Congestion Events Research Articles

HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

Abstract. Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

Modelling the impact of traffic incidents on travel time reliability

Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics.

In Vivo Evaluation of Active and Passive Physiological Control Systems for Rotary Left and Right Ventricular Assist Devices.

Preventing ventricular suction and venous congestion through balancing flow rates and circulatory volumes with dual rotary ventricular assist devices (VADs) configured for biventricular support is clinically challenging due to their low preload and high afterload sensitivities relative to the natural heart. This study presents the in vivo evaluation of several physiological control systems, which aim to prevent ventricular suction and venous congestion. The control systems included a sensor-based, master/slave (MS) controller that altered left and right VAD speed based on pressure and flow; a sensor-less compliant inflow cannula (IC), which altered inlet resistance and, therefore, pump flow based on preload; a sensor-less compliant outflow cannula (OC) on the right VAD, which altered outlet resistance and thus pump flow based on afterload; and a combined controller, which incorporated the MS controller, compliant IC, and compliant OC. Each control system was evaluated in vivo under step increases in systemic (SVR ∼1400-2400 dyne/s/cm(5) ) and pulmonary (PVR ∼200-1000 dyne/s/cm(5) ) vascular resistances in four sheep supported by dual rotary VADs in a biventricular assist configuration. Constant speed support was also evaluated for comparison and resulted in suction events during all resistance increases and pulmonary congestion during SVR increases. The MS controller reduced suction events and prevented congestion through an initial sharp reduction in pump flow followed by a gradual return to baseline (5.0 L/min). The compliant IC prevented suction events; however, reduced pump flows and pulmonary congestion were noted during the SVR increase. The compliant OC maintained pump flow close to baseline (5.0 L/min) and prevented suction and congestion during PVR increases. The combined controller responded similarly to the MS controller to prevent suction and congestion events in all cases while providing a backup system in the event of single controller failure.

S-TFRC: An efficient rate control scheme for multimedia handovers

The integration of heterogeneous wireless access technologies has numerous issues for multimedia applications, such as to smoothly continue over new connections without any service disruption during vertical handovers. We propose State-Aware Feedback extension to Datagram Congestion Control Protocol (DCCP) that meets QoS requirements of multimedia applications throughout the handover process. We consider movement of mobile subscribers among heterogeneous access technologies from highly unstable to more stable environment and model their mobility patterns as Uniform, Pareto, and Exponentially distributed. We present detailed analysis on the performance of proposed mechanism in terms of resuming transmissions during the handover process. In particular, we propose Markov model of TFRC capturing TCP timeouts, loss rates, and no-feedback timer expirations. We validate the proposed analytical model through simulations and show that it accurately predicts various congestion events that may happen during the handover process. We then evaluate the performance of S-TFRC via both simulations and analytical model and observe the sender rates and throughputs achieved. We also consider transmission delays and transmission rates as performance metrics and compare performance of S-TFRC with the standard TFRC. Our results show that S-TFRC is capable of providing better QoS to multimedia applications than standard TFRC by significantly reducing transmission delays and provides better throughput to multimedia applications.

Evaluation of Driver Compliance to Displayed Variable Advisory Speed Limit Systems: Comparison between Germany and the U.S.

Variable Speed Limit (VSL) and Variable Advisory Speed (VAS) systems are applications of a growing field of active traffic management systems (ATM). This technology aims to improve safety while reducing congestion and emissions. VSL is common on German freeways, harmonizing traffic flow during congestion and weather events. Portland, Oregon installed a VAS system (advisory meaning it is not automatically enforced) on an eleven km (7 mile) segment of heavily congested urban freeway. The Portland region maintains archived, high-resolution data of both VAS sign messages and speed detection loop feedback, permitting reconstruction of traffic and sign data. This work analyses over 30 days of archived data from the Portland site in order to study driver compliance to the VAS signs. The focus is to suggest methods and parameters to score system performance. Such an analysis could benefit new rollouts of VAS corridors by providing system performance feedback and shed light on options for improving system performance.

