Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations

Essays on Demand Estimation, Financial Economics and Machine Learning

In this era of big data, we often rely on techniques ranging from simple linear regression, structural estimation, and state-of-the-art machine learning algorithms to make operational and financial decisions based on data. This calls for a deep understanding of practical and theoretical aspects of methods and models from statistics, econometrics, and computer science, combined with relevant domain knowledge. In this thesis, we study several practical, data-related problems in the particular domains of sharing economy and financial economics/financial engineering, using appropriate approaches from an arsenal of data-analysis tools. On the methodological front, we propose a new estimator for classic demand estimation problem in economics, which is important for pricing and revenue management. In the first part of this thesis, we study customer preference for the bike share system in London, in order to provide policy recommendations on bike share system design and expansion. We estimate a structural demand model on the station network to learn the preference parameters, and use the estimated model to provide insights on the design and expansion of the system. We highlight the importance of network effects in understanding customer demand and evaluating expansion strategies of transportation networks. In the particular example of the London bike share system, we find that allocating resources to some areas of the station network can be 10 times more beneficial than others in terms of system usage, and that currently implemented station density rule is far from optimal. We develop a new method to deal with the endogeneity problem of the choice set in estimating demand for network products. Our method can be applied to other settings, in which the available set of products or services depends on demand. In the second part of this thesis, we study demand estimation methodology when data has a long-tail pattern, that is, when a significant portion of products have zero or very few sales. Long-tail distributions in sales or market share data have long been an issue in empirical studies in areas such as economics, operations, and marketing, and it is increasingly common nowadays with more detailed levels of data available and many more products being offered in places like online retailers and platforms. The classic demand estimation framework cannot deal with zero sales, which yields inconsistent estimates. More importantly, biased demand estimates, if used as an input to subsequent tasks such as pricing, lead to managerial decisions that are far from optimal. We introduce two new two-stage estimators to solve the problem: our solutions apply machine learning algorithms to estimate market shares in the first stage, and in the second stage, we utilize the first-stage results to correct for the selection bias in demand estimates. We find that our approach works better than traditional methods using simulations. In the third part of this thesis, we study how to extract a signal from option pricing models to form a profitable stock trading strategy. Recent work has documented roughness in the time series of stock market volatility and investigated its implications for option pricing. We study a strategy for trading stocks based on measures of their implied and realized roughness. A strategy that goes long the roughest-volatility stocks and short the smoothest-volatility stocks earns statistically significant excess annual returns of 6% or more, depending on the time period and strategy details. Standard factors do not explain the profitability of the strategy. We compare alternative measures of roughness in volatility and find that the profitability of the strategy is greater when we sort stocks based on implied rather than realized roughness. We interpret the profitability of the strategy as compensation for near-term idiosyncratic event risk. Lastly, we apply a heterogeneous treatment effect (HTE) estimator from statistics and machine learning to financial asset pricing. Recent progress in the interdisciplinary area of causal inference and machine learning has proposed various promising estimators for HTE. We take the R-learner algorithm by [73] and adapt it to empirical asset pricing. We study characteristics associated with standard factors, size, value and momentum through the lens of HTE. Our goal is to identify sub-universes of stocks, ``characteristic responders", in which size, value or momentum trading strategies perform best, compared with the performance had they been applied to the entire universe. On the other hand, we identify subsets of ``characteristic traps" in which the strategies perform the worst. In our test period, the differences in average monthly returns between long-short strategies restricted to ``characteristic responders" and ``characteristic traps" range from 0.77% to 1.54% depending on treatment characteristics. The differences are statistically significant and cannot be explained by standard factors: a long-short of long-short strategy generates alpha of significant magnitude from 0.98% to 1.80% monthly, with respect to standard Fama-French plus momentum factors. Simple interaction terms between standard factors and ex-post important features do not explain the alphas either. We also characterize and interpret the characteristic traps and responders identified by our algorithm. Our study can be viewed as a systematic, data-driven way to investigate interaction effects between features and treatment characteristic, and to identify characteristic traps and responders

Master Plan: Circulation element suggestions for implementation

This is the Consultant’s report to the Master Plan Circulation Group, which is a subgroup of the Master Plan Committee. It attempts to supplement and amplify the information provided in the Master Plan of March 21, 2001, and create an effective circulation system balancing the automobile with the pedestrian, bicycle, and bus. The report is in agreement with the Master Plan and proposes steps for implementation along with minor changes to the plan

