Congestion Duration Research Articles

Estimating the Value of Time Considering Perceived Congestion and Congestion Duration on Urban Rail Transit

Estimating the Value of Time Considering Perceived Congestion and Congestion Duration on Urban Rail Transit

Establishment and analysis of an animal model for nutcracker syndrome.

This study investigated the effects of renal vein congestion of varying durations on kidney damage by establishing an animal model of Nutcracker Syndrome (NCS). In addition, we assessed whether renal damage improved after relieving the congestion. We established a rat model of NCS and relieved renal congestion through secondary surgery, with renal vein reperfusion confirmed by animal ultrasound. Histopathological changes in congested kidneys were evaluated at 3, 6, and 9days using HE and Masson staining, and the expression of renal injury factors was determined using q-PCR. Renal tissue pathology changes were evident with prolonged obstruction time. Compared to the sham-operated group, no apparent fibrosis was observed in the kidneys at 3days post-congestion, whereas fibrosis was most severe at 9days post-congestion, with no significant improvement observed even after blood flow restoration. Six days post-congestion relief, some alleviation of renal fibrosis occurred. q-PCR results indicated a significant reduction in the expression of fibrosis markers after 3 or 6days of renal vein ligation release. Following blood flow restoration on the 9th day post-congestion, no significant changes in fibrosis markers were noted. Prolonged renal vein congestion in a rat model of NCS resulted in severe kidney damage and significant fibrosis after 9days. While relieving congestion within 6days led to some improvement, no reduction in fibrosis occurred after 9days, underscoring the importance of congestion duration in renal pathology.

Congestions and spectral transitions in time-lagged correlations of motorway traffic

The understanding of congestions contributes to the development of effective traffic management strategies. The propagation of congestions from a motorway section to the neighboring ones results in correlations. Here, we study symmetrized time-lagged correlation matrices and show how their spectral properties reveal congestion durations. We first carry out an empirical analysis of velocities for two local motorway networks, then set up a numerical simulation for indicator time series of traffic phases, and further propose a simplified, analytical model capturing various scenarios of congestion durations. Our empirical analysis reveals a transition behavior for the dominant eigenvalue as function of time lags, reflecting changes in traffic dynamics. Furthermore, both the numerical simulations and the analytical model disclose a nonlinear relation between the spectral transition and the congestion duration.

Assessing the impact of vaccination and medical resource allocation on infectious disease outbreak management: a case study of COVID-19 in Taiyuan City.

Amidst an emerging infectious disease outbreak, the rational allocation of vaccines and medical resources is crucial for controlling the epidemic's progression. Analysing COVID-19 data in Taiyuan City from December 2022 to January 2023, this study constructed a dynamics model to assess the impact of COVID-19 vaccination and resource allocation on epidemic trends. Vaccination significantly reduces infection rates, hospitalisations, and severe cases, while also curtailing strain on medical resources by reducing congestion periods. An early and sufficient reserve of medical resources can delay the onset of medical congestion, and with increased maximum capacity of medical resources, the congestion's end can be accelerated. Stronger resource allocation capabilities lead to earlier congestion resolution within a fixed total resource pool. Integrating vaccination and medical resource allocation can effectively reduce medical congestion duration and alleviate the epidemic's strain on medical resource capacity (CCMR).

A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with re-routing since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of re-routing. Against this backdrop, a Prediction-based Route Guidance Method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: perception about traffic congestion with three indexes (i.e., speed index, dense index, acceleration index); judgment about congestion type with four defined congestion types; prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); route planning about vehicles considering congestion duration and the extra time overhead of re-routing. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

Blockchain-Enabled Online Traffic Congestion Duration Prediction in Cognitive Internet of Vehicles

The real-time intelligent perception and prediction of traffic situation can assist connected automated vehicles (CAVs) in path planning and reduce traffic congestion in Cognitive Internet of Vehicles (CIoVs). The centralized traffic congestion prediction solutions generally fail to adapt to the dynamic traffic environment and lead to significant communication overheads. Blockchain technology has attracted great attention in the information sharing of vehicular networks for its advantages in decentralization, transparency, traceability, and tamper-proof capability. However, due to the bottlenecks, such as high computational cost, current blockchains are incapable actuate on efficient online traffic situational cognition and prediction for CIoVs. Motivated by this, we propose a blockchain-enabled cognitive segments sharing framework for online multistep congestion duration prediction. We design a cognitive model of traffic situation based on anomaly detection and filtering mechanism to guarantee the accuracy of the cognitive segments before being packaged into the block. Furthermore, to improve the consensus efficiency, we design a credit evaluation mechanism and propose a credit-based delegated Byzantine fault tolerance (CDBFT) algorithm. Finally, we propose an online multistep prediction algorithm based on long short-term memory (LSTM) to predict future traffic congestion duration. Experimental results demonstrate that the proposed algorithms achieve shorter consensus latency and higher predictive accuracy than the existing algorithms.

