Mobility Sharing as a Preference Matching Problem

Traffic congestion, dominated by single-occupancy vehicles, reflects not only transportation system inefficiency and negative externalities but also a sociological state of human isolation. Advances in information and communication technology are enabling the growth of real-time ridesharing to improve system efficiency. While most ridesharing algorithms optimize fellow passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time, or vehicle-distance traveled), very few explicitly consider passengers' preference for their peers as the matching objective. The existing literature either considers the bipartite driver-passenger matching problem, which is structurally different from the monopartite passenger-passenger matching, or only considers the passenger-passenger problem in a simplified one-origin-multiple-destination setting. We formulate a general monopartite passenger matching model in a road network and illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1000 randomly generated preference orders and the other considering four sets of group-based preference orders. In both scenarios, compared with efficiency-based matching models, preference-based matching improves the average ranking of paired fellow passenger to the near-top position of people's preference orders with only a small efficiency loss at the individual level and a moderate loss at the aggregate level. The near-top-ranking results fall in a narrow range even with the random variance of passenger preference as inputs.Singapore-MIT Alliance (Future Mobility Program)Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Seed Fun

Competition between shared autonomous vehicles and public transit: A case study in Singapore

Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it. This study examines the competitive perspective where both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one focuses on unconstrained competition to find the Nash Equilibrium. We evaluate the competition process as well as the system performance from the standpoints of four stakeholders -- the AV operator, the PT operator, passengers, and the transport authority. We also examine the impact of PT subsidies on the competition results including both demand-based and supply-based subsidies. A heuristic algorithm is proposed to update supply strategies for AV and PT based on the operators' historical actions and profits. An agent-based simulation model is implemented in the first-mile scenario in Tampines, Singapore. We find that the competition can result in higher profits and higher system efficiency for both operators compared to the status quo. After the supply updates, the PT services are spatially concentrated to shorter routes feeding directly to the subway station and temporally concentrated to peak hours. On average, the competition reduces the travel time of passengers but increases their travel costs. Nonetheless, the generalized travel cost is reduced when incorporating the value of time. With respect to the system efficiency, the bus supply adjustment increases the average vehicle load and reduces the total vehicle kilometer traveled measured by the passenger car equivalent (PCE), while the AV supply adjustment does the opposite

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

Therapeutics of Charcot neuroarthropathy and pharmacological mechanisms: A bone metabolism perspective

Charcot neuroarthropathy (CN) is a chronic, destructive, and painless damage of the skeletal system that affects the life quality of patients. CN, with an unclear mechanism, is characterized with invasive destruction of bones and a serious abnormality of bone metabolism. Unfortunately, development of an effective prevention and treatment strategy for CN is still a great challenge. Of note, recent studies providing an insight into the molecular mechanisms of bone metabolism and homeostasis have propelled development of novel CN therapeutic strategies. Therefore, this review aims to shed light on the pathogenesis, diagnosis, and treatment of CN. In particular, we highlight the eminent role of the osteoprotegerin (OPG)-receptor activator of nuclear factor-κB (RANK)-RANK ligand (RANKL) system in the development of CN. Furthermore, we summarize and discuss the diagnostic biomarkers of CN as well as the potential pharmacological mechanisms of current treatment regimens from the perspective of bone metabolism. We believe that this review will enhance the current state of knowledge on the diagnosis, prevention, and therapeutic efficacy of CN

The RANK/RANKL/OPG system and tumor bone metastasis: Potential mechanisms and therapeutic strategies

With the markedly increased diagnosis and incidence of cancer in the population, tumor bone metastasis has become a frequent event in tumor patients. Healthy bone integrity is maintained by a delicate balance between bone formation and bone resorption. Unfortunately, many tumors, such as prostate and breast, often metastasize to the bone, and the alterations to the bone homeostasis can particularly favor tumor homing and consequent osteolytic or osteoblastic lesions. Receptor activator of NF-κB ligand (RANKL), its receptor RANK, and osteoprotegerin (OPG) are involved in the regulation of the activation, differentiation, and survival of osteoclasts, which play critical roles in bone metastasis formation. High rates of osteoclastic bone resorption significantly increase fracture risk, cause severe bone pain, and contribute to homing tumor cells in bone and bone marrow. Consequently, suppression of the RANK/RANKL/OPG system and osteoclastic activity can not only ameliorate bone resorption but may also prevent tumor bone metastases. This review summarizes the important role of the RANK/RANKL/OPG system and osteoclasts in bone homeostasis and its effect on tumor bone metastasis and discusses therapeutic strategies based on RANKL inhibition

Chloroplast translational regulation uncovers nonessential photosynthesis genes as key players in plant cold acclimation

Plants evolved efficient multifaceted acclimation strategies to cope with low temperatures. Chloroplasts respond to temperature stimuli and participate in temperature sensing and acclimation. However, very little is known about the involvement of chloroplast genes and their expression in plant chilling tolerance. Here we systematically investigated cold acclimation in tobacco seedlings over 2 days of exposure to low temperatures by examining responses in chloroplast genome copy number, transcript accumulation and translation, photosynthesis, cell physiology, and metabolism. Our time-resolved genome-wide investigation of chloroplast gene expression revealed substantial cold-induced translational regulation at both the initiation and elongation levels, in the virtual absence of changes at the transcript level. These cold-triggered dynamics in chloroplast translation are widely distinct from previously described high light-induced effects. Analysis of the gene set responding significantly to the cold stimulus suggested nonessential plastid-encoded subunits of photosynthetic protein complexes as novel players in plant cold acclimation. Functional characterization of one of these cold-responsive chloroplast genes by reverse genetics demonstrated that the encoded protein, the small cytochrome b6f complex subunit PetL, crucially contributes to photosynthetic cold acclimation. Together, our results uncover an important, previously underappreciated role of chloroplast translational regulation in plant cold acclimation