Different Levels Of Congestion Research Articles

Congestive mode-switching and economies of scale on a bus route

This paper introduces a type of circular causation called Congestive Mode-Switching (CMS) that may arise when an increase in congestion penalizes transit relative to driving. In turn, rising congestion persuades some transit riders to drive, which exacerbates congestion further, and so on. This circular causation can beget multiple equilibria with different levels of congestion and transit ridership. The paper explores this logic with a static model of a bus route. When the bus fleet size is fixed, CMS applies because congestion raises the bus cycle time and thus lowers bus frequency, resulting in higher wait times. When the fleet size depends on bus ridership, CMS is joined by economies of scale as a second source of circular causation. We derive the system’s equilibria using a static model in the vein of Walters (1961), which permits us to graphically characterize equilibria in useful ways. The comparative statics of a road improvement show how feedback alters first-order effects. A Downs-Thomson paradox is not possible, because a road improvement aids buses even more than cars. Continuous-time stability analysis shows that multiple equilibria may be stable.

Measuring congestion-induced performance imbalance in Internet load balancing at scale

Internet load balancers typically use congestion-oblivious hashing algorithms to distribute traffic across load-balanced (LB) paths, resulting in imbalanced load distribution. When congestion occurs, LB paths could experience different levels of congestion, called congestion imbalance. Congestion imbalance has been extensively studied in datacenters (DCs). It is, however, still under-explored in the Internet. In this paper, we take a first step towards measuring congestion imbalance among Internet LB paths at scale. We present Congi, our lightweight prober, that leverages support vector machine (SVM) classifiers to efficiently detect congestion imbalance using latency samples. Our experiments show that Congi is capable of detecting congestion imbalance between uncongested and congested LB paths: on average, the uncongested path has 3x greater throughput and 0.4% lower packet loss rate than the congested one. To measure congestion imbalance at scale, we then use Congi to conduct measurement campaigns from worldwide DCs to millions of/24s. We find that most DCs experience significant congestion imbalance to 36%–43% of end hosts. Lastly, we use Congi to direct web page downloads under congestion imbalance from a campus network, and show that the download time can be reduced by 44% on average for Alexa top sites for clients experiencing network congestion under congestion imbalance.

Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

This paper proposes models and algorithms for the estimation of traffic flows generated by the centralised management of autonomous vehicles under exclusive and mixed traffic conditions. It is assumed that the autonomous vehicle manager wants to obtain the system optimum that minimises the total travel time on the network, of autonomous vehicles in the case of exclusive traffic, and of all flows in the case of mixed traffic. In the case of mixed traffic, the drivers of the human-guided vehicles choose their routes from a user-optimal perspective; in this case, a fixed-point problem has to be solved, which represents a state of equilibrium between the flows of human-guided vehicles, the flows of autonomous vehicles, which follow paths aimed at the system optimum, and the link costs. The proposed models and algorithms are tested on a small network with a single origin-destination pair, and on the Sioux Falls network, with different levels of congestion and different percentages of autonomous vehicles. The results show the validity of the proposed methods and the impact of autonomous vehicles on network performance. The proposed static/equilibrium approach is necessary to evaluate transport planning, design or policy interventions that include the presence of autonomous vehicles in traffic flows.

Strategies for Assuring Low Latency, Scalability and Interoperability in Edge Computing and TSN Networks for Critical IIoT Services

In recent years, there has been an exponential growth of IoT (Internet of Things) devices in the market and increasingly, they are being part of critical mission functionalities for many industries. For this reason, processes associated with these devices are generating new requirements at the level of interconnectivity, security, and scalability. Due to the large volume of devices, this growth process has tested the existing communications infrastructure, deployment architecture, different support services, technical requirements and the intrinsic characteristics of the services offered. In this sense, the use of Edge Computing schemes and TSN networks (Time Sensitive Networks), is proposed as a tool for supporting IIoT (Industrial Internet of Things) services to achieve optimization and assurance processes of latency, scalability and interoperability parameters required by critical mission services such as health, transportation and production. In this sense, we propose several strategies for assuring low latency, scalability and interoperability in Edge Computing and TSN networks for critical IIoT services. These strategies are evaluated considering different levels of congestion and considering different schemes such as best effort, TSN and Edge Computing.