Variable Speed Limit Strategy With Dynamic Control Cycle Based on Predictive Control

In recent years, variable speed limit (VSL) strategies have proven to be an efficient control measure to mitigate traffic congestion at freeway bottlenecks. In general, the VSL change time is a constant value. However, there are certain limitations in traffic situations handled by a constant cycle VSL. Therefore, in this paper, we develop a dynamic cycle strategy of VSL based on predictive control. We first analyze the applicable situation of the dynamic control cycle and establish a probability model to determine the range of cycle selection. Then, the cell transmission model predicts the parameters of macroscopic traffic flow. Finally, an optimization algorithm is designed that is suitable for this strategy, which optimizes the cycle and speed limit options. An objective optimization function is formulated to minimize the total travel time. A sensitivity analysis is applied to compare different control strategies under a variety of road bottleneck structures by both the numerical analysis and simulation experiments. The simulation results show that the strategies and algorithms proposed in this paper can effectively reduce traffic congestion duration and enhance the service level of a freeway network.

A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio

• Cross-resolution modelling approach for understanding the dynamic relationship between demand and supply and the resulting congestion. • Describe oversaturated system dynamics with parsimonious macroscopic analytical formulations with consistent mesoscopic queue vehicular fluid model. • Unified integration of multi-scale models provides city planners with a valid analytical framework to analyze queue saturation evolution process. • Estimate key model parameters from real-world data sets on heavily congested corridors. Although the macroscopic volume-delay function (VDF) has been widely used in static traffic assignment for transportation planning, the planning community has long recognized its deficiencies as a static function in capturing traffic flow dynamics and queue evolution process. In the existing literature, many queueing-based and simulation-based dynamic traffic assignment (DTA) models involving various traffic flow parameters have been proposed to capture traffic system dynamics on different spatial scales; however, how to calibrate these DTA models could still be a challenging task in its own right, especially for real-world congested networks with complex traffic dynamics. By extending the fluid-based polynomial arrival queue (PAQ) model with quadratic inflow rates proposed by Newell (1982) and cubic inflow rates by Cheng et al. (2022), this paper attempts to propose a cross-resolution Queueing-based Volume-Delay Function (QVDF) to explicitly establish a coherent connection between (a) the macroscopic average travel delay performance function in a long-term planning horizon and (b) the mesoscopic dynamic queuing model during a single oversaturated period. By introducing two types of elasticity functional forms, this paper develops a relationship from the macroscopic inflow demand-to-capacity (D/C) ratio to the congestion duration of a bottleneck, from the congestion duration to the magnitude of speed reduction. The QVDF can be directly utilized to provide closed-form expressions for both average travel delay performance and the time-dependent speed profiles. The proposed cross-resolution QVDF provides a numerically reliable and theoretically rigorous performance function to characterize oversaturated bottlenecks at both macroscopic and mesoscopic scales.

Estimating key traffic state parameters through parsimonious spatial queue models

As an active performance evaluation method, the fluid-based queueing model plays an important role in traffic flow modeling and traffic state estimation problems. A critical challenge in the application of traffic state estimation is how to utilize heterogeneous data sources in identifying key interpretable model parameters of freeway bottlenecks, such as queue discharge rates, system-level bottleneck-oriented arrival rates, and congestion duration. Inspired by Newell’s deterministic fluid approximation model, this paper proposes a spatial queue model for oversaturated traffic systems with time-dependent arrival rates. The oversaturated system dynamics can be described by parsimonious analytical formulations based on polynomial functional approximation for virtual arrival flow rates. With available flow, density and end-to-end travel time data along traffic bottlenecks, the proposed modeling framework for estimating the key traffic queueing state parameters is able to systematically map various measurements to the bottleneck-level dynamics and queue evolution process. The effectiveness of the developed method is demonstrated based on three case studies with empirical data in different metropolitan areas, including New York, Los Angeles, and Beijing.

Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach

This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.

The Use of Dynamic Message Signs (DMSs) on the Freeways: An Empirical Analysis of DMSs Logs and Survey Data

This study evaluates the Dynamic Message Signs (DMSs) use to dissipate incident information on the freeways in Las Vegas, Nevada. It focuses on the DMSs message timing, extent, and content, from the operators’ and drivers’ perspectives, considering the variability in drivers’ freeway experience. Two-week incidents data with fifty-nine incidents, DMS log data, and responses from a survey questionnaire were used. The descriptive analysis of the incidents revealed that about 54% of the incidents had their information posted on the DMSs; however, information of only 18.6% of the incidents was posted on time. The posted information covered the incident type (54.2%), location (49.2%), and lane blockage (45.8%), while the expected delay or the time the incident has lasted are rarely posted. Further, the standard DMSs are the most preferred sources of traffic information on the freeway compared to the travel time only DMSs, and the graphical map boards. The logistic regression applied to the survey responses revealed that regular freeway users are less likely to take an alternative route when they run into congestion, given no other information is available. Conversely, when given accurate information through DMSs, regular freeway users are about 2.9 times more likely to detour. Furthermore, regular freeway users perceive that the DMSs show clear information about the incident location. Upon improving the DMSs usage, 73% of respondents suggested that the information be provided earlier, and 54% requested improvements on congestion duration and length information. These findings can be used by the DMSs operators in Nevada and worldwide to improve freeway operations.

