Bottleneck Locations Research Articles

Assessing the impact of heavy rainfall on the Newcastle upon Tyne transport network using a geospatial data infrastructure

Extreme weather conditions can adversely impact transport networks and driver behaviour, leading to variations in traffic volumes and travel times and increased accident rates. Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making. However, real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research, we demonstrate how we obtain historical time-series and real-time data from sensors operated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne, UK. In the study, we assess the impact of rainfall on traffic volume and travel time, and the cascading impacts during a storm event in Newcastle during early October 2021. We also estimate the economic cost of the storm, with regards to transport disruption, as the cost of travel, using the “value of time” based on Department for Transport guidelines (2021).Using spatial-temporal analysis, we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm. We identified increases in travel times of up to 600% and decreases in traffic volume of up to 100% when compared to historical data. Further, we assessed cascading impacts at important traffic locations and their broader implications for city areas. We estimated that the storm's economic impact on one sensor location increased by up to 370% of the reference value.By analysing historical and real-time data, we detected and explained patterns in the data that would have remained uncovered if they had been examined individually. The combination of different data sources, such as traffic and weather, helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.

An Automated Process for Identification of Bottlenecks in the Traffic System Using Large Data Sets

Traffic breakdowns are frequently observed phenomena on roads in larger cities, especially during peak hours. Locations along a road stretch with frequently observed breakdowns are known as recurrent bottlenecks. Knowledge about bottleneck locations are important for improvement of traffic conditions at these locations. Bottleneck locations can be identified through manual inspection of data. However, due to the comprehensive amount of data that are available today, it becomes impractical to manually identify breakdowns and instead, an automated process can be used. We propose such an automated method. The proposed method is applied to a use case south of Stockholm in Sweden. One month of data collected at densely spaced detectors is used to investigate the sensitivity of the parameter settings. After calibration of the threshold values, 100% of the larger breakdowns and 40% of the medium size breakdowns are identified. Smaller breakdowns, not giving significant impact on the traffic conditions, are only detected in 10%–20% of the cases. Thereafter, the method is applied to 1 year of data to illustrate the applicability of the method on a larger data set. The results show that the method is promising to use for identification of recurrent bottleneck locations.

Predicting future production system bottlenecks with a graph neural network approach

Predicting throughput bottlenecks has received an increasing research attention in the recent years. Several statistical and machine learning models have been developed, with the objective of identifying future bottleneck locations for more efficient and cost-saving productions. However, bottleneck prediction remains a challenging task due to the complexity of manufacturing systems and the interactive effects from many impacting factors. This paper proposed the BSTAN (bottleneck spatial-temporal attention network) – an interpretable modeling framework to improve system bottlenecks prediction capability for complex manufacturing systems by: (1) characterizing line topological features using graph representations; (2) modeling spatial-temporal relationships and interactions between stations using graph attention network with gated recurrent units. In this proposed framework, the production line is first transformed into the representation of a weighted undirected graph to depict network structural information of a production line. An attention-based graph neural network with gated recurrent units is applied to capture both the temporal trends and station interactions to predict each station’s blockage and starvation. Future bottleneck stations are identified by analyzing the forecasted blockage and starvation distribution. To evaluate the model performance, an industrial case study is performed with data collected from a one-year production at an automotive powertrain assembly line. With the proper model construction, BSTAN has outperformed both statistical and machine learning benchmark models, achieving up to 15% lower root mean square error compared to the best benchmark method in predicting overall system blockage and starvation time.

