Congestion Patterns Research Articles

A Novel Travel Time Estimation Model for Modeling a Green Time-Dependent Vehicle Routing Problem in Food Supply Chain

In a green time-dependent vehicle routing optimisation problem, if the travel time is not well estimated, the values of the objective function (emissions) for different solutions will be obtained with less accuracy, which leads to wrong decision making. The purpose of this paper is to propose an accurate travel-time estimation model. The model was developed by considering the different multiple traffic modes through a continuous approach. Moreover, in this model, it was assumed that the route between two nodes was a combination of different segments (i.e., expressway, boulevard, main street, auxiliary street, alley, etc.), and the speed level varied along the route and depended on the segments of the route. Congestion is a key factor affecting the delivery time of food to customers. To assess the reliability of the model, data from the delivery and sales network of the oldest and biggest beverage manufacturer in Iran was used. A new congestion pattern was identified based on the analysis of 272 routes and several rules were described that proved the above assumption about congestion. The travel time estimated for 30 random vehicles with random departure times were compared with data from Google Maps through statistical analyses.

Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach

Understanding the spatiotemporal characteristics of traffic congestion is the cornerstone of generating traffic management and control strategies. Based on the large-scale taxi trajectory data in Shenzhen, China, this study designs an effective framework to explore the spatiotemporal patterns of traffic congestions. To bridge trajectory data with urban road networks, we develop a two-stage map-matching method from the aspects of distance and angle. Then, the free-flow speed of each road segment is extracted and employed to identify traffic congestion. In this way, a novel complex network method, named chronological network (chronnet), is utilized for traffic congestion modeling, and we employ an overlapping community detection algorithm to identify region-level bottlenecks. According to the network properties, we explore the influence scope of traffic congestions and uncover the role of each road segment in the propagation process. Meanwhile, community detection results indicate that there are typical local clustering structures in traffic congestions, and each community also has its unique traffic characteristics. Overall, these findings reveal that the complex network can effectively mine the consecutive patterns of traffic congestion.

A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns

The congestion propagation pattern in urban traffic networks has long been viewed as a complex question. Inspired by the fact that virus spread patterns are similar to traffic congestion propagation in some ways, this paper proposes a congestion propagation model (SIS-CP) to describe the congestion propagation patterns in large-scale traffic networks with very few parameters. In this SIS-CP model, the attribute of the real traffic network in the actual operation process is considered, a method based on Hilbert–Huang transform (HHT-based) is proposed to determine traffic states of links, and the congestion propagation patterns are analyzed. The real-world data of ‘DiDi Chuxing GAIA Open Dataset Initiative of Chengdu city from March to June is used to calibrate and validate the proposed models. The results show that the basic and limit value of congestion links’ ratio do exist in real traffic networks; the relationship between congestion links’ ratio and its derivative is conformed to the parabolic function; the HHT-based method can determine traffic states of links accurately; and the SIS-CP model can describe the congestion propagation patterns correctly.

Exploring the spatiotemporal pattern of traffic congestion performance of large cities in China: A real-time data based investigation

The socioeconomic costs of traffic congestion are crippling in urbanising China. This study is a first attempt to explore the spatiotemporal pattern of traffic congestion performance in 77 Chinese large cities by using real-time big data. Based upon the hourly real-time traffic performance index data collected between August 27, 2019 to September 27, 2019, four clusters of cities have been captured in line with their urban traffic congestion pattern between different days of the week. Empirical results unveil that urban traffic congestion performance varies substantially between surveyed cities on the same day of the week. Cities with advanced planning and delivery of urban road network, and well-developed urban public transportation system exhibit better traffic performance. Cities with relatively smaller scale of urban population, higher per capita road area and less amount of vehicles have also achieved relatively smooth traffic congestion performance. Particularly, cities in Northeast China tend to have earlier morning and evening peak hours than other sample cities, and the Northeastern cities are overwhelmingly diagnosed with more severe traffic congestion. Moreover, the traffic congestion patterns of surveyed large cities in China present obvious variations between different days of the week. This study provides valuable lens to understand the variegated pattern of traffic congestion performance between different regions in urban China, based upon which targeted policy recommendations have been synthesised to help alleviate urban traffic congestion and further improve urban well-being across the country.

