Congestion Patterns Research Articles

Large-scale vented deflagration tests

This paper presents results from a test program carried out to determine the peak deflagration pressure achieved within a congested enclosure vented through one wall of the enclosure. The industry standard in the United States for predicting the peak pressure developed in a vented deflagration is the National Fire Protection Association's Standard on Explosion Protection by Deflagration Venting (NFPA 68). The NFPA Explosion Protection Committee has compiled a database of published and unpublished explosion venting test data. This data was summarized in a 2008 report (Zalosh) that served as the foundation of the development for the vented deflagration correlations in the latest (2013) edition of NFPA 68. In this latest edition, NFPA 68 (2013), the vent area correlation accounts for varying degrees of congestion if the ratio of the obstacle surface area (Aobs) to that of the enclosure internal surface area (As) is greater than 0.4 (i.e., Ar = Aobs/As > 0.4). Congestion is accounted for within the correlation at all values of Ar, however when Ar is < 0.4, variations in the level of congestion are not accounted for. The tests described in this paper were performed using an obstacle array with an Ar ratio of less than 0.4.These tests were conducted in a rig with a 48-foot width, 24-foot depth, and 12-foot height. The rig is enclosed with solid walls, roof, and floor, allowing for venting through one of the long walls (i.e., 48-foot by 12-foot). The venting face of the rig was sealed with a 6 mil (0.15 mm) thick plastic vapor barrier to allow for the formation of a near-stoichiometric propane-air mixture. The flammable gas cloud was ignited near the center of the rear wall. Steel vent panels (20-gauge, 2 lbm/ft2) were installed over the plastic vapor barrier using explosion relief fasteners. The vent panels were configured to release at 0.3 psig; vent panel restraint devices were not utilized. The congestion inside the rig was provided by a regular array of vertical cylinders (2-inch schedule 40 pipe and 2-inch outer diameter cylinders) giving area and volume blockage ratios (ABR and VBR) within the congestion array of 4.9% and 0.5%, respectively. The obstacle-to-enclosure surface area ratio (Ar) is 0.3 with the array extended throughout the rig and vent panels installed, which is less than the critical value to account for congestion in the NFPA 68 correlation.Four series of tests were conducted with varying vent parameters, flammable gas cloud sizes, and congestion levels. Baseline tests were performed with the congestion array and flammable gas cloud extending throughout the entire rig without vent panels present (i.e., vapor barrier only). The second test series included the addition of vent panels for the same congestion pattern as that employed for the baseline tests. The third test series utilized a flammable gas cloud that filled only the back half of the rig. For the fourth test series, the congestion array occupied only ¼ of the rig. The peak pressures and impulses for each test series are provided, along with pressure histories internal and external to the rig for selected tests. The steel vent panel throw distance is also provided as a function of internal peak pressure.The test data were compared with the predictions of the vent area correlations provided in NFPA 68. For all but the fourth test series (i.e., congestion array occupying ¼ of the rig), the average internal peak pressures were approximately a factor of 2 larger than those predicted by NFPA 68. Adjustments to the NFPA 68 correlation were investigated to improve the agreement with the current test data.

Analysis of speed disturbances in empirical single vehicle probe data before traffic breakdown

In single vehicle probe data measured on German freeways the authors have revealed free flow→synchronised flow→free flow transitions that occur before traffic breakdown at a bottleneck occurs. Thus resulting in the formation of a congested pattern. The empirical findings of these phase transitions confirm a recent microscopic stochastic theory of traffic breakdown developed by Kerner. Only because of the recently introduced possibility of gathering larger amounts of anonymised vehicle data – including a sequence of positions of each car – the authors are able to show the phenomenon of these phase transitions in measured floating car data. This contribution reveals empirical findings in microscopic data which support and prove some of the recent theoretical findings of the nature of traffic breakdown.

Measuring and visualizing space–time congestion patterns in an urban road network using large-scale smartphone-collected GPS data

Congestion is a dynamic phenomenon with elements of space and time, making it a promising application of probe vehicles. The purpose of this paper is to measure and visualize the magnitude and variability of congestion on the network scale using smartphone GPS travel data. The sample of data collected in Quebec City contained over 4000 drivers and 21,000 trips. The congestion index (CI) was calculated at the link level for each hour of the peak period and congestion was visualized at aggregate and disaggregate levels. Results showed that each peak period can be viewed as having an onset period and dissipation period lasting one hour. Congestion in the evening is greater and more dispersed than in the morning. Motorways, arterials, and collectors contribute most to peak period congestion, while residential links contribute little. Further analysis of the CI data is required for practical implementation in network planning or congestion remediation.

