term congestion in San Francisco. The short-term congestion monitoring application is timely given the spread of the COVID-19 pandemic and the region’s rapidly changing traffic conditions. Several valuable lessons learned from use of probe data for roadway performance monitoring are shared. Developing tools to ensure consistency of the data product and to reduce reliance on any one data vendor is of key importance.
Origin-destination (OD) data collection methods are steadily attempting to move from conventional survey techniques (roadside interview, license plate, etc.) toward using passively collected big data ...sources such as those based on global positioning system (GPS) and cell phone call detail records (CDR). In this study, a new passive data source, Google’s Aggregated and Anonymized Trips (AAT), was used to derive hourly OD demand matrices for the San Francisco Bay Area. Since the AAT dataset contains relative flows or weights as opposed to absolute trips, machine learning techniques were applied to convert them with the help of observed OD flows from expanded household travel survey. Several machine learning models were trained to perform quite well for both training and test data. However, it was found that the multi-layer perceptron (MLP), a neural networks approach, resulted in the best performing model for the conversion. Additionally, all models were used for predictions in a hypothetical application context where input AAT data were scaled by different growth factors. This exercise showed that, even though the trip predictions of all models were close to each other initially, they varied widely for different magnitudes of OD markets and growth factors.
With rising urban freeway congestion and limited funds available for highway expansion, it may be essential to manage traffic growth by using high-occupancy toll lanes and other travel demand ...management (TDM) measures. To prepare for and help guide freeway corridor management planning in the US-101 and I-280 corridors in San Francisco, California, information describing trip origins and destinations by time of day was desired. Observed roadway facility-specific origin–destination (O-D) flows can help researchers to understand spatial distribution of demand and impute willingness to pay, actions that are useful in evaluating various TDM strategies. This paper describes a new passively collected O-D data source—Google’s aggregated and anonymized trip (AAT) data—obtained under Google’s Better Cities program. Aggregate hourly flow matrices for 85 districts covering California’s nine-county Bay Area specific to four freeway segments in San Francisco were obtained. Because AAT data account for only a sample of travelers, Google provides relative flows rather than absolute counts. Linear regression models were estimated to relate relative flows in the AAT data set and observed traffic volumes from the California Department of Transportation’s Performance Measurement System. The models were applied to convert relative flows to trips and derive facility-specific, time-dependent O-D matrices. Comparison of these facility-specific O-D matrices to select link O-D matrices from a regional travel demand model show that there is a higher correlation in terms of productions at origin districts and attractions at destination districts than at the O-D flow level. Some opportunities and limitations of the new data source are discussed, along with recommendations for future research.
This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these ...characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.
This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these ...characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.PUBLICATION ABSTRACT
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