Assessment of clinical and hemodynamic congestion as predictors of mortality in elderly outpatients with heart failure

IntroductionThis work aims to evaluate whether a clinical examination and measurement of N-terminal pro-brain natriuretic peptide can predict poor prognosis in outpatients with heart failure. Patients and methodsWe carried out a retrospective study from 2010 to 2018 in 238 patients diagnosed with heart failure. At baseline, we evaluated the presence of pulmonary rales and bilateral leg edema (clinical congestion) together with N-terminal pro-brain natriuretic peptide ≥ 1500 pg/mL (hemodynamic congestion). Patients were classified into 4 groups depending on their congestion pattern: no congestion (G1) (n = 50); clinical congestion (G2) (n = 43); hemodynamic congestion (G3) (n = 73); and clinical and hemodynamic congestion (G4) (n = 72). The primary outcome was all-cause mortality at one year of follow-up. ResultsA total of 238 patients were included. The mean age was 82 years, 61.8% were women, and 20.7% had reduced left ventricular ejection fraction. Thirty patients died in the first year of follow-up (12.6%). After controlling for confounding variables (sex, recent discharge for heart failure, estimated glomerular filtration rate, and left ventricular ejection fraction), the independent risk of death in each group compared to G1 as the reference group was: G2: HR 4.121 (95%CI 1.131-15.019); G3: HR 2.511 (95%CI 1.007-6.263); and G4: HR 7.418 (95%CI 1.630-33.763). ConclusionCongestion in outpatients with heart failure correlates with prognosis. Patients with both clinical and hemodynamic congestion had the highest risk of all-cause death at one year.

Temporal Change in Traffic Congestion Pattern of the Intersection at Lucknow Smart City Using Geospatial Technology

Abstract: Traffic congestion is the most common problem in an urban area. The presence of various facilities or intersections of major as well as minor roads can cause hindrance in the path. Congestion on the intersection, near the markets, government offices, etc. can cause problems in the free flow of traffic at an intersection. Road connectivity is also affected due to the presence of congestion on the routes. Traffic congestion varies as time passes, due to an increase in illegal parking, unauthorized vending sites, unauthorized stoppage points for transit vehicles, accidents, etc. therefore there is a change in the traffic pattern at the intersection which affects the public. Keywords: Traffic Congestion, Intersection, Peak hours, Connectivity, Traffic congestion pattern, Level of Service.

Exploiting COVID-19 related traffic changes to evaluate flow dependency of an FCD-defined congestion measure

Traffic congestion poses a significant problem in urban areas globally, and yet no measure of congestion is universally applied. Various studies have evaluated congestion measures, however, none have identified and demonstrated a best-practice congestion metric that can compare congestion between road segments, networks, and city zones. Furthermore, no studies have proven the link between a congestion metric and traffic flow, despite suggestion by Lomax et al. in their seminal 1997 Quantifying Congestion report that an appropriate congestion metric should vary predictably according to flow. This paper aims to address these gaps in traffic congestion literature. Various congestion measures are evaluated according to standard criteria. Although this process has been followed before, this paper adds a unique criterion that requires congestion to be quantifiable from commercial floating car data (FCD), due to its extensive availability and relative affordability. The most appropriate congestion measure is evaluated to be the speed reduction index (SRI). The application of the SRI to describe spatiotemporal congestion patterns and flow dependency is then demonstrated in a case-study analysis in South Africa. This analysis exploited traffic impacts of the COVID-19 pandemic in 2020 (particularly, the stepwise increase from severely reduced traffic flows as lockdown levels eased), to evaluate SRI. The wide range of flows enabled an unprecedented regression analysis comparing congestion level and flow. The results of the regression analysis are highly significant (p < 0.001) indicating that SRI-based congestion measurement tracks flow variation. This study further identified that unidirectional congestion, quantified by the SRI, is impacted by high bi-directional flow along arterials. These findings confirm the appropriateness of the SRI quantified from commercial FCD to measure congestion.

Cellular automaton model with the multi-anticipative effect to reproduce the empirical findings of Kerner’s three-phase traffic theory

As the important spatiotemporal state of traffic flow discovered by Kerner’s three-phase traffic theory, the synchronized traffic flow describes a new traffic phase in congested traffic. However, until now most models within the standard traffic theories cannot reproduce it. The average space gap model (ASGM) is a simple cellular automaton model aimed to reproduce various empirical findings discovered by Kerner’s three-phase traffic theory by incorporating the influence of the multi-anticipative effect on vehicle’s deceleration. However, this paper shows that the simulated synchronized flow by ASGM is not consistent with the reality. To this end, the Multi-Anticipative Model (MAM) based on ASGM is proposed, which describes the influence of the multi-anticipative effect on both the accelerations and the decelerations. Simulations indicate that the empirical consistent synchronized flow and related congested patterns can be well reproduced by MAM. Moreover, MAM can reproduce the speed drop in the car following vehicle platoons reported by the empirical observations. Generally, MAM indicates that the multi-anticipative effect can shed light on the understanding and capturing the complex characteristics of traffic flow especially reported by Kerner’s three-phase traffic theory.

