Large-scale simulation of traffic flow using Markov model.

Modeling and simulating movement of vehicles in established transportation infrastructures, especially in large urban road networks is an important task. It helps in understanding and handling traffic problems, optimizing traffic regulations and adapting the traffic management in real time for unexpected disaster events. A mathematically rigorous stochastic model that can be used for traffic analysis was proposed earlier by other researchers which is based on an interplay between graph and Markov chain theories. This model provides a transition probability matrix which describes the traffic's dynamic with its unique stationary distribution of the vehicles on the road network. In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution which can handle the traffic's dynamic together with the vehicles' distribution. In addition, the weighted least squares estimation method is applied for estimating this new parameter matrix using trajectory data. In a case study, we apply our method on the Taxi Trajectory Prediction dataset and road network data from the OpenStreetMap project, both available publicly. To test our approach, we have implemented the proposed model in software. We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory and superior to the frequency based maximum likelihood method. In a real application, we have unfolded a stationary distribution on the map graph of Porto, based on the dataset. The approach described here combines techniques which, when used together to analyze traffic on large road networks, has not previously been reported.

Specific enhancer selection by IRF3, IRF5 and IRF9 is determined by ISRE half-sites, 5' and 3' flanking bases, collaborating transcription factors and the chromatin environment in a combinatorial fashion.

IRF3, IRF5 and IRF9 are transcription factors, which play distinct roles in the regulation of antiviral and inflammatory responses. The determinants that mediate IRF-specific enhancer selection are not fully understood. To uncover regions occupied predominantly by IRF3, IRF5 or IRF9, we performed ChIP-seq experiments in activated murine dendritic cells. The identified regions were analysed with respect to the enrichment of DNA motifs, the interferon-stimulated response element (ISRE) and ISRE half-site variants, and chromatin accessibility. Using a machine learning method, we investigated the predictability of IRF-dominance. We found that IRF5-dominant regions differed fundamentally from the IRF3- and IRF9-dominant regions: ISREs were rare, while the NFKB motif and special ISRE half-sites, such as 5'-GAGA-3' and 5'-GACA-3', were enriched. IRF3- and IRF9-dominant regions were characterized by the enriched ISRE motif and lower frequency of accessible chromatin. Enrichment analysis and the machine learning method uncovered the features that favour IRF3 or IRF9 dominancy (e.g. a tripartite form of ISRE and motifs for NF-κB for IRF3, and the GAS motif and certain ISRE variants for IRF9). This study contributes to our understanding of how IRF members, which bind overlapping sets of DNA sequences, can initiate signal-dependent responses without activating superfluous or harmful programmes.