scholarly journals Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks

2022 ◽  
Vol 2 (1) ◽  
Author(s):  
Yalong Pi ◽  
Nick Duffield ◽  
Amir H. Behzadan ◽  
Tim Lomax
Keyword(s):  
Visual Recognition ◽  
Optimization Technique ◽  
Data Retrieval ◽  
Traffic Volume ◽  
Video Data ◽  
Urban Traffic ◽  
Traffic Data ◽  
Pixel Size ◽  
Lighting Condition ◽  
Cost Efficient

AbstractAccurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera’s pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.

Analisis Kinerja dan Pengaruh Pelebaran Jalan serta Pembuatan Median Jalan (Studi Kasus Jalan Temanggung Tilung Kota Palangkaraya)

2018 ◽  
Vol 7 (1) ◽  
pp. 51-60
Author(s):  
Fitri Wulandari ◽  
Nirwana Puspasari ◽  
Noviyanthy Handayani
Keyword(s):  
Data Retrieval ◽  
Service Level ◽  
Traffic Volume ◽  
Primary Data ◽  
Volume Data ◽  
Saturation Degree ◽  
The Road ◽  
Before And After ◽  
Road Widening ◽  
The Way

Jalan Temanggung Tilung is a 2/2 UD type road (two undirected two-way lanes) with a road width of 5.5 meters, which is a connecting road between two major roads, namely the RTA road. Milono and the path of G. Obos. Over time, the volume of traffic through these roads increases every year, plus roadside activities that also increase cause congestion at several points of the way. To overcome this problem, the local government carried out road widening to increase the capacity and level of road services. The study was conducted to determine the amount of traffic volume, performance, service level of the Temanggung Tilung road section at peak traffic hours before and after road widening. Data retrieval is done by the direct survey to the field to obtain primary data in the form of geometric road data, two-way traffic volume data, and side obstacle data. Performance analysis refers to the 1997 Indonesian Road Capacity Manual (MKJI) for urban roads. From the results of data processing, before increasing the road (Type 2/2 UD), the traffic volume that passes through the path is 842 pcs/hour and after road widening (Type 4/2 UD) the traffic volume for two directions is 973 pcs/hour, with route A equaling 528 pcs/hour and direction B equaling 445 pcs/hour. Based on the analysis of road performance before road enhancement, the capacity = 2551 pcs/hour, saturation degree = 0.331, and the service level of the two-way road are level B. Based on the analysis of the performance of the way after increasing the way, the direction capacity A = 2686 pcs/hour and direction B = 2674 pcs /hour, saturation degree for direction A = 0.196 and direction B = 0.166, service level for road direction A and direction B increase to level A

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

Predicting of atmospheric corrosivity and durability of structural materials. Part II. Impact of urban traffic pollution

2021 ◽  
Vol 26 (2) ◽  
pp. 25-33
Author(s):  
Joanna Kobus ◽  
Rafał Lutze
Keyword(s):  
Zinc Coating ◽  
Environmental Parameters ◽  
Structural Materials ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Traffic Pollution ◽  
Street Traffic ◽  
The Impact

The results of the atmospheric corrosivity assessment in the immediate vicinity of streets of different traffic volume in Warsaw, Krakow and Katowice are derived . On the bases of annual exposures in 2014–2018 years an equation describing the impact of environmental parameters and street traffic volume on corrosion losses of zinc and zinc coating on steel was selected.

Taxi Global Positioning System Data in Urban Road Network: A Methodology to Identify Key Road Clusters Based on Travel Speed–Traffic Volume Correlation

2021 ◽  
pp. 036119812110366
Author(s):  
Yi Li ◽  
Weifeng Li ◽  
Qing Yu ◽  
Han Yang
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Travel Speed ◽  
Detection Algorithm ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Mitigation Strategies ◽  
Operating Characteristics ◽  
The Road ◽  
Road Segments

Urban traffic congestion is one of the urban diseases that needs to be solved urgently. Research has already found that a few road segments can significantly influence the overall operation of the road network. Traditional congestion mitigation strategies mainly focus on the topological structure and the transport performance of each single key road segment. However, the propagation characteristics of congestion indicate that the interaction between road segments and the correlation between travel speed and traffic volume should also be considered. The definition is proposed for “key road cluster” as a group of road segments with strong correlation and spatial compactness. A methodology is proposed to identify key road clusters in the network and understand the operating characteristics of key road clusters. Considering the correlation between travel speed and traffic volume, a unidirectional-weighted correlation network is constructed. The community detection algorithm is applied to partition road segments into key road clusters. Three indexes are used to evaluate and describe the characteristic of these road clusters, including sensitivity, importance, and IS. A case study is carried out using taxi GPS data of Shanghai, China, from May 1 to 17, 2019. A total of 44 key road clusters are identified in the road network. According to their spatial distribution patterns, these key road clusters can be classified into three types—along with network skeletons, around transportation hubs, and near bridges. The methodology unveils the mechanism of congestion formation and propagation, which can offer significant support for traffic management.

Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

2018 ◽  
Vol 2672 (38) ◽  
pp. 128-137 ◽  
Author(s):  
Angela E. Kitali ◽  
Emmanuel Kidando ◽  
Paige Martz ◽  
Priyanka Alluri ◽  
Thobias Sando ◽  
...  
Keyword(s):  
Real Time ◽  
Random Effect ◽  
Random Effect Model ◽  
Probit Model ◽  
Severe Injury ◽  
Traffic Data ◽  
Vehicle Crashes ◽  
Lighting Condition ◽  
Crash Data ◽  
Real Time Traffic

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.

scholarly journals An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaomin Yu ◽  
Huiqiang Wang ◽  
Hongwu Lv ◽  
Junqiang Fu
Keyword(s):  
Data Storage ◽  
Optimization Technique ◽  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Storage Structure ◽  
Storage Model ◽  
Indoor Space ◽  
Close Relationship ◽  
Time Requirements ◽  
Map Data

The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average.

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