From the Cover: Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Vehicular traffic congestion––and the air pollution that results from it––is one of the greatest challenges facing cities all over the world. It comes at great monetary and human cost: in the 83 largest urban areas of the United States alone, the amount of wasted time and fuel caused by congestion has been placed at US$ 60 billion (1). At the same time, the World Health Organization has estimated that over one million deaths per year worldwide can be attributed to outdoor air pollution (2), which is to a large part caused by vehicular traffic (3). Further adverse effects include fatalities through road accidents and economic losses from missed business activities. For these reasons, great hope is placed today in the rapid deployment of digital information and communication technologies that could help make cities “smarter” (4), and, in particular, that could help manage vehicular traffic more efficiently. The use of real-time information allows the monitoring of the urban mobility infrastructure to an unprecedented extent, and opens up new potential for the exploitation of unused capacity. One major example is the public mobility infrastructure: taking advantage of the widespread use of smart phones and their capabilities for running real-time applications, it is possible to design new, smarter transportation systems based on the sharing of cars or minivans, effectively providing services that could replace public transportation with the on-demand qualities of individual mobility or taxis (5). However, although this option has been proposed in the past, municipal authorities, city residents, and other stakeholders may be reluctant to invest in it until its benefits have been quantified (6). This is the goal of the present paper.At the basis of a shared taxi service is the concept of ride sharing or carpooling, a long-standing proposition for decreasing road traffic, which originated during the oil crisis in the 1970s (6). During that time, economic incentives outbalanced the psychological barriers on which successful carpooling programs depend: giving up personalized transportation and accepting strangers in the same vehicle. Surveys indicate that the two most important deterrents to potential carpoolers are the extra time requirements and the loss of privacy (7, 8). However, the lack of correlations between socio-demographic variables and carpooling propensity (8), the design of appropriate economic incentives (9), and recent practical implementations of taxi-sharing systems in New York City (http://bandwagon.io) give ample hope that many social obstacles might be overcome in newly emerging “sharing economies” (10, 11).Besides psychological considerations, it is fundamental to understand the logistic limitations of realistic taxi-sharing systems, which is our focus here. From a theoretical perspective, trip sharing is traditionally seen as an instance of “dynamic pickup and delivery” problems (12, 13), in which a number of goods or customers must be picked up and delivered efficiently at specific locations within well-defined time windows. Such problems are typically solved by means of linear programming, in which a function of the system variables is optimized subject to a set of equations that describe the constraints. Whereas linear programming tasks can be solved with standard approaches of Operations Research or with constraint programming (14), their computational feasibility heavily depends on the number of variables and equations, e.g., the pickup and delivery time windows of each customer, used to describe the problem at hand. Most previous taxi studies have therefore focused on small-scale routing problems, such as within airport perimeters (15, 16). Large urban taxi systems, in contrast, involve thousands of vehicles performing hundreds of thousands of trips per day. A first step toward practical taxi ride-sharing systems is ref. 17, where the authors present the design of a dynamic ride-sharing system inclusive of a taxi dispatching strategy and fare management. Due to computational reasons trip sharing in ref. 17 is decided based on a heuristic approach tailored to the specific taxi dispatching strategy at hand. Our approach, by contrast, is the development of a framework which enables investigation in general terms the fundamental tradeoff between the benefit and the passenger discomfort induced by taxi-sharing systems at the city level, as an example from a wide class of spatial sharing problems.Here we introduce the notion of shareability network to model trip sharing in a simple static way, and apply classical methods from graph theory to solve the taxi trip-sharing problem in a provably efficient way. The differences between static trip sharing as considered herein, and dynamic sharing as considered, e.g., in ref. 17, are discussed in detail in SI Appendix. The starting point of our analysis is a dataset composed of the records of over 150 million taxi trips originating and ending in Manhattan in the year 2011 by all 13,586 registered taxis. For each trip, the record reports the vehicle ID, the Global Positioning System (GPS) coordinates of the pickup and drop-off locations, and corresponding times. Pickup and drop-off locations have been associated with the closest street intersection in the road map of Manhattan (Materials and Methods). We impose a natural network structure on an otherwise unstructured, gigantic search space of the type explored in traditional linear programming. To this end we define two parameters: the shareability parameter k, standing for the maximum number of trips that can be shared, and the quality of service parameter Δ, which stands for the maximum delay a customer tolerates in a shared taxi service trip, mathematically equivalent to the notion of “time window” used in other approaches (13, 17). To ease the analysis, we use the Δ formalism; however, when presented in a real implementation to passengers, it might be psychologically more effective to use the neutral wording “time window” rather than explicitly mentioning the maybe more negatively connoted word “delay.” The choice