Research of Road Traffic State Classification Threshold Value of Expressway and Arterial Roads in Cities Based on Travel Speed

CICTP 2012 ◽  
2012 ◽  
Author(s):  
Hongtong Qiu ◽  
Yin Zhu ◽  
Xiaofeng Wu ◽  
Ruimin Li
Keyword(s):  
Road Traffic ◽  
Threshold Value ◽  
Travel Speed ◽  
Traffic State ◽  
State Classification

Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ji-hua Hu ◽  
Jia-xian Liang
Keyword(s):  
Road Traffic ◽  
Travel Speed ◽  
Bayesian Framework ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Important Indicator ◽  
Main Road ◽  
Traffic Lights ◽  
Speed Distribution ◽  
Hybrid Bus

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.

The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data

2014 ◽  
Vol 694 ◽  
pp. 80-84
Author(s):  
Xiao Tong Yin ◽  
Chao Qun Ma ◽  
Liang Peng Qu
Keyword(s):  
Road Traffic ◽  
Velocity Estimation ◽  
Least Square ◽  
Single Type ◽  
Estimation Model ◽  
Average Speed ◽  
Urban Road ◽  
Traffic State ◽  
Floating Car Data ◽  
Different Types

The analysis of the unban road traffic state based on kinds of floating car data, is based on the model and algorithm of floating car data preprocessing and map matching, etc. Firstly, according to the characteristics of the different types of urban road, the urban road section division has been carried on the elaboration and optimization. And this paper introduces the method of calculating the section average speed with single floating car data, also applies the dynamic consolidation of sections to estimate the section average velocity.Then the minimum sample size of floating car data is studied, and section average velocity estimation model based on single type of floating car data in the different case of floating car data sample sizes has been built. Finally, the section average speed of floating car in different types is fitted to the section average car speed by the least square method, using section average speed as the judgment standard, the grade division standard of urban road traffic state is established to obtain the information of road traffic state.

scholarly journals Research on Traffic Acoustic Event Detection Algorithm Based on Sparse Autoencoder

2020 ◽  
Vol 308 ◽  
pp. 05002
Author(s):  
Xiaodan Zhang ◽  
Yongsheng Chen ◽  
Guichen Tang
Keyword(s):  
Feature Vector ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Research Direction ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Acoustic Event ◽  
Traffic State ◽  
Sparse Autoencoder ◽  
New Research

Road traffic monitoring is very important for intelligent transportation. The detection of traffic state based on acoustic information is a new research direction. A vehicles acoustic event classification algorithm based on sparse autoencoder is proposed to analysis the traffic state. Firstly, the multidimensional Mel-cepstrum features and energy features are extracted to form a feature vector of 125 features; Secondly, based on the computed features, the five-layers autoencoder is trained. Finally, vehicle audio samples are collected and the trained autoencoder is tested. The experimental results show that detection rate of the traffic acoustic event reaches 94.9%, which is 12.3% higher than that of the traditional Convolutional Neural Networks (CNN) algorithm.

scholarly journals Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5213 ◽  
Author(s):  
Donato Impedovo ◽  
Fabrizio Balducci ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Machine Learning Techniques ◽  
Visual Features ◽  
Learning Approaches ◽  
Traffic State ◽  
State Classification ◽  
Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

scholarly journals Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878148 ◽  
Author(s):  
Wan-Xiang Wang ◽  
Rui-Jun Guo ◽  
Jing Yu
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Travel Speed ◽  
Service Level ◽  
Evaluation Indexes ◽  
Traffic Conditions ◽  
Calculation Results ◽  
Congestion Index ◽  
Operation Status ◽  
Road Traffic Congestion

Traffic congestion index reflects the state of traffic flow. The detection and analysis on traffic congestion index can be used to estimate the operation status of roads, to plan and organize road traffic for traffic managers, and to make the reasonable decisions of travelers to travel. The traffic conditions of several evaluation indexes were analyzed. Based on the theory of fuzzy mathematics, some membership functions of the evaluating indexes were designed. Three calculation methods of traffic congestion index were proposed. Their calculation results were compared mutually. The conclusion revealed that using saturation calculated by the corresponding service level of traffic congestion index not well reflect the traffic situation, what’s more, travel speed is used to calculate the congestion index of the first method. Using comprehensive parameters can calculate the congestion index of the third method. Both them are roughly similar and in line with the actual traffic phenomenon.

scholarly journals Analysis of traffic-related air pollution using Shanghai road traffic state index

2018 ◽  
Vol 189 ◽  
pp. 10003 ◽  
Author(s):  
Ruoyu Chen ◽  
Lianliang Chen ◽  
Wenhao Fan
Keyword(s):  
Air Pollution ◽  
Significant Contribution ◽  
Road Traffic ◽  
Traffic Emissions ◽  
Diurnal Variability ◽  
Traffic State ◽  
Road Traffic Emissions ◽  
State Index ◽  
Pollution Concentrations ◽  
Statistical Summary

Recently, due to the rapid economic development and the acceleration of urbanization, haze events have occurred frequently in most parts of China, which has attracted widespread attention at home and abroad. This study presents a statistical summary of air pollution concentrations and traffic state indexes from August 2014 to April 2015 in Shanghai, China. We find PM2.5 concentrations show a remarkable seasonal variability with ``winter > spring > autumn > summer'' in Shanghai. Concentrations of PM2.5, CO, NO2, SO2 are generally higher in winter than in summer due to enhanced anthropogenic and biogenic emissions and unsuitable meteorological conditions for pollution diffusion, contrary to concentrations of O3. The weekly changes of NO2 are highly consistent with that of traffic state indexes, suggesting a significant contribution to NO2 concentrations from road traffic emissions. Two moderate peaks are found in the diurnal variability of concentrations of PM2.5, CO and NO2, similar to road traffic indexes, indicating the important contribution of road traffic emissions every day. We find that SO2, NO2, CO are the dominant factors contributing to PM2.5 pollution, where NO2 and CO are mainly from road traffic emissions. The average annual Spearman correlation coefficient is r = 0.689 (p < 0.01), r = 0.564 (p < 0.01), r = 0.812 (p < 0.01), respectively.

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