Enhancing the Shared Mobility Market: Dissolving Market Segmentation and Understanding Market Friction

Over the past decade, the growth of ride-sharing companies, also known as Transportation Network Companies (TNCs), providing on-demand transportation services for passengers, has been one of the fastest worldwide. However, in the governance of the shared mobility market of a city or metropolitan area, two conflicting principles emerge: the healthy competition between multiple platforms, such as Uber and Lyft in the United States, and economies of network scale, which leads to higher chances for trips to be matched and thus higher operation efficiency, but which also implies a monopoly. The current shared mobility markets, as observed in different cities in the world, are either monopolistic, or largely segmented by multiple platforms, the latter with significant efficiency loss. This thesis addresses the efficiency loss issues due to segmentation by proposing new market designs while keeping the competition between platforms. We first propose a theoretical framework for describing shared mobility markets and then propose four market structure designs thereupon. The framework and four designs are first discussed as an abstract model, without losing generality, thus not constrained to any specific city. High-level perspectives and detailed mechanisms for each proposed market structure are both examined. Then, to assess the real-world performance of these market structure designs, we used a ride-sharing simulator with real-world ride-hailing trip data from New York City to simulate. The proposed market designs can reduce the total vehicle-miles traveled (VMT) by 6\% while serving more customers with 8.4\% fewer total number of trips. In the meantime, customers receive better services with an on-average 5.4\% shorter waiting time. On the other hand, platform drivers in the shared mobility market frequently switch or work for multiple platforms, providing a natural way of dissolving the market segmentation. However, the presence of significant market friction preventing platform drivers from multi-homing has been found in a recent survey distributed in Jakarta, Indonesia. In this thesis, we taxonomize and estimates perceived switching and multi-homing frictions on mobility platforms. Based on a structural model of driver labor supply, we estimate switching and multi-homing costs in a platform duopoly using public and limited high-level survey data in a shared mobility market with a transportation network company duopoly. Estimated costs are sizeable, and reductions in multi-homing and switching costs significantly affect platform market shares and driver welfare. Driver labor supply elasticity with respect to platform wage is also discussed considering both multi-homing and switching frictions.S.M

Shareability Network Based Decomposition Approach for Solving Large-scale Multi-modal School Bus Routing Problems

We consider the classic School Bus Routing Problem (SBRP) with a multi modal generalization, where students are either picked up by a fleet of school buses or transported by an alternate transportation mode, subject to a set of constraints. The constraints that are typically imposed for school buses are a maximum fleet size, a maximum walking distance to a pickup point and a maximum commute time for each student. This is a special case of the Vehicle Routing Problem (VRP) with a common destination. We propose a decomposition approach for solving this problem based on the existing notion of a shareability network, which has been used recently in the context of dynamic ridepooling problems. Moreover, we simplify the problem by introducing the connection between the SBRP and the weighted set covering problem (WSCP). To scale this method to large-scale problem instances, we propose i) a node compression method for the shareability network based decomposition approach, and ii) heuristic-based edge compression techniques that perform well in practice. We show that the compressed problem leads to an Integer Linear Programming (ILP) of reduced dimensionality that can be solved efficiently using off-the-shelf ILP solvers. Numerical experiments on small-scale, large-scale and benchmark networks are used to evaluate the performance of our approach and compare it to existing large-scale SBRP solving techniques.Comment: 41 pages, 27 figure

