Periods Of Congestion Research Articles

A Bi-Level Programming Model of Liquefied Petroleum Gas Transportation Operation for Urban Road Network by Period-Security

As a clean energy, Liquefied Petroleum Gas (LPG) is consistent with the coordinated and sustainable development of both the economy and environment. However, LPG is a hazardous material (hazmat) and is thus always transported in cylinders by vehicles on urban road networks to meet varying demand. This transport can threaten the surrounding citizens, vehicles, and even the whole urban area. Hence, LPG transportation should be focused on maintaining its security while simultaneously minimizing shipping costs. When LPG is moved through an urban area, its threat level fluctuates with the network congestion level, which continually varies by different time periods. So, variation in the magnitude of the threat posed by LPG transportation causes additional changes in the safe-related cost as well as the shipping cost. This study aims to solve the problem of an LPG transportation operation on an urban road network according to congested periods; the solution is based on cutting its two types of cost. In general, we should choose an LPG transport period that results in a lower safety cost, however optimization of an LPG transportation operation must minimize both the safety cost and shipping cost. This paper presents the problem of LPG flow distribution and vehicle dispatch scheme by “period-security” to rationalize the LPG transport risk level. Firstly, the impedance function of LPG flow distribution was constructed with a focus on the safety cost in different periods. Meanwhile, a bi-level programming model was built, in which the upper mixed binary integer programming model aims to minimize the shipping cost and the lower model is a user-equilibrium model that is aimed at calculating the distribution of the LPG demands on the given lines and in feasible periods. Then, we designed a heuristic algorithm based on the Genetic Algorithm to solve the upper model and embedded the Frank-Wolfe Algorithm to get the optimal LPG flow distribution solution. Numerical examples are presented which validate that the LPG optimal operation can realize a minimal safety cost and the minimum shipping cost for three LPG demand values by considering the congestion situation.

Adaptive access class barring for efficient mMTC

In massive machine-type communications (mMTC), an immense number of wireless devices communicate autonomously to provide users with ubiquitous access to information and services. The current 4G LTE-A cellular system and its Internet of Things (IoT) implementation, the narrowband IoT (NB-IoT), present appealing options for the interconnection of these wireless devices. However, severe congestion may arise whenever a massive number of highly-synchronized access requests occur. Consequently, access control schemes, such as the access class barring (ACB), have become a major research topic. In the latter, the precise selection of the barring parameters in a real-time fashion is needed to maximize performance, but is hindered by numerous characteristics and limitations of the current cellular systems. In this paper, we present a novel ACB configuration (ACBC) scheme that can be directly implemented at the cellular base stations. In our ACBC scheme, we calculate the ratio of idle to total available resources, which then serves as the input to an adaptive filtering algorithm. The main objective of the latter is to enhance the selection of the barring parameters by reducing the effect of the inherent randomness of the system. Results show that our ACBC scheme greatly enhances the performance of the system during periods of high congestion. In addition, the increase in the access delay during periods of light traffic load is minimal.

Trace Elements and Polycyclic Aromatic Hydrocarbons Variation Along the Guang-Shen Expressway Before and After the 2016 Qingming Festival in Guangzhou.

PM2.5 samples (particles with aerodynamic diameter < 2.5μm) were collected along the Guang-Shen expressway around the Qingming Festival, one of the most congested periods in China, which started from April 2-4, in 2016. Twenty-five trace elements and 16 priority polycyclic aromatic hydrocarbons (PAHs) of the samples were analyzed. Their major sources at different periods were identified. The concentrations of PAHs distinctly increased with growing traffic flow 2days before the Qingming Festival (March 31th and April 1st), decreased gradually on the first 2days of the 3-day festival (April 2nd and 3rd) and rose again on the last day (April 4th). The proportion changing of higher molecular weight containing 5- and 6-ring PAHs (HMW PAHs) closely related to the traffic flow variation were consistent with the concentration variation of PAHs during the experimental period. Indicators of gasoline/diesel engines emission, i.e., Mo, Co, Mn, and Pb showed similar concentration variation with PAHs. The concentrations of trace elements, mainly derived from wear instead of combustion process, such as Cu, Zn, Ti, and Sb, raised significantly during the rainy days. Incremental lifetime cancer risk (ILCR) values were calculated to evaluate the health risk caused by PAH around the Qingming Festival. The ILCR values increased by 3-10 times 2days before and on the last day of the festival comparing with other days, as a result of traffic related sources, including engine emission and wearing of tires. It concluded by recommending the necessity of traffic diversion to alleviate the health risk to drivers and nearby residents during important festivals.

