Future Traffic Conditions Research Articles

Deep reinforcement learning enabled self-learning control for energy efficient driving

To address the air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions; or heavily relying on accurate prediction of future traffic conditions. Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks (e.g., play chess) in recent years, which shows the great potential of such methods in practical engineering problems. In this study, a deep reinforcement learning (Deep Q-network or DQN) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment. It is a fully data-driven and self-learning model that does not rely on any prediction, predefined rules or even prior human knowledge. The experiment results show that the proposed model is capable of achieving 16.3% energy savings (with the designed PHEV simulation model) on a typical commute trip, compared to conventional binary control strategies. In addition, a dueling Deep Q-network with dueling structure (DDQN) is also implemented and compared with single DQN in particular with respect to the convergence rate in the training process.

Proactive highway traffic control with intelligent multi-objective optimisation algorithm

The great increase in car ownership has led to the daily recurrence of traffic congestion. Thus, traffic mobility, safety and emission concerns have become the most serious challenges for transportation researchers. To mitigate traffic congestion, a variety of proactive traffic-control strategies, such as ramp metering (RM), have been intensively investigated and deployed. With the aim of improving freeway traffic conditions, RM regulates the on-ramp flows dynamically in response to dynamic road conditions. However, most early RM strategies focus on optimising the traffic from one single aspect. This paper presents an RM control algorithm that predicts and evaluates the RM-controlled future traffic states. The impact of RM control was evaluated using a macroscopic traffic-flow model. The designed RM control algorithm possesses a multi-objective optimisation module, which improves the traffic network from the aspects of mobility, safety and emissions. The designed algorithm is evaluated through simulation and calibrated using field data collected over an 11 km major freeway stretch in Edmonton, Alberta, Canada. The comparison of the proposed algorithm-controlled scenario and the uncontrolled scenario shows that the proposed RM control algorithm can effectively relieve traffic congestion, improve safety and reduce carbon emissions concurrently.

Traffic Flow Data Prediction Using Residual Deconvolution Based Deep Generative Network

Traffic flow prediction is quite crucial for estimating the future traffic states, efficient and accurate prediction models greatly contribute to the smooth traffic of road networks. However, existing methods mainly concentrate on short-term prediction. The challenging task of long-term flow prediction for the next day, as the important reference of traffic management, is still not well solved. In this paper, we present a residual deconvolution based deep generative network (RDBDGN) to handle the problem of long-term traffic flow prediction. The proposed method consists of a generator and a discriminator. The generator is composed of multi-channel residual deconvolutional neural networks, and the discriminator contains a convolutional neural network which aims to optimize the adversarial training process. The experiments are evaluated based on the traffic flow data of elevated highways, presented results demonstrate that our approach outperforms the state-of-the-art works.

A hybrid prediction approach for road tunnel traffic based on spatial-temporary data fusion

In this paper, we propose a hybrid prediction model based on spatial-temporal data fusion to predict future tunnel traffic. Our approach consists of a local predictor, a global predictor, an outlier predictor, and a prediction integrator. Firstly, the local predictor forecast tunnel traffic based on the collected local data. It is more concerned with the historical and future traffic conditions, that is, the temporal correlation. Then, the global predictor uses data collected from peripheral road segments to predict tunnel volume, which models the spatial correlation based on a deep learning network. Thirdly, the prediction integrator dynamically integrates the prediction results of local and global predictors on the basis of the current weather conditions. In addition, we detect the abnormal traffic volume for training an individual outlier predictor. Finally, we integrate it with the output of the prediction integrator and accumulate the current tunnel traffic volume to calculate final prediction results. In our experiments, we collected the multisource urban-awareness data from Shanghai to evaluate the proposed hybrid prediction model. Our approach is obviously superior to the baseline when dealing with the general condition. The MREs of 30 min and 60 min tunnel traffic volume prediction are less than 6.5%. In addition, the outlier predictor of the proposed model significantly enhances the ability to predict the abnormal tunnel traffic under extreme weather conditions or unexpected traffic accidents.

Construction of traffic state vector using mutual information for short-term traffic flow prediction

Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction.

Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections.

Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.

Modeling and Characterization of Traffic Flows in Urban Environments.

Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles.

