Recurrent Bottlenecks Research Articles

Variable Speed Limit Control for the Motorway–Urban Merging Bottlenecks Using Multi-Agent Reinforcement Learning

Traffic congestion is a typical phenomenon when motorways meet urban road networks. At this special location, the weaving area is a recurrent traffic bottleneck. Numerous research activities have been conducted to improve traffic efficiency and sustainability at bottleneck areas. Variable speed limit control (VSL) is one of the effective control strategies. The primary objective of this paper is twofold. On the one hand, turbulent traffic flow is to be smoothed on the special weaving area of motorways and urban roads using VSL control. On the other hand, another control method is provided to tackle the carbon dioxide emission problem over the network. For both control methods, a multi-agent reinforcement learning algorithm is used (MAPPO: multi-agent proximal policy optimization). The VSL control framework utilizes the real-time traffic state and the speed limit value in the last control step as the input of the optimization algorithm. Two reward functions are constructed to guide the algorithm to output the value of the dynamic speed limit enforced within the VSL control area. The effectiveness of the proposed control framework is verified via microscopic traffic simulation using simulation of urban mobility (SUMO). The results show that the proposed control method could shape a more homogeneous traffic flow, and reduces the total waiting time over the network by 15.8%. In the case of the carbon dioxide minimization strategy, the carbon dioxide emission can be reduced by 10.79% in the recurrent bottleneck area caused by the transition from motorways to urban roads.

Immigration and seasonal bottlenecks: high inbreeding despite high genetic diversity in an oscillating population of Culicoides sonorensis (Diptera: Ceratopogonidae).

Most population genetic studies concern spatial genetic differentiation, but far fewer aim at analyzing the temporal genetic changes that occur within populations. Vector species, including mosquitoes and biting midges, are often characterized by oscillating adult population densities, which may affect their dispersal, selection, and genetic diversity over time. Here, we used a population of Culicoides sonorensis from a single site in California to investigate short-term (intra-annual) and long-term (inter-annual) temporal variation in genetic diversity over a 3 yr period. This biting midge species is the primary vector of several viruses affecting both wildlife and livestock, thus a better understanding of the population dynamics of this species can help inform epidemiological studies. We found no significant genetic differentiation between months or years, and no correlation between adult populations and the inbreeding coefficient (FIS). However, we show that repeated periods of low adult abundance during cooler winter months resulted in recurring bottleneck events. Interestingly, we also found a high number of private and rare alleles, which suggests both a large, stable population, as well as a constant influx of migrants from nearby populations. Overall, we showed that the high number of migrants maintains a high level of genetic diversity by introducing new alleles, while this increased diversity is counterbalanced by recurrent bottleneck events potentially purging unfit alleles each year. These results highlight the temporal influences on population structure and genetic diversity in C. sonorensis and provide insight into factors effecting genetic variation that may occur in other vector species with fluctuating populations.

TD3LVSL: A lane-level variable speed limit approach based on twin delayed deep deterministic policy gradient in a connected automated vehicle environment

Variable speed limit (VSL) control plays a vital role in the emerging connected automated vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by capacity drop and accidents. However, the effect of the VSL on improving traffic efficiency is still controversial. It is necessary to study how to explore the potential benefits of the VSL by balancing its influence on reducing crash risk and enhancing traffic efficiency. To fill the technological gap above, this paper proposes a reinforcement learning-based lane-level VSL (LVSL) control approach to conduct refined traffic control on the mainlines. Firstly, an actor-critic framework is developed to generate and evaluate the discrete speed limits of each lane in continuous action space. To optimize traffic control performance, a hybrid reward function is developed by synchronously considering traffic safety and traffic efficiency of the bottleneck area. Then, to solve the overestimation bias problem of the actor-critic methods caused by function approximation error, a twin delayed deep deterministic policy gradient (TD3) method is introduced to train the framework of the LVSL method. Finally, a real-world recurrent bottleneck of the State Route 91 highway in California is simulated with consideration of the connected automated vehicles to examine the performance of the TD3-based LVSL (TD3LVSL) controller. The simulation results reveal that the proposed method is capable of reducing crash risk and improving traffic efficiency synchronously. Compared with the LVSL controller based on the deep deterministic policy gradient approach, the TD3LVSL controller shows better performance in terms of traffic safety and efficiency. These findings indicate that the proposed controller could contribute to future traffic control in a CAVH environment.