VAYU: Accelerating stream processing applications through dynamic network-aware topology re-optimization

Stream processing applications for online analytics are commonly used in domains ranging from sensor data processing to social networking. To achieve high-throughput, stream processing engines support pipelined execution, low-overhead fault-tolerance, and efficient group communication overlays. The throughput of pipelined application workflows is significantly impacted by dynamic system state. In particular, we show that a single bottleneck in the pipeline (congested link or an overloaded operator) can drastically impact the system throughput. In this paper, we present a number of techniques for addressing bottlenecks in stream engines. Our techniques fall into two major classes – network-aware routing for fine grained control of streams; and dynamic overlay generation for optimizing performance of group communication operations. To enable fast workflow re-optimization, we present a light-weight protocol for consistent modification of pipelines. We present detailed algorithms, their implementation in a real system, and address issues of fault tolerance and performance. We evaluate performance of the proposed techniques in the context of three real applications. We show that our techniques improve performance by 20% to 200%, under various overheads, relative to a baseline representative of current implementations. We demonstrate that our techniques are robust to highly dynamic state, as well as complex congestion patterns. Given the widespread use of streaming systems and the need for dealing with dynamic system state, our techniques represent a significant and practical improvement.

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on 24-hour congestion pattern of road segments in urban area, so that the spatial autoregressive moving average model (SARMA) was introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained 12 traffic-related factors and land use factors’ impact on road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use and so on have large impact on congestion formation. The Fuzzy C-means clustering was proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service of traffic from the congestion perspective.

An Integrated Framework for Allocation of New Emergency Response Facilities in Developing Nations

Developing countries are majorly resource-constrained societies which do not have the financial power to invest in meeting all infrastructure needs. Although there are multiple types of emergency response systems (ERS), the most important ones are the basic essentials—fire, medical and police. Along with emergency response planning for man-made emergencies in an urban scenario, there is a gap in the recent literature about integration of basic ERS into a single framework. We propose a novel framework that first evaluates the existing ERS in place and then provides solutions for improvement with the inclusion of additional facilities at optimal locations. Keeping in mind dynamic congestion patterns and land use resource constraints in different areas of the urban space, the proposed facilities are labeled as either permanent or floating. We also propose three different schemes, each with two competing strategies that cater to a specific type of constraint common to developing nations. Through comparison of system performance metrics like first response time, area coverage, and average response time, the ERS pre- and post-implementation of the framework are compared to check for degree of improvement. A case study of the framework is shown using the city of Chandigarh in India as an example. The framework proposed in the paper takes into account the growth and compactness of urban spaces and can be used to continually assess and improve ERS in mixed use urban spaces after fixed periods of time.

Time-dependent green Weber problem

We consider an extension of the classical Weber problem, named as the green Weber problem (GWP), in which the customers have one-sided time windows. The GWP decides on the location of the single facility in the plane and the speeds of the vehicles serving the customers from the facility within the one-sided time windows so as to minimize the total amount of carbon dioxide emitted in the whole distribution system. We also introduce time-dependent congestion which limits the vehicle speeds in different time periods and call the resulting problem as the time-dependent green Weber problem (TD-GWP). In the TD-GWP, the vehicles are allowed to wait during more congested time periods. We formulate the GWP and TD-GWP as second order cone programming problems both of which can be efficiently solved to optimality. We show that if the traffic congestion is non-increasing, then there exists an optimal solution in which the vehicles do not wait at all. Computational results are provided comparing the locations of the facility and the resulting carbon dioxide emissions of the classical Weber problem with those of the GWP and comparing the GWP with the TD-GWP in terms of carbon dioxide emissions in different traffic congestion patterns.

Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data

Traffic congestion is one of the most serious problems that impact urban transportation efficiency, especially in big cities. Identifying traffic congestion locations and occurring patterns is a prerequisite for urban transportation managers in order to take proper countermeasures for mitigating traffic congestion. In this study, the historical GPS sensing data of about 12,000 taxi floating cars in Beijing were used for pattern analyses of recurrent traffic congestion based on the grid mapping method. Through the use of ArcGIS software, 2D and 3D maps of the road network congestion were generated for traffic congestion pattern visualization. The study results showed that three types of traffic congestion patterns were identified, namely: point type, stemming from insufficient capacities at the nodes of the road network; line type, caused by high traffic demand or bottleneck issues in the road segments; and region type, resulting from multiple high-demand expressways merging and connecting to each other. The study illustrated that the proposed method would be effective for discovering traffic congestion locations and patterns and helpful for decision makers to take corresponding traffic engineering countermeasures in order to relieve the urban traffic congestion issues.