A Protection Method Based on Message Identification and Flow Monitoring for Managing the Congestion Arising From Network Attacks on Smart Substation

The information security of smart substations has become a major issue, as substations become increasingly dependent on communication networks. This letter analyzes the data flows in the communication networks of smart substations and proposes a protection method based on message identification and flow monitoring for managing the congestion arising from network attacks. This method implements the rapid identification and filtering of messages by condensing key information into a message information tag and then examines the tag along with the traffic rates at switches to identify and discard counterfeit messages. OPNET simulation results show that the method successfully identifies and discards counterfeit messages and avoids timeouts and packet drops in the transmission process of authentic messages during periods of congestion.

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods.

Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model.

Choice of High Occupancy/Toll Lanes under Alternative Pricing Strategies

High occupancy/toll (HOT) lanes typically vary tolls charged to single occupant vehicles, with the toll increasing during congested periods. The toll is usually tied to time of day or to the density of vehicles in the HOT lane. The purpose of raising the toll with congestion is to discourage demand sufficiently to maintain travel speeds in the HOT lane. However, it has been demonstrated that the HOT toll may act as a signal of downstream congestion (in both general purpose (GP) and HOT lanes), causing an increase in demand for the HOT lane, at least at lower prices. This paper develops a model of lane choice to evaluate alternative HOT lane pricing strategies, including the use of GP density, to more accurately reflect the value of the HOT lane. In addition, the paper explores the potential effect these strategies would have on the HOT lane vehicle share through a partial equilibrium analysis. This analysis demonstrates the change in demand elasticity with price, showing the point at which drivers switch from a positive to negative elasticity.

Travel Time Variability and Macroscopic Fundamental Diagram Relationships in Multimodal Networks

Travel time variability (TTV) must be considered in evaluations of the effect of traffic control on transport network performance. The objective of this paper was to investigate the relationship between a travel time variability diagram (TTVD), which relates average travel time to its standard deviation, and a macroscopic fundamental diagram (MFD), which relates average network flow to its average density. Recent studies indicated hysteresis loops in TTVD (for journeys with the same average travel time, the TTVs for departures as congestion dissipates are higher than the TTVs for departures during congestion onset) and in the MFD (with the same network density, higher network flows occur during congestion onset than during congestion offset). Based on a simulation results, we evaluated the correlations between these loops in a multimodal network. Strong correlations were found between TTVD and MFD loops. Next, we developed combined diagrams of travel time standard deviations against network flow and density. These diagrams revealed the presence of a critical network flow and density during congested periods; beyond these critical points, the TTV increases sharply. These findings may facilitate evaluation of management strategies that consider both traffic flow and reliability measures.

Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km2. The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.

Online monitoring of local taxi travel momentum and congestion effects using projections of taxi GPS-based vector fields

Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on pre-processing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a four-day period, we demonstrate how to generate in real time a series of projections from a continuously updated vector field of taxi travel momentum to query a point of interest anywhere in a city, such as the CBD or the airport. This method allows a policy-maker to automatically identify temporal net influxes of travel demand to a location. The proposed methodology is shown to be over twenty times faster than a conventional selection query of trajectories. We also demonstrate, using taxi data entering the Beijing Capital International Airport and the CBD, how we can quantify in nearly real time the occurrence and magnitude of inbound or outbound queueing and congestion periods due to taxis cruising or waiting for passengers, all without having to fit any mathematical queueing model to the data.

