Yanjie Duan

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Social media based transportation research: the state of the work and the networking

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis methods.We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified.

A deep learning based approach for traffic data imputation

Traffic data is a fundamental component for applications and researches in transportation systems. However, real traffic data collected from loop detectors or other channels often include missing data which affects the relative applications and researches. This paper proposes an approach based on deep learning to impute the missing traffic data. The proposed approach treats the traffic data including observed data and missing data as a whole data item and restores the complete data with the deep structural network. The deep learning approach can discover the correlations contained in the data structure by a layer-wise pre-training and improve the imputation accuracy by conducting a fine-tuning afterwards. We analyze the imputation patterns that can be realized with the proposed approach and conduct a series of experiments. The results show that the proposed approach can keep a stable error under different traffic data missing rate. Deep learning is promising in the field of traffic data imputation.

Travel time prediction with LSTM neural network

Travel time is one of the key concerns among travelers before starting a trip and also an important indicator of traffic conditions. However, travel time acquisition is time delayed and the pattern of travel time is usually irregular. In this paper, we explore a deep learning model, the LSTM neural network model, for travel time prediction. By employing the travel time data provided by Highways England, we construct 66 series prediction LSTM neural networks for the 66 links in the data set. Through model training and validation, we obtain the optimal structure within the setting range for each link. Then we predict multi-step ahead travel times for each link on the test set. Evaluation results show that the 1-step ahead travel time prediction error is relatively small, the median of mean relative error for the 66 links in the experiments is 7.0% on the test set. Deep learning models considering sequence relation are promising in traffic series data prediction.

Managing Emergency Traffic Evacuation With a Partially Random Destination Allocation Strategy: A Computational-Experiment-Based Optimization Approach

Natural or man-made disasters can cause huge losses of human life and property. One of the effective and widely used response and mitigation strategies for these disasters is traffic evacuation. Evacuation destination choice is critical in evacuation traffic planning and management. In this paper, we propose a partially random destination allocation strategy for evacuation management. We present a metamodel-based simulation optimization method to design the strategy. The proposed method uses a quadratic polynomial as a metamodel, within which a degree-free trust region algorithm is developed to solve the proposed model. The performance of the proposed method is evaluated based on a subnetwork of Beijing with two different traffic demands. Computational experiments demonstrate that the proposed method can yield a well-performed strategy, leading to reduced network clearance times.

Performance evaluation of the deep learning approach for traffic flow prediction at different times

Traffic flow prediction is very important in the deployment of intelligent transportation system. Based on our previous research on deep learning approach for traffic data prediction, we further evaluates the performance of the SAE model for traffic flow prediction at daytime and nighttime. Through 250 experimental tasks training a SAE model and evaluating its performance at daytime and nighttime with 3 different criteria, we obtain the best combination of hyper parameters for each criterion at different times on weekday and non-weekday, respectively. Experimental results show that the MAE and RMSE at daytime are larger than that at nighttime, while the MRE at daytime are smaller than that at nighttime. For different criteria, the hyper parameters of the SAE model should vary accordingly. The results in this paper indicate that in real applications, traffic flow prediction using the deep learning approach can be a combination of multiple SAE models with different parameters suitable for different periods, which is of significance in future research.

Latent factor model for traffic signal control

The increased ownership of motor vehicles has brought many urban problems, such as traffic congestion, environmental pollution. Traffic signal control is recognized as one of effective ways to alleviate these problems. However, it is still hard to automatically choose appropriate traffic signal timing plans for different traffic conditions due to the dynamics and uncertainty of transportation systems. In this paper, we propose a latent factor model based traffic signal timing plan recommendation method to address this problem. In the proposed method, we model the abstract traffic states as the “users” in recommendation systems, and timing plans as the “items”. And there are many explicit or implicit factors in the interactions between “users” and “items”. The latent factor model is successfully used to deal with uncertain factors which cannot be modeled accurately in math. The novel method adopted the model-free adaptive idea to solve the problem of modeling from the perspective of data mining and machine learning framework. And, the proposed method is tested by using simulation data generated by a microscopic traffic simulator called Paramics. The results are compared to the baseline Webster method. The results indicate that the proposed latent factor model based recommendation method outperforms the Webster method on reducing the delay.

Semi-actuated arterial coordination for traffic control: A practical method

Arterial coordination is a common method in urban traffic control. Traditional arterial coordination methods are usually off-line control methods based on mixed-integer linear program. These methods cannot adapt to changes of traffic flow, for instance, although there are very few cars in a branch road, timing plan will not adjust. Thus, we proposed a semi-actuated arterial coordination method which is a combination of practical actuated traffic control and traditional arterial coordination control. Five adjacent intersections of Huanghe Second Road in Binzhou City, Shandong Province, China are selected to test our method. We use microscopic traffic simulation software Q-PARAMICS to simulate and simulation results show that semi-actuated arterial coordination can effectively improve the performance index.

Improved information feedback in symmetric dual-channel traffic

Information feedback is very important in traffic systems. Real-time information feedback can improve traffic flow using existing facilities. This chapter proposes a real-time information feedback strategy called improved mean number feedback strategy. Based on a two-route scenario, simulation results show that the strategy is much more effective with different lengths of roads or a different percentage of dynamic vehicles than the old strategies, i.e., congestion coefficient feedback strategy and mean velocity feedback strategy.