Improve urban traffic efficiency with prohibiting left-turn policy

Enhancing traffic flow plays an important role in the traffic management of urban arterial networks. The policy of prohibiting left-turn (PLT) at selected highly demanded intersections has been adopted as an attempt to increase the efficiency at these intersections. In this paper, we study the impact of PLT by mathematical analysis and simulations based on the cellular automaton model. Using the flow-density relation, three system performance indexes are examined: the average trip completion rate, the average traffic flow, and the average velocity of vehicles. Different route guidance strategies, including the shortest path and the quickest path, are investigated. We show that when left turn is prohibited, vehicles are distributed more homogeneously in the road network, and the system performs better and reaches a higher capacity. We also derive a critical length of link, above which the benefit of PLT will decrease.

Arc Routing with Time-Dependent Travel Times and Paths

Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems involving services on edges of a road network. To fill this gap, we formally define the time-dependent capacitated arc routing problem (TDCARP), with travel and service speed functions given directly at the network level. Under these assumptions, the quickest path between locations can change over time, leading to a complex problem that challenges the capabilities of current solution methods. We introduce effective algorithms for preprocessing quickest paths in a closed form, efficient data structures for travel time queries during routing optimization, and heuristic and exact solution approaches for the TDCARP. Our heuristic uses the hybrid genetic search principle with tailored solution-decoding algorithms and lower bounds for filtering moves. Our branch-and-price algorithm exploits dedicated pricing routines, heuristic dominance rules, and completion bounds to find optimal solutions for problems counting up to 75 services. From these algorithms, we measure the benefits of time-dependent routing optimization for different levels of travel-speed data accuracy.

Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing

Around five percent of the population is affected by a rare disease, most often due to genetic variation. A genetic test is the quickest path to a diagnosis, yet most suffer through years of diagnostic odyssey before getting a test, if they receive one at all. Identifying patients that are likely to have a genetic disease and therefore need genetic testing is paramount to improving diagnosis and treatment. While there are thousands of previously described genetic diseases with specific phenotypic presentations, a common feature among them is the presence of multiple rare phenotypes which often span organ systems. Here, we hypothesize that these patients can be identified from longitudinal clinical data in the electronic health record (EHR). We used diagnostic information from the EHRs of 2,286 patients that received a chromosomal microarray and 9,144 matched controls to train and test a prediction model. We identified high prediction accuracy (AUROC = 0.97, AUPR = 0.92) in a held-out test sample and in 172,265 hospital patients where cases were defined broadly as interacting with a genetics provider (AUROC = 0.9, AUPR = 0.63). High probabilities (median = 0.97) were associated with 46 patients carrying a known pathogenic copy number variant (CNV) among a subset of 6,445 genotyped patients. Our model identified many more patients needing a genetic test while increasing the proportion having a putative genetic disease compared to the current nonsytematic approach. Taken together, we demonstrate that phenotypic patterns representative of a genetic disease can be captured from EHR data and provide an opportunity to systematize decision making on genetic testing to speed up diagnosis, improve care, and reduce costs.

Network modelling and computation of quickest path for service-level agreements using bi-objective optimization

This article addresses the problem related to the reliability of path after transmitting the given amount of data with the service-level agreement cooperation in the computer communication network. The links have associated with service performance factor parameter during the data transmission, and each node is associated with the requested service performance factor. In this article, first we have considered the single objective to minimize the transmission time of the quickest path problem. An algorithm for quickest path problem has been proposed for results, and furthermore, its time complexity has been shown. The problem has been extended with bi-objective optimization of the quickest path problem, which minimizes the transmission time and hybrid logarithmic reliability. An algorithm is proposed for getting the number of efficient solutions for the quickest path problem using label-correcting algorithm. The algorithms are implemented and tested on different standard benchmark network problems provided with the set of Pareto front of the results.

A Secure, Energy- and SLA-Efficient (SESE) E-Healthcare Framework for Quickest Data Transmission Using Cyber-Physical System

Due to advances in technology, research in healthcare using a cyber-physical system (CPS) opens innovative dimensions of services. In this paper, the authors propose an energy- and service-level agreement (SLA)-efficient cyber physical system for E-healthcare during data transmission services. Furthermore, the proposed phenomenon will be enhanced to ensure the security by detecting and eliminating the malicious devices/nodes involved during the communication process through advances in the ad hoc on-demand distance vector (AODV) protocol. The proposed framework addresses the two security threats, such as grey and black holes, that severely affect network services. Furthermore, the proposed framework used to find the different network metrics such as average qualifying service set (QSS) paths, mean hop and energy efficiency of the quickest path. The framework is simulated by calculating the above metrics in mutual cases i.e., without the contribution of malevolent nodes and with the contribution of malevolent nodes over service time, hop count and energy constraints. Further, variation of SLA and energy shows their expediency in the selection of efficient network metrics.