Study of video traffic In telecommunican systems for the purpose of service quality

The self-similar network traffic can have a detrimental impact on network performance, including amplified queuing delay, retransmission rate and packet loss rate. Modern network traffic consists of more bursts than Poisson models predict over many time scales. This difference has implications for performance. The video traffic research is important because self-similar nature of network traffic leads to a number of undesirable effects like high buffer overflow rates, large delays and persistent periods of congestion and the severity of these conditions is directly proportional to the degree of self-similarity. On the other hand, the long memory property of self-similar traffic is able to help to forecast traffic for the purpose of quality of service (QoS) provision. This work presents the results of the analysis of video traffic performed and study the structure of the video traffic to identify its characteristic features

Application of LiDAR and Connected Vehicle Data to Evaluate the Impact of Work Zone Geometry on Freeway Traffic Operations

Extensive literature exists regarding recommendations for lane widths, merging tapers, and work zone geometry to provide safe and efficient traffic operations. However, it is often infeasible or unsafe for inspectors to check these geometric features in a freeway work zone. This paper discusses the integration of LiDAR (Light Detection And Ranging)-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. Connected vehicle speed data can be used at both a system-wide (statewide) or segment-level view to identify periods of congestion and queueing. Examples of regional trends, localized incidents, and recurring bottlenecks are shown in the data in this paper. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. In total, 350 directional miles were covered, resulting in approximately 360 GB of data. Two case studies, where geometric anomalies were identified, are discussed in this paper: a short segment with a narrow lane width of 10–10.5 feet and a merging taper that was about 200 feet shorter than recommended by the Manual on Uniform Traffic Control Devices. In both case studies, these work zone features did not conform to project specifications but were difficult to assess safely by an inspector in the field because of the high volume of traffic. The paper concludes by recommending the use of connected vehicle data to systematically identify work zones with recurring congestion and the use of LiDAR to assess work zone geometrics.

Performance Analysis and Optimal Access Class Barring Parameter Configuration in LTE-A Networks With Massive M2M Traffic

Over the coming years, it is expected that the number of machine-to-machine (M2M) devices that communicate through long term evolution advanced (LTE-A) networks will rise significantly for providing ubiquitous information and services. However, LTE-A was devised to handle human-to-human traffic, and its current design is not capable of handling massive M2M communications. Access class barring (ACB) is a congestion control scheme included in the LTE-A standard that aims to spread the accesses of user equipments (UEs) through time so that the signaling capabilities of the evolved Node B are not exceeded. Notwithstanding its relevance, the potential benefits of the implementation of ACB are rarely analyzed accurately. In this paper, we conduct a thorough performance analysis of the LTE-A random access channel and ACB as defined in the 3GPP specifications. Specifically, we seek to enhance the performance of LTE-A in massive M2M scenarios by modifying certain configuration parameters and by the implementation of ACB. We observed that ACB is appropriate for handling sporadic periods of congestion. Concretely, our results reflect that the access success probability of M2M UEs in the most extreme test scenario suggested by the 3GPP improves from approximately $30\%$ , without any congestion control scheme, to $100\%$ by implementing ACB and setting its configuration parameters properly.

Aerobic Fitness of Starter and Non-Starter Soccer Players in the Champion's League.

To identify individual response patterns in selected aerobic fitness variables of regular starters (ST; N = 7) and non-starters (Non-ST; N = 10), top level professional soccer players were tested for maximal oxygen uptake (VO2max), velocity at 4 mM of lactate (V4), velocity at maximal oxygen uptake (νVO2max) and oxygen pulse (O2-pulse) in July and December following consecutive periods of fixture congestion. V4 was the only variable that increased significantly in December compared to July (15.1 ± 0.5 vs. 14.6 ± 0.5, p = 0.001). There was an almost certain beneficial large mean team change for V4 (ES = 1.2 (0.67; 1.57), 100/0/0), while beneficial mean team changes were less likely for νVO2max and O2-pulse [ES = 0.31 (-0.08; 0.70), 68/30/2 and ES = 0.24 (0.01; 0.49), 64/36/0, respectively] and unclear for VO2max (ES = 0.02 (-0.31; 0.70), 18/69/13). With the exception of V4 where 10 out of 17 players (7 ST and 3 Non-ST) showed positive changes higher than the biological variability, all other variables were characterized by a substantial proportion of changes lower than the biological variability. The present study demonstrated that aerobic fitness variables that require maximal effort may be characterized by greater variability of the individual response pattern compared to that of submaximal aerobic fitness variables irrespective of the accumulated game time. Submaximal aerobic fitness variables appear to be more informative in the physiological evaluation of top level soccer players and this may be an advantage during exposure to periods of consecutive games.

