Moving from Walkability? Evaluation Traditional and Merging Data Sources for Evaluating Changes in Campus-Generated Greenhouse Gas Emissions

Universities are increasingly committing to reduce campus-generated greenhouse gas emissions, whether voluntarily or in response to a legal mandate. As an initial step to keeping these commitments, universities need an accounting of baseline greenhouse gas emissions levels and means of monitoring changes in campus-generated greenhouse gas emissions over time. Commute-generated greenhouse gas emissions from travel to and from campus by students and employees are among the most difficult to quantify. This report examines some of the challenges associated with estimating campus-generated greenhouse gas emissions and evaluates ways to address those challenges. The purpose of this study is to identify changes in campus-generated travel behavior at California Polytechnic State University based on the results of three successive campus-wide travel surveys; to evaluate alternative data sources that have the potential to supplement or replace campus travel surveys as a source of data for campus-generated greenhouse gas emissions; and to evaluate alternate methods to estimating greenhouse gas emissions from campus-generated vehicle miles traveled, depending on the presence of campus-specific information about vehicle fleet characteristics. The results of successive travel surveys suggest that the campus population has become more car-dependent over time. Comparison of survey results with data collected from automating traffic counting devices and mobile device data suggest that surveys that are limited to members of the campus community are likely to undercount campus-generated vehicle miles traveled by excluding infrequent, but potentially long, trips by campus visitors. Finally, we find that using campus-specific information on the model years of vehicles used to commute to campus yields higher estimates of campus-generated greenhouse gas emissions, relative to average regional emissions rates

Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10), available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019-2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne and Moscow. The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. We detail the efficient matching approach mapping the data to the OpenStreetMap road graph. We evaluate the dataset by comparing it with publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). The comparison highlights the differences across datasets in spatio-temporal coverage and variations in the reported traffic caused by the binning method. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS), DOI: https://doi.org/10.1109/TITS.2023.329173

QUANTIFYING THE IMPACT OF TRANSPORTATION NETWORK COMPANIES (TNCs) ON TRAFFIC CONGESTION IN SAN FRANCISCO

This research investigates whether Transportation Network Companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion by complementing transit and reducing car ownership in major cities. The objective of this research study is to answer the question: are TNCs are correlated to traffic congestion in the city of San Francisco? If found to be so, do they increase or decrease traffic congestion for the case of San Francisco? If and how TNC pickups and drop-offs impact traffic congestion within San Francisco? And finally, how does the magnitude of this measured command of TNCs on congestion compare to that caused by pre-existing conventional drivers of traffic and congestion change? Apart from answering these questions, it is also sought to establish a framework to be able to include TNCs, a seemingly fledgling mode of transportation but one that is demonstrably shaping and modifying extant transportation and mode choice trends, as part of the travel demand models estimated by any geographic jurisdiction. Traffic congestion has worsened noticeably in San Francisco and other major cities over the past few years. Part of this change could reasonably be explained by strong economic growth or other standard factors such as road and transit network changes. The sharp increase in travel times and congestion also corresponds to the emergence of TNCs, raising the question of whether the two trends may be related. Existing research has produced conflicting results and been hampered by a lack of data. Using data scraped from the Application Programming Interfaces (APIs) of two TNCs, combined with observed travel time data, this research finds 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. This research also 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. It is so done through a series of controlled travel demand model runs, supplemented with observed TNC data. The results show that road and transit network changes over this period have only a small effect on congestion, population and employment growth are important contributors, 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 a recent empirical analysis showing that their net effect is to increase 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

Quantifying the uneven efficiency benefits of ridesharing market integration

Ridesharing is recognized as one of the key pathways to sustainable urban mobility. With the emergence of Transportation Network Companies (TNCs) such as Uber and Lyft, the ridesharing market has become increasingly fragmented in many cities around the world, leading to efficiency loss and increased traffic congestion. While an integrated ridesharing market (allowing sharing across TNCs) can improve the overall efficiency, how such benefits may vary across TNCs based on actual market characteristics is still not well understood. In this study, we extend a shareability network framework to quantify and explain the efficiency benefits of ridesharing market integration using available TNC trip records. Through a case study in Manhattan, New York City, the proposed framework is applied to analyze a real-world ridesharing market with 3 TNCs$-$Uber, Lyft, and Via. It is estimated that a perfectly integrated market in Manhattan would improve ridesharing efficiency by 13.3%, or 5% of daily TNC vehicle hours traveled. Further analysis reveals that (1) the efficiency improvement is negatively correlated with the overall demand density and inter-TNC spatiotemporal unevenness (measured by network modularity), (2) market integration would generate a larger efficiency improvement in a competitive market, and (3) the TNC with a higher intra-TNC demand concentration (measured by clustering coefficient) would benefit less from market integration. As the uneven benefits may deter TNCs from collaboration, we also illustrate how to quantify each TNC's marginal contribution based on the Shapley value, which can be used to ensure equitable profit allocation. These results can help market regulators and business alliances to evaluate and monitor market efficiency and dynamically adjust their strategies, incentives, and profit allocation schemes to promote market integration and collaboration