Traffic State Transition Research Articles

Insights into vehicle conflicts based on traffic flow dynamics

The utilization of traffic conflict indicators is crucial for assessing traffic safety, especially when the crash data is unavailable. To identify traffic conflicts based on traffic flow characteristics across various traffic states, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) thresholds. Different traffic states and corresponding transitions are identified with the three-phase traffic theory using high-resolution trajectory data. Meanwhile, the SSMs are mapped to the corresponding traffic states from the perspectives of time, space, and deceleration. Three models, including k-means, GMM, and Mclust, are investigated and compared to optimize the identification of traffic conflicts. It is observed that Mclust outperforms the others based on the evaluation metrics. According to the results, there is a variation in the distribution of traffic conflicts among different traffic states, wide moving jam (phase J) has the highest conflict risk, followed by synchronous flow (phase S), and free flow (phase F). Meanwhile, the thresholds of traffic conflicts cannot be fully represented by the same value through different traffic states. It reveals that the heterogeneity of thresholds is exhibited across traffic state transitions, which justifies the necessity of dynamic thresholds for traffic conflict analysis.

Elucidating Transition State Behaviour from Mobility Data by Cascades of Markov Chains

With the ongoing trend towards digitisation, vast amounts of, often very fine-grained, data are being collected. The ultimate goal is to capture and understand the behaviour of a system, such as the traffic in a city. However, making sense of such data is not straightforward due to its high level of detail and complex dependencies in time and space. Exploring heuristic approaches is essential to arrive at data representations that enable better insights into the underlying system dynamics by zooming out from the detail. In this paper, a novel approach for representing and reasoning about traffic state transition behaviour via a multitude of parameterised Markov chains models, cleverly designed to fit in a cascade, is proposed. The benefits of working with a multitude of individual Markov chains are outlined and subsequently, it is illustrated how to combine them into daily transition graphs such that their graph representation can be exploited to extract insights about daily traffic behaviour. In addition, targeting context-specific studies, an alternative approach is introduced combining in a dynamic fashion a cascade of Markov chains covering longer and overlapping time windows. A recursive algorithm is conceived and validated allowing to exploit this cascade structure for computing state transition probabilities over time. The potential of the proposed approach for mining traffic state transitions is demonstrated on a use case derived from real-world data.

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.

Spatiotemporal trajectory characteristic analysis for traffic state transition prediction near expressway merge bottleneck

The theoretical analysis of traffic flow with empirical vehicle trajectory data contained within this study allows for the explanation, reconstruction, and prediction of spatiotemporal transition characteristics of traffic conditions. Using an unmanned aerial vehicle (UAV) during a morning rush hour on a working day, observations of congestion evolution near an on-ramp bottleneck of an expressway was captured. The empirical high-fidelity trajectory data of 621 vehicles were extracted. The major findings include:(1) Macroscopic perspective: Three traffic states (free flow, metastable traffic flow, and jam) were observed with different spatiotemporal physical structures and their critical characteristics in a continuous first-order phase transition. The spontaneous spatiotemporal traffic breakdown and capacity drop were also captured, the critical points of which were also recorded. The features of widening synchronized flow pattern, general pattern, and the nature of nucleation described in the three-phase traffic theory were identified, which provides a new observational cognition for congestion formation and development.(2) Microscopic perspective: The impact of lane changes on immediate vehicles were modeled and quantified with the fluctuation magnitude of speed and the theoretical lag distance, which provides a method to identify the source of perturbation and determine the microscopic critical threshold of vehicle between macroscopic phase transitions. The research shows a lane changer could cause a forced deceleration of an immediate vehicle on a target lane after inserting itself with critical spacing headway below approximately 15–20 m. The average deceleration duration of vehicles in different traffic states can be captured as crucial driving features from downstream to help predict traffic state transition in a real bottleneck.

Hybrid Dynamic Traffic Model for Freeway Flow Analysis Using a Switched Reduced-Order Unknown-Input State Observer.