Smart systems approach for development of explicit congestion marking and traffic engineering model for Diffserv/MPLS networks

The scheduling, control, and management of traffic load in multi-protocol label switching (MPLS) are important aspects supporting the quality of service (QoS) and optimizing the network performance. In addition, ensuring the QoS in the Internet of Things (IoT) is one of the main issues of current research in this area. The IoT provides the ability for humans and computers to learn and interact with billions of things, including sensors, actuators, services, and other Internet-connected objects. MPLS offers the possibility for effective traffic control mechanisms within limited network capabilities. Traffic engineering in the MPLS domain provides alterations of the traffic routes to improve network resources or avoid network traffic congestion. Static management of traffic flows has failed to enhance the QoS of the network performance. Therefore, many researchers have attempted to use a dynamic management of traffic flow to enhance the QoS; however, more enhancements are needed. In this paper, we propose a new scheme to solve the problem of dynamic management of traffic flow through various links and routers in the network, and describe its application to an MPLS network. The proposed scheme works by utilizing queues, re-distribution, and re-balancing of streams before or during periods of congestion. It solves the problem of QoS violation, and tags, upgrades, or downgrades the traffic between queues based on the availability of free space of other queues and QoS parameters. We simulate the proposed scheme using OPNET, and the effect of implementing our method on low- and high-priority queues is analyzed and finally validated in conjunction with the ITU QoS measures.

Running Performance in Brazilian Professional Football Players During a Congested Match Schedule

Palucci Vieira, LH, Aquino, R, Lago-Peñas, C, Munhoz Martins, GH, Puggina, EF, and Barbieri, FA. Running performance in Brazilian professional football players during a congested match schedule. J Strength Cond Res 32(2): 313-325, 2018-The purpose of the study was to examine the influence of playing congested fixtures (2 matches a week) on running performance of Brazilian professional football players, pooled (all performance files regardless players' rotation between games) or individualized data treatment (including only files of athletes' who played pair(s) of noncongested-congested matches which occurred on the same week). Forty participants were observed 1-37 times during 59 official matches over 3 years. Global positioning system devices were used to compute total distance covered, maximal sprinting speed mean speed (VAVERAGE), and high-intensity activities (HIA: ≥15 km·h). Independent variables included match status (win, draw, and lose), location (home and away), opponent quality (weak and strong), competitive standard (national and state), team formation (4-4-2 and 4-3-3), and position (defender, midfielder, and forward). Student's t-test, effect sizes (d), and magnitude-based inferences were performed to detect differences between congested and noncongested fixtures for each independent factor. Overall, pooled and individualized data treatment revealed contrasting results. The first showed chances of positive outcomes playing congested matches (possibly to very likely; d = 0.09-0.55), for all variables, as using 4-4-2 and state tournament. Conversely, individualized data analysis revealed that during national leagues (possibly; d = 0.24) or forward position (likely; d = 0.70), the players presented reduced HIA when played congested than noncongested periods (p ≤ 0.05). Using 4-3-3 (d = 0.26) or when draw (d = 0.41), possibly lower HIA was also identified. Coaching staff should be aware of performance oscillations such as detrimental effects on HIA during congested periods.

The health impacts of weekday traffic: A health risk assessment of PM2.5 emissions during congested periods

Little work has accounted for congestion, using data that reflects driving patterns, traffic volume, and speed, to examine the association between traffic emissions and human health. In this study, we performed a health risk assessment of PM2.5 emissions during congestion periods in the Greater Toronto and Hamilton Area (GTHA), Canada. Specifically, we used a micro-level approach that combines the Stochastic User Equilibrium Traffic Assignment Algorithm with a MOVES emission model to estimate emissions considering congestion conditions. Subsequently, we applied a concentration-response function to estimate PM2.5-related mortality, and the associated health costs. Our results suggest that traffic congestion has a substantial impact on human health and the economy in the GTHA, especially at the most congested period (7:00am). Considering daily mortality, our results showed an impact of 206 (boundary test 95%: 116; 297) and 119 (boundary test 95%: 67; 171) deaths per year (all-cause and cardiovascular mortality, respectively). The economic impact from daily mortality is approximately $1.3 billion (boundary test 95%: 0.8; 1.9), and $778 million (boundary test 95%: 478; 981), for all-cause and cardiovascular mortality, respectively. Our study can guide reliable projections of transportation and air pollution levels, improving the capability of the medical community to prepare for future trends.