Dynamic Control Cycle Speed Limit Strategy for Improving Traffic Operation at Freeway Bottlenecks

In recent years, variable speed limit (VSL) has been used to decrease the total travel time and alleviate the traffic congestion on freeway. To improve traffic efficiency, we proposed a novel VSL strategy considering dynamic control cycle in this study. An extension of the cell transmission model was used to depict the traffic characteristics under VSL control, and the VSL strategy was designed to provide multiple control cycles. A probability formula was developed and used to determine the optimal quantity of control cycles in the VSL strategy. An objective optimization function was formulated with the aim of minimizing total travel time. A sensitivity analysis was applied to compare different control strategies under a variety of road bottleneck structures by both the numerical analysis and simulation experiments. The results show that dynamic control cycle VSL strategy outperformed other control scenarios and effectively reduced traffic congestion duration and enhanced the service level of freeway network.

Traffic Congestion and Duration Prediction Model Based on Regression Analysis and Survival Analysis

With the current situation of traffic congestion becoming more and more serious, how to accurately predict the time of traffic congestion has been widely concerned. In this article, we will build two models to better predict traffic congestion time. First, we use methods to collect the data we need, and through the preliminary cleaning, processing, deletion of missing data, combined calculation of data according to indicators and other steps to screen and integrate the data we need. Then, the multivariate linear regression method is used to construct the traffic prediction congestion model for the existing data, and the actual situation of traffic congestion is obtained. Secondly, the non-parametric method Kaplan-Meier model in the survival analysis method is used to obtain the survival function of traffic congestion duration, and the traffic congestion duration model is constructed. The software programming is solved by MATLAB, Stata, SPSS, etc., and the congestion prediction is obtained. The fitting degree between the predicted value and the actual value of the model is above 0.96, which can better quantify the conclusion that the road traffic operation congestion degree and congestion duration model can identify the characteristics of congestion distribution and duration. Finally, the paper evaluates the advantages and disadvantages of the model objectively, and considers the aspects that can be promoted and applied. I hope that this model can contribute to the prediction research of traffic congestion time!

Spatiotemporal Congestion Recognition Index to Evaluate Performance under Oversaturated Conditions

We developed a congestion index for traffic management that uses link travel time based on global positioning system probe vehicle data. The congestion index can be used to evaluate the performance of an arterial network under oversaturated conditions. It considers congestion intensity and congestion duration, unlike existing congestion indices. After investigating current big traffic data, we selected the average link travel speed that could be collected at all times over a wide range to develop the congestion index. We used extra travel time as a new measure of effectiveness. The congestion criterion for monitoring oversaturated conditions was set as extra travel time with three congestion levels when traffic flow exceeded capacity. We devised the new congestion index by reflecting the three congestion levels to monitor oversaturated conditions macroscopically and to examine congestion characteristics microscopically. A spatiotemporal congestion recognition index (SCRIN) was defined based on congestion intensity and congestion duration. We analyzed SCRIN to monitor the traffic congestion of Seoul city using the data on travel speeds from the seoul transport operation and information service (TOPIS) for 2016.

Research on road traffic flow status based on survival analysis

: Based on the data of bayonet and floating car, this paper studies the traffic flow state of the main roads in Shenzhen, analyses the relationship between speed and flow, and establishes a speed-flow model. It studies the characteristics of traffic flow in Shenzhen and the overall distribution of congestion duration. The results demonstrate that there is a strong correlation between flow and speed, which have obvious characteristics of morning and evening peak within a day, and the flow and speed show significant differences between work days and non-work days. As for the congestion duration of each road, they are generally under 16 minutes. Specifically, when the congestion duration is less than 8 minutes, there is a high probability that the congestion state ends; while the congestion lasts more than 10 minutes, the congestion status is less likely to end in a short time. In view of the above analysis, it is recommended that relevant departments strengthen monitoring and control of real-time road traffic so that the traffic environment in Shenzhen can be improved.