Analysis of the Impact of Variable Speed Limits on Environmental Sustainability and Traffic Performance in Urban Networks

This work focuses on evaluating the potential of variable speed limits (VSLs) in a synthetic urban network to improve both environmental sustainability and traffic performance. The traffic system is modeled using the microscopic traffic simulator SUMO, and a physical fuel consumption and NOx emission model is used to assess the vehicles’ energy efficiency. Speed limits are controlled through a nonlinear model predictive control (NMPC) approach, in which the traffic evolution and fuel consumption are respectively predicted with a macroscopic traffic model, namely the cell transmission model (CTM), and a pre-calibrated artificial neural network (ANN). The results reveal that in transient phases between different levels of congestion, the proposed eco-VSL controller is faster to decongest the network, resulting in an improvement of the environmental sustainability and the traffic performance both in the controlled network, and at its boundary roads.

Characterization of heavy vehicle headways in oversaturated interrupted conditions: Towards development of passenger car equivalency factors

Passenger car equivalency (PCE) of heavy trucks is often studied using filed observation and microscopic simulation models, especially for signalized intersections. While the Highway Capacity Manual recommends a single value regardless of the percentage of those heavy vehicles, literatures have shown that this equivalency is affected by different factors, including the trucks percentage. This research aims to examine the PCE under different level of traffic and heavy trucks demands. First, field measurements were collected and used to examine the characteristics of heavy vehicle and passenger car headways in oversaturated interrupted flow conditions. Field observations were then used to calibrate a microscopic simulation model. The model was then used to evaluate impact on headways of different levels of congestion and heavy vehicle percentages. Field results show that truck headways are about 2.3 those of passenger cars. The results also show that trucks are 1.5 more likely to be first in a queue when compared to passenger cars and 1.7 times more likely to be in first four vehicles in a standing queue. Passenger cars immediately behind trucks had longer than average headway. The simulation results suggest that PCE increases nonlinearly with increase in congestion level and with percentage of trucks; PCE's increase becomes less marked once sever congestion (stop-and-go with increasing queue lengths) conditions set in.

The impact of optimal rail access charges on frequencies and fares

Sweden has been a front runner in vertical separation. We use data from the business long-distance corridor in Sweden to calibrate and define a demand and supply model. We simulate how the profit, welfare, fares, frequencies, modal shares and train size depend on the level of the track charges. We simulate the welfare optimal track charges, given different levels of congestion on the track, hence using the charges as a pricing instrument to allocate the train slots efficiently. We find that increases in charges have a limited impact on fares but larger impacts on the frequency. When the length of the trains can be extended and when the crowding penalty is high, the impact of higher track charges on the frequencies is larger. Higher track charges increase the length of the trains if possible. The intermodal competition from road and air has a significant impact on rail fares.

A Color Histogram Based Large Motion Trend Fusion Algorithm for Vehicle Tracking

Due to the static nature of roadside cameras, targets of images have the characteristics of near big and far small. In a short time, as distances between targets and cameras get more faraway and the pixel ratios of the same target decrease sharply, which leads to a decrease in appearance information. However, the loss of appearance information makes tracking more challenging in vehicle tracking. To solve this problem, we propose a color histogram based large motion trend fusion (LMT-CH) algorithm for vehicle tracking, suitable for the traffic scene taken by the roadside camera. Based on Generalized Intersection-over-Union and Hungarian algorithm, LMT-CH uses targets of the large motion trend combined with the color histogram to modify the associated results. The algorithm includes four key steps: 1) train model based on the EfficientDet framework as an object detector. 2) First, calculate the correlation matrix between two consecutive frames. Second, get the matching results based on the Hungarian algorithm. Then, divide targets into four different sets based on the matching results. 3) Calculate the color histogram and large motion trend of the target in the suspected increase set of current frame's targets. 4) Re-identification trajectories based on large motion trend and color histogram. We evaluate the algorithm for the UA-DETRAC datasets with different levels of congestion. In several experiments, we show that the algorithm can effectively deal with the problem of target occlusion and loss, and it performs well in the ID switch of long-distance targets.