Work Zone Management in the District of Columbia: Deploying a Citywide Transportation Management Plan and Work Zone Project Management System

In a growing and congested capital city, the challenges of maintaining traffic during construction of a multimillion dollar transportation program are immense. Managers are tasked with budgeting, planning, designing, procuring, constructing, and monitoring hundreds of projects in a dynamic and politically charged environment. Critically important infrastructure, utility, maintenance, and developer projects—as well as capital area special events—are frequently on a national (or international) stage with a high risk of traffic congestion, safety concerns, and negative public perception. Washington, D.C., is often listed as one of the top 10 tourist destinations in the United States. With a growing resident population, commuters nearly double the number of people in the city each weekday. In addition, Washington, D.C., is often listed as the most congested urban area in the nation. Aside from recurring congestion, project construction and special events frequently strain the existing roadway network. A proactive, citywide approach to managing all work zones is helping to reduce congestion and provide the District Department of Transportation with better control of how best to schedule and approve projects, as well as budget for cost-effective strategies to mitigate work zone congestion. A unique Citywide Transportation Management Plan, including a dynamic Work Zone Project Management System, has been deployed to coordinate and analyze project work zones and special events throughout the city for the next 5 years. Graphical, web-based software tools track and assess cumulative work zone effects. From these results, mitigation strategies are aimed at improving safety and mobility throughout Washington, D.C.

Non-recurrent traffic congestion detection on heterogeneous urban road networks

This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space–time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network.

Accurate monitoring of intravascular fluid volume: A novel application of intrathoracic impedance measures for the guidance of volume reduction therapy

BackgroundA significant proportion of patients admitted for acute decompensated heart failure (ADHF) that undergo volume reduction therapy are discharged with unchanged or increased bodyweight suggesting that the endpoints for these therapies are not optimally defined. We aimed to identify vectors that can help monitor changes in intravascular fluid volume, that in turn may more accurately guide volume reduction therapy. MethodsData from six different impedance vectors and corresponding changes in intravascular volume derived from changes in hematocrit were obtained from 132 clinical congestion events in 56 unique patients enrolled in a multisite trial of early detection of clinical congestion events (DEFEAT PE). Mixed effects regression models were used to determine the relation between changes in impedance derived from six different vectors and changes in intravascular plasma volume. ResultsChanges in impedance were negatively associated with changes in plasma volume. Two vectors, the right atrial ring to left ventricular ring and the left ventricular ring to the right ventricular ring, were most closely associated with changes in intravascular plasma volume. ConclusionImpedance vectors derived from a multivector monitoring system reflect changes in intravascular plasma volume. Two of these vectors most closely track changes in plasma volume and may be used to more accurately guide and optimize volume reduction therapy.

An SDN-Based CDN/ISP Collaboration Architecture for Managing High-Volume Flows

The collaboration of Internet service providers (ISPs) and content distribution network (CDN) providers was shown to be beneficial for both parties in a number of recent works. Influencing CDN edge server (surrogate) selection allows the ISP to manage the rising amount of traffic emanating from CDNs to reduce the operational expenditures (OPEX) of his infrastructure, e.g., by preventing peered traffic. At the same time, including the ISP's hidden network knowledge in the surrogate selection process influences the quality of service a CDN provider can deliver positively. As a large amount of CDN traffic is video-on-demand traffic, this paper investigates the topic of CDN/ISP collaboration from a perspective of high-volume long-living flows. These types of flows are hardly manageable with state-of-the-art Dynamic Name Service (DNS)-based redirection, as a reassignment of flows during the session is difficult to achieve. Consequently, varying load of surrogates caused by flash crowds and congestion events in the ISP's network are hard to compensate. This paper presents a novel approach promoting ISP and CDN collaboration based on a minimal deployment of software-defined networking switches in the ISP's network. The approach complements standard DNS-based redirection by allowing for a migration of high-volume flows between surrogates in the backend even if the communication has state information, such as Hyper Text Transfer Protocol sessions. In addition to a proof-of-concept, the evaluation identifies factors influencing performance and shows large performance increases when compared to standard DNS-based redirection.

Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks

Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on urban road networks. In this paper we propose an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined as those LJTs that are greater than a threshold. The threshold is calculated by multiplying the expected LJTs with a congestion factor. To evaluate the effectiveness of the proposed NRC detection method, we propose two novel criteria. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be associated with incidents. The proposed NRC detection methodology is tested for London’s urban road network. The optimum value of the congestion factor is determined by sensitivity analysis by using a Weighted Product Model (WPM). It is found out those LJTs that are at least 40% higher than their expected values should belong to an NRC; as such NRCs are found to maintain the best balance between the proposed evaluation criteria.

