A mathematical framework and a solution approach are presented for the simultaneous calibration of the demand and supply parameters and inputs to microscopic traffic simulation models as well as a large-scale application emphasizing practical issues. Microscopic traffic simulation models provide detailed estimates of evolving network conditions by modeling time-varying demand patterns and individual drivers' detailed behavioral decisions. Such models are composed of elements that simulate different demand and supply processes and their complex interactions. Several model inputs (such as origin-destination flows) and parameters (car-following and lane-changing coefficients) must be specified before these simulation tools can be applied, and their values must be determined so that the simulation output accurately replicates the reality reflected in traffic measurements. A methodology is presented here for simultaneously estimating all microscopic simulation model parameters by using general traffic measurements. A large-scale case study for the calibration of the MITSimLab microscopic traffic simulation model by using the network of Lower Westchester County, New York, is employed to demonstrate the feasibility, application, and benefits of the proposed methodology.

The management of severe congestion in complex urban networks calls for dynamic traffic assignment (DTA) models that can replicate real traffic situations with long queues and spillbacks. DynaMIT-P, a mesoscopic traffic simulation system, was enhanced and calibrated to capture the traffic characteristics in the city of Beijing, China. All demand and supply parameters were calibrated simultaneously using sensor counts and floating car travel time data. Successful calibration was achieved with the Path-size Logit route choice model, which accounted for overlapping routes. Furthermore, explicit representations of lane groups were required to properly model traffic delays and queues. A modified treatment of acceptance capacity was required to model the large number of short links in the transportation network (close to the length of one vehicle). In addition, even though bicycles and pedestrians were not explicitly modeled, their impacts on auto traffic were captured by dynamic road segment capacities.

Autoimmune hemolytic anemia (AIHA) is a clinically relevant complication after allogeneic hematopoietic stem cell transplantation (HSCT). Currently, there is no established consensus regarding the optimal therapeutic approach. Whether AIHA contributes to increased mortality is still somewhat controversial.We investigated the incidence, risk factors, and outcome of post-transplant AIHA in 265 consecutive pediatric patients undergoing allo-HSCT over a 17-year period. Onset of AIHA was calculated from the first documented detection of AIHA by either clinical symptoms or positive direct agglutinin test. Resolution of AIHA was defined as normalization of hemoglobin and biochemical markers of hemolysis with sustained transfusion independence.We identified 15 cases of AIHA after allo-HSCT (incidence rate, 6%). Ten (67%) of these patients had a positive direct antiglobulin test. Data were obtained for 9 boys and 6 girls after a median follow-up of 53 months (range 4–102). The median age was 5.1 years (range 0.5–15.4) at the time of HSCT and the median time to emergence was 149 days (range 42–273). No significant risk factor for post-transplant AIHA has emerged from our data to date. In the majority (14 of 15; 93%) of AIHA patients, multiple agents for treatment were required, with 12 of 15 (80%) patients achieving complete resolution of AIHA. No splenectomy was performed in any of our patients.For various reasons, post-transplantation AIHA poses an extraordinary challenge to transplant physicians. Despite the advancements in diagnostic tools, therapeutic challenges remain due to the myriad interacting pathways in AIHA.