Evaluación del grado de congestión clínica y hemodinámica como predictores de mortalidad en pacientes ambulatorios con insuficiencia cardíaca de edad avanzada

IntroductionThis work aims to evaluate whether a clinical examination and measurement of N-terminal pro-brain natriuretic peptide can predict poor prognosis in outpatients with heart failure. Patients and methodsWe carried out a retrospective study from 2010 to 2018 in 238 patients diagnosed with heart failure. At baseline, we evaluated the presence of pulmonary rales and bilateral leg edema (clinical congestion) together with N-terminal pro-brain natriuretic peptide≥1500 pg/mL (hemodynamic congestion). Patients were classified into 4 groups depending on their congestion pattern: no congestion (G1) (n=50); clinical congestion (G2) (n=43); hemodynamic congestion (G3) (n=73); and clinical and hemodynamic congestion (G4) (n=72). The primary outcome was all-cause mortality at one year of follow-up. ResultsA total of 238 patients were included. The mean age was 82 years, 61.8% were women, and 20.7% had reduced left ventricular ejection fraction. Thirty patients died in the first year of follow-up (12.6%). After controlling for confounding variables (sex, recent discharge for heart failure, estimated glomerular filtration rate, and left ventricular ejection fraction), the independent risk of death in each group compared to G1 as the reference group was: G2: HR 4.121 (95%CI 1.131-15.019); G3: HR 2.511 (95%CI 1.007-6.263); and G4: HR 7.418 (95%CI 1.630-33.763). ConclusionCongestion in outpatients with heart failure correlates with prognosis. Patients with both clinical and hemodynamic congestion had the highest risk of all-cause death at one year.

On the Morning Commute Problem in a Y-shaped Network with Individual and Household Travelers

This paper examines the morning commute problem when both household commuters and individual commuters are considered in a Y-shaped network with two upstream links and a single downstream link. The household parents daily pass through an upstream bottleneck with a limited capacity before a school and drop off their children. Then, they traverse the downstream bottleneck common to both household and individual commuters and arrive at the workplace. We explore the effects of staggering policy, that is, staggering work and school start times, on the distribution of traffic congestion and social welfare. We analytically solve all the equilibrium cases and reveal all the traffic congestion patterns. The results reveal that the staggering policy may be harmful in certain cases. When the demand of individuals is relatively low, the staggering policy may not improve social welfare. When the demand of individuals is high, social welfare can be significantly improved if the schedule gap between the work start time and school start time is optimized. The effects of the staggering policy on system performance are examined. We derive a Pareto frontier, which provides a good candidate set for policymakers when the two system performance measures, that is, the total system cost and the total congestion cost, are considered. Our results show that the optimal staggering policy on system performance depends on the demand distribution of the two groups. When the demand of individuals is high, there exists a unique optimal staggering policy that optimizes system performance. However, when the demand of individuals is low, the optimal staggering policy should be selected from the Pareto frontier. Furthermore, we re-examine the capacity expansion paradox under the staggering policy. Our study shows the capacity expansion at the downstream bottleneck can always reduce the total system cost. However, the paradoxical phenomenon may arise when the capacity of the upstream bottleneck is expanded, but it can be eliminated if the schedule gap is properly designed.

Spatiotemporal analysis of urban road congestion during and post COVID-19 pandemic in Shanghai, China

Coronavirus Disease 2019 (COVID-19) has become one of the most serious global health crises in decades and tremendously influence the human mobility. Many residents changed their travel behavior during and after the pandemic, especially for a certain percentage of public transport users who chose to drive their owned vehicles. Thus, urban roadway congestion has been getting worse, and the spatiotemporal congestion patterns has changed significantly. Understanding spatiotemporal heterogeneity of urban roadway congestion during and post the pandemic is essential for mobility management. In this study, an analytical framework was proposed to investigate the spatiotemporal heterogeneity of urban roadway congestion in Shanghai, China. First, the matrix of average speed in each traffic analysis zones (TAZs) was calculated to extract spatiotemporal heterogeneity variation features. Second, the heterogenous component of each TAZ was extracted from the overall traffic characteristics using robust principal component analysis (RPCA). Third, clustering analysis was employed to explain the spatiotemporal distribution of heterogeneous traffic characteristics. Finally, fluctuation features of these characteristics were analyzed by iterated cumulative sums of squares (ICSS). The case study results suggested that the urban road traffic state evolution was complicated and varied significantly in different zones and periods during the long-term pandemic. Compared with suburban areas, traffic conditions in city central areas are more susceptible to the pandemic and other events. In some areas, the heterogeneous component shows opposite characteristics on working days and holidays with others. The key time nodes of state change for different areas have commonness and individuality. The proposed analytical framework and empirical results contribute to the policy decision-making of urban road transportation system during and post the COVID-19 pandemic.