A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting

Accurate and timely traffic flow forecasting is essential for many intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Over the past two decades, a variety of traffic flow forecasting models have been proposed. While each model has its merits and can achieve satisfactory forecasting results under certain traffic conditions, it is difficult for a single model to deal with various conditions well. In this paper, we proposed a novel deep learning-based multimodel integration framework in order to overcome the limitations of previous methods in dealing with large variations and uncertainties of traffic flow and hence improve the forecasting accuracy. Our framework can dynamically choose an optimal model or an optimal subset of models from a set of candidate models to forecast the future traffic flow conditions according to current input data. We employ stacked autoencoder (SAE), a simple yet efficient deep learning architecture, to extract the implicit relationships hidden in the traffic flow data and employed labeled data to fine tune the parameters of the architecture. Compared with the hand-crafted features and explicable dependence relations leveraged in previous models, the features learning from SAE are more representative and hence have more powerful forecasting capability. In addition, we propose a model-driven scheme to automatically label the training data and develop three strategies to integrate multiple models. Extensive experiments performed on three typical traffic flow datasets demonstrate the proposed framework outperforms state-of-the-art models and achieves much more accurate forecasting results under large and sudden variations.

A methodology for forecasting freeway travel time reliability using GPS data

Abstract The objective of this paper is to develop a methodology for forecasting freeway vehicle travel time reliability for transportation planning using probe GPS data. Travel time reliability is measured using the coefficient of variation of the GPS spot (instantaneous) speed distribution. The proposed approach establishes relationships between travel time reliability and roadway traffic density in order to forecast reliability given future traffic conditions. The travel time reliability and traffic density datasets are segmented into different homogenous groups using the K-means cluster algorithm and the corresponding reliability-density relationship of each cluster is fitted by minimizing squared errors. This paper employs a truck probe GPS dataset as an example to demonstrate the proposed approach. The approach can be applied with any GPS datasets for forecasting reliability.

Evaluating Traffic Congestion Using the Traffic Occupancy and Speed Distribution Relationship: An Application of Bayesian Dirichlet Process Mixtures of Generalized Linear Model

Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.

Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion

With the development of connected vehicle (CV) and Vehicle to X (V2X) communication, more traffic data is being collected from the road network. In order to predict future traffic condition from connected vehicles’ data in real-time, we present an online traffic condition evaluation model utilizing V2X communication. This model employs the Analytic Hierarchy Process (AHP) and the multilevel fuzzy set theory to fuse multiple sources of information for prediction. First, the contemporary vehicle data from the On Board Diagnostic (OBD) is fused with the static road data in the Road Side Unit (RSU). Then, the real-time traffic evaluation scores are calculated using the variable membership model. The real data collected by OBU in field test demonstrates the feasibility of the evaluation model. Compared with traditional evaluation systems, the proposed model can handle more types of data but demands less data transfer.

A Study on the Traffic Predictive Cruise Control Strategy With Downstream Traffic Information

A vehicle traffic predictive cruise control (TPCC) system, responding to the change of downstream traffic situation, has been proposed to improve traffic operation and the fuel efficiency of vehicle based on the asymmetric traffic theory. The proposed predictive cruise control system consists of four parts: 1) deceleration-based safety surrogate measure (DSSM); 2) single-vehicle control algorithm; 3) multivehicle safety measurement (Co-DSSM); and 4) TPCC. The single-vehicle control algorithm basically decides the acceleration action based on estimated safety state between a subject vehicle and the immediate preceding vehicle, and the control strategy is determined by asymmetric driving behavior. Then, TPCC adjusts the amount of acceleration based on the Co-DSSM from multiple downstream vehicles, which contains the information on future traffic condition of the subject vehicle. A simulation using the real vehicle trajectories from the Next Generation Simulation (NGSIM) data validates the proposed TPCC system, and we compare the results with the real vehicles' car-following patterns. It is found that the proposed TPCC system can contribute to both the energy consumption and traffic flow operation by obtaining a higher level of traffic stability. Such results are due to the effects of suppressing the shockwave from downstream traffic and removing the unnecessary deceleration and acceleration actions.

