Traffic flow prediction is a very challenging task in traffic networks. Travel order quantity prediction is of great value to the analysis of traffic flow. However, the number of travel orders is closely related to time, leading to its short-term proximity and long-term cyclical dependence. Therefore, it is difficult to predict travel order quantity using traditional methods. To capture both the long-term and short-term correlations, in this paper, we propose an attention-based bidirectional long short-term memory network (AT-BLSTM) model, which consists of bidirectional long short-term memory (BLSTM) layer and attention layer. The BLSTM layer contains forward and backward long short-term memory (LSTM), which uses a novel method to combine forward and backward output. The attention layer utilizes novel self-attention algorithm to assign different weights according to the correlation between features. In this case, AT-BLSTM can predict travel order quantity more accurately than other time series models. For example, AT-BLSTM achieves the lowest MAE (0.0647), the lowest RMSE (0.0836) and the lowest MAPE (0.1239) among all the methods on dataset Xiuying. Extensive experiments on real-world travel datasets offer evidence that the proposed approach matches or outperforms state-of-the-art methods.

Travel order quantity prediction via attention-based bidirectional LSTM networks

In recent years, many cities have formed a complete modern transportation system, but the phenomenon of “phantom traffic jam” is also frequent, the reason of which has been concerned by people. At present, much research has analyzed this phenomenon, but there are still many questions about its occurrence. Therefore, this paper makes an in-depth study and analysis of the phenomenon of “phantom traffic jam”, it can be found that the causes of this phenomenon are the bad driving habits of drivers and unreasonable road planning. In view of this situation, this paper adopts MATLAB as the analysis tool, and establishes a MATLAB traffic congestion prediction model according to the main characteristics of the phenomenon of “phantom traffic jam”. The occurrence mechanism of the phenomenon of “phantom traffic jam” has been combined and it can control the existing traffic network intelligently. According to different congestion conditions, the traffic congestion prediction model is tested separately in the form of secondary classification. It can be found that the accuracy rate of the prediction system in this paper reaches more than 90% from the test results. Therefore, the model can meet the current requirement of traffic congestion prediction.

Phantom Traffic Jam Based on MATLAB

While bringing convenience to people, the growing number of vehicles already on roads causes inevitable traffic congestion. Traffic congestion can happen for both observable reasons, such as accidents, or occur naturally due to human driving style in high vehicles density scenarios. In the latter case, traffic congestion are referred to as phantom jams. To alleviate the traffic instability caused by phantom jams, bilateral control models have been proposed with the development of intelligent transportation system (ITS) in ideal situations. However, in road scenarios, some factors that severely affect the performance of existing models cannot be ignored, such as uncertainties of vehicle state measurements caused by on-board sensors, time delay caused by intervehicle communications and control systems of vehicles. In this paper, a novel predictable bilateral control model (PBCM) with local wireless communication, which consists of best estimation and state prediction, is proposed to accurately control the host vehicle in traffic flow to alleviate traffic instability. Compared with other models, theoretical analysis and simulation results show that more accurate control decisions could be obtained with the PBCM, which could distribute all vehicles more evenly, and our model could maintain higher velocities when disturbances occurred. The experimental results also reveal that our proposed model could more effectively suppress traffic instability (i.e., the improvement in control decision $a$ is more than 10%) and could restore traffic balance 20% faster than other models on average.

A Vehicle Control Model to Alleviate Traffic Instability

While bringing convenience to people, the growing number of vehicles on road already cause inevitable traffic congestion. Some traffic congestion happen with observable reasons, but others occur without apparent reasons or bottlenecks, which referred to as phantom jams, are caused by traditional vehicle following model. In order to alleviate the traffic instability caused by phantom jam, several models have been proposed with the development of intelligent transportation system (ITS). these have been proved to be able to suppress traffic instability in the ideal situation. But in road scenarios, uncertainties of vehicle state measurements and time delay caused by on-board sensors, inter-vehicle communications and control system of vehicles will affect the performance of the existing models severely, and cannot be ignored. In this paper, a novel predictable bilateral control model-PBCM, which consists of best estimation and state prediction is proposed to determine accurate acceleration values of the host vehicle in traffic flow to alleviate traffic instability. Theoretical analysis and simulation results show that our model could reduce the influence of the measurement errors and the delay caused by communication and control system effectively, control the state of the vehicles in traffic flow accurately, thus achieve the goal of restrain the instability of traffic flow.

A Vehicles Control Model to Alleviate Traffic Instability

Traffic jams that appear without distinguishable reason is called phantom traffic jam. To study this phenomenon, macroscopic modelling using the second-order Payne-Whitham equation was adopted. In this article, the staggered conservative scheme applied on a staggered grid was implemented to solve the equation. Using this scheme, different behavior of a perturbed equilibrium solution was simulated; it might either decay or grow, depending on the critical threshold parameter. When unstable, a small perturbation was amplified into a local peak of high traffic density. This type of traveling wave is called a jamiton. On a circular road of a certain length and with a fixed number of vehicles, the growing process of these traveling jamiton waves was simulated. The shape and propagation speed of these numerical jamitons are shown to confirm the analytical formulas. A good understanding of this phenomenon may support decision-makers and engineers to determine the judicious selection of speed limits of a certain road section.

N. Malvin

Papers

Staggered Conservative Scheme for Simulating the Emergence of a Jamiton in a Phantom Traffic Jam