The Effect of Crosswalks on Traffic Flow

In urban areas and especially in inner cities, pedestrians crossing the road considerably influence the road traffic flow. For political reasons, priority could be given to pedestrians. A larger number of crossings reduces the pedestrian load per crossing and facilitates both the pedestrian flow and the car flow; the ultimate case is a “cross anywhere” scenario. Earlier work shows that the road capacity decreases with the square of the pedestrian crossing time, hence a short crossing time is desired. Crosswalks can ensure pedestrians cross orthogonally, and thus quickly, and can thereby improve traffic flow. Moreover, a limited number of crosswalks is less stressful than a “cross anywhere” scenario for a car driver since (s)he only needs to expect crossing pedestrians at dedicated crosswalks. This paper studies the effect of the distances between crosswalk and road traffic capacity. The paper’s goal is finding a single formula or universal set of charts that can describe the effect of pedestrian crosswalks on traffic flow under virtually all scenarios (with long blocks). This type of result would obviate the need for simulations of specific situations when only a rough assessment of the effect of crosswalks is desired. Traffic flow for several distances between pedestrian crossings is simulated, and moreover, a non-constant inter-crosswalk spacing is considered. The simulation results can be used for other situations, using transformations and an interpolation recipe. Overall, the closer the crosswalks, the better the flow. However, spacings closer than approximately 25-50 meters do not add much. Speed of traffic under a broad array of pedestrian crossing scenarios is given

An Area-Aggregated Dynamic Traffic Simulation Model

Microscopic and macroscopic dynamic traffic models not fast enough to run in an optimization loop to coordinate traffic measures over areas of twice a trip length (50x50 km). Moreover, in strategic planning there are models with a spatial high level of detail, but lacking the features of traffic dynamics. This paper introduces the Network Transmission Model (NTM), a model based on areas, exploiting the Macroscopic or Network Fundamental Diagram (NFD). For the first time, a full operational model is proposed which can be implemented in a network divided into multiple subnetworks, and the physical properties of spillback of traffic jams for subnetwork to subnetwork is ensured. The proposed model calculates the traffic flow between to cell as the minimum of the demand in the origin cell and the supply in the destination cell. The demand first increasing and then decreasing as function of the accumulation in the cell; the supply is first constant and then decreasing as function of the accumulation. Moreover, demand over the boundaries of two cells is restricted by a capacity. This system ensures that traffic characteristics move forward in free flow, congestion moves backward and the NFD is conserved. Adding the capacity gives qualitatively reasonable effects of inhomogeneity. The model applied on a test case with multiple destinations, and re-routing and perimeter control are tested as control measures

The Influence of Spillback Modelling when Assessing Consequences of Blockings in a Road Network

Robustness of a network is a main objective for road network managers these days, and has therefore become an important study area for transportation scientists. This article discusses one specific aspect in assessing road network robustness: the consequences of the closure of a link. These spillback effects have been examined in a dedicated traffic simulator in which the representation of spillback can be switched on and off. The impacts are studied in a simulation study of a road network of a regional size in which sequentially links are blocked. Two scenarios for route choice are considered: a fixed route choice based on a daily congestion pattern and a route choice adapted to the actual congestion caused by the closure. The study has also shown the influence of information which makes travellers adapt their routes. Road network robustness and characteristics of vulnerable links are evaluated for both spillback and non-spillback cases. It is found that a valid spillback modelling is a prerequisite for correctly analysing the robustness of the network as a whole, as well as for correctly indicating the locations in the network where a closure causes the largest delays. Furthermore, without simulating spillback, it is not possible to identify correctly the most vulnerable links for the network performance

Game-theoretical approach to decentralized multi-drone conflict resolution and emergent traffic flow operations

This paper introduces decentralized control concepts for drones using differential game theory. The approach optimizes the behavior of an ego drone, assuming the anticipated behavior of the opponent drones using a receding horizon approach. For each control instant, the scheme computes the Nash equilibrium control signal which is applied for the control period. This results in a multi-drone conflict resolution scheme that is applied to all drones considered. The paper discusses the approach and presents the numerical algorithm, showing several examples that illustrate the performance of the model. We examine at the behavior of the ego drone, and the resulting collective drone flow operations. The latter shows that while the approach aims to optimize the operation cost of the ego drone, the experiments provide evidence that resulting flow operations are very efficient due to the self-organization of various flow patterns. The presented work contributes to the state of the art in providing a generic approach to multi-drone conflict resolution with good macroscopic flow performance characteristics. The approach enables relatively straightforward inclusion of error due to sensing and communication. The approach also allows for including different risk levels (e.g., for malfunctioning of sensor and communication technology), priority rules, regulations, and higher-level control signals (e.g., routing, dynamic speed limits).Comment: Submitted to the TRB Annual Meeting 202

Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?