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.

Investigation of Traffic and Evacuation Aspects at Kumamoto Earthquake and the Future Issues

This study reports the results of the analysis of probe data, collected in the periods immediately before and after the foreshock in Kumamoto on April 14, 2016. Data were gathered under actual urban traffic conditions, and the traffic activity evaluated. The study also identifies any issues to be addressed in future, based on this analysis. The analytical results quantitatively show that movement from Fukuoka to central Kumamoto was impacted by closure of the Kyushu Expressway; it also shows that travel times during the day along alternative paths significantly increased, whilst congestion on highways decreased. The results show that locations such as shelters, supermarkets, and public baths were the beginning and end points of travel, thereby causing deviations from normal congestion patterns.

Congested traffic patterns of two-lane lattice hydrodynamic model with on-ramp

This paper attempts to reproduce the empirical phenomena of congested traffic flow with an on-ramp through a microscopic traffic model. First, an improved two-lane lattice hydrodynamic traffic flow model is proposed, which is capable of avoiding vehicles backward moving in original lattice hydrodynamic model. Then, the deterministic and stochastic on-ramps are designed and mapped into the new lattice model to reproduce the empirical phenomena. For the stochastic case, many empirical congested patterns are reproduced, such as moving localized cluster, triggered stop-and-go traffic (TSG), pinned localized cluster (PLC), oscillating congested traffic (OCT) and homogeneous congested traffic. For the deterministic case, a number of combination patterns of HST, PLC, TSG and OCT are found. Taken together, these results suggest that the present model is able to predict the congested traffic patterns.

Large-scale, high-fidelity dynamic traffic assignment: framework and real-world case studies

We present a highly detailed, microscopic Dynamic Traffic Assignment (DTA) framework with sufficient fidelity to address emergent and future planning and operations applications. Congestion patterns are estimated at the lane level with explicit modeling of complex signal timing algorithms and their impacts on queues and spillbacks. A Geographic Information System (GIS) ensures the most accurate network representation. The flexible representation of travel demand at the resolution of individual vehicles facilitates the capturing of sufficient vehicle and driver classes, vehicle performance and driving behavior distributions, inter-vehicle interactions, and temporally fine trajectories. Model outputs are saved at any desired granularity, and may be used to assess entire distributions of performance metrics to support reliability studies. Applications of the framework include the study of connected vehicles, Intelligent Transportation Systems (ITS), advanced tolling systems, and emissions modeling, and safety analysis. The above features are implemented with unparalleled computational performance so that large-scale networks may be handled without the need for accuracy-running time tradeoffs. We describe four real-world projects that clearly demonstrate the advantages of the microscopic DTA in practice.

Departure Time and Route Choices with Bottleneck Congestion: User Equilibrium under Risk and Ambiguity

Abstract: This paper examines commuters’ departure time and route choices in the morning commute problem when travel time is described as a bounded distributional uncertainty set. The preferences towards risk and ambiguity are distinguished by adopting the ambiguity-aware constant absolute risk aversion travel time criteria. We first examine the dynamic user equilibrium for a single-route model with homogeneous risk/ambiguity preference. Compared with risk-neutral commuters, we find that departure time window is shifted earlier for the risk-averse commuters and shifted later for the risk-seeking commuters. We then study the single bottleneck with a risk-averse class and a risk-seeking class. It is shown that with a larger gap between the two classes’ preferences, the congestion pattern will change from one peak to two peaks. It implies that preference heterogeneity may stagger the departure time choice and thereby relieve the average congestion. At last, we examine a two-routes problem with homogeneous preference towards risk and ambiguity. The commuters will choose between a faster route (highway) and a less risky route (local arterial). We prove that the flow distribution between the two routes will monotonically change with the maximum variation in travel time. That is, the highway flow will decrease with uncertainty on the highway for risk-averse commuters. In contrast, it will increase with uncertainty on the highway for risk-seeking commuters. The price of anarchy is analyzed in the numerical section by varying the risk preference and the ambiguity preference.

Traffic Congestion Pattern Classification Using Multiclass Active Shape Models

Identifying and classifying traffic and congestion patterns are essential parts of modern traffic management underpinned by the emerging intelligent transport systems. This paper explores the potential of using a combination of image processing methods to identify and classify regions of congestion within spatiotemporal traffic (speed, flow) contour maps. The underlying idea is to use these regions as (archetype) shapes that in many combinations can make up a wide variety of larger-scale traffic patterns. In this paper, use of a so-called statistical shape model is proposed as a low-dimensional representation of the archetype shape, and an active shape model algorithm coupled with linear classification is developed to classify the patterns of interest. Application of the proposed method is demonstrated with a preliminary set of speed contour maps reconstructed from loop detector data in the Netherlands. The results show that the extended active shape model can be used as a multiclass classifier. In particular, 70% of the traffic patterns in the test data were correctly classified with use of only two archetype shapes and simple logistic classifiers. The results point to the importance of use of expert knowledge by means of (a priori) manual classification of the training examples. This work opens many research directions, including semiautomated searches through traffic databases, automatic detection, and classification of new traffic patterns.