Method to extract difficult-to-evacuate areas by using tsunami evacuation simulation and numerical analysis

Extracting the area where people have difficulty evacuating (hereafter difficult-to-evacuate areas, DEA) when tsunamis hit after an earthquake is important for effective disaster mitigation measures. The DEA was conventionally extracted by simply considering the walking speed, distance to the evacuation destination, and time needed for evacuation after considering the estimated tsunami inundation area. However, evaluating the DEA from such a simple scheme is insufficient because the behavior of residents and the road conditions to the evacuation destinations after an earthquake are not properly reflected in the scheme.In this study, agent-based tsunami evacuation simulations that can reflect the behavior of residents and real-time changes in the situation were conducted in Zihuatanejo, Guerrero, Mexico. It is a prime sightseeing destination under the high risk of megathrust events in the Guerrero Gap. First, by checking the simulation images at the tsunami arrival time, bottleneck locations were identified, and five additional models with different measures for the bottleneck locations were constructed and tested to find the best model with 195 casualties. Then, focusing on the best model, three indices for the casualties were proposed to extract the DEA effectively and quantitatively, and numerical analyses using the three indices was conducted. Finally, the subdistrict in the center of the target area (subdistrict 5) was quantitatively found to be the district that should be given the highest priority for measures. Moreover, an example model with a new measure in subdistrict 5 was validated to have 101 casualties. The key points for applying the proposed method for extraction of DEA in other areas are summarized.

An Automated Detection Framework for Multiple Highway Bottleneck Activations

Highway bottlenecks are responsible for the majority of traffic congestion. Although the problem of bottleneck detection is not new, contemporary methods have not solved the problem thoroughly with regards to bottleneck locations, activation time, and related congestion tracking. These elements are essential for identifying and characterizing a bottleneck. This paper proposes a comprehensive framework for detecting and extracting these features of highway bottlenecks from traffic data. We particularly focus on questions (i) whether a bottleneck is the primary source of congestion or (ii) whether it is activated due to congestion caused by another downstream bottleneck. The underlying principles of the proposed method include the detection of congestion (in spatio-temporal patterns of traffic congestion), and the detection of speed discontinuities in traffic data (since this is an important indicator of a bottleneck activation). The method is data-driven and automatic therefore can be easily applied to different highways and used to obtain meaningful statistics of existing bottlenecks. We have tested the method on simulated data and also demonstrated it on real data from a busy highway section in the Netherlands. The results suggest that the method is robust to different implementations, i.e. locations, of loop-detectors which measure traffic at discrete locations.

Clustering breakdown observations of speed-flow relations for capacity estimation

Capacity estimation at bottleneck locations along urban motorways are of importance for traffic management purposes. If the capacity is known, bottlenecks with observed low capacity can be identified and improvements, such as active control strategies or infrastructure modifications, can be initiated to reduce the number of breakdowns. In this paper, we propose a methodology for estimating the capacity distribution for clusters with similar speed-flow relations before a breakdown for large data sets. Explanatory variables are identified to show that the proposed methodology can be used to categorize each cluster based on the bottleneck characteristics. The methodology consist of the following steps: (1) an automated process to identify breakdowns, (2) a clustering method to cluster breakdown days and locations with similar capacity levels, and (3) a statistical approach for estimating the capacity distribution for each cluster. Further, we illustrate how the proposed methodology can be used to identify bottleneck characteristics with the greatest impact on the capacity level by using one year of empirical observations of speed and flow from an urban motorway stretch south of Stockholm in Sweden. The results show that the proposed methodology has potential to identify bottlenecks with frequently observed low capacity and to find bottleneck characteristics with a large impact on breakdown capacity.

Coordinated Control Algorithm at Non-Recurrent Freeway Bottlenecks for Intelligent and Connected Vehicles

Intelligent and Connected Vehicle (ICV) technology is considered to be a solution to improve the traffic performance. Through the information exchange in real-time among the vehicles, the roadside infrastructures, and the cloud platform, the sensing of the vehicles can be enhanced. This also enables coordinated driving decisions, which can improve traffic operations, especially at bottleneck locations. This paper addresses the problem of coordinating the vehicles near the bottleneck locations to help the vehicles passing the area quickly and smoothly. A lane advisory algorithm is designed to reduce conflicts by encouraging early lane changes. A coordinated vehicle movement planning algorithm is proposed to achieve a smooth longitudinal reference speed profiles for vehicles in the subject area. The algorithm can open enough headway for vehicles to change the lane and continue their trips. The effectiveness of the algorithm is evaluated using SUMO (Simulation of Urban MObility) as the simulation tool with no communication between vehicles as the benchmark case as well as the case where the vehicular traffic follows the so-called First-in-First-Out (FIFO) principle. The results of the evaluation summarize and indicate that the Coordinated Control Algorithm (CCA) proposed in this paper can improve traffic performance in terms of the average speed, the waiting time, the total travel time, and the traffic flow rate under different levels of service.