Network-level traffic condition forecasting has been intensively studied for decades. Although prediction accuracy has been continuously improved with emerging deep learning models and ever-expanding traffic data, traffic forecasting still faces many challenges in practice. These challenges include the robustness of data-driven models, the inherent unpredictability of traffic dynamics, and whether further improvement of traffic forecasting requires more sensor data. In this paper, we focus on this latter question and particularly on data from loop detectors. To answer this, we propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models. Firstly, the model design combines traffic flow theory with graph neural networks, ensuring the robustness of prediction and uncertainty quantification. Secondly, evidential learning is employed to quantify different sources of uncertainty in a single pass. The estimated uncertainty is used to "distil" the essence of the dataset that sufficiently covers the information content. Results from a case study of a highway network around Amsterdam show that, from 2018 to 2021, more than 80\% of the data during daytime can be removed. The remaining 20\% samples have equal prediction power for training models. This result suggests that indeed large traffic datasets can be subdivided into significantly smaller but equally informative datasets. From these findings, we conclude that the proposed methodology proves valuable in evaluating large traffic datasets' true information content. Further extensions, such as extracting smaller, spatially non-redundant datasets, are possible with this method.Comment: 13 pages, 5 figure

Macroscopic analysis and modelling of multi-class, flexible-lane traffic

An excessive demand of vehicles to a motorway bottleneck leads to traffic jams. Motorbikes are narrow and can drive next to each other in a lane, or in-between lanes in low speeds. This paper analyses the resulting traffic characteristics and presents numerical scheme for a macroscopic traffic flow model for these two classes. The behavior included is as follows. If there are two motorbikes behind each other, they can travel next to each other in one lane, occupying the space of one car. Also, at low speeds of car traffic, they can go in between the main lanes, creating a so-called filtering lane. The paper numerically derives functions of class-specific speeds as function of the density of both classes, incorporating flexible lane usage dependent on the speed. The roadway capacity as function of the motorbike fraction is derived, which interesting can be in different types of phases (with motorbikes at higher speeds or not). We also present a numerical scheme to analyse the dynamics of this multi-class system. We apply the model to an example case, revealing the properties of the traffic stream , queue dynamics and class specific travel times. The model can help in showing the relative advantage in travel time of switching to a motorbike

A randomized controlled trial of liposomal cyclosporine A for inhalation in the prevention of bronchiolitis obliterans syndrome following lung transplantation

Bronchiolitis obliterans; Clinical research; Lung transplantationBronquiolitis obliterante; Investigación clínica; Trasplante de pulmónBronquiolitis obliterant; Recerca clínica; Trasplantament de pulmóLong-term survival after lung transplantation is limited by chronic allograft dysfunction. The aim of this study was to investigate the effect of locally augmented immunosuppression with liposomal cyclosporine A for inhalation (L-CsA-i) for the prevention of bronchiolitis obliterans syndrome (BOS). In a randomized, double-blind, placebo-controlled, multi-center Phase 3 study, 180 LT recipients in BOS grade 0 were planned to receive L-CsA-i or placebo in addition to triple-drug immunosuppression. L-CsA-i was administered twice daily via an Investigational eFlow nebulizer to recipients of single (SLT) and bilateral lung transplants (BLT) within 6–32 weeks posttransplant, and continued for 2 years. The primary endpoint was BOS-free survival. 130 patients were enrolled before the study was prematurely terminated for business reasons. Despite a 2-year actuarial difference in BOS-free survival of 14.1% in favor of L-CsA-i in the overall study population, the primary endpoint was not met (p = .243). The pre-defined per protocol analysis of SLT recipients (n = 24) resulted in a treatment difference of 58.2% (p = .053). No difference was observed in the BLT (n = 48) subpopulation (p = .973). L-CsA-i inhalation was well tolerated. Although this study failed to meet its primary endpoint, the results warrant additional investigation of L-CsA-i in lung transplant recipients.The study was funded by PARI Pharma GmbH. Open access funding enabled and organized by ProjektDEAL

Impact of Lung Function Decline on Mortality in Lung Transplant Recipients: Long-Term Results From the L-CsA-i Study for the Prevention of Bronchiolitis Obliterans Syndrome

Cyclosporine (CsA); Chronic rejection; Lung transplantationCiclosporina (CsA); Rechazo crónico; Trasplante de pulmónCiclosporina (CsA); Rebuig crònic; Trasplantament de pulmóBackground: Chronic lung allograft dysfunction (CLAD) is defined by a progressive loss of FEV1 and is associated with premature mortality. The aim of this study was to investigate the direct association between FEV1 decline and risk of mortality in patients after lung transplantation (LTx). Methods: 10-year follow up data from lung transplant recipients participating in randomized placebo-controlled clinical trial investigating the role of liposomal Cyclosporine A for inhalation (L-CsA-i) in the prevention of bronchiolitis obliterans syndrome (NCT01334892) was used. The association between the course of FEV1 over time and the risk of mortality was assessed using joint modeling and Cox regression analysis. Results: A total of 130 patients were included. Predictors of FEV1 decline were a higher absolute FEV1 at baseline and male sex. The joint model analysis indicated a significant association of change of FEV1 and risk of mortality (p < 0.001), with a predicted 3.4% increase in mortality risk for each 1% decline in FEV1. Significant predictors of a progressive phenotype were single LTx and treatment with placebo (as opposed to L-CsA-i). At the end of follow-up, 82 patients (63.1%) were still alive. Cox regression analyses for mortality identified only single LTx as a predictor of higher risk. Conclusion: Based on our observation of a close association between FEV1 and mortality over a period of 10 years we suggest FEV1 as a valid predictor of mortality and a suitable surrogate endpoint in the investigation of early interventions.This study was funded by Zambon S.p.A., Milan, Italy