scholarly journals Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

2019 ◽  
Vol 11 (18) ◽  
pp. 4989 ◽  
Author(s):  
Wei Yu ◽  
Hua Bai ◽  
Jun Chen ◽  
Xingchen Yan
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Rapid Development ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Space Time ◽  
Smart Cards ◽  
The Sustainable Development ◽  
Passenger Flow ◽  
Challenges And Opportunities

The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

The Image Process Based on Intelligent Transportation System

2014 ◽  
Vol 945-949 ◽  
pp. 1789-1793 ◽  
Author(s):  
Wen Qian An ◽  
Xu Fang Bo ◽  
Chao Jia
Keyword(s):  
Automobile Industry ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Gaussian Mixture ◽  
Transportation Systems ◽  
Background Model ◽  
Image Process

With the rapid development of China's automobile industry, intelligent transportation systems have become an important means of modern traffic management. The paper analyzes the prospects for detection of several sports: frame difference method, optical flow method and the background model, and compared the effects of several methods of detection, using a strong adaptability to disturbance Gaussian mixture background model to detect moving targets. Then the detected motion foreground image OTSU threshold, using morphological methods de-noising and holes filled, get a complete moving target. In this paper, the characteristics of the target shadow and existing methods are analyzed, using a shadow removal method based on color features.

scholarly journals A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode

2021 ◽  
Vol 11 (15) ◽  
pp. 6831
Author(s):  
Yue Chen ◽  
Jian Lu
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Road Traffic ◽  
Low Cost ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Counting Method ◽  
Moving Vehicle ◽  
Available Information

With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.

scholarly journals Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3059
Author(s):  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Seung-Bae Jeon ◽  
Minkyo Youm
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Moving Average ◽  
Computational Cost ◽  
Nonlinear Problems ◽  
Transportation Systems ◽  
Mobility Data ◽  
Challenges And Opportunities ◽  
Moving Average Model ◽  
Gated Recurrent Unit

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

scholarly journals Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment

2021 ◽  
Vol 13 (6) ◽  
pp. 3474
Author(s):  
Guang Yu ◽  
Shuo Liu ◽  
Qiangqiang Shangguan
Keyword(s):  
Communication Technology ◽  
Intelligent Transportation Systems ◽  
Rapid Development ◽  
Safety Margin ◽  
Practical Method ◽  
Transportation Systems ◽  
Systematic Evaluation ◽  
Connected Vehicle ◽  
Car Following ◽  
Cooperative Driving

With the rapid development of information and communication technology, future intelligent transportation systems will exhibit a trend of cooperative driving of connected vehicles. Platooning is an important application technique for cooperative driving. Herein, optimized car-following models for platoon control based on intervehicle communication technology are proposed. On the basis of existing indicators, a series of evaluation methods for platoon safety, stability, and energy consumption is constructed. Numerical simulations are used to compare the effects of three traditional models and their optimized counterparts on the car-following process. Moreover, the influence of homogenous and heterogeneous attributes on the platoon is analyzed. The optimized model proposed in this paper can improve the stability and safety of vehicle following and reduce the total fuel consumption. The simulation results show that a homogenous platoon can enhance the overall stability of the platoon and that the desired safety margin (DSM) model is better suited for heterogeneous platoon control than the other two models. This paper provides a practical method for the design and systematic evaluation of a platoon control strategy, which is one of the key focuses in the connected and autonomous vehicle industry.

scholarly journals Sustainable urban transportation development in China: A behavioral perspective

2021 ◽  
Author(s):  
Shuai Ling ◽  
Shoufeng Ma ◽  
Ning Jia
Keyword(s):  
Traffic Management ◽  
Rapid Development ◽  
Demand Management ◽  
Urban Transportation ◽  
Transportation Systems ◽  
Transportation Demand ◽  
Special Cases ◽  
Traffic Systems ◽  
Four Levels ◽  
Transportation Development

