Jianming Hu

Bio: Jianming Hu is an academic researcher from Tsinghua University. The author has contributed to research in topics: Traffic flow & Traffic generation model. The author has an hindex of 19, co-authored 142 publications receiving 1849 citations. Previous affiliations of Jianming Hu include The Chinese University of Hong Kong & Ontario Ministry of Transportation.

PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach

Abstract: The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.

The retrieval of intra-day trend and its influence on traffic prediction

Abstract: In this paper, we discuss three problems that occur within short-term traffic prediction when the information from only a single point loop detector is used. First, we analyze the retrieval of intra-day trend for traffic flow series and determine whether this retrieval process improves traffic prediction. We compare different highway traffic prediction models that use either the original traffic flow series or the residual time series with the intra-day trend removed. Test results indicate that the prediction performance MAY be significantly improved in the latter scenario. Second, we address two other related questions: the influence of missing data and traffic breakdown prediction. We show that the Probabilistic Principal Component Analysis (PPCA) method, which also utilizes the intra-day trend of traffic flow series, can be a useful tool in imputing the missing data. It can simultaneously ensure that the prediction error remains at an acceptable level, especially when the missing ratio is relatively low. We also show that almost all the known predictors have hidden assumptions of smoothness and, thus, cannot predict the burst points that deviate too far from the intra-day trend. As a result, traffic breakdown points can only be identified but not predicted.

Short-time traffic flow prediction with ARIMA-GARCH model

Abstract: Short-time traffic flow prediction is a significant interest in transportation study, and it is essential in congestion control and traffic network management In this paper, we propose an Autoregressive Integrated Moving Average with Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) model for traffic flow prediction The model combines linear ARIMA model with nonlinear GARCH model, so it can capture both the conditional mean and conditional heteroscedasticity of traffic flow series The model is calibrated, validated and used for prediction based on PeMS single loop detector data The performance of the hybrid model is compared with that of standard ARIMA model The results show that the introduction of conditional heteroscedasticity cannot bring satisfactory improvement to prediction accuracy, in some cases the general GARCH(1,1) model may even deteriorate the performance Thus for ordinary traffic flow prediction, the standard ARIMA model is sufficient

Short-term traffic flow forecasting based on Markov chain model

Abstract: In this paper, the traffic flow is modeled as a high order Markov chain. And the transition probability from one state to the other state describes, given the current and recent values of the traffic flow, what the future value will be. Under the criteria of minimum mean square error, the optimal prediction is given as the conditional expectation according to the transition probability. However, in general, the transition probability is not known beforehand and we even don't know its form exactly. Gaussian Mixture Model (GMM), whose parameters are estimated with Expectation Maximum (EM) algorithm, is applied to approximate the transition probability. Then the representation of the optimal forecasting is given in terms of the parameters in GMM. A case study with real traffic data obtained from UTC/SCOOT system in Beijing shows the applicability and effectiveness of our proposed model.

Metaheuristics: From Design to Implementation

Abstract: A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Abstract: 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.

Data-Driven Intelligent Transportation Systems: A Survey

Abstract: For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.