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.

Sweepstakes reproduction facilitates rapid adaptation in highly fecund populations.

Adaptation enables natural populations to survive in a changing environment. Understanding the mechanics of adaptation is therefore crucial for learning about the evolution and ecology of natural populations. We focus on the impact of random sweepstakes on selection in highly fecund haploid and diploid populations partitioned into two genetic types, with one type conferring selective advantage. For the diploid populations, we incorporate various dominance mechanisms. We assume that the populations may experience recurrent bottlenecks. In random sweepstakes, the distribution of individual recruitment success is highly skewed, resulting in a huge variance in the number of offspring contributed by the individuals present in any given generation. Using computer simulations, we investigate the joint effects of random sweepstakes, recurrent bottlenecks and dominance mechanisms on selection. In our framework, bottlenecks allow random sweepstakes to have an effect on the time to fixation, and in diploid populations, the effect of random sweepstakes depends on the dominance mechanism. We describe selective sweepstakes that are approximated by recurrent sweeps of strongly beneficial allelic types arising by mutation. We demonstrate that both types of sweepstakes reproduction may facilitate rapid adaptation (as defined based on the average time to fixation of a type conferring selective advantage conditioned on fixation of the type). However, whether random sweepstakes cause rapid adaptation depends also on their interactions with bottlenecks and dominance mechanisms. Finally, we review a case study in which a model of recurrent sweeps is shown to essentially explain population genomic data from Atlantic cod.

The Prospects of gene introgression from crop wild relatives into cultivated lentil for climate change mitigation.

Crop wild relatives (CWRs), landraces and exotic germplasm are important sources of genetic variability, alien alleles, and useful crop traits that can help mitigate a plethora of abiotic and biotic stresses and crop yield reduction arising due to global climatic changes. In the pulse crop genus Lens, the cultivated varieties have a narrow genetic base due to recurrent selections, genetic bottleneck and linkage drag. The collection and characterization of wild Lens germplasm resources have offered new avenues for the genetic improvement and development of stress-tolerant, climate-resilient lentil varieties with sustainable yield gains to meet future food and nutritional requirements. Most of the lentil breeding traits such as high-yield, adaptation to abiotic stresses and resistance to diseases are quantitative and require the identification of quantitative trait loci (QTLs) for marker assisted selection and breeding. Advances in genetic diversity studies, genome mapping and advanced high-throughput sequencing technologies have helped identify many stress-responsive adaptive genes, quantitative trait loci (QTLs) and other useful crop traits in the CWRs. The recent integration of genomics technologies with plant breeding has resulted in the generation of dense genomic linkage maps, massive global genotyping, large transcriptomic datasets, single nucleotide polymorphisms (SNPs), expressed sequence tags (ESTs) that have advanced lentil genomic research substantially and allowed for the identification of QTLs for marker-assisted selection (MAS) and breeding. Assembly of lentil and its wild species genomes (~4Gbp) opens up newer possibilities for understanding genomic architecture and evolution of this important legume crop. This review highlights the recent strides in the characterization of wild genetic resources for useful alleles, development of high-density genetic maps, high-resolution QTL mapping, genome-wide studies, MAS, genomic selections, new databases and genome assemblies in traditionally bred genus Lens for future crop improvement amidst the impending global climate change.

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.

Modeling the genetic footprint of fluctuating balancing selection: from the local to the genomic scale.