Update schemes of multi-velocity floor field cellular automaton for pedestrian dynamics

Modeling pedestrian movement is an interesting problem both in statistical physics and in computational physics. Update schemes of cellular automaton (CA) models for pedestrian dynamics govern the schedule of pedestrian movement. Usually, different update schemes make the models behave in different ways, which should be carefully recalibrated. Thus, in this paper, we investigated the influence of four different update schemes, namely parallel/synchronous scheme, random scheme, order-sequential scheme and shuffled scheme, on pedestrian dynamics. The multi-velocity floor field cellular automaton (FFCA) considering the changes of pedestrians’ moving properties along walking paths and heterogeneity of pedestrians’ walking abilities was used. As for parallel scheme only, the collisions detection and resolution should be considered, resulting in a great difference from any other update schemes. For pedestrian evacuation, the evacuation time is enlarged, and the difference in pedestrians’ walking abilities is better reflected, under parallel scheme. In face of a bottleneck, for example a exit, using a parallel scheme leads to a longer congestion period and a more dispersive density distribution. The exit flow and the space–time distribution of density and velocity have significant discrepancies under four different update schemes when we simulate pedestrian flow with high desired velocity. Update schemes may have no influence on pedestrians in simulation to create tendency to follow others, but sequential and shuffled update scheme may enhance the effect of pedestrians’ familiarity with environments.

Redesigning subway map to mitigate bottleneck congestion: An experiment in Washington DC using Mechanical Turk

This paper explores the possibility of using subway maps as a planning tool to influence passenger route choice to mitigate congestion. Specifically, it tests whether extending the appearance of an overcrowded subway line on the Washington DC subway map would encourage passengers to use other underutilized lines. The experiment was conducted through the Mechanical Turk, a crowdsourcing platform, with 3056 participants, producing 21,240 route choice decisions on the official and six alternative maps. Results show that redesigned maps significantly affect participants’ route choices. Depending on the specific design, a 20% length increase of the overcrowded line could move 1.9–5.7 percentage points of ridership to an alternative, underutilized line. The change could remove up to 10 passengers per car during the highest peak, reducing the number of highly congested half-hour periods (max load=100–120 passengers per car) on the overcrowded line from 4 to 1, and the number of crush periods (max load>120 passengers per car) from 3 to 1. This is done at minimal or zero cost. The paper calls for more attention from transit agencies to the planning potential of transit maps.

Sensitivity analysis of the mainline travel lane pavement service life when utilizing part-time shoulder use with full depth paved shoulders

Part-time Shoulder Use (PTSU) is an Active Traffic Management (ATM) strategy that utilizes the shoulder as an additional travel lane during high congestion periods to temporarily increase roadway capacity. This strategy has become increasingly popular on freeways around major metropolitan areas in the United States (U.S.) and Europe. This research focused on freeways near major metropolitan areas where the roadways are structurally sound, but widening roadways has become increasingly expensive to implement. These areas typically utilize full-depth paved shoulders, defined as shoulders built to the same pavement structural profile as the mainline travel lanes and structurally sufficient to carry heavy vehicle loading. As a result of PTSU, the load repetitions on the mainline travel lanes can be reduced for multiple hours during each day by shifting vehicles to the shoulder and this load reduction may indirectly increase the mainline pavement’s service life. This research explored the potential benefits of PTSU based on a variety of conditions, including climate conditions, pavement types, and traffic loading levels. The results suggest that across all of these conditions an extension of 10–20% in service life can be expected for both flexible and rigid pavements in the mainline travel lanes.