Urban Public Transportation Planning with Endogenous Passenger Demand

An effective and efficient public transportation system is crucial to people\u27s mobility, economic production, and social activities. The Operations Research community has been studying transit system optimization for the past decades. With disruptions from the private sector, especially the parking operators, ride-sharing platforms, and micro-mobility services, new challenges and opportunities have emerged. This thesis contributes to investigating the interaction of the public transportation systems with significant private sector players considering endogenous passenger choice. To be more specific, this thesis aims to optimize public transportation systems considering the interaction with parking operators, competition and collaboration from ride-sharing platforms and micro-mobility platforms. Optimization models, algorithms and heuristic solution approaches are developed to design the transportation systems. Parking operator plays an important role in determining the passenger travel mode. The capacity and pricing decisions of parking and transit operators are investigated under a game-theoretic framework. A mixed-integer non-linear programming (MINLP) model is formulated to simulate the player\u27s strategy to maximize profits considering endogenous passenger mode choice. A three-step solution heuristic is developed to solve the large-scale MINLP problem. With emerging transportation modes like ride-sharing services and micro-mobility platforms, this thesis aims to co-optimize the integrated transportation system. To improve the mobility for residents in the transit desert regions, we co-optimize the public transit and ride-sharing services to provide a more environment-friendly and equitable system. Similarly, we design an integrated system of public transit and micro-mobility services to provide a more sustainable transportation system in the post-pandemic world

EQUITY AND EFFICIENCY IN MULTI-MODAL TRANSPORTATION SYSTEMS

The land-use pattern for many cities is a central business district surrounded by sprawling suburbs. This pattern can lead to an inefficient and congestion-prone transportation system due to a reliance on automobiles, because high-capacity transit is not efficient in low-density areas where insufficient travelers can access transit. This also poses an equity concern as the monetary cost of faster and more expensive travel disproportionately burdens low income travelers. This dissertation presents a deterministic approximation of a discrete choice model for mixed access and mainline transportation modes, meaning that travelers may use different modes to access a mainline system, such as transit. The purpose is to provide a tractable computationally efficient model to address the first/last mile problem using a system-wide pricing policy that can account for heterogeneous values of time; a problem that is difficult to solve efficiently using a stochastic model. The model is structured for a catchment area around a central access point for a mainline mode, approximating choice by comparing modal utility costs. The underlying utility model accommodates both fixed prices (e.g., parking, fixed tolls, and fares) and distance-based unit prices (e.g. taxi fare, bike-share, and distance tolls) that may be set in a coordinated way with respect to value of time. Using numerical analysis, the deterministic model achieved results within 4% accuracy of a stochastic logit-based model, and within 6\% of measured values. The final model achieved a 57% reduction in generalized travel time and improved the Gini inequity measure from 0.21 to 0.03

Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management

The goal of this Special Issue is to discuss new challenges in the simulation and management problems of both traditional and innovative bike-sharing systems, to ultimately encourage the competitiveness and attractiveness of BSSs, and contribute to the further promotion of sustainable mobility. We have selected thirteen papers for publication in this Special Issue

A Systematic Literature Review on Machine Learning in Shared Mobility

Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels

Bicycle Sharing Systems: Fast and Slow Urban Mobility Dynamics

In cities all around the world, new forms of urban micromobility have observed rapid and wide-scale adoption due to their benefits as a shared mode that are environmentally friendly, convenient and accessible. Bicycle sharing systems are the most established among these modes, facilitating complete end-to-end journeys as well as forming a solution for the first/last mile issue that public transportation users face in getting to and from transit stations. They mark the beginnings of a gradual transition towards a more sustainable transportation model that include greater use of shared and active modes. As such, understanding the way in which these systems are used is essential in order to improve their management and efficiency. Given the lack of operator published data, this thesis aims to explore the utility of open bicycle sharing system data standards that are intended for real-time dissemination of bicycle locations in uncovering novel insights into their activity dynamics over varying temporal and geographical scales. The thesis starts by exploring bicycle sharing systems at a global-scale, uncovering their long-term growth and evolution through the development of data cleaning and metric creation heuristics that also form the foundations of the most comprehensive classification of systems. Having established the values of these metrics in conducting comparisons at scale, the thesis then analyses the medium-term impacts of mobility interventions in the context of the COVID-19 pandemic, employing spatio-temporal and network analysis methods that highlight their adaptability and resilience. Finally, the thesis closes with the analysis of granular spatial and temporal dynamics within a dockless system in London that enable the identification of the variations in journey locations throughout different times of the day. In each of these cases, the research highlights the indispensable value of open data and the important role that bicycle sharing systems play in urban mobility

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019