The main form of freeway traffic congestion is the familiar stop-and-go wave, characterized by wide moving jams that propagate indefinitely upstream provided enough traffic demand. They cause severe, long-lasting adverse effects, such as reduced traffic efficiency, increased driving risks, and higher vehicle emissions. This underscores the crucial importance of artificial intervention in the propagation of stop-and-go waves. Over the past two decades, two prominent strategies for stop-and-go wave suppression have emerged: variable speed limit (VSL) and jam-absorption driving (JAD). Although they share similar research motivations, objectives, and theoretical foundations, the development of these strategies has remained relatively disconnected. To synthesize fragmented advances and drive the field forward, this paper first provides a comprehensive review of the achievements in the stop-and-go wave suppression-oriented VSL and JAD, respectively. It then focuses on bridging the two areas and identifying research opportunities from the following perspectives: fundamental diagrams, secondary waves, generalizability, traffic state estimation and prediction, robustness to randomness, simulation scenarios for strategy validation, and field tests and practical deployment. We expect that through this review, one area can effectively address its limitations by identifying and leveraging the strengths of the other, thus promoting the overall research goal of freeway stop-and-go wave suppression.

The time-space (TS) traffic diagram serves as a crucial tool for characterizing the dynamic evolution of traffic flow, with its resolution directly influencing the effectiveness of traffic theory research and engineering applications. However, constrained by monitoring precision and sampling frequency, existing TS traffic diagrams commonly suffer from low resolution. To address this issue, this paper proposes a refinement method for TS traffic diagrams based on neighborhood-adaptive linear regression. Introducing the concept of neighborhood embedding into TS diagram refinement, the method leverages local pattern similarity in TS diagrams, adaptively identifies neighborhoods similar to target cells, and fits the low-to-high resolution mapping within these neighborhoods for refinement. It avoids the over-smoothing tendency of the traditional global linear model, allows the capture of unique traffic wave propagation and congestion evolution characteristics, and outperforms the traditional neighborhood embedding method in terms of local information utilization to achieve target cell refinement. Validation on two real datasets across multiple scales and upscaling factors shows that, compared to benchmark methods, the proposed method achieves improvements of 9.16%, 8.16%, 1.86%, 3.89%, and 5.83% in metrics including MAE, MAPE, CMJS, SSIM, and GMSD, respectively. Furthermore, the proposed method exhibits strong generalization and robustness in cross-day and cross-scenario validations. In summary, requiring only a minimal amount of paired high- and low-resolution training data, the proposed method features a concise formulation, providing a foundation for the low-cost, fine-grained refinement of low-sampling-rate traffic data.

Travel data containing personal identification information needs to be anonymized before being shared and analyzed for privacy considerations. While this approach protects personal privacy, it makes it difficult for researchers and planners to identify the same traveler from different databases and to construct complete multi-modal trips, which greatly reduces the value of data. To address this challenge, this paper develops TBLink, a method to match individual travelers between different modes. The underlying idea is that if a traveler makes a transfer, the spatiotemporal signatures of the previous and next trips will be similar and must satisfy certain conditions. When this pattern occurs for two travelers in the two datasets repeatedly, we can infer that the two travelers are actually the same person. The matching of travelers is regarded as a stable matching problem, and the Gale-Shapley algorithm is used to solve the problem. TBLink is demonstrated using metro and bus trip data from Chengdu City between January and March 2021. The results show that the precision, recall, and recovery rate of user matching are 80.83%, 92.82%, and 10.56% respectively, and the matching is more reliable as the dataset increases. Sensitivity analysis is performed to study the effect of several model parameters on the matching performance.