Oxide nanoparticle-doped molten carbonate salts for thermal energy storage

An increased percentage of renewable energy is being deployed in the total amount of global energy supply in light of the challenging criteria and scenarios for the reduction of greenhouse gas emissions from using fossil fuels. However, the biggest drawback of renewable energy is the intermittence of energy generation, most of which is quite time-and-climate dependant. As such, various technologies have been pursued to address the issue. Energy storage technology is increasingly accepted as an indispensable and effective approach towards reliable conversion of the unstable and intermittent renewables to a stable, secure and sustainable energy supply. In recent years, the great progress of concentrating/concentrated solar power (CSP) technologies has provided an efficient and sustainable route to reflection and collection of the solar irradiance. Meanwhile, the integration of thermal storage capability with CSP makes it possible to motivate and accelerate the generation of solar thermal electricity (STE) in many regions in the world, specially owing to the flexibility and energy security it can provide to power systems. Rapid development of the thermal energy storage (TES) technology and the successful utilisation of ‘Solar Salt’ in an increasing number of commercial CSP plants in recent decades have encouraged the extensive research on molten salts as TES materials. The thesis considers carbonate-based molten salts as promising candidates of high-temperature sensible TES materials. Different oxide nanoparticles (Al2O3, SiO2 and MgO) were utilised in the molten carbonate salts. For each kind of oxide nanoparticles, a series of salt mixtures were prepared using the static fusing method at different concentrations. Besides, other methods of preparation were employed for comparison to pursue the optimal behaviour of heat capacity enhancement. Thermophysical properties including liquidus temperature, specific heat capacity, and thermal stability were characterised by an assortment of testing devices, primarily DSC and TGA. The results showed that the specific heat capacity of prepared molten salt mixtures was well enhanced by those three nanoparticles. The largest specific heat capacity enhancement of over 30% was obtained by the molten carbonate salt with MgO nanoparticles which was prepared by the in-situ method. At a concentration of 1 wt.%, each type of nanoparticles achieved the largest enhancement. Moreover, all prepared samples were proved to have excellent thermal stability up to 800oC, showing great potentials in serving as the high-temperature TES materials. Besides, extra ex-situ analytical devices, primarily SEM, XRD, EDX were employed to investigate the mechanism of the divergent results of the thermophysical properties, especially the specific heat capacity. Different factors including the size, concentration, morphology, the principle of formation of the nanostructures and the specific heat capacity prediction model were all discussed in detail. These preliminary findings and understanding warrant and encourage more effort to systematically analyse and integrate previous findings and contradictions originated from the scattered methods and techniques applied to facilitate more comparable and repeatable outputs in the further work

The comparison of optical variability of broad-line Seyfert 1 and narrow-line Seyfert 1 galaxies from the view of Pan-STARRS

By means of the data sets of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), we investigate the relationship between the variability amplitude and luminosity at 5100 \AA, black hole mass, Eddington ratio, $R_{\rm Fe \, II}$ ( the ratio of the flux of Fe II line within 4435-4685 \AA ~to the broad proportion of $\rm H\beta$ line) as well as $R_{5007}$ (the ratio of the flux [O III] line to the total $\rm H\beta$ line) of the broad line Seyfert 1 (BLS1) and narrow line Seyfert 1 (NLS1) galaxies sample in g,r,i,z and y bands, respectively. We also analyze the similarities and differences of the variability characteristics between the BLS1 galaxies and NLS1 galaxies. The results are listed as follows. (1). The cumulative probability distribution of the variability amplitude shows that NLS1 galaxies are lower than that in BLS1 galaxies. (2). We analyze the dependence of the variability amplitude with the luminosity at 5100 \AA, black hole mass, Eddington ratio, $R_{\rm Fe \,II}$ and $R_{5007}$, respectively. We find significantly negative correlations between the variability amplitude and Eddington ratio, insignificant correlations with the luminosity at 5100 \AA. The results also show significantly positive correlations with the black hole mass and $R_{5007}$, significantly negative correlations with $R_{\rm Fe\, II}$ which are consistent with Rakshit and Stalin(2017) in low redshift bins (z<0.4) and Ai et al.(2010). (3). The relationship between the variability amplitude and the radio loudness is investigated for 155 BLS1 galaxies and 188 NLS1 galaxies. No significant correlations are found in our results.Comment: 10 pages, 5 figures, accepted by Astrophysics and Space Science, in Pres

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

Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.Comment: 31 pages, 6 figure