Comparative analysis of implicit models for real‐time short‐term traffic predictions

Predicting future traffic conditions in real-time is a crucial issue for applications of intelligent transportation systems devoted to traffic management and traveller information. The increasing number of connected vehicles equipped with locating technologies provides a ubiquitous updated source of information on the whole network. This offers great opportunities for developing data-driven models that extrapolate short-term future trend directly from data without modelling traffic phenomenon explicitly. Among several different approaches to implicit modelling, machine-learning models based on a network structure are expected to be more suitable to catch traffic phenomenon because of their capability to account for spatial correlations existing between traffic measures taken on different elements of the road network. The study analyses and applies different implicit models for short-term prediction on a large road network: namely, time-dependent artificial neural networks and Bayesian networks. These models are validated and compared by exploiting a large database of link speeds recorded on the metropolitan area of Rome during seven months.

A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.

Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction

Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.

Dynamic route planning with real-time traffic predictions

Situation aware route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio-Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows us to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system with a real-world use-case from Dublin city, Ireland.

Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed.

Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

Virtual Patrolling

Approximately 25 per cent of all congestion on motorways is caused by incidents. By virtual patrolling, incidents e.g. queues, accidents and car breakdowns on a road network, can be predicted or detected in an early stage. This early detection and prediction of an incident likely to happen, offers the opportunity to act faster and therefore reduce congestion compared to current practice. A module is developed capable of virtual patrolling and based on four key features: data fusion, real-time estimation of the fundamental diagram, fuzzy traffic state estimation and artificial neural networks. In CHARM, the cooperation between the Highways England (UK) and Rijkswaterstaat (NL), the potential of this module has been demonstrated in a simulation environment and in 2015 further developed, deployed and tested in real life.Extended Kalman filtering and traffic flow theory algorithms are used to fuse data from loop detectors with FVD (floating vehicle data) to provide detailed traffic state data (i.e. flow and speed for 10seconds for every segment of approximately 200 meters). For the prediction of the future traffic state, information on the actual road capacity is necessary. Road capacity varies for weather, lightness, number of heavy vehicles et cetera. A self-adapting module is used to make a real time estimation of the fundamental diagram and road capacity. The traffic state estimation itself is based on fuzzy logic to determine to what extent the traffic state is free flow or congested. If only speed data is available, alternative simplified fuzzy logic rules are applied. The fuzzy traffic state estimations for separate segments is combined into a combined traffic state estimation. This traffic state estimation is used as input for artificial neural networks (ANN) to predict whether it is likely that a queue will form within the short-term future (within 10minutes) on a relevant segment. This is defined as a segment that suffers from queues on a frequent basis.The results of these estimations will be used for virtual patrolling to distinguish between normal and expected congestion and other situations. Information from the modules can also be used for the detection of prediction of shockwaves. This can be of help to act faster when incidents happen and to prevent or solve shockwaves by giving (in-car) advices on speed and lane choice to car users.

Future Impact of Current Toll-Gates on the Capacity of the Southern Expressway

The Southern Expressway plays a vital role in national road network in Sri Lanka. Vehicles on Expressways can travel at uniform speeds throughout their journey offering advantages on fuel consumption and reduced travel time. The expressways ensure a hassle free safe journey due to the minimum obstructions to the traffic. At present, all the interchanges along the Southern Expressway operate with manually operated toll gates. The efficiency of current toll gates was studied for the existing exit ramp lengths and it investigated whether their current efficiencies are sufficient for higher vehicle volume of the future traffic. To investigate the current toll gate efficiency on the capacity of Southern Expressway, the necessary data were collected from relevant organizations. From the collected data, peak days and peak hours were identified for each and every exit ramp of the Southern Expressway. By considering the peak hours, critical exit ramps were identified. After identifying critical exit ramps of the interchanges, traffic studies were conducted to obtain arrival and service rates in peak hours during the peak days. Then, after analyzing survey data, maximum queue lengths and longest individual delays for critical exit ramps along the Southern Expressway were found. Simultaneously the analysis was carried-out for a different number of gates. Finally, maximum queue lengths and longest individual delays were compared with existing ramp lengths, and shortcomings were identified. By using suitable growth rates for the Southern Expressway, future traffic conditions were predicted. Subsequently, by using the maximum vehicular queue lengths, individual delays that could occur in the future were computed. Hence, these values were used to analyze the effect of the capacity of Southern Expressway due to the present toll gate arrangement. Therefore, it was able to recommend suitable off ramp lengths considering queue lengths and deceleration lengths. Recommendations were also proposed for future improvements to the service rates at toll gates.