Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.

Urban traffic congestion has an obvious spatial and temporal relationship and is relevant to real traffic conditions. Traffic speed is a significant parameter for reflecting congestion of road networks, which is feasible to predict. Traditional traffic forecasting methods have poor accuracy for complex urban road networks, and do not take into account weather and other multisource data. This paper proposes a convolutional neural network (CNN)-based bidirectional spatial–temporal network (CNN-BDSTN) using traffic speed and weather data by crawling electric map information. In CNN-BDSTN, the spatial dependence of traffic network is captured by CNN to compose the time-series input dataset. Bidirectional long short-term memory (LSTM) is introduced to train the convolutional time-series dataset. Compared with linear regression, autoregressive integrated moving average, extreme gradient boosting, LSTM, and CNN-LSTM, CNN-BDSTN presents its ability of spatial and temporal extension and achieves more accurately predicted results. In this case study, traffic speed data of 155 roads and weather information in Urumqi, Xinjiang, People’s Republic of China, with 1-min interval for 5 months are tested by CNN-BDSTN. The experiment results show that the accuracy of CNN-BDSTN with input of weather information is better than the scenario of no weather information, and the average predicted error is less than 5%.

Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GAN) by combining bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN) as the generative model and discriminative model, respectively, to keep learning with continuous feedback. BiLSTM constantly generates temporal candidate flows based on valuable memory units, and CNN screens out the best spatial prediction by returning the feedback gradient to BiLSTM. Multi-dimensional indicators are selected to map the multi-view fusion local trend for accurate prediction. To balance computing efficiency and accuracy, different batch sizes are pre-tested and allocated to different lanes. The models are trained with rectified adaptive moment estimation (RAdam) by dividing the dataset into the training and testing sets with a rolling time-domain scheme. In comparison with the autoregressive integrated moving average (ARIMA), BiLSTM, generating adversarial network for traffic flow (GAN-TF), and generating adversarial network for non-signal traffic (GAN-NST), the proposed improved generating adversarial network for traffic flow (IGAN-TF) successfully generates more accurate and stable flows and performs better.

Variable speed limit (VSL) system has been recognised as an active traffic management strategy to improve traffic safety and operational efficiency. The development of connected vehicle (CV) technologies have a potential to improve VSL systems because CVs can provide continuous and precise traffic condition as the input of VSL algorithm as well as real‐time recommended speed to drivers. To demonstrate the improvement of the VSL performance from CV technologies, this study presents the evaluation of a VSL system's safety and operational impact on CVs, non‐CVs, and overall traffic conditions, based on a field test in interstate 5 corridor, Seattle. A hierarchical evaluation framework was proposed to explore the effectiveness of the implemented algorithm. The framework consists of three parts, i.e. manoeuvring, safety, and operation. Each part conducts data analysis and statistical tests from traffic sensors and CVs. The findings indicate that the VSL system is able to inform drivers to follow the recommended speed and improve traffic operation even at a low penetration rate. However, the stability of traffic flow is decreased in certain sections. Therefore, the potential improvement of the VSL algorithm is presented based on the findings.