This paper introduces a new methodology for reconstructing vehicle densities of freeway segments by utilizing the limited data collected by traffic-counting sensors and developing a macroscopic traffic stream model formulated as a switched reduced-order state observer design problem with unknown or partially known inputs. Specifically, the traffic network is modeled as a hybrid dynamic system in a state space that incorporates unknown inputs. For freeway segments with traffic-counting sensors installed, vehicle densities are directly computed using field traffic count data. A reduced-order state observer is designed to analyze traffic state transitions for freeway segments without field traffic count data to indirectly estimate the vehicle densities for each freeway segment. A simulation-based experiment is performed applying the methodology and using data of a segment of Beijing Jingtong freeway in Beijing, China. The model execution results are compared with the field data associated with the same freeway segment, and highly consistent results are achieved. The proposed methodology is expected to be adopted by traffic engineers to evaluate freeway operations and develop effective management strategies.

Predicting Short-Term Traffic Speed Using a Deep Neural Network to Accommodate Citywide Spatio-Temporal Correlations

The traffic speed on a given road segment is affected by the current and past speeds on nearby segments, and the influence further cascades into the rest of a transport network. Thus, a successful forecasting model should consider not only the impact of neighboring road segments but also that of distant segments. Based on this principle, the approach proposed here projects the topology of a real traffic network into the structure of a deep neural network in order to accommodate citywide spatial correlations as well as temporal dependencies. This approach leads to interesting model interpretations in terms of traffic state transition and propagation, which form a basis for extending the proposed forecasting model. The present study was conducted with a large-scale data set collected over 10 months, and traffic speeds were successfully forecasted for 170 road segments in Gangnam, Seoul, Korea.

A novel generative adversarial network for estimation of trip travel time distribution with trajectory data

Knowledge of trip travel times serves an important role in transportation management and control. Existing travel time estimation approaches generally cover empirical ones, statistical ones and hybrid ones. Despite strong tractability, the empirical approaches cannot sufficiently capture diverse travel time distributions (TTDs) and often encounter some issues (e.g., assumption of a predefined distribution, failure of significance tests). Statistical and hybrid methods possess better generalization in estimating heterogeneous TTDs, but fail to model the network-wide spatiotemporal correlations, which have been found useful in the TTD estimation. To address these drawbacks, this paper proposes a deep learning based Trip Information Maximizing Generative Adversarial Network (T-InfoGAN). In this method, the trip TTD is estimated by modeling the joint distribution of travel times of two successive links with the consideration of network-wide spatiotemporal correlations. Meanwhile, a dynamic clustering with Wasserstein distance (DCWD) algorithm is used to explore the traffic state transitions for link pairs and cluster the link pairs with similar TTDs into one group, which benefits the training and estimation processes of T-InfoGAN. Then, based on GPS trajectory data from Didi Chuxing in Chengdu city, China, numerical results show that the T-InfoGAN with DCWD can well estimate three mini trip TTDs with various features, and performs better than three other counterparts (i.e., Convolution method, MC-Grid method, and MC-GMMS method) in estimating the TTDs of two longer trips. In summary, this study is the first successful try to estimate trip TTDs within the framework of Generative Adversarial Networks (GANs), and the deep learning based T-InfoGAN is a promising approach to estimate heterogeneous trip TTDs with the better generalization and flexibility in the big data era.

On the Spatial Evolution of Traffic State Transitions: Empirical Observations and General Features

Features of transitions between stationary traffic states for both congestion onset and dissipation were evaluated through empirical observations at two freeway sites. Results reveal that transition durations vary significantly between sites and that they can be rather long, with observed average transition durations of 42–45 min in one site. It was also observed that transition durations remain relatively constant within a queue, while transition rates vary as the transition zone travels upstream of the bottleneck. In addition, results also suggest that complex site geometry, particularly the presence of multiple lane reductions, has an effect on the spatial evolution of transition rates, while single merging and diverging maneuvers do not seem to affect the mechanism of transitions. Finally, it was observed that the presence of non-stationary traffic periods could have a considerable effect on the estimation of key traffic variables in locations where long transition durations are observed.

Traffic-Aware Energy Optimizing Strategies for Multi-Cell Coordinated Green Cellular Networks

The ongoing 5G cellular networks are expected to meet the demands posed by the energy and radio resource crises, especially exploiting flexible management for the traffic in idle zone. In this paper, two energy optimizing solutions are proposed for macro-tier with the tradeoff consideration regarding spectrum efficiency and quality of service (QoS), which adapt to the instantaneous traffic variance based upon dynamic cell configuration. To characterize the optimal energy savings for base stations (BSs), an active cell rotation (ACR) scheme is cyclic activated to control the number of active BSs by appropriately scheduling different work patterns in the cell group, and hence, guarantee QoS requirement of the user equipment by a recovery mechanism. An improved cooperative ACR scheme is also proposed, which enables BSs to achieve better spectrum utilization while pursuing energy efficiency in terms of traffic load. In addition, closed-form stationary results as well as asymptotic analysis are also derived for formulating the traffic variance and state transitions by a Markov-based framework. The numerical performances indicate that the proposed methodologies can have positive effects on addressing greenness in both energy and spectrum domains while meeting the upcoming intense traffic and QoS requirements.