Exploring the biomechanics of walking and crowd “flow”

SummaryCrowd movement simulation models are generally based on aggregated speed and flow data collected more than 50 years ago. There appears to be no validated modelling capability to include the impact of recent and future changes in population demographics, resulting from an ageing population and increasing obesity rates. New analytical approaches and data gathering are required to successfully model crowd movement and safety for current and future generations. This study carried out (a) a review of the primary components of crowd movement, demographics and analytical techniques, (b) prototype experiments to investigate age‐related aspects of space and potential points of contact and (c) a new predictive model for crowd flow analysis based on pedestrian biomechanics and anthropometric data. The model uses the physical space taken up by the biomechanical walking process and the spatial buffer between points of potential contact with other pedestrians to predict the speed of movement at different levels of congestion. The new analytical model was used to predict single file speeds (for people with different demographics in congested space), which compared well with published experimental data. The next steps for model development for wider “flows” and additional experiments to provide data sets for wider demographics are also proposed.

Traffic Management System in Abuja City Center, using Geographic Information Systems (GIS) and Global Positioning System (GPS). A case study of FCT, Nigeria

In any city in the world, moving from one point to another is an endurance task. Regardless of the social status of the traveller, the situations under which persons travel are becoming challenging gradually. The condition of Abuja traffic is no diverse. Efforts were made in this study, to map traffic conditions along selected corridors of Federal Capital Territory (FCT) Abuja using Geographical Information System (GIS) and Global Positioning System (GPS) Techniques. The primary data used includes the geographical coordinates of the chosen traffic corridors using GPS, the extent of vehicle traffic congestion and traffic counts along the corridors. Whereas the secondary data used are Abuja FCT’s topographical and road map from the Abuja Geographical Information System (AGIS). The data obtained were entered and used to develop an information system for traffic conditions (TCIS). Data recovered and attribute spatial analysis were displayed using ArcGIS 10. The results were presented in a map format, making it easy to interpret and quick to make decisions. The results display different levels of congestion and vehicular volume along digital traffic corridors. The results will be beneficial for agencies in charge of traffic conditions planning and management in Abuja FCT. GIS-GPS are powerful tools for both spatial and non-spatial data analyses. From the analysis, it can provide traffic data (Speed, Level of Service, Traffic and Delay Time) to transport planners in road controlling authorities in order to understand congestion better, determine its causes and prioritise investment where it is most needed.

A multi-criteria multi-commodity flow model for analysing transportation networks

This article proposes a novel multi-criteria multi-commodity network flow (MCMCNF) model to help transport planners and other analysts holistically assess different types of transportation systems (TS). This model provides a tool to autonomously analyse the effect of expansions, tolls, different levels of congestion and accidents leading to potential insights into a network's resilience and vulnerability, emissions distribution and risk. Unlike the mono criterion network flow models used for some time, we propose the application of multiple objectives. In this article we investigate the application of two objectives. The first maximises the flow of commodities and the second minimises travel related costs. The travel cost is modelled generically and may include the distance travelled, travel time and access charges. The considered cost function is non-linear, so different linearization strategies are suggested. These permit the model to be solved efficiently using Separable Programming techniques and the ɛ-constraint method (ECM). We have applied the proposed model to a variety of case studies and demonstrate how different forms of sensitivity analysis can be performed. The numerical investigations have highlighted the specific features of the Pareto frontiers and the resilience and flexibility of the networks considered.