Accurate and refined passenger flow prediction is essential for optimizing the collaborative management of multiple collection and distribution modes in large-scale transportation hubs. Traditional methods often focus only on the overall passenger volume, neglecting the interdependence between different modes within the hub. To address this limitation, we propose MM-STFlowNet, a comprehensive multi-mode prediction framework grounded in dynamic spatial-temporal graph modeling. Initially, an integrated temporal feature processing strategy is implemented using signal decomposition and convolution techniques to address data spikes and high volatility. Subsequently, we introduce the Spatial-Temporal Dynamic Graph Convolutional Recurrent Network (STDGCRN) to capture detailed spatial-temporal dependencies across multiple traffic modes, enhanced by an adaptive channel attention mechanism. Finally, the self-attention mechanism is applied to incorporate various external factors, further enhancing prediction accuracy. Experiments on a real-world dataset from Guangzhounan Railway Station in China demonstrate that MM-STFlowNet achieves state-of-the-art performance, with an average improvement of 52.56% in MSE and 36.38% in MAE. Especially during peak hours, it demonstrates excellent forecasting performance, providing valuable insights for transportation hub management. Our model is also demonstrated strong generalization in low-resource scenarios and different traffic scenarios. Our code is available at https://github.com/BMRETURN/MM-STFlowNet

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.

For on-demand dynamic ride-pooling services, e.g., Uber Pool and DiDi Pinche, a well-designed vehicle dispatching strategy is crucial for platform efficiency and passenger experience. Most existing dispatching strategies overlook incoming pairing opportunities, therefore suffer from short-sighted limitations. In this paper, we propose a forward-looking vehicle dispatching strategy, which first predicts the expected distance saving that could be brought about by future orders and then solves a bipartite matching problem based on the prediction to match passengers with partially occupied or vacant vehicles or keep passengers waiting for next rounds of matching. To demonstrate the performance of the proposed strategy, a number of simulation experiments and comparisons are conducted based on the real-world road network and open trip data from Haikou, China. Results show that the proposed strategy outperforms the baseline strategies by generating approximately 31% more distance saving and 18% less average passenger detour distance. It indicates the significant benefits of considering future pairing opportunities in dispatching, and highlights the effectiveness of our innovative forward-looking vehicle dispatching strategy in improving system efficiency and user experience for dynamic ride-pooling services.

A major shortcoming of the conventional car-following models is that these models only consider the current spacing and speeds of the target vehicle and its immediate leading vehicle, without taking into account prior driving actions, even for those from the same driver. In other words, the numerous prior experiences have no influence in predicting vehicular movements for the next time step. In this research, we propose a machine-learning-based data-driven methodology that is able to take advantage of the high-resolution historical traffic data in the current data-rich era, to predict vehicular movements in an accurate manner with high computational efficiency. The proposed car-following model has a simple model structure based on a fixed-radius near neighbors (FRNN) search algorithm and it can be applied to high-resolution, real-time vehicle movement prediction, modeling, and control. A comprehensive performance comparison is also conducted among the proposed car-following model, another similar data-driven model, and two conventional formula-based models. The results indicate that the FRNN algorithm-based car-following model is superior to all other three models in terms of prediction accuracy and is more computationally efficient compared to its data-driven-based counterpart. Some extensive applications of the proposed car-following model are also discussed at the end of this article.

As one of the most frequently occurred driving behaviors, the lane-changing’s impact on surrounding traffic is important. From the aspect of macro-traffic flows, its impact on traffic efficiency and safety has been investigated. However, as a non-essential lane-changing type, a single discretionary lane-changing’s impact on surrounding traffic is still difficult to be quantified by real-word traffic data. To solve this problem, this paper first proposes a trajectory data-based analytic method to calculate response time of the lane-changing vehicle and its following vehicles, then develops a time sequence data processing method based on feature points to analyze the change pattern of the variable minimum space, and then a lane-changing’s temporal and spatial on surrounding traffic can be captured by the decreasing-increasing pattern of minimum space. Besides, a high-precision trajectory data set, called Zen-traffic, is employed to demonstrate the effectiveness of the proposed methods and reach conclusions. Given the condition that the speed of a discretionary lane-changing vehicle is between 6 to 20 m/s, i.e., the traffic condition at which lane changes more likely occur, the results of the paper include (1) on average, 4 or 5 vehicles are usually impacted by a discretionary lane change; (2) the average impact time is 12 to 13 seconds and it has little relationship with the distance between the lane-changing vehicle and the following vehicle on the target/initial lane; (3) the lateral movement direction (i.e., changing to left or to right) of a lane change has a significant impact on the number of the vehicles affected.