Proffering Secure Energy Aware Network-On-Chip (Noc) Using Incremental Cryptogine

Network-on-chip (NoC) is an integrated circuit network-based communications subsystem that usually operates between System on chip (SoC) systems. Security risks, including hidden data gathering from IP cores, contributing to unnecessary efficiency and energy degradation, are however revealed. So, to deal with this scenario, a Proffering Secure Energy-Aware Network-on-Chip (NoC) using Incremental Cryptogine is designed as the proposed work for the secure transmission of encrypted data and reducing the power and space consumption to enhance the performance. Unique NoC characteristics are implemented on the network interface for effectual lightweight encryption by Incremental Cryptogine to transmit encrypted data in a secured path. Aberrant Congestion Pattern Detection is used to identify the unusual traffic produced on the data transmission path. Spiking neural networks classifies the traffic and at the destination node, the encrypted data are decrypted into the original packet for secure data transmission. Cognizest Bipartite Buffer develops a unified buffer with the input ports on NoC and reduces an under-utilized buffer space by emphasizing an observation for the switch traversals to attain the competent performance for reducing both power and space consumption. Thus, better accuracy and performance in attack detection is achieved by using an incremental cryptogine to secure the data in Network-on-chip (NoC) thereby reducing both power and space consumption.

Spatio-temporal variations of traffic congestion under work from home (WFH) arrangements: Lessons learned from COVID-19

Traffic congestion has been a persistent problem in cities globally. Theoretically, commuting-related congestion can be relieved by promoting working from home (WFH). Amid the COVID-19 pandemic, WFH arrangement has been encouraged or enforced to reduce the spread of the coronavirus. Under these circumstances, it was reported that traffic congestion has been alleviated in many cities. However, changes in congestion patterns within a city have not been studied in-depth. In this study, we analysed the congestion index (CI) at peak hours, when commuting-related congestion is typically most serious, throughout different waves of the pandemic in Hong Kong. Results show that under WFH arrangement, peak-hour congestion has been alleviated. Within a day, morning peak congestion was more relieved. Spatially, significant drops in CI were found not only in the central business district and urban cores but also in some new town areas. This paper has significant implications for urban planners in creating more sustainable cities that duly consider the commuting needs of residents, and cautions against the optimism that WFH can relieve urban transport problems despite jobs-housing imbalance. While the WFH arrangement has potentials to ease commuting congestion, future e-working and transport measures need to take spatial and temporal dimensions into account.

Road network vulnerability analysis considering the probability and consequence of disruptive events: A spatiotemporal incident impact approach

This paper proposes a new data-driven impact area vulnerability analysis approach that considers both the probability of impact of incidents and their resulting effects on an impact area. Multi-year observed travel time and incident data are deployed to investigate the underlying traffic propagation dynamics. These datasets contain a wealth of information that reflects the occurrences of link closure/disruption by space and time and the network-wide impacts stemming from these disruptions. A novel approach is developed to identify the resulting spatial and temporal impacts of non-recurrent incidents. Rather than focusing solely on the travel time changes of the link subject to incident, this approach captures all aspects of the impacts of incidents by monitoring the dynamic propagation of congestion patterns in the set of links that are in the vicinity of the link subject to incident (i.e., impact area). These spatial and temporal dimensions of the impacts are subsequently used in a new vulnerability analysis. The performance of the developed approach was examined using historical travel time and incident data from the City of Calgary, Canada. The results indicate that the recorded temporal impact of incidents is not representative enough of the true impact of incidents because the dynamic spatial propagation of the effect of incidents on the impact area is overlooked. The proposed approach is capable of capturing the enduring spatiotemporal impact of incidents in a large-scale road network. The primary improvement of the developed vulnerability index is its ability to model multi-dimensional aspects of non-recurrent travel delays caused by non-current incidents in a road network.

A Quasi-Contagion Process Modelling and Characteristic Analysis for Real-World Urban Traffic Network Congestion Patterns

A Quasi-Contagion Process Modelling and Characteristic Analysis for Real-World Urban Traffic Network Congestion Patterns

An agent-based model for modal shift in public transport

Modal shift in public transport as a consequence of a disruption on a line has in some cases unforeseen consequences such as an increase in congestion in the rest of the network. How information is provided to users and their behavior plays a central role in such configurations. We introduce here a simple and stylised agent-based model aimed at understanding the impact of behavioural parameters on modal shift. The model is applied on a case study based on a stated preference survey for a segment of Paris suburban train network. We systematically explore the parameter space and show non-trivial patterns of congestion for some values of discrete choice parameters linked to perceived wait time and congestion. We also apply a genetic optimisation algorithm to the model to search for optimal compromises between congestion in different modes.

