Optimal Operations Management of Mobility-on-Demand Systems

The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.

Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors

The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.

Laboratory findings associated with severe illness and mortality among hospitalized individuals with coronavirus disease 2019 in Eastern Massachusetts

ImportanceThe coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed.ObjectiveTo quantify admission laboratory and comorbidity features associated with critical illness and death and mortality risk across 6 Eastern Massachusetts hospitals.DesignRetrospective cohort study using hospital course, prior diagnoses, and laboratory values through June 5, 2020.SettingEmergency department and inpatient settings from 2 academic medical centers and 4 community hospitals.ParticipantsAll individuals with hospital admission and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing across these 6 hospitals.Main Outcome or Measuresevere illness defined by ICU admission, mechanical ventilation, or death.ResultsAmong 2,511 hospitalized individuals who tested positive for SARS-CoV-2, 215 (8.6%) were eventually admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded area under ROC curve (AUC) of 0.823 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 78% of deaths occurred in the highest-risk mortality quintile.Conclusions and RelevanceSpecific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitate risk stratification among individuals hospitalized for COVID-19.Funding1R56MH115187-01Trial RegistrationNoneKey PointsQuestionHow well can sociodemographic features, laboratory values, and comorbiditeis of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020 predict severe illness course?FindingsAmong 2,511 hospitalized individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and were admitted to one of six hospitals, 215 (8.6%) were eventually admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 78% of deaths occurred in the top mortality-risk quintile.MeaningSimple prediction models may assist in risk stratification among hospitalized COVID-19 patients.

Electronic Health Record Documentation of Psychiatric Assessments in Massachusetts Emergency Department and Outpatient Settings During the COVID-19 Pandemic

Key PointsQuestionHow did documentation of psychiatric symptoms in outpatient and emergency room settings change with onset of COVID-19 infection in Eastern Massachusetts?FindingsIn this cohort study spanning 2 academic medical centers and 3 community hospitals, prevalence of narrative notes referencing depression or anxiety decreased 75-81% in outpatient settings following onset of coronavirus in March 2019, and by 44–45% in emergency departments.MeaningThe observation that documentation of psychiatric symptoms declined sharply with increasing coronavirus infection in Massachusetts, even as prevalence of such symptoms is anticipated to increase, suggests additional efforts may be required to address these symptoms in the context of COVID-19.

The Hub

This is the first of two chapters (Chapters 6 and 7) that analyze fieldwork results in eastern Massachusetts. This chapter analyzes the eastern Massachusetts “Hub” region as a whole, providing a statistical overview of speakers interviewed in the Dartmouth-based fieldwork in this area. It examines the results in terms of major traditional Eastern New England dialect features, including Linear Mixed Effects regression modeling in terms of phonetic environments and social factors like age, gender, social class, and ethnicity. The chapter also plots these dialect features in terms of speakers’ birth year and other factors, showing how these features are changing over time.

Focus on Subgroups within the Hub

This is the second of two chapters (Chapters 6 and 7) that analyze the Dartmouth-based fieldwork data in eastern Massachusetts. This chapter “zooms in” to focus on particular subgroups within the Hub data set. First, the chapter provides statistical and graphical comparisons of traditional New England dialect features by contrasting two nearby groups: White speakers in the traditional working-class South Boston neighborhood, and Black/African American speakers in nearby Dorchester, Hyde Park, and other neighborhoods. The chapter concludes with a fieldwork project in Cape Cod. In each case, the chapter provides detailed plots of dialect features and statistical analyses with respect to age, gender, social class, ethnicity, and other factors