Real-Time Associative Memory–Based Rear-End Collision Warning System

In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network–based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.

Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy

The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following.(1)Volume-density relationships of three submodels are unanimous with detector data.(2)When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger.(3)Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.

A traffic flow state transition model for urban road network based on Hidden Markov Model

Traffic guidance and prompt information could induce the change of traffic states on road sections, and in turn the effects of these changes will be transited to their relative upstream and downstream sections, which lead to dynamic variations in traffic states of urban regional road networks. In this paper, the rule of dynamic transition in traffic state of urban road networks under the effect of traffic information is studied. Specifically, the hidden Markov model is selected to represent the dynamic transition process. Then Expectation Maximization (EM) Algorithm is presented for potential traffic state estimation. Finally, verification is carried out through simulation with Variable Message Sign (VMS) selected as the information release terminal and then a certain regional road network in Beijing is chosen as the study object. The results show that the model in this paper can describe transition process of road traffic state under the effect of VMS information, and also can be used for real-time traffic state estimation of urban road networks. The study has both theoretical and practical values in evaluation of service quality of traffic information and in making traffic dispersion and control strategies for traffic management department.

Optimal velocity model with dual boundary optimal velocity function

ABSTRACTThis paper proposes a dual boundary optimal velocity model (DBOVM) by substituting a dual boundary optimal velocity function (DBOVF) for the original one in the optimal velocity model. The speed adjustment mechanism is introduced to describe the rule of car-following within the dual boundary steady region. Field data are studied, which verify the assumption of DBOVM. Properties of traffic state transition in the law of DBOVM are analysed. Besides, the dynamic properties and the stability of DBOVM are studied by means of numerical simulations. It is found that the DBOVM can describe the capacity drop and hysteresis loop phenomena quite well. The stability of DBOVM is found related to the initial conditions on both boundaries of DBOVF. The speed adjustment mechanism has positive effect on the stability of DBOVM.

An empirical study on the traffic state evolution and stop-and-go traffic development on freeways

Stop-and-go traffic, related to traffic breakdown and instability, is the core mechanism of traffic state transition to congestion. The purpose of this paper is to provide improved empirical understanding on the development of the stop-and-go traffic from asymmetric theory's viewpoint. We analyse traffic state transition before and after stop-and-go wave by observing platoon trajectories using NGSIM data. We observe that stop-and-go wave develops into growth (dissipation) in unstable (stable) traffic, which becomes stable (unstable) by passing the wave, respectively. Alternating patterns can explain recurring oscillatory traffic. Besides, we investigate the relationship between the evolution of stop-and-go wave and lane-changes. We can find that the growth region in the flow-density plane is located on the higher flow and density area above dissipation region. Also, as the number of lane-change increases (decreases), traffic will be developed to growth (dissipation), respectively. The findings of this research have potential for detailed traffic control over stop-and-go waves’ development.

Adaptive fine pollutant discharge control for motor vehicles tunnels under traffic state transition

Traffic flow dynamics is an important issue for implementing effective pollutant discharge control of tunnels. Longitudinal ventilation using jet fans is the most popular system for pollutant discharge control of tunnels. Nowadays, jet fans equipped with the frequency conversion technology in the tunnel can shorten the control cycle and even conduct manipulation of step-less jet speeds. The longitudinal ventilation system has considerable inertia and non-linear characteristics, which are partly resulted from traffic flow dynamics such as traffic state transition. Therefore, in this paper an adaptive control method based on the artificial neural-network theory is proposed to be tailored to the traffic state transition. The model is based on aerodynamic equations and takes vehicle speed and density as main system disturbances, whose value can be determined by fundamental diagram when having incomplete field traffic data. The proposed controller can also cope with the parameters and uncertainties of the time-varying model. The author's simulation results show that the adaptive control method can track the desirable system output effectively whenever the traffic condition changes gently or dramatically. The results also show that our method performs better than the common-used proportional integral derivative (PID) controller in terms of system adaptability following the traffic state transition.