of defining the quality of service parameter as an absolute time, instead of as a percentage increase of the travel time, is in line with similar realizations in the literature (17), and is motivated by the fact that absolute delay information is likely more valuable than percent estimation of travel time increase for potential customers of a shared taxi service. Further, let Ti=(oi,di,tio,tid),i=1…k be k trips where o_i denotes the origin of the trip, d_i the destination, and tio,tid the starting and ending times, respectively. We say that multiple trips T_i are shareable if there exists a route connecting all of the o_i and d_i in any order where each o_i precedes the corresponding d_i, except for configurations where single trips are concatenated and not overlapped like o₁ → d₁ → o₂ → d₂, such that each customer is picked up and dropped off at the respective origin and destination locations with delay at most Δ, with the delay computed as the time difference to the respective single, individual trip. Imposing a bound of k on shareability implies that the k trips can be combined using a taxi of corresponding capacity (Fig. 1G). Deciding whether two or more trips can be shared necessitates knowledge of the travel time between arbitrary intersections in Manhattan, which we estimated using an ad hoc heuristic (SI Appendix, Fig. S2 and Table S1).Open in a separate windowFig. 1.Shareability networks translate spatiotemporal sharing problems into a graph-theoretic framework that provides efficient solutions. (A) Example of seven trips, T₁,…, T₇, requested and to be shared in Manhattan, New York City. (B) Construction of shareability network for k = 2. Trips that could potentially be shared are connected, given the necessary time constraints to hold which we assume here to be the case. Trips 1 and 4 cannot be shared because the total length of the best shared route would be longer than the sum of the single routes. Likewise, trip 7 is an isolated node because it cannot possibly be shared with other trips. (C) Maximum matching of the shareability network gives the maximum number of trip pairs, i.e., the maximum number of shared trips. (D) Implementation (routing) of the maximum matching solution. (E) Alternatively, maximum weighted matching of the shareability network gives the solution with the minimal total travel time, which in this case leads to a different solution than unweighted maximum matching. Here only two pairs of trips are shared, but the amount of travel time saved, given by the sum of link weights of the matching, 30 + 16, is optimal. (F) Implementation (routing) of the weighted maximum matching solution. (G) k sharing and taxi capacity. Each of the three cases involves a number of trips T_i to be shared, but ordered differently in time t. (Top) This case corresponds to a feasible sharing according to our model with k = 2, and the trips can be accommodated in a taxi with capacity ≥2. (Middle) This case corresponds to a model with k = 3 because three trips are combined, but the three trips can be combined in a taxi with capacity = 2 because two of the trips are nonoverlapping. (Bottom) This case corresponds to k = 3, but here a taxi capacity ≥3 is needed to accommodate the combined trips. Here we are assuming one passenger per trip, in line with the data reported in ref. 18, according to which the average number of passengers per trip is 1.3.For the case k = 2, the shareability network associated with a set ?? of trips is obtained by assigning a node T for each trip in ??, and by placing a link between two nodes T_i and T_j if the two trips can be shared for the given value of Δ (Fig. 1 A and B). The value of Δ has a profound impact on topological properties of the resulting shareability network. Increasing Δ capitalizes on well-known effects of time-aggregated networks such as densification (19, 20), capturing the intuitive notion that the more patient the customers, the more opportunities for trip sharing arise (Fig. 2 A and B). For values of k > 2, the shareability network has a hypergraph structure in which up to k nodes can be connected by a link simultaneously. Because of computational reasons, the shareability parameter k has a substantial impact on the feasibility of solving the problem. A solution is tractable for k = 2, heuristically feasible for k = 3, whereas it becomes computationally intractable for k ≥ 4 (SI Appendix). This constraint implies that taxi-sharing services, and social-sharing applications in general, will likely be able to combine only a limited number of trips. However, as we show below, even the minimum possible number of trip combinations (k = 2) can provide immense benefits to a dense enough community like the city of New York.Open in a separate windowFig. 2.Shareability networks densify with longer time aggregation, increasing sharing opportunities. This exemplary subset of the shareability network corresponds to 100 consecutive trips for values of (A) Δ = 30 s and (B) Δ = 60 s. Open links point to trips outside the considered set of trips. Isolated nodes are represented as self-loops. Node positions are not preserved across the networks. A similar, although visually not insightful, densification effect is observed in shareability networks obtained when k = 3.With the shareability network, classical algorithms for solving maximum matching on graphs (21, 22) can be used to determine the best trip-sharing strategy according to two optimization criteria: (i) maximizing the number of shared trips, or (ii) minimizing the cumulative time needed to accommodate all trips. To find the best solution according to (i) or (ii), it is sufficient to compute a maximum matching or a weighted maximum matching on the shareability network, respectively (Fig. 1 C and E, Materials and Methods). Because a shared trip can be served by a single taxi instead of two, the number of shared trips can be used as a proxy for the reduction in number of circulating taxis. For instance, an 80% rate of shared trips translates into a 40% reduction of the taxi fleet. Other important objectives such as total system cost and emissions are reasonably approximated by criterion (ii).     