Natural selection not only affects the actual loci under selection but also leaves "footprints" in patterns of genetic variation in linked genetic regions. This offers exciting opportunities for inferring selection and for understanding the processes shaping levels of genetic variation in natural populations. Here, we develop analytical approximations based on coalescent theory to characterize the genetic footprint of a complex, but potentially common type of natural selection: balancing selection with seasonally fluctuating allele frequencies. As we show analytically and confirm with stochastic simulations, seasonal allele frequency fluctuations can have important (and partly unexpected) consequences for the genetic footprint of balancing selection. Fluctuating balancing selection generally leads to an increase in genetic diversity close to the selected site, the effect of balancing selection, but reduces diversity further away from the selected site, which is a consequence of the allele-frequency fluctuations effectively producing recurrent bottlenecks of allelic backgrounds. This medium- and long-range reduction usually outweighs the short-range increase when averaging diversity levels across the entire chromosome. Strong fluctuating balancing selection even induces a loss of genetic variation in unlinked regions, e.g. on different chromosomes. If many loci in the genome are simultaneously under fluctuating balancing selection this can lead to substantial genome-wide reductions in genetic diversity, even when allele-frequency fluctuations are small and local footprints are difficult to detect. Thus, together with genetic drift, selective sweeps and background selection, fluctuating selection could be a major force shaping levels of genetic diversity in natural populations.

Viability Selection at Linked Sites

Evolutionary ecology may be described as explaining ecology through evolution and vice versa, but one may also view it as an integration of the two fields, where one takes the view that ecology and evolution are inseparable, and one can only begin to understand the biology of organisms by synthesizing the two fields. An example of such a synthesis is the biology of high fecundity and the associated concept of sweepstakes reproduction, or skewed individual recruitment success. As an illustration, we consider selection at linked sites under various dominance and epistasis mechanisms in a diploid population evolving according to random sweepstakes and experiencing recurrent bottlenecks. Using simulations, we give a few examples of the impact of the stated elements on selection. We show that depending on the dominance mechanisms, random sweepstakes can shorten the time to fixation (conditional on fixation) of the fit type at all sites. Bottlenecks tend to increase the fixation time, with random sweepstakes counteracting the effects of bottlenecks on the fixation time. Understanding the effect of random sweepstakes, recurrent bottlenecks, dominance mechanisms and epistasis on the fate of selectively advantageous mutations may help with explaining genetic diversity in natural highly fecund populations possibly evolving under sweepstakes reproduction.

Pre-domestication bottlenecks of the cultivated seaweed Gracilaria chilensis.

Gracilaria chilensis is the main cultivated seaweed in Chile. The low genetic diversity observed in the Chilean populations has been associated with the over-exploitation of natural beds and/or the founder effect that occurred during post-glacial colonization from New Zealand. How these processes have affected its evolutionary trajectory before farming and incipient domestication is poorly understood. In this study, we used 2232 single nucleotide polymorphisms (SNPs) to assess how the species' evolutionary history in New Zealand (its region of origin), the founder effect linked to transoceanic dispersion and colonization of South America, and the recent over-exploitation of natural populations have influenced the genetic architecture of G. chilensis in Chile. The contrasting patterns of genetic diversity and structure observed between the two main islands in New Zealand attest to the important effects of Quaternary glacial cycles on G. chilensis. Approximate Bayesian Computation (ABC) analyses indicated that Chatham Island and South America were colonized independently near the end of the Last Glacial Maximum and emphasized the importance of coastal and oceanic currents during that period. Furthermore, ABC analyses inferred the existence of a recent and strong genetic bottleneck in Chile, matching the period of over-exploitation of the natural beds during the 1970s, followed by rapid demographic expansion linked to active clonal propagation used in farming. Recurrent genetic bottlenecks strongly eroded the genetic diversity of G. chilensis prior to its cultivation, raising important challenges for the management of genetic resources in this incipiently domesticated species.