A spatial–temporal-based state space approach for freeway network traffic flow modelling and prediction

Effective traffic flow prediction is an essential component of any proactive traffic control system and one of the pillars of an advanced traffic management system. Hence, the main objective of this study is to develop a general and flexible prediction model that simulates the freeway system as it responds to traffic state transition. To this end, a spatial–temporal-based model without the prior need for off-line parameter calibration is proposed for traffic flow prediction. The model predicts flow rates at a given freeway network based on contributions from their recent temporal profiles as well as the spatial contributions from neighbouring sites. An in-depth investigation of the variables pertinent to traffic flow prediction is conducted to examine the extent of the temporal and spatial contributions and their effects on prediction accuracy. The predictions of the proposed models have been compared with the field-measured flow rates, and show that the results predicted by the proposed model are quite close to the measurement values for all the investigated sites. The benefit from full-spatial contribution as opposed to no contribution at all (ARIMA model) is evident in all investigated sites, and the accuracy can be improved by 5% as an average. Compared with the linear regression model with only spatial contributions, the average prediction error of the proposed model is 9% lower. Through comparison, the result shows that the temporal input factor provides more accurate information to the flow rate prediction, but it can hardly track the flow rate drops during the congestion. During the flow rate drops, the spatial input factor would secure the prediction accuracy.

Adaptive fine pollutant discharge control for motor vehicles tunnels under traffic state transition

Adaptive fine pollutant discharge control for motor vehicles tunnels under traffic state transition

Adaptive fine pollutant discharge control for motor vehicles tunnels under traffic state transition

Traffic flow dynamics is an important issue for implementing effective pollutant discharge control of tunnels. Longitudinal ventilation using jet fans is the most popular system for pollutant discharge control of tunnels. Nowadays, jet fans equipped with the frequency conversion technology in the tunnel can shorten the control cycle and even conduct manipulation of step-less jet speeds. The longitudinal ventilation system has considerable inertia and non-linear characteristics, which are partly resulted from traffic flow dynamics such as traffic state transition. Therefore, in this paper an adaptive control method based on the artificial neural-network theory is proposed to be tailored to the traffic state transition. The model is based on aerodynamic equations and takes vehicle speed and density as main system disturbances, whose value can be determined by fundamental diagram when having incomplete field traffic data. The proposed controller can also cope with the parameters and uncertainties of the time-varying model. The author's simulation results show that the adaptive control method can track the desirable system output effectively whenever the traffic condition changes gently or dramatically. The results also show that our method performs better than the common-used proportional integral derivative (PID) controller in terms of system adaptability following the traffic state transition.

Exploring Bias in Traffic Data Aggregation Resulting from Transition of Traffic States

Transportation engineers and researchers heavily use traffic data, which are generally aggregated by predetermined time intervals (e.g., 5 to 15 min). The aggregation process often discards essential information of traffic state transition (e.g., breakdowns). However, the transition of traffic conditions within an aggregation interval is not well understood. This study explored traffic state transition from uncongested to congested regimes that occurred within a predetermined time interval. From two urban freeway locations in Norfolk, Virginia, traffic data archived at 15-min intervals were obtained. A heuristic method based on a Gaussian mixture model was developed to detect the aggregate traffic data that exhibit the transition of traffic states as well as to partition the data statistically into uncongested and congested traffic states. Results show a substantial difference in travel speed (approximately 20 mph) between the two states. In addition, these results illustrate that aggregating these different traffic conditions can cause substantial traffic data aggregation bias by lowering travel speed and flow rates, especially in high traffic flow situations. Finally, new insights into valid traffic data aggregation and speed–flow–concentration relationship development are discussed.

Urban Regional Traffic State Analysis Software System Emphasizing Pattern Transition

Urban traffic state evolution analysis is very significant and constructive for traffic guidance and control. In this paper, firstly, a quantitative method for analyzing regional traffic state evolution was proposed by constructing traffic state pattern transition network to mine regional traffic state information and state pattern transition characteristics from massive data. Secondly, a GIS-based urban regional traffic state analysis soft ware system URTSAS based on the method was designed and implemented. Application scenarios and effect shows that traffic state transition analysis in URTSAS has a wide application prospect in improving urban traffic management and decision-making.