The effects of dietary supplementation with n-3 polyunsaturated fatty acids in patients with rheumatoid arthritis: a randomized, double blind trial

STUDY OBJECTIVE: To determine the effect of dietary supplementation with n-3 polyunsaturated fatty acids (n-3 PUFA) on disease variables in patients with rheumatoid arthritis. DESIGN: Multicenter, randomized, placebo controlled, double blind. SETTING: Three Danish hospital Departments of Rheumatology. PATIENTS: Fifty-one patients with active rheumatoid arthritis. INTERVENTION: Random allocation to 12 weeks of treatment with either six n-3 PUFA capsules (3.6 g) or six capsules with fat composition as the average Danish diet. MAIN RESULTS: Significant improvement of morning stiffness and joint tenderness. No significant effect on the four other assessed clinical parameters. No serious side effects. CONCLUSIONS: Dietary supplementation with n-3 PUFA in patients with rheumatoid arthritis improved two out of six patient reported disease parameters. Further studies are needed to clarify the more precise role of n-3 PUFA in the treatment of rheumatoid arthritis.     

Molecular characterization of CTX‐M‐15‐producing clinical isolates of Escherichia coli reveals the spread of multidrug‐resistant ST131 (O25:H4) and ST964 (O102:H6) strains in Norway

Nationwide, CTX‐M‐producing clinical Escherichia coli isolates from the Norwegian ESBL study in 2003 (n=45) were characterized on strain and plasmid levels. Bla_CTX‐M allele typing, characterization of the genetic environment, phylogenetic groups, pulsed field gel electrophoresis (PFGE), serotyping and multilocus sequence typing were performed. Plasmid analysis included S1‐nuclease‐PFGE, polymerase chain reaction‐based replicon typing, plasmid transfer and multidrug resistance profiling. Bla_CTX‐M‐15 (n=23; 51%) and bla_CTX‐M‐14 (n=11; 24%) were the major alleles of which 18 (78%) and 6 (55%), respectively, were linked to ISEcp1. Thirty‐two isolates were of phylogenetic groups B2 and D. Isolates were of 29 different XbaI‐PFGE‐types including six regional clusters. Twenty‐three different O:H serotypes were found, dominated by O25:H4 (n=9, 20%) and O102:H6 (n=9, 20%). Nineteen different STs were identified, where ST131 (n=9, 20%) and ST964 (n=7, 16%) were dominant. Bla_CTX‐M was found on ≥100 kb plasmids (39/45) of 10 different replicons dominated by IncFII (n=39, 87%), FIB (n=20, 44%) and FIA (n=19, 42%). Thirty‐nine isolates (87%) displayed co‐resistance to other classes of antibiotics. A transferable CTX‐M phenotype was observed in 9/14 isolates. This study reveals that the majority of CTX‐M‐15‐expressing strains in Norway are part of the global spread of multidrug‐resistant ST131 and ST‐complex 405, associated with ISEcp1 on transferrable IncFII plasmids.     