AbstractThe rapid development of economics requires highly efficient and environment-friendly urban transportation systems. Such requirement presents challenges in sustainable urban transportation. The analysis and understanding of transportation-related behaviors provide one approach to dealing with complicated transportation activities. In this study, the management of traffic systems is divided into four levels with a structural and systematic perspective. Then, several special cases from the perspective of behavior, including purchasing behaviors toward new energy vehicles, choice behaviors toward green travel, and behavioral reactions toward transportation demand management policies, are investigated. Several management suggestions are proposed for transportation authorities to improve sustainable traffic management.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Deriving Traffic-Performance Measures and Levels of Service from Second-Order Statistical Features of Spatiotemporal Traffic Contour Maps

2003 ◽  
Vol 1858 (1) ◽  
pp. 148-157 ◽  
Author(s):  
Sherif Ishak
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Performance Monitoring ◽  
Transportation Systems ◽  
Second Order ◽  
Level Of Service ◽  
Large Set ◽  
Contour Map ◽  
Contour Maps ◽  
Statistical Measures

Little information has been successfully extracted from the wealth of data collected by intelligent transportation systems. Such information is needed for the efficiency of operations and management functions of traffic-management centers. A new set of second-order statistical measures derived from texture characterization techniques in the field of digital image analysis is presented. The main objective is to improve the data-analysis tools used in performance-monitoring systems and assessment of level of service. The new measures can extract properties such as smoothness, homogeneity, regularity, and randomness in traffic operations directly from constructed spatiotemporal traffic contour maps. To avoid information redundancy, a correlation matrix was examined for nearly 14,000 15-min speed contour maps generated for a 3.4-mi freeway section over a period of 5 weekdays. The result was a set of three second-order measures: angular second moment, contrast, and entropy. Each measure was analyzed to examine its sensitivity to various traffic conditions, expressed by the overall speed mean of each contour map. The study also presented a tentative approach, similar to the conventional one used in the Highway Capacity Manual, to evaluate the level of service for each contour map. The new set of level-of-service criteria can be applied in real time by using a stand-alone module that was developed in the study. The module can be readily implemented online and allows traffic-management center operators to tune a large set of related parameters.

Smart Traffic Management for Collision Avoidance Using V2V

2021 ◽  
Vol 11 (1) ◽  
pp. 01-07
Author(s):  
V. Naren Thiruvalar ◽  
E. Vimal
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Emergency Situation ◽  
Transportation Systems ◽  
Pollution Monitoring ◽  
Inappropriate Use ◽  
V2v Communication ◽  
Use Of Resources ◽  
Smart Traffic

The main objective of this project is to connect the vehicles together and avoid accidents by using V2V Communication. The vehicles are to be connected together by means of DSRC algorithm which is used for transceiving alert messages among the connected vehicles, in case of any emergency situation such as accidents. The Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behaviour. An increase in the infrastructure growth is a possible solution but turns out to be costly in terms of both time and effort. Various applications that target transport efficiency could make use of the vast information collected by vehicles: safety, traffic management, pollution monitoring, tourist information, etc.

A Survey of Approaches for Estimating Meteorological Visibility Distance Under Foggy Weather Conditions

2020 ◽  
pp. 65-92 ◽  
Author(s):  
Faouzi Kamoun ◽  
Hazar Chaabani ◽  
Fatma Outay ◽  
Ansar-Ul-Haque Yasar
Keyword(s):  
Intelligent Transportation Systems ◽  
Situational Awareness ◽  
Wide Spectrum ◽  
Distance Estimation ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Driver Assistance Systems ◽  
Research Issues ◽  
Visibility Distance ◽  
Assistance Systems

The immaturity of fog abatement technologies for highway usage has led to growing interest towards developing intelligent transportation systems that are capable of estimating meteorological visibility distance under foggy weather conditions. This capability is crucial to support next-generation cooperative situational awareness and collision avoidance systems as well as onboard driver assistance systems. This chapter presents a survey and a comprehensive taxonomy of daytime visibility distance estimation approaches based on a review and synthesis of the literature. The proposed taxonomy is both comprehensive (i.e., captures a wide spectrum of earlier contributions) and effective (i.e., enables easy comparison among previously proposed approaches). The authors also highlight some open research issues that warrant further investigation.

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