A realistic two-lane traffic model for highway traffic

A two-lane extension of a recently proposed cellular automaton model for traffic flow is discussed. The analysis focuses on the reproduction of the lane usage inversion and the density dependence of the number of lane changes. It is shown that the single-lane dynamics can be extended to the two-lane case without changing the basic properties of the model which are known to be in good agreement with empirical single-vehicle data. Therefore it is possible to reproduce various empirically observed two-lane phenomena, like the synchronization of the lanes, without fine-tuning of the model parameters

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

A Transit-Based Evacuation Model for Metropolitan Areas

This paper develops a decision-support model for transit-based evacuation planning occurring in metropolitan areas. The model consists of two modules executed in a sequential manner: the first deals with determining pick-up locations from candidate locations based on the spatial distribution of the evacuees, and the second plans for the route and schedule for each transit vehicle based on vehicle availability and evacuee demand pattern. An overlapping clustering algorithm is first adopted in allocating the demands to several nearby clusters. Then, an optimization model is proposed to allocate available buses from the depots to transport the assembled evacuees between the pick-up locations and different safety destinations and public shelters. A numerical example based on the city of Baltimore demonstrates the applicability of the proposed model and the advantages compared to state-of-the-art models with overly strict and unrealistic assumptions

A Transit-Based Evacuation Model for Metropolitan Areas

This paper develops a decision-support model for transit-based evacuation planning occurring in metropolitan areas. The model consists of two modules executed in a sequential manner: the first deals with determining pick-up locations from candidate locations based on the spatial distribution of the evacuees, and the second plans for the route and schedule for each transit vehicle based on vehicle availability and evacuee demand pattern. An overlapping clustering algorithm is first adopted in allocating the demands to several nearby clusters. Then, an optimization model is proposed to allocate available buses from the depots to transport the assembled evacuees between the pick-up locations and different safety destinations and public shelters. A numerical example based on the city of Baltimore demonstrates the applicability of the proposed model and the advantages compared to state-of-the-art models with overly strict and unrealistic assumptions

Estimation of Freeway Travel Time Based on Sparsely Distributed Detectors

This study presents a new approach for estimating travel time information along freeway corridors, which experience recurrent congestions but have only a limited number of available detectors due to budget constraints. The proposed iterative estimation procedure, based on a set of empirically calibrated regression models, intends to rebuild the relations between travel times and accumulated flows within each segment of the target freeway corridor. To evaluate the effectiveness of the proposed methodology, this study has conducted extensive numerical experiments with simulated data from a CORSIM simulator. Experimental results under various traffic volume levels have revealed that the proposed method offers a promising property for use in travel time estimation based on sparsely distributed sensors

Estimation of time-varying origin-destination distributions with dynamic screenline flows

A variety of methods for dynamic origin-destination (O-D) estimation is available in the transportation literature. Among those, the research direction pursued by Bell (1991)(Transpn Res. 25B, 115-125) and others in the same category offers the best promise for use in practice as it employs only link flow information from existing surveillance systems. Following the same direction, this paper presents an innovative method for estimating the dynamic network O-D matrices with time series of link and screenline flows. The proposed method takes full advantage of available link flow information, and considerably increases the observability of the dynamic interrelations between network O-D patterns and the resulting link flow distributions. Hence, one can estimate the dynamic O-D matrices with much fewer model parameters and less execution burden. With properly selected screenlines and efficient computing algorithms, the proposed model also offers the potential for real-time applications.

An integrated model for estimating time-varying network origin-destination distributions

This paper presents an integrated method for estimating time-varying O-Ds in urban networks. The proposed method starts with our previously developed two-stage, non-assignment-based model that can yield a time-varying O-Ds without a reliable prior O-D set and a dynamic traffic assignment model (DTA). With the initial estimated O-D set, one can compute the link-incident matrix with any acceptable DTA model and generate a revised distribution of network O-Ds. To further improve the estimation accuracy and also account for the impact of urban signals, we have developed an intersection O-D estimation model that can produce an additional set of system observation constraints based on either existing or estimated intersection turning fractions. Although the statistical properties and variances of such a system under a large-scale network remain to be investigated, the results of simulation experiments have clearly indicated that our proposed method for integrated estimation of time-varying network O-D distributions is quite promising.

Recursive estimation of time-varying origin-destination flows from traffic counts in freeway corridors

This research presents a dynamic system model and its on-line estimation algorithm for time-varying freeway origin-destination (O-D) matrices. The proposed model employs information formation form mainline traffic counts, ramp flow measurements, and macroscopic traffic characteristics to construct a set of dynamic equations, which realistically consider the interrelations between O-D distributions and observed flows under congested conditions. To improve the operational efficiency necessary for real-time applications, a revised model with some approximation have also been developed. Due to the difficulty in acquiring the real-world data, the proposed model was evaluated with simulation experiments. The results of laboratory evaluation indicate that the proposed methods offers a promising direction for tackling this complex issue.

A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix

This study presents a generalized model for estimating the dynamic freeway O-D distribution, based on measurable time series of mainline and ramp flows, and estimated travel time distributions. The proposed model captures the speed discrepancy among drivers, due either to their desired speeds or responses to congestion, with an embedded travel time distribution function and the identified interrelations between time-varying ramp and mainline flows. With the employed mainline information and travel time function, the proposed system equation has increased its observability with less parameters. The proposed model with a specially designed algorithm offers the potential for use in a network of realistic size, such as the I-95 freeway corridor between the Maryland I-695 and I-495 beltways. Extensive numerical analyses with respect to the sensitivity of both input measurement errors and the selection of initial parameters on the estimation results have revealed that the proposed model is sufficiently robust for real-world applications.