We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, comparing to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quickly regardless of charging speed.

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This paper presents two novel models for land use and transportation to address the development of different functional zones in urban areas by considering the design of an efficient transportation network and reducing air pollution. Objective functions of the first model are maximizing utility function and maximizing reliability index. The utility is formulated as a function of travel cost and zonal attractiveness. Reliability index is defined as the probability that flow in each link of the network is less than the design capacity. Maximizing this probability is equivalent to minimizing congestion in the network. In addition, maximizing utility and minimizing carbon monoxide emission in the network are considered as objective functions in the second model. The formulated models are nonlinear and stochastic. We implement the e-constraint method for solving these bi-objective optimization problems. We analyze the models and solution characteristics of some examples. In addition, we evaluate the relation between computing time and complexity of the model. In this study, for the first time in the open literature, stochastic bi-objective optimization models are formulated to analyze interaction among land use, transportation network and air pollution. We also extract and summarize some useful insights on the relationship among land use, transportation network and environmental impact associated with them.

Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task. Ultimately, with limited resources and time, we were able to develop simple yet useful models for 12-hour census prediction and design a dashboard application to display this output to our hospital’s decision-makers. Specifically, we found that linear models with ElasticNet regularization performed well for this task with relative 95% error of +/− 3.4% and that this work could be completed in approximately 7 months.