A review on influence of age of vehicle and vehicle traffic on air pollution dispersion

Vehicular pollution is considered as a bigger issue especially in urban cities. Vehicular emissions contribute substantially to the air pollution with increase in pollutant levels of various organic compounds. Traffic characteristics like speed, acceleration, traffic volume and congestion patterns of vehicles have a major impact and influence on the rate of emissions. As vehicular volume and congestion increases, vehicular emission levels also increases and exposure to the pollutants causes many chronic health impacts for road users and pedestrians. This paper reviews various research articles on traffic characteristics and its high influence on the pollutant emission levels.

Empirical investigation of the emission-macroscopic fundamental diagram

• We analyse vehicle emissions in a multi-modal urban area using the pNEUMA dataset. • EPA’s microscopic emission model, project level MOVES is implemented. • We investigate aggregated relations between emissions and congestion patterns. • The macroscopic fundamental diagram (MFD) is extended to the emissions-MFD (e-MFD). • The spatiotemporal distribution of vehicle emissions is revealed in high detail. Vehicle emissions are a major contributor of air pollution in urban areas, with severe consequences for public health and the environment. This paper focuses on analyzing vehicle emissions in a multimodal urban context to better understand its determinants and investigate relations between emissions and congestion patterns. Thousands of naturalistic trajectories collected with a swarm of drones during the pNEUMA experiment are analyzed and EPA’s microscopic emission model, project level MOVES, is implemented to estimate vehicular emissions. The method is developed and illustrated with a CO 2 analysis. The results empirically show the aggregated relationships between congestion and vehicular emissions, while accounting for the individual characteristics of each trajectory. These systematic relationships on a network scale with the use of empirical data, allow us to extend the macroscopic fundamental diagram (MFD) to the emissions-MFD (e-MFD). The spatiotemporal distribution of emissions is also studied, highlighting areas with high concentration over the urban area.

Spatial Patterns of Carbon Monoxide Distribution to Traffic Jam in East Jakarta

The rapid growth of cities will certainly also increase traffic jams and emissions in the air. This study aims to analyze the increase in car volume and the CO distribution pattern in East Jakarta. Data for traffic jam patterns were recorded based on Google Maps on weekdays in the morning and evening. The spatial analysis method used to find the CO distribution pattern is the IDW interpolation, and the mathematical model calculates the moving emission based on the distance travelled (VKT). The spatial pattern of CO distribution in 2020 was scattered with high concentrations in Pasar Rebo, Ciracas, Cipayung, Kramat Jati, and Makasar Districts, with CO levels above 4,500 ppm. The spatial pattern of CO distribution from the mobile emission model differs from the air station IDW interpolation. The CO distribution pattern from the mobile emission model is very concentrated in Makassar, and Kramat Jati District was 6,740.91 tons/year. The result concluded that the increase in vehicle volume is not related to the distribution of the CO model from air station IDW interpolation, and the other hand, the congestion pattern was related to the distribution pattern of the CO model from vehicles from the level of congestion.

Causes of Traffic Congestion and Possible Solutions along Akesan-Igando Road, Lagos Nigeria

Many urban cities in Nigeria are bedeviled with traffic congestion which tends to defy various remedial measures adopted by different governments over the years. Journey times from one point to another within a town have remained unreliable and residents have continued to face disturbing in conveniences in transportation especially traffic congestion. This study, therefore, addresses the problems of traffic congestion impacts on commuters along Akesan-Igando road. The set objectives incudes: identifying pattern of traffic congestion at the Corridor, ascertaining the cause(s) of traffic congestion, determine what can be done to reduce traffic congestion in the study area. Basically, primary data was used for the research through direct observation, interview and field measurement. Drones were also deployed to capture images from the field. It was discovered from the field that the causes of traffic congestion along this corridor include: narrow road conditions, increase in population leading to increase in vehicle ownership, activities of road traders, illegal parking and poor enforcement. The study recommended that t here should be proper and adequate enlightment for the drivers on the dangers inherent in congestion , t he law enforcement agencies (Police and LASTMA) should be motivated in other to act professionally and not to take bribe from public bus drivers, street trader should be completely banned from displaying their wares on the set back which impedes the free flow of vehicular movement, the state government should intensify effort to ensure BRT is operational on this corridor and t here is need for improved traffic management system and of the road condition along the corridor .