A prognostic algorithm to prescribe improvement measures on throughput bottlenecks

Throughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively.

Effect of on-ramp demand and flow distribution on capacity at merge bottleneck locations

ABSTRACT Past research acknowledged the impact of ramp vehicles on the occurrence of a breakdown event, but little has been done to quantify the effect of the ramp vehicles on the resulting bottleneck capacity. The objective of this research is to explore the relationship between ramp flow and capacity and to recommend capacity values for merge bottleneck locations. To explore the relationship between ramp flow and capacity, a capacity model has been developed using linear regression. The Kansas City area freeway network was considered for the analysis. Multiple locations that experienced ‘true breakdowns’, i.e., breakdowns caused by merging operations and not due to downstream spillback, were selected for the analysis. Demand-derived variables such as ramp-to-freeway demand ratio and flow distribution were found to be statistically significant. The regression model was used to predict capacity values for different ramp and freeway demands. By observing the calculated capacity values it was concluded that capacity decreases as the ramp-to-freeway demand ratio increases.

Integrated Traffic Control for Freeways using Variable Speed Limits and Lane Change Control Actions

The wide deployment of vehicle automation and communication systems (VACS) in the next decade is expected to influence traffic performance on freeways. Apart from safety and comfort, one of the goals is the alleviation of traffic congestion which is a major and challenging problem for modern societies. The paper investigates the combined use of two feedback control strategies utilizing VACS at different penetration rates, aiming to maximize throughput at bottleneck locations. The first control strategy employs mainstream traffic flow control using appropriate variable speed limits as an actuator. The second control strategy delivers appropriate lane-changing actions to selected connected vehicles using a feedback-feedforward control law. Investigations of the proposed integrated scheme have been conducted using a microscopic simulation model for a hypothetical freeway featuring a lane-drop bottleneck. The results demonstrate significant improvements even for low penetration rates of connected vehicles.

Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach

In this study, we focus on one of practically important research problems of integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network. The task is accomplished by using a triple of flow, density, and speed to construct a discretized passenger flow state, further constructing a space-time-state (STS) hyper network so that we can utilize a better defined three-dimensional solution space to integrate structurally heterogeneous data sources. The monitoring data include passenger transaction records and identification space-time samples observed over a possible range of a few hours from origins to destinations (Lagrangian observations), and time-dependent passenger counts collected at some key bottleneck locations (Eulerian observations). To describe the complex urban-rail passenger flow evolution, passenger traveling and fixed sensor state transition processes can be unified within a STS path representation. To estimate the consistent system internal states between two different types of observations, we formulate a hyper network-based flow assignment model in a generalized least squares estimation framework. For applications in large-scale transportation networks, we decompose the proposed model into three easy-to-solve sub-problems. The proposed model is applied to a real-world case based on the Beijing subway network with complete smart card data for each passenger at his/her origin and destination and time-dependent passenger counts in several key transfer corridors, while the specific space-time trajectories of all passengers and high-resolution time-dependent congestion levels at platforms, in trains, and in transfer corridors are estimated. This proposed passenger flow state inference method can provide a rich set of state inferences for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

Bottleneck mitigation through a variable speed limit system using connected vehicles

ABSTRACTVariable speed limit (VSL) systems are used to improve the traffic conditions by adjusting the speed limits based on the current traffic situation. Advances in vehicle technology have made it possible to use connected vehicles in VSL systems. Connected vehicles can continuously transmit information about their speed and location, which can be used to estimate the current traffic conditions at arbitrary locations. In this study, we propose a VSL system based on connected vehicles. The aim is to also allow application of VSLs for non-recurrent bottleneck mitigation at arbitrary locations, unlike today's systems which require densely placed detectors or are limited to beforehand known bottleneck locations. The proposed system is evaluated by microscopic traffic simulation. The results indicate that the VSL system manage to improve traffic efficiency in a simulated incident scenario.