Integrated Traffic Control for Freeway Recurrent Bottleneck Based on Deep Reinforcement Learning

Recent advances in deep reinforcement learning have shown promising results in solving sophisticated control problems with high dimensional states and action space. Inspired by this, we use the latest deep reinforcement learning (DRL) methods to improve freeway traffic mobility and alleviate recurring bottlenecks and congestion. More specifically, this paper proposes a centralized traffic control system that can coordinate multiple ramp metering (RM) and variable speed limit (VSL) traffic controllers on freeways to minimize the total travel time. The system uses a novel double-layer structure to synchronize different traffic controllers and introduces the actor-critic-based DRL methods to learn joint actions in a high-dimensional traffic environment. The reward function takes into account the waiting time of vehicles, the average speed of different road sections, and the on-ramp queuing limit to improve traffic mobility. We also proposed an integrated feedback controller as a benchmark. The simulation results show that the actor-critic-based methods are superior to other methods and can save more than 20% of the total travel time. We also analyzed the curse of dimensionality problem by comparing the performance of two scenarios in the simulation: one is a single-ramp interweaving area scenario; the other is a large freeway corridor with multiple on-ramps and off-ramps. The results show that our system can effectively handle these two situations without significant performance degradation, which means that the centralized control system can effectively control freeway corridors by directly guiding various traffic controllers. This also leads to the conclusion that we can use a centralized actor-critic-based control unit to manage medium-scale freeway traffic to save computing resources instead of using complex collaboration strategies.

Resolution strategies for cooperative vehicle fleets for reducing rear-end collision risks near recurrent freeway bottlenecks

Advanced cooperative vehicle fleet technology has shown potential for improving safety and efficiency near freeway bottlenecks. This study focuses on resolution strategies for cooperative vehicle fleets for reducing rear-end collision risks near recurrent bottlenecks. Based on different vehicle types and time requirements and intentions, different resolution strategies are proposed to determine the travel rules and sequence of vehicle flows when a cooperative vehicle fleet needs to change its original fixed formation. Then, a series of experimental scenarios is designed by setting different vehicle types (conventional vehicles and connected and automated vehicles), requirements and intentions in different areas of multilane freeways. The experimental scenarios are simulated based on the MATLAB platform to test the proposed strategy. The results show that the proposed strategy can effectively reduce vehicle rear-end collisions, and the average cost savings of the conflict resolution process is 25.71%. The proposed strategies in this research provide autonomous resolutions for vehicle conflicts in different scenarios.

Statistical Distance-Based Travel-Time Reliability Measurement for Freeway Bottleneck Identification and Ranking

Freeway bottleneck identification is an essential component in the process of deploying mitigation strategies to reduce congestion at freeway bottlenecks. Most previous studies on bottleneck identification focus on recurrent bottlenecks, and limited work has been conducted to identify the locations of non-recurrent bottlenecks. Therefore, in this study, we propose a new travel time reliability (TTR) measurement and develop a freeway bottleneck identification method based on this measurement, which can identify with high probability not only recurrent bottlenecks but also the locations of non-recurrent bottlenecks. The TTR measurement is developed based on statistical distance between travel time distributions. Three statistical distance measurements, Jensen–Shannon divergence, Wasserstein distance, and Hellinger distance, are applied in the TTR measurement. The bottleneck identification method is evaluated in a case study on I-15 freeway corridor in Salt Lake City, Utah. The three statistical distance measurements show good consistency in ranking locations by the impacts of recurrent and non-recurrent congestion, especially for extreme cases with very high or low variation between travel time distributions. The recurrent bottlenecks identified in this study show their clustering characteristics, which is similar to the generating and dismissing process of recurrent congestion. The locations with high probability of non-recurrent bottlenecks scatter both spatially and temporally, which agrees with the random characteristic of non-recurrent congestion.

Genetic differentiation in the genus Characodon: implications for conservation and taxonomy.