Road intersections play an important role in the operation of an urban road transportation system. However, channelized segment (C-segment) spillovers frequently occur during peak hours, making the vehicle queue on a lane spatially and temporally interact with the queue on another lane. Intersection traffic efficiency is thus largely degraded. Unfortunately, most of the existing models cannot capture the spatial-temporal queue interactions and thus are incapable of developing efficient intersection optimization plans. To fill this gap, the paper proposes a highly efficient approach traffic flow model to capture C-segment spillovers. An optimization problem that takes C-segment spillovers into account is formulated as a nonlinear programming model, and it is solved by using a Markov chain Monte Carlo method. The proposed model and solution are tested in several scenarios with various intersection settings, and the effectiveness of the proposed models is demonstrated. This study is beneficial to understanding C-segment spillovers and the models can be applied to improve traffic efficiency at recurrently-congested intersections.

For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents (e.g., vehicles, bicycles, pedestrians) are essential information. The prediction of future trajectories is challenging as the motion of traffic agents is constantly affected by spatial-temporal interactions from agents and road infrastructure. To take those interactions into account, this study proposes a Graph Attention Transformer (Gatformer) in which a traffic scene is represented by a sparse graph. To maintain the spatial and temporal information of traffic agents in a traffic scene, Convolutional Neural Networks (CNNs) are utilized to extract spatial features and a position encoder is proposed to encode the spatial features and the corresponding temporal features. Based on the encoded features, a Graph Attention Network (GAT) block is employed to model the agent-agent and agent-infrastructure interactions with the help of attention mechanisms. Finally, a Transformer network is introduced to predict trajectories for multiple agents simultaneously. Experiments are conducted over the Lyft dataset and state-of-the-art methods are introduced for comparison. The results show that the proposed Gatformer could make more accurate predictions while requiring less inference time than its counterparts.

To accurately estimate freeway traffic carbon dioxide (CO2) emissions, this paper proposes a spatiotemporal cell-based model by taking traffic dynamics into account. High-fidelity vehicle trajectory data is used to construct a spatiotemporal traffic (ST) diagram and to calculate the exact CO2emissions of the traffic in the ST diagram. The factors impacting the CO2emissions in the ST diagram are selected and taken as model inputs. Firstand second-order regression models are employed to fit the exact CO2emissions. It is found that the relationship between complicated traffic dynamics and CO2emissions can be simply described by using a linear or nearly linear function; i.e., for larger cells (such as 90·150 sec·m) that are used to construct an ST diagram, a first-order regression model is able to well reflect the relationship, while for small cells (such as 30·50 sec·m) a second-order model is more accurate. To validate the proposed model, another trajectory dataset that was collected in a different freeway segment is introduced, and the transferability and predictability of the model are demonstrated. The proposed spatiotemporal cell-based model allows us to accurately estimate CO2emissions by inputting the prevailing ST diagram. It opens a gate for estimating CO2emissions from widely available low-fidelity traffic data, since the ST diagram can be constructed by using various traffic flow data, such as loop detector data and floating car data.

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.

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.

In transportation networks, traditional fixed sensors are used to monitor the operation of transportation systems. However, fixed sensors cannot move once they are installed. In this paper, the motion ability of traffic sensors is introduced to improve the performance of transportation network surveillance. A mobile traffic sensor routing problem is proposed, modeled as a novel vehicle routing problem. A measure of traffic information acquisition benefits is developed and used to gauge the surveillance performance. To solve this mobile-sensor routing problem, a hybrid two-stage heuristic algorithm is designed, which is based on particle swarm optimization and ant colony optimization. Numerical experiments are conducted. The results show that the mobile traffic sensor has a better network surveillance performance than the fixed sensor in most experimental cases.