A Framework for Identifying Recurring Bottlenecks in the State Planning Process in Colorado

Traditional methods used in transportation planning to identify bottlenecks and mobility issues, such as the Volume-to-Capacity Ratio and the Planning Time Index, have limited usefulness in identifying the exact location and extent of bottlenecks. Moreover, existing bottleneck identification tools flag all bottleneck types without distinction, despite the fact that strategies and resources used to address each type are different. This research first proposes a taxonomy of congestion to distinguish between different types of bottlenecks. It then describes a new methodology for identifying the location of recurring bottlenecks. Recurring bottleneck locations must experience a reduction in speed, an upstream accumulation of vehicles, and recurrence at the same location over three consecutive months. The methodology is currently used in long-term transportation planning and project selection in Colorado to identify and address the most severe bottleneck locations in the state.

Comparison of Highway Capacity Estimation Methods

The capacity of a freeway segment is a critical factor for planning, design, and operational analysis of freeway facilities. This research aimed to perform a comparison among well-known freeway capacity estimation methods in order to investigate their application, as well as their advantages and disadvantages. Single estimate capacity methods such as the Van Aerde method, and breakdown probability methods, such as the product limit method (PLM), the Highway Capacity Manual (HCM) method, and the sustainable flow index (SFI) method, were applied at six merge bottleneck locations in the Kansas City area. The results from all methods were compared and the advantages and the disadvantages of each method were discussed. The HCM results showed a significant variability in the estimated breakdown probability function and the resulting capacities. The HCM method was also found to be sensitive to the breakdown probability ratios as a single breakdown observation can significantly shift the fitted distribution and the corresponding capacity estimate. The PLM model provided the highest capacity estimates, followed by the Van Aerde model capacities. The Van Aerde capacities were also found to be closer to the average pre-breakdown flow rates. Finally, the PLM and the SFI method showed consistent performance in comparison to the remaining methods, and flexibility in being applied on different sites with various characteristics.

New Framework for Automatic Identification and Quantification of Freeway Bottlenecks Based on Wavelet Analysis

AbstractAs the amount of traffic congestion continues to grow, pinpointing freeway bottleneck locations and quantifying their impacts are crucial activities for traffic management and control. Amon...

Data‐driven approach for identifying spatiotemporally recurrent bottlenecks

Identification of recurrent bottlenecks is an effective way to hone an appropriate investment in current facilities to relieve congestion. Furthermore, it would enable the ranking or prioritisation of bottlenecks since bottleneck removal and its associated impact alleviation are hampered by limited sources. It is imperative that transportation jurisdiction understand and identify the basis for ranking bottlenecks by exploring: how often they are active; how long it takes the congestion to disappear; and how many miles of road are affected. Previous bottleneck identification schemes have focused on identifying congestion with little attention to distinguishing the recurrent level at the same ‘bottleneck’ location. In contrast to traditional schemes, a data-driven approach for identifying recurrent bottlenecks is introduced, using probe vehicle speed reports. The historical spatiotemporal characteristics of bottlenecks are investigated through a comprehensive analysis of 2253 miles of all state-wide interstates in North Carolina. Using the characteristics determined the recurrent bottleneck locations with a historical time span of bottleneck activation are revealed and tested. The findings of the proposed identification schemes generate critical information in order to quantify and diagnose a bottleneck and its associated impact area.

