scholarly journals Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Deng Ming-jun ◽  
Qu Shi-ru
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Road Traffic ◽  
Short Term ◽  
Traffic Forecasting ◽  
Traffic State ◽  
Forecasting Error ◽  
Trend Forecasting ◽  
State Variation ◽  
Fuzzy State

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting.

Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

2021 ◽  
pp. 036119812199347
Author(s):  
Lei Lin ◽  
Siyuan Gong ◽  
Srinivas Peeta ◽  
Xia Wu
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Road Transportation ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

Research of Short-Term Traffic Flow Forecast Method Based on the Kalman Filter

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Feng Chen ◽  
Yuanhua Jia ◽  
Wenjuan An ◽  
Na Zhang ◽  
Zhonghai Niu
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Forecast Method ◽  
Short Term ◽  
Traffic Flow Forecast ◽  
Flow Forecast

A noise-immune Kalman filter for short-term traffic flow forecasting

2019 ◽  
Vol 536 ◽  
pp. 122601 ◽  
Author(s):  
Lingru Cai ◽  
Zhanchang Zhang ◽  
Junjie Yang ◽  
Yidan Yu ◽  
Teng Zhou ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Forecasting

scholarly journals A Novel Multisensor Traffic State Assessment System Based on Incomplete Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Yiliang Zeng ◽  
Jinhui Lan ◽  
Bin Ran ◽  
Yaoliang Jiang
Keyword(s):  
Traffic Flow ◽  
Incomplete Data ◽  
Road Traffic ◽  
Flow Speed ◽  
State Assessment ◽  
Assessment System ◽  
Flow Data ◽  
The Road ◽  
Traffic State ◽  
Probe Vehicle

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

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