Evaluation of Ramp Metering Impacts on Travel Time Reliability and Traffic Operations through Simulation

Ramp metering has been found to improve traffic conditions on the freeway mainline by breaking the platoons of ramp vehicles minimizing turbulence at the merge locations. The majority of the ramp metering evaluation studies have examined traffic performance under specific demand conditions, whereas travel time reliability and variability aspects have not been adequately addressed. This paper focuses on evaluating two well-known ramp metering algorithms in terms of travel time reliability as well as other performance measures such as queue lengths, throughput, and congestion duration, looking at a wide range of traffic demands throughout a calendar year. The evaluation was done through simulating an 8-mile corridor in Kansas City, KS. The results showed localized improvements due to ramp metering at the northern section of the facility, in terms of travel time reliability, throughput, and congestion duration. It was also shown that ramp metering may cause a new (possibly “hidden”) bottleneck to occur downstream, thus diluting its overall benefits when looking at an entire freeway facility. It is further noted that although ALINEA performed better than HERO on the mainline, traffic operations on the on-ramps significantly deteriorated using isolated control.

Study on the vehicle routing problem considering congestion and emission factors

To meet the requirement of greening transportation in poor traffic condition, vehicle routing problem (VRP) with consideration of fuel consumption and congestion is studied. We formulated a time-dependent green vehicle routing problem (TD-GVRP) model with minimised total cost as the objective function which includes fuel consumption cost, and the measurement of fuel consumption is based on the Comprehensive Modal Emissions Model (CMEM). In the model, the situation of waiting at customer nodes to avoid bad traffic is defined. To solve this model, a Response Surface Method (RSM)-based hybrid algorithm (HA) that combines genetic algorithm (GA) and particle swarm optimisation (PSO) is constructed. Finally, using instances from PRPLIB database, the following experiments are carried out and the corresponding conclusions are drawn. (i) Comparison of the proposed objective and traditional VRP objectives shows that fuel consumption can be greatly reduced by introducing fuel consumption factor into the objective function. (ii) Sensitivity analysis of congestion duration provides the influence of congestion duration on fuel consumption and travel time. (iii) Experiments based on different waiting time reveal that the optimisation of departure time can reduce fuel consumption and total cost to some extent.

A linked-bottleneck control method for urban expressway on-ramp

ABSTRACTIn order to alleviate the congestion in the mainline of urban expressway caused by the uncertainty of the traffic flow, a linked-bottleneck control method for urban expressway on-ramp is proposed in this paper. Firstly, the downstream dynamic critical occupancy of the on-ramp under uncertainty is predicted by using ESN (Echo State Network). Then, a function for dividing the master-slave ramps is designed to dynamically determine the ramp numbers required coordination and expand the range of coordination control. Considering the designed function changes over time and some factors such as the traffic accidents, weather and so on also affect it, the BP neural network is used to improve its accuracy. The proposed method calculates the on-ramp regulating rate based on the downstream dynamic critical occupancy and expand the range of coordination control by the designed function, which can adapt the changes of actual traffic flow and overcome the defects of some methods such as Bottleneck, Linked-control, Swarm and so on. The simulations show that, compared with some existing methods, the congestion duration and the high density duration in the mainline of the urban expressway are significantly reduced by using the proposed method in this paper.

User-centric interdependent urban systems: Using time-of-day electricity usage data to predict morning roadway congestion

Urban systems are interdependent as individuals’ daily activities engage using those urban systems at certain time of day and locations. There may exist clear spatial and temporal correlations among usage patterns across all urban systems. This paper explores such a correlation among energy usage and roadway congestion. We propose a general framework to predict congestion starting time and congestion duration in the morning using the time-of-day electricity use data from anonymous households with no personally identifiable information. We show that using time-of-day electricity data from midnight to early morning from 322 households in the City of Austin, can make reliable prediction of congestion starting time of several highway segments, at the time as early as 2 am. This predictor significantly outperforms a time-series predictor that uses only real-time travel time data up to 6 am. We found that 8 out of the 10 typical electricity use patterns have statistically significant affects on morning congestion on highways in Austin. Some patterns have negative effects, represented by an early spike of electricity use followed by a drastic drop that could imply early departure from home. Others have positive effects, represented by a late night spike of electricity use possible implying late night activities that can lead to late morning departure from home.

Construction of intelligent traffic information recommendation system based on long short-term memory

Traffic information service can improve road utilization and reduce traffic congestion and accidents. In this paper, we design the intelligent traffic information recommendation system based on deep learning. The recommendation system first preprocesses the traffic flow data through Internet of Things (IoT) technology, and then it uses the deep learning network to predict traffic parameters. The traffic congestion duration and spatial diffusion evolution trend are predicted respectively based on long short-term memory (LSTM), which is a typical time-recurrent neural network of deep learning. To the best of our knowledge, it is the first time to construct the intelligent traffic information recommendation system to improve the practicality of traffic information service. The experimental results show that the proposed recommendation system can expand the time horizon of traffic congestion prediction and further improve the reliability and predictability of decision-making basis for traffic managers and travelers.