The subfamily Goodeinae is a group of fishes endemic to the Mexican highlands. Most of the species are restricted to small and isolated streams or springs. Within this subfamily, the genus Characodon is the earliest diverging lineage of which three species have been described: C. lateralis, C. audax, and C. garmani, with the latter, considered extinct. Characodon lateralis and C. audax are classed as endangered, and have been the subject of taxonomic controversy since their description: previous studies have recognized a genetic differentiation in two groups separated by the El Salto waterfall, but morphological analyses contradict these genetic results. We perform a phylogeographic study using the mitochondrial cytb gene and d-loop region to elucidate the evolutionary history of C. lateralis and C. audax. The results with both markers show the presence of two highly differentiated haplogroups; one distributed north and the other distributed south of the waterfall, with genetic distances of 1.7 and 13.1% with cytb and d-loop respectively, and divergence calculated to have occurred 1.41 Mya. Significant genetic structure was found within each haplogroup and suggests the existence of at least four Evolutionary Significant Units (ESUs) within the examined populations. The possible processes identified as contributing to the formation of differentiated genetic groups are isolation, low population size, recurrent bottlenecks, and the strong sexual selection exhibited by the genus.

Evaluating Safety of Type-A Weaving Sections Using Geometric and Traffic Operational Factors

The efficiency and safety of type-A freeway weaving sections in urban areas are constrained by recurrent bottlenecks. Limited space in freeway weaving sections cause traffic congestion and crashes during peak-hours. Various factors, including length of weaving section, continuity of lanes, and number of lanes will have significant effects on the level of service and safety performance of the weaving sections. Eight years (2010-2017) of crash data in the type-A weaving sections was used in this analysis. The objective of this study aims to evaluate geometric design factors and operational factors on total crashes and each of the four crash types: rear-end, sideswipe, angle, and single-vehicle in type-A weaving sections using traditional negative binomial approach and develop crash modification factors (CMFs) to improve safety in the type-A weaving section. The results revealed that on-ramp traffic per hour, off-ramp traffic per hour, non-weaving traffic per hour, weaving ratio, length of the weaving section, direction of the freeway, width of inside shoulder, and width of outside shoulder were influencing crashes in type-A weaving sections. Furthermore, the estimated crash modification factors (CMFs) result revealed that total crashes gradually decrease as inside shoulder width increases. This implies that widening inside shoulder width have positive effects on weaving section safety. In addition, ramp metering, and advisory warning signs could improve safety in type-A weaving sections.

Evolved to vary: genome and epigenome variation in the human pathogenHelicobacter pylori

Helicobacter pylori is a Gram-negative, spiral shaped bacterium that selectively and chronically infects the gastric mucosa of humans. The clinical course of this infection can range from lifelong asymptomatic infection to severe disease, including peptic ulcers or gastric cancer. The high mutation rate and natural competence typical of this species are responsible for massive inter-strain genetic variation exceeding that observed in all other bacterial human pathogens. The adaptive value of such a plastic genome is thought to derive from a rapid exploration of the fitness landscape resulting in fast adaptation to the changing conditions of the gastric environment. Nevertheless, diversity is also lost through recurrent bottlenecks and H. pylori's lifestyle is thus a perpetual race to maintain an appropriate pool of standing genetic variation able to withstand selection events. Another aspect of H. pylori's diversity is a large and variable repertoire of restriction-modification systems. While not yet completely understood, methylome evolution could generate enough transcriptomic variation to provide another intricate layer of adaptive potential. This review provides an up to date synopsis of this rapidly emerging area of H. pylori research that has been enabled by the ever-increasing throughput of Omics technologies and a multitude of other technological advances.

Reliability Measure-Based Data Analytics Approach to Identifying and Ranking Recurrent Bottlenecks in Urban Rail Transit Networks

AbstractRecurrent bottlenecks in the urban rail transit (URT) system during rush hours have become a significant concern for both passengers and URT operators. In order to alleviate the resulting p...

Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm

Variable speed limit (VSL) control is a flexible way to improve traffic conditions, increase safety, and reduce emissions. There is an emerging trend of using reinforcement learning methods for VSL control. Currently, deep learning is enabling reinforcement learning to develop autonomous control agents for problems that were previously intractable. In this paper, a more effective deep reinforcement learning (DRL) model is developed for differential variable speed limit (DVSL) control, in which dynamic and distinct speed limits among lanes can be imposed. The proposed DRL model uses a novel actor-critic architecture to learn a large number of discrete speed limits in a continuous action space. Different reward signals, such as total travel time, bottleneck speed, emergency braking, and vehicular emissions are used to train the DVSL controller, and a comparison between these reward signals is conducted. The proposed DRL-based DVSL controllers are tested on a freeway with a simulated recurrent bottleneck. The simulation results show that the DRL based DVSL control strategy is able to improve the safety, efficiency and environment-friendliness of the freeway. In order to verify whether the controller generalizes to real world implementation, we also evaluate the generalization of the controllers on environments with different driving behavior attributes. and the robustness of the DRL agent is observed from the results.

A dynamic merge assistance method based on the concept of instantaneous virtual trajectory for vehicle-to-infrastructure connected vehicles

Lane-changing activities near freeway merging, diverging, and weaving sections are one of the major factors leading to recurrent bottleneck congestion. In recent years, microscopic dynamic merging assistance (DMA) methods are found efficient to improve mobility and safety in merging areas. The article proposes a solution, the connected vehicle (CV) vehicle-to-infrastructure (V2I)-based dynamic merge assistance (DMA) method, analyzing vehicle trajectory data collected at CV roadside units (RSU) and implements a “vehicle-gap” pairing process for vehicle-gap synchronized control. The vehicle-gap pairing is determined by dynamically predicting their merging potential per the relationship between the “instantaneous virtual trajectories” (IVTs) of mainline and onramp vehicles. The IVTs are generated according to the prevailing speed profiles on both ramp and mainline through lane. Other than those automated control-based methods, this article focuses on providing a realistic speed instruction to human drivers through CV communication. A Cooperative Adaptive Cruise Control model is adopted to calculate such speed instruction. The paired onramp/mainline vehicles who follow the speed instruction can form a putative platoon so that their speed and location can be synchronized all through the merging area to ensure the gap-opening and catching-up. The proposed method is evaluated within a simulation network based on the field data collected on Interstate 35 at Austin, TX. The proposed method is tested in a microscopic traffic simulation environment using VISSIM external driver model Application Programing Interface. The simulation results indicated significant reduced average travel time at merge sections during congestion and increased time-to-collision during merging compared with other existing microscopic merge control models.

Recurrent Congestion Impact based on Spatiotemporally Historic Congested Information — Case Study: Separating Collision-Induced Congestion

Transportation jurisdictions should monitor mobility and reliability of roadway systems in order to adequately invest capacity expansion and deployment of ITS technologies to alleviate congestion effectively and efficiently. In recent years, several link-based bottleneck identification schemes have estimated bottleneck impact factors on freeways based on the characteristics of congestion. However, those have used congestion data with no attention to distinguishing recurrent level of at the same “bottleneck” location. Most existing studies that distinguish between recurrent and non-recurrent congestion have focused on separating non-recurrent congestion from recurrent congestion only for intensity of congestion using parameters for the speed distribution in a time of day in a segment or point. As such, this study introduced a data-driven procedure for quantifying spatiotemporal “recurrent” congestion impact. In addition, this study used spatiotemporally historic congestion information and generated stochastic spatiotemporal congestion distributions in terms of congestion types. Using the relationship between the distributions of recurrent and non-recurrent congestion occurring at bottlenecks, the bottleneck impacts were estimated by capturing spatial and temporal impact of recurrent bottleneck from that of non-recurrent congestion occurring at recurrent bottleneck. The proposed approach represents a significant improvement in the understanding and monitoring of mobility on freeways. This can be directly applied to evaluate and rank bottlenecks.