A Simple Closed-Form Approximation for the Packet Loss Rate of a TCP Connection Over Wireless Links

This letter presents a new and simple model for a TCP flow experiencing random packet losses due to both transmission errors and congestion events. From this model, we will derive a straightforward expression of a unified loss probability (ULP). This ULP gives the opportunity to reuse classical analytic models to analyze the performance of TCP and to size the buffer to optimize the wireless link utilization. Extensive simulations using TCP Reno in ns-2 demonstrate that our model is valid not only for the extreme cases where either transmission errors or congestion losses dominate but also in the situations where both types of losses are significant.

Analysis of Different Device-Based Intrathoracic Impedance Vectors for Detection of Heart Failure Events (from the Detect Fluid Early from Intrathoracic Impedance Monitoring Study)

Detect Fluid Early from Intrathoracic Impedance Monitoring (DEFEAT-PE) is a prospective, multicenter study of multiple intrathoracic impedance vectors to detect pulmonary congestion (PC) events. Changes in intrathoracic impedance between the right ventricular (RV) coil and device can (RVcoil→Can) of implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy ICDs (CRT-Ds) are used clinically for the detection of PC events, but other impedance vectors and algorithms have not been studied prospectively. An initial 75-patient study was used to derive optimal impedance vectors to detect PC events, with 2 vector combinations selected for prospective analysis in DEFEAT-PE (ICD vectors: RVring→Can + RVcoil→Can, detection threshold 13 days; CRT-D vectors: left ventricular ring→Can + RVcoil→Can, detection threshold 14 days). Impedance changes were considered true positive if detected <30 days before an adjudicated PC event. One hundred sixty-two patients were enrolled (80 with ICDs and 82 with CRT-Ds), all with ≥1 previous PC event. One hundred forty-four patients provided study data, with 214 patient-years of follow-up and 139 PC events. Sensitivity for PC events of the prespecified algorithms was as follows: ICD: sensitivity 32.3%, false-positive rate 1.28 per patient-year; CRT-D: sensitivity 32.4%, false-positive rate 1.66 per patient-year. An alternative algorithm, ultimately approved by the US Food and Drug Administration (RVring→Can + RVcoil→Can, detection threshold 14 days), resulted in (for all patients) sensitivity of 21.6% and a false-positive rate of 0.9 per patient-year. The CRT-D thoracic impedance vector algorithm selected in the derivation study was not superior to the ICD algorithm RVring→Can + RVcoil→Can when studied prospectively. In conclusion, to achieve an acceptably low false-positive rate, the intrathoracic impedance algorithms studied in DEFEAT-PE resulted in low sensitivity for the prediction of heart failure events.

ABEONA Monitored Traffic: VANET-Assisted Cooperative Traffic Congestion Forecasting

The existing mechanisms to monitor vehicular traffic, such as the use of induction loops and cameras, are expensive to deploy and maintain. Vehicular communications opens up a new world of optimization opportunities as each vehicle can be used as a sensor to measure the fundamental variables defining the traffic state (flow, density, and speed). In this article, we propose ABEONA, a beacon-based traffic congestion algorithm and also the name of the Roman goddess of journey, which captures the current and recent-past traffic trends to forecast the near-future road conditions. Compared to the existing monitoring approaches, ABEONA allows for the estimation of the vehicular density and reduces installation and maintenance costs. ABEONA's algorithm incurs low overhead and enables drivers to use forecast traffic congestion events to replan their route accordingly.

Identifying Urban Traffic Congestion Pattern from Historical Floating Car Data

With the increasing amount of traffic information collected through floating car data, it is highly desirable to find meaningful traffic patterns such as congestion patterns from the accumulated massive historical dataset. It is however challenging due to the huge size of the dataset and the complexity and dynamics of traffic phenomena. A novel floating car data analysis method based on data cube for congestion pattern exploration is proposed in this paper. This method is different from traditional methods that depend only on numerical statistics of traffic data. The view of the event or spatial-temporal progress is adapted to model and measure traffic congestions. According to a multi-dimensional analysis framework, the traffic congestion event is first identified based on spatial-temporal related relationship of slow-speed road segment. Then, it is aggregated by a cluster style to get the traffic pattern on a different level of detail of spatial-temporal dimension. Aggregated location, time period and duration time for recurrent and important congestions are used to represent the congestion pattern. We evaluate our methods using a historical traffic dataset collected from about 12000 taxi-based floating cars for one week in a large urban area. Results show that the method can effectively identify and summarize the congestion pattern with efficient computation and reduced storage cost.