Robust routing based on urban traffic congestion patterns

Abstract: In your daily journeys, you are driving from a departure location to another destination location situated in the road network. There are a number of alternative routes that you could use. For each section of the network, we know the travel time required under normal traffic conditions to move from one endpoint to the other. What we do not know is the occurrence of incidents that slow down traffic and cause traffic congestion and therefore delays. This work considers the development of simple traffic model based on the semi-microscopic traffic assumption. The simulator based on the proposed model makes it possible to estimate the travel time on a road link. From variables, provided by simulator we can derive travel time index and therefore to characterize the traffic state of the urban transportation network. Another objective of this paper is to determine a robust itinerary in an urban transport network, taking into account the dynamic aspects of traffic congestion.

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

Acute Otitis Media and the Crying Child.

Acute otitis media (AOM) can be difficult to diagnose in a crying child. The vessels of the ear canal and tympanic membrane (TM) can engorge simulating AOM. We observed the pattern of vascular congestion during modified Valsalva maneuver. Sparing of the pars tensa, lack of TM bulging help to differentiate a crying child's red TM from that seen in AOM.

Time-dependent congestion pricing system for large networks: Integrating departure time choice, dynamic traffic assignment and regional travel surveys in the Greater Toronto Area

Congestion pricing is one of the widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a framework for large-scale variable congestion pricing policy determination and evaluation. The proposed framework integrates departure time choice and route choice models within a regional dynamic traffic assignment (DTA) simulation environment. The framework addresses the impact of tolling on: (1) road traffic congestion (supply side), and (2) travelers’ choice dimensions including departure time and route choices (demand side). The framework is applied to a simulation-based case study of tolling a major freeway in Toronto while capturing the regional effects across the Greater Toronto Area (GTA). The models are developed and calibrated using regional household travel survey data that reflect the heterogeneity of travelers’ attributes. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The case study examined two tolling scenarios: flat and variable tolling. The results indicate that: (1) more benefits are attained from variable pricing, that mirrors temporal congestion patterns, due to departure time rescheduling as opposed to predominantly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic demand are observed across the regional network in response to tolling a significant, yet relatively short, expressway serving Downtown Toronto, and (3) flat tolling causes major and counterproductive rerouting patterns during peak hours, which was observed to block access to the tolled facility itself.

Investigating the Empirical Existence of Static User Equilibrium

Abstract: The Static User Equilibrium is a powerful framework for the theoretical study of traffic. Despite the restricting assumption of stationary flows that intuitively limit its application to real traffic systems, many operational models implementing it are still used without an empirical validation of the existence of the equilibrium. We investigate its existence on a traffic dataset of three months for the region of Paris, FR. The implementation of an application for interactive spatio-temporal data exploration allows to hypothesize a high spatial and temporal heterogeneity, and to guide further quantitative work. The assumption of locally stationary flows is invalidated in a first approximation by empirical results, as shown by a strong spatial and temporal variability in shortest paths and in network topological measures such as betweenness centrality. Furthermore, the behavior of spatial autocorrelation index of congestion patterns at different spatial ranges suggest a chaotic evolution at the local scale, especially during peak hours. We finally discuss the implications of these empirical findings and describe further possible developments based on the estimation of Lyapunov dynamical stability of traffic flows.

Optimizing congestion and emissions via tradable credit charge and reward scheme without initial credit allocations

Abstract This paper investigates the revenue-neutral tradable credit charge and reward scheme without initial credit allocations that can reassign network traffic flow patterns to optimize congestion and emissions. First, we prove the existence of the proposed schemes and further decentralize the minimum emission flow pattern to user equilibrium. Moreover, we design the solving method of the proposed credit scheme for minimum emission problem. Second, we investigate the revenue-neutral tradable credit charge and reward scheme without initial credit allocations for bi-objectives to obtain the Pareto system optimum flow patterns of congestion and emissions; and present the corresponding solutions are located in the polyhedron constituted by some inequalities and equalities system. Last, numerical example based on a simple traffic network is adopted to obtain the proposed credit schemes and verify they are revenue-neutral.