A traffic control framework for urban networks based on within-day dynamic traffic flow models

ABSTRACTThe paper aims to provide a further development of traffic control strategies, to propose an enhanced version of two traffic flow models and to integrate these models within an urban traffic control framework. Concerning the traffic control method, the synchronisation approach is adopted and three objective functions are considered and compared: two are mono-criterion and the third is multi-criteria. Simulated annealing and multi-objective simulated annealing are adopted as a solution algorithm. In terms of traffic flow representation the approaches analysed are macroscopic cell-based and mesoscopic link-based, both able to model path choice behaviours and vehicle dispersion phenomena. Furthermore, traffic flow prediction is pursued through a Kalman filter and a rolling horizon approach is adopted as a forecasting framework for the optimisation procedure. In order to test the framework and to compare two traffic flow models, a 15-node grid network was considered, including different levels of congestion and demand profiles.

Enhancing average speed emission models to account for congestion impacts in traffic network link-based simulations

This study aims to demonstrate a new approach for estimating fuel consumption from vehicles circulating on urban roads with different levels of congestion. A microscopic dynamic traffic model (AIMSUN) was coupled with an instantaneous emissions model (CRUISE) and their output was used to refine the average speed model (COPERT) functions. The integrated methodology was applied in an urban corridor in Turin using two validated vehicle models of Euro 5 passenger cars. Three traffic conditions were examined, corresponding to free-flow, normal and congested traffic. New average speed – fuel consumption functions were developed for free and congested traffic, which successfully estimated the differences in fuel consumption when moving from normal to other conditions. The results showed that under congested conditions the fuel consumption can increase by more than 18% in some cases, indicating the significance of incorporating similar congestion algorithms in macro emission models. Moreover, if a more comprehensive method does not exist, the standard COPERT functions could be used to estimate fuel consumption under congested traffic conditions.

Evaluation of the Operational Performance of Continuous Green T-Intersection under Different Levels of Congestion

The main objective of this paper is to evaluate and compare the operational efficiency of a conventional signalized T-intersection with an unconventional Continues Green T-intersection under different congestion levels. The analysis was performed using Synchro.8 micro-simulation software. A total of 48 hypothetical scenarios, 24 scenarios for each design, were created by changing the approach volumes and turning percentages on the major / minor intersecting roadways to reflect different levels of congestion that may occur on any urban intersection. Total intersection delay, Level of Service, maximum queue length and volume-to-capacity ratio (v/c) were the measures of effectiveness used for comparison purposes. These performance measures were selected because they demonstrated the overall efficiency of the intersection design. The simulation results showed that the Continuous Green T-intersection operates the best under stable traffic conditions and that it is not an effective solution for signalized T-intersections under heavy traffic volume.

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

Using Traffic Micro-Simulation to Test Route Diversion as a Real-Time Crash Prevention Strategy on Freeways

The purpose of this study is to examine the potential of route diversion for reducing the real-time crash risk along a typical urban freeway. The PARAMICS micro-simulation software package was used to simulate the Interstate-4 corridor in Florida. The crash risk along the freeway was determined using models created by the authors that yielded values of both the rear-end and lane-change crash risks using real-time loop detector data. Route diversion was applied at different network loading levels to determine the effects of diverting vehicles at different levels of congestion. The study shows that route diversion is an effective crash prevention strategy when the freeway is operating in uncongested conditions. In this situation, the crash risk decreases between the locations where vehicles are diverted from and where the diverted vehicles re-enter the freeway. In general, diverting more vehicles and diverting vehicles further downstream serves to improve the overall safety benefits experienced. The only downside to diverting more vehicles or diverting them further downstream is the increased overall travel time; however, the travel time increase encountered in this study was within acceptable limits of about 5% during the uncongested conditions.