A multilevel information fusion approach for road congestion detection in VANETs

As city road congestion problems become more serious, many researchers have started to use the technique of vehicle ad hoc networks (VANETs) for road congestion detection. However, various on-board sensors equipped in vehicles may generate lots of atomic messages, which usually cause serious channel competition problems. In this paper, we propose a multilevel information fusion approach by combining the fuzzy clustering-based feature level information fusion (FCMA) and the modified Dempster–Shafer evidence reasoning-based decision level information fusion (D-SEMA). The FCMA can extract the key features from atomic messages, thereby greatly reducing the network traffic load. Furthermore, the D-SEMA mechanism is used to judge whether the road congestion event occurs. Performance analysis and simulation results under ONE simulator show that the proposed multilevel information fusion approach can detect road congestion efficiently with low bandwidth consumption.

Traffic Congestion and Social Media in China

Research on social media has been applied to various academic fields. During this year's Chinese National Holiday traffic congestion event, online users showed great enthusiasm on social media, such as forums, Weibo, communities, and other platforms. This article describes the construction of a dynamic evolution network, analyzes the transformation of online users' concentration, and studies the geographic distribution of travelers by analyzing online users' attributes.

Congestion Control in 4G Wireless Networks Using Coalition Game

In 4G-domain multi home mobile host (MH) will have access to different wireless networks. In this 4G wireless networks real time applications are initiated to support services like VOIP, videoconference. These applications use RTP over UDP. These protocols are unresponsive to congestion events. Our proposed congestion management scheme minimizes congestion level in 4G domains using coalition game. Its main objective is to decide target networks for handoff with minimum congestion level. Our mechanism is based on the coalition game formation. It aims at maximizing the utilization of the resources available and meeting QoS requirement of users as much as possible. This will reduce congestion level with vertical handoff initiation.

Time Series Analysis and Models of Freeway Performance

AbstractThis paper describes a study whose primary purpose is to better understand the relationship between freeway traffic flow and speed. Incidents of recurrent and nonrecurrent congestion were encountered at six radar collection northbound locations on New Hampshire interstate I-93 in July 2010. The root cause for the onset of the recurrent congestion is explained with exploratory data analyses and a time series modeling approach. A complex combination of present and past values of traffic flow, speed, and congestion state, a congestion history of lingering effect variables, can explain the triggering and mitigation of congestion events for a highly volatile traffic environment. The approach includes two mathematical models: (1) a generalized additive binomial model to forecast the probability of congestion and (2) state-space models of speed and flow. The state-space models use a dynamic linear model (DLM) with switching structures to describe the bimodal distribution of speed and flow in the free-flo...

Performance Enhancement of DCCP TCP-like over Long Delay Link Networks

The performance of DCCP TCP-like degrades significantly over long delay link networks. Despite the TCP-like congestion control mechanism follows the TCP SACK, the performance is really affected by the congestion window growth algorithms as employed by Jacobson based TCP variants. In this paper, all the experiments are done using Network Simulator ns-2, and we manipulated the congestion window size drop during congestion avoidance phase to enhance the performance of DCCP TCP-like over long delay link networks. Instead of halving the current congestion window when congestion events are detected, the reduction of current congestion window drop has been shown to improve the DCCP TCP-like throughput with minimal drop packet percentage.

Relating Travel Time Reliability and Nonrecurrent Congestion with Multistate Models

The measurement of travel time reliability is of increasing importance as agencies seek to understand better the performance of their facilities and communicate trip expectations to the traveling public. Recent research suggests that modeling historical travel times with multistate distributions can add value to the process of monitoring reliability by linking travel times with the underlying traffic state. Although that research demonstrated the feasibility of breaking down travel time distributions into different operational states, the researchers did not evaluate the causal factors of the states. Models that link the travel time states of agencies' facilities with the factors that cause the time states, such as incidents or special events, allow agencies to predict travel times more accurately when these events occur in real time and enable agencies to develop targeted projects for the improvement of reliability over the long term. This paper extends existing reliability modeling in three ways. First, where past research has used bimodal distributions to model the two primary operating regimes (free flow and congested), this paper presents a methodology for determining the optimal number of states for modeling data to ensure that the distinct effects of nonrecurrent congestion events are captured. Second, the paper presents a process that is easily implemented for determining when and how the different sources of nonrecurrent congestion influence the travel time state and its variability. Last, with the development of multistate models for aggregate travel times, the paper proves that a methodology originally developed to model individual vehicle trips can be applied to a broader array of data sets.