•Propose Graph Convolutional Neural Network with Data Driven Graph Filter (GCNN-DDGF).•Propose Recurrent GCNN-DDGF to capture the temporal dependencies.•Explore two GCNN-DDGFs that outperforms four ...normal GCNNs and seven benchmark models.•Obtain insights on the “black box” of GCNN-DDGF by conducting graph network analysis.•The DDGF uncovers hidden correlations between stations to improve demand predictions.
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic ...dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.
In the upcoming decades, connected vehicles will join regular vehicles to appear on roads, and the characteristics of traffic flow will be changed accordingly. To model the heterogeneous traffic ...mixing regular and connected vehicles, a generic car-following framework is first proposed in this paper. A linear stability condition is theoretically derived, which indicates that the stability of the heterogeneous traffic is closely related to the penetration rate and the spatial distribution of connected vehicles. The generic car-following framework is applied by taking the Intelligent Driver Model as an example, and it is shown that connected vehicles can obviously enhance the stability of traffic flow and improve traffic efficiency in particular when traffic is in congestion. Moreover, a driver assistance strategy based on distributed feedback control is developed for connected vehicles, and the simulation results show that the proposed driver assistance strategy performs satisfactorily in stabilizing traffic as well as improving traffic efficiency.
•Provide a comprehensive review of trajectory based traffic flow studies.•Make a survey focusing on the new phenomena and the new models in the last 15 years.•Highlight the future research ...directions.
In this paper, we review trajectory data-based traffic flow studies that have been conducted over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest in the latest developments of traffic flow theory that have been stimulated by the availability of trajectory data. We first highlight the critical role of trajectory data (especially the next generation simulation (NGSIM) trajectory dataset) in the recent history of traffic flow studies. Then, we summarize new traffic phenomena/models at the microscopic/mesoscopic/macroscopic levels and provide a unified view of these achievements perceived from different directions of traffic flow studies. Finally, we discuss some future research directions.
In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing ...probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping‐to‐cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space‐space and time‐space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.
To mitigate traffic oscillations that usually sustainably propagate upstream, this paper proposes a jam-absorption driving (JAD) strategy in the framework of Newell's car-following theory. The basic ...idea of the JAD strategy is to guide a vehicle to move slowly before being captured by an oscillation and terminate the slow movement when the vehicle would start to leave the jam if no such slow movement was implemented. To practically implement the idea, a two-step method is proposed to estimate the time-space ending point of the strategy, and a proper vehicle is selected to implement the JAD strategy based on a given expected absorbing speed and current traffic conditions. To test the JAD strategy, two simulated traffic scenarios are constructed based on a realistic data-driven car-following model. The first scenario, which only reproduces one oscillation, directly shows the effectiveness of the JAD idea in preventing wave propagation and capacity drop. The second scenario, which contains a series of traffic oscillations induced by the rubbernecking behavior, validates the proposed JAD strategy in more complicated and realistic conditions. It is indicated that the JAD strategy is able to absorb traffic oscillations; thus, the side effects incurred by the oscillations could be subsequently mitigated. The significance of this paper is to provide us a new idea to mitigate traffic oscillations, i.e., the JAD strategy.
•The general assumptions of automated vehicles in traffic flow studies are discussed.•The automated vehicle-involved traffic flow studies are systematically reviewed.•The review is conducted from the ...microscopic, mesoscopic and macroscopic perspectives.•The pros and cons of the existing models and approaches are critically discussed.
Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing road capacity, and stabilizing traffic. To materialize these widely expected potentials of AVs, a sound understanding of AVs’ impacts on traffic flow is essential. Not surprisingly, despite the relatively short history of AVs, there have been numerous studies in the literature focusing on understanding and modeling various aspects of AV-involved traffic flow and significant progresses have already been made. To understand the recent development and ultimately inspire new research ideas on this important topic, this survey systematically and comprehensively reviews the existing AV-involved traffic flow models with different levels of details, and examines the relationship among the design of AV-based driving strategies, the management of transportation systems, and the resulting traffic dynamics. The pros and cons of the existing models and approaches are critically discussed, and future research directions are also provided.
The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network ...congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the 'Pareto optimal' to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks.
In modern society, an urban agglomeration is a highly developed spatial form of integrated cities. Travel activities driven by various travel demands frequently take place within an urban ...agglomeration. Understanding urban agglomeration travel demand is a basic but significant task. The paper constructs spatial–temporal networks for urban agglomeration travel demand via spatial–temporal decomposition and makes explicit analyses of the attributes of the networks. Taiwan and taxi demand, which are a typical urban agglomeration and representative travel demand, respectively, are taken as the empirical objects of the study. It is found that the degree distributions of the spatial–temporal travel demand networks follow power laws, whose exponents monotonically decrease with the growth of the square cells that are used to divide an urban agglomeration to aggregate travel demand. Nevertheless, the fact of following power laws is not influenced by the spatial–temporal granularity of network construction, indicating a spatial–temporal fractal property of urban agglomeration travel demand. The findings contribute to understanding the nature of travel demand and human mobility in the scale of an urban agglomeration.
•This study contributes to understanding the human mobility in an urban agglomeration.•Spatial-temporal networks for urban agglomeration travel demand is constructed.•It is found that the degree distributions of the networks follow power laws.•The power law exponent is negatively correlated with the cell size of the networks.
A time-space (TS) traffic diagram, which presents traffic states in time-space cells with color, is an important traffic analysis and visualization tool. Despite its importance for transportation ...research and engineering, most TS diagrams that have already existed or are being produced are too coarse to exhibit detailed traffic dynamics due to the difficulty of collecting high-fidelity traffic data. To increase the resolution of a TS diagram and enable it to present ample traffic details, this paper introduces the TS diagram refinement problem and proposes a multiple linear regression-based solution. Data collected at different times, in different locations and even in different countries are employed to thoroughly evaluate the accuracy and transferability of the proposed model. Two tests, which attempt to increase the resolution of a TS diagram 4 and 16 times, are carried out to evaluate the performance of the proposed model. In the increase-4-times test, the errors represented by Mean Absolute Percentage Error are all less than 0.1, and in the increase-16-times test all less than 0.17. Model comparison demonstrates that the proposed model outperforms the classic adaptive smoothing method in refining TS diagrams. All the strict tests with diverse data show that the proposed model, despite its simplicity, is able to refine a TS diagram with promising accuracy and reliable transferability. The proposed refinement model will "save" widely existing TS diagrams from their blurry "faces" and enable TS diagrams to show more traffic details.
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