Detection of traffic congestion and incidents from GPS trace analysis

This paper presents an expert system for detecting traffic congestion and incidents from real-time GPS data collected from GPS trackers or drivers’ smartphones. First, GPS traces are pre-processed and placed in the road map. Then, the system assigns to each road segment of the map a traffic state based on the speeds of the vehicles. Finally, it sends to the users traffic alerts based on a spatiotemporal analysis of the classified segments. Each traffic alert contains the affected area, a traffic state (e.g., incident, slowed traffic, blocked traffic), and the estimated velocity of vehicles in the area. The proposed system is intended to be a valuable support tool in traffic management for municipalities and citizens. The information produced by the system can be successfully employed to adopt actions for improving the city mobility, e.g., regulate vehicular traffic, or can be exploited by the users, who may spontaneously decide to modify their path in order to avoid the traffic jam. The elaboration performed by the expert system is independent of the context (urban o non-urban) and may be directly employed in several city road networks with almost no change of the system parameters, and without the need for a learning process or historical data. The experimental analysis was performed using a combination of simulated GPS data and real GPS data from the city of Pisa. The results on incidents show a detection rate of 91.6%, and an average detection time lower than 7min. Regarding congestion, we show how the system is able to recognize different levels of congestion depending on different road use.

Joint design of parking capacities and fleet size for one-way station-based carsharing systems with road congestion constraints

This paper formulates one-way station-based carsharing systems as a mixed queuing network model and proposes a profit-maximization model for the joint design of fleet size and station capacities. We explicitly model the road congestion by formulating each route as a queue where the travel time is an increasing function of the state. The booking process is also modeled in the rental station queue so that the efficiency loss caused by the reservation policy can be captured. The mixed queuing network falls into Baskett, Chandy, Muntz and Palacios (BCMP) networks with unique product-form equilibrium distribution. We derive the asymptotic behavior as the parking capacities and fleet size grows, and show that the performance of carsharing systems will be proportionally bounded by that of the bottleneck route. The exact mean value analysis (MVA) algorithm and the approximate Schweitzer-Bard mean value analysis (SB-MVA) algorithm are extended here to solve networks with different sizes. The numerical experiments reveal some interesting findings: (1) The higher customer service rate (the smaller pick-up time window) will generate the optimal design with lower parking capacities and lower fleet size; (2) Neglecting the efficiency loss due to reservation will lead to an overestimate of the profit and other system performances as well; and (3) Given different levels of congestion on the existing road network (the non-shared car traffic), the net revenue is maximized when the existing traffic congestion is moderate.

The treatment of uncertainties in reactive pollution dispersion models at urban scales

The ability to predict NO2 concentrations ([NO2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street-scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties which should be taken into account when using models within decision making. This paper presents an analysis of mean [NO2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, and a Reynolds averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. The analysis focuses on the sensitivity of predicted in-street increments of [NO2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO + O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO2], particularly for regions of high NOx emissions such as close to traffic queues.

Applying Projection-Based Methods to the Asymmetric Traffic Assignment Problem

This article examines the application of a path-based algorithm to the static and fixed demand asymmetric traffic assignment problem. The algorithm is of the simplicial decomposition type and it solves the equilibration or master problem step by means of five existing projection methods for variational inequality problems to evaluate their performance on real traffic networks. The projection methods evaluated are: (1) a cost approximation-based method for minimizing the Fukushima's gap function, (2) the modified descent method of Zhu and Marcotte (1988), (3) the double projection method of Khobotov (1987) and three of its recently developed variants (Nadezhkina and Takahashi, 2006; Wang et al., 2010; and He et al., 2012); (4) the method of Solodov and Svaiter (1999); and (5) the method of Solodov and Tseng (1996). These projection methods do not require evaluation of the Jacobians of the path cost functions. The source for asymmetries are link costs with interactions, as in the case of priority ruled junctions. The path-based algorithm has been computationally tested using the previous projection methods on three medium to large networks under different levels of congestion and the computational results are presented and discussed. Comparisons are also made with the basic projection algorithm for the fixed demand asymmetric traffic assignment problem. Despite the lack of monotonicity properties of the test problems, the only method that failed to converge under heavy congestion levels was the basic projection algorithm. The fastest convergence was obtained in all cases solving the master problem step using the method of He et al. (2012), which is a variant of Khobotov's method.