Femur length shortening in the detection of Down syndrome: is prenatal screening feasible?

The potential utility of screening for femur length shortening in prenatal detection of Down syndrome (trisomy 21) was evaluated by comparing 49 consecutive fetuses with Down syndrome with 572 chromosomally normal fetuses before genetic amniocentesis. Ratios of measured femur length/predicted femur length and biparietal diameter/femur length were calculated for each fetus. The predicted femur length was calculated from a regression equation relating the biparietal diameter and femur length derived from a sample control group. With this normal regression equation, 7 of 49 (14.3%) fetuses with Down syndrome had short femur lengths (measured femur length/predicted femur length ratio of less than or equal to 0.91) compared with 35 of 572 (6.1%) fetuses with a normal karyotype (p less than 0.05). However, the maximum positive predictive value for identification of Down syndrome based on short femur lengths was only 0.93% for a high-risk population (prevalence of Down syndrome, 1:250) and 0.33% for a low-risk population (prevalence of Down syndrome, 1:700). We conclude that ultrasonographic screening of short femur length is less effective for prenatal detection of Down syndrome than initially suggested.     

Quantitative morphometrical investigation of basal cell layer in laryngeal premalignant lesions

Histological diagnosis of laryngeal dysplasia is quite subjective. Since morphometry is highly reproducible, this method was applied to compare shape and size variations of the basal nuclei of the laryngeal epithelium in normal, laryngeal intraepithelium neoplasia (LIN) and invasive carcinoma to assess the reliability of light microscopic criteria used in grading dysplasia according to Friedman classification. Morphometrical analysis was carried out by Shape Analytical Morphometry (S.A.M) system. The logical architecture assumes that each irregular shape contains elements of two distinct logical domains: gross distortions that interest the contour and its local perturbations. These features were investigated separately by analytical procedures to acquire independent parameters both on the logical level and the numerical one. The nuclear area significantly increased from normal to carcinoma (p<.001). The increasing of the nuclear area was evident also in LIN I. Nuclear distortions were present in LIN II and LIN III. The highest nuclear contour irregularities were found in LIN III. Multivariate analysis showed a difficulty in discriminating various grades of dysplasia, especially between LIN I and LIN II (31% of error). In conclusion, our results indicate that nuclear pleomorphism of the basal cells layer, using a unique evaluator, is an unsatisfying criterion to distinguish moderate dysplasia.     

Identification and use of biomarkers in Gaucher disease and other lysosomal storage diseases

The value of biomarkers in the clinical management of lysosomal storage diseases is best illustrated by the present use of plasma chitotriosidase levels in the diagnosis and monitoring of Gaucher disease. The enzyme chitotriosidase is specifically produced and secreted by the pathological storage macrophages (Gaucher cells). Plasma chitotriosidase levels are elevated on average 1000-fold in symptomatic patients with Gaucher disease and reflect the body burden on storage cells. Changes in plasma chitotriosidase reflect changes in clinical symptoms. Monitoring of plasma chitotriosidase levels is nowadays commonly used in decision making regarding initiation and optimization of costly therapeutic interventions (enzyme replacement therapy or substrate reduction therapy). A novel substrate has been developed that further facilitates the measurement of chitotriosidase in plasma samples. Moreover, an alternative Gaucher-cell marker, CCL18, has been very recently identified and can also be employed to monitor the disease, particularly in those patients lacking chitotriosidase due to a genetic mutation. There is a need for comparable surrogate markers for other lysosomal storage diseases and the search for such molecules is an area of intense investigation.
Conclusion: The use of biomarkers can provide valuable insight into the molecular pathogenesis of LSDs, such as Gaucher disease and Fabry disease.