Investigation of Effect of Machine Layout on Productivity and Utilization Level: What If Simulation Approach

Designing and selecting the material handling system is a vital factor for any production line, and as result for the whole manufacturing system. Poor design and unsuitable handling equipment may increase the risk of having bottlenecks, longer production time and as a result the higher total production cost. One of the useful and effective tools are using “what if” simulation techniques. However, this technique needs effective simulation software. The main objective for this research is to simulate different types of handling system using what if scenario. To achieve the objective of the research, Delmia Quest software has been used to simulate two different systems: manual system and conveyers system for the same production line and analyses the differences in terms of utilization and production rate. The results obtained have been analysed and appraised to induce the bottleneck locations, productivity and utilizations of the machines and material handling systems used in the design system. Finally, the best model have been developed to increase the productivity, utilizations of the machines, material handling systems and to minimize the bottleneck locations.

A Sequential Decomposition Framework and Method for Calibrating Dynamic Origin—Destination Demand in a Congested Network

This paper presents a two-stage model to calibrate the time-dependent, dynamic origin-destination (O–D) demand under congested traffic conditions. The first-stage model estimates O–D trip rates by minimizing link demand deviation with a one-norm formulation approach, so that over the calibration time period, the traffic demand on calibration links matches with the link demand from the field data. Due to its linear model structure, the first-stage model is more computationally effective and solvable on large real-life networks compared with the commonly seen least-square formulation. Then, a time-dependent user equilibrium traffic assignment model is formulated at the second stage to adjust the departure time profile iteratively, aiming to match the calibrated result with the field observed dynamic traffic condition, i.e., time-dependent speed profile. The second-stage model starts from the concept of a demand–capacity–volume relationship at a congested road segment, where demand exceeds supply, and utilizes shockwave theory to capture the differences between true demand and volume output, together with the idea of using travel time propagation between origin and bottleneck locations to infer real demand at the origin location. The two-stage model was implemented and tested in a case study in Tucson, AZ, USA, as an experimental proof of concept, which demonstrated the effectiveness of the proposed calibration framework and method under circumstances, in which the departure time profile was systematically distorted and initial demand solutions deviated from the true O–D matrices.

Lane-Changing Feedback Control for Efficient Lane Assignment at Motorway Bottlenecks

A feedback control strategy is proposed for lane assignment at bottleneck locations. The strategy assumes that some vehicles equipped with vehicle automation and communication systems are capable of receiving and executing specific lane-changing orders or recommendations. From a previously proposed optimal control strategy based on a simplified multilane motorway traffic flow model and formulated as a linear quadratic regulator, a feedback control problem was designed. The aim was to maximize the throughput at bottleneck locations while distributing the total density at the bottleneck area over the lanes according to a given policy by optimal lane assignment of the vehicles upstream of the bottleneck. The feedback control decisions were based on real-time measurements of the traffic state and inflow. The proposed strategy was tested on a nonlinear first-order macroscopic multilane traffic flow model, which also accounted for the capacity drop phenomenon.

Detection of urban traffic patterns from Floating Car Data (FCD)

Abstract: Real time data collection in traffic engineering is crucial for better traffic corridor control and management. In the literature, many data collection methods have been used such as; magnetic loops, road tube counters, piezo sensors, radars, Bluetooth etc. to estimate the link occupancy, average speed or density of a corridor. More recently, Floating Car Data (FCD) has become another important traffic data source and has an increasing usage due to its lower cost and higher coverage despite its reliability problems. FCD obtained from GPS equipped vehicles moving in the traffic can provide speed or travel speed data for many segments for even 1-min intervals in real-time. Though not totally diverse providing more than one of the traffic flow parameters, measuring the effectiveness of this extensive data source in detecting some critical urban traffic states is the ultimate goal of this study. As a case study, 1-min interval FCD for an urban arterial in Ankara has been collected during the morning peak hour for 2 months. Average speed values were transformed into a qualitative 4-scale state parameter based on the Level of Service (LOS) definitions for urban roads. Pattern searches over consecutive segment states using different search length (i.e. 2 segments, 3 segments, etc.) showed that FCD is capable to detect recurrent congestion or bottleneck locations, and even have an idea about the length of queue formed before the bottlenecks.