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 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.

Linear Acceleration Car-Following Model Development and Validation

1998 ◽  
Vol 1644 (1) ◽  
pp. 10-19 ◽  
Author(s):  
M.F. Aycin ◽  
R.F. Benekohal
Keyword(s):  
Intelligent Transportation Systems ◽  
Field Data ◽  
Statistical Tests ◽  
Reaction Times ◽  
Linear Acceleration ◽  
Transportation Systems ◽  
Cruise Control ◽  
Vehicle Simulation ◽  
Car Following ◽  
Car Following Model

A linear acceleration car-following model has been developed for realistic simulation of traffic flow in intelligent transportation systems (ITS) applications. The new model provides continuous acceleration profiles instead of the stepwise profiles that are currently used. The brake reaction times of the drivers are simulated effectively and are independent of the simulation time steps. Chain-reaction times of the drivers are also simulated and perception thresholds are incorporated in the model. The preferred time headways are utilized to determine the simulated drivers’ separation during car-following. The features of the model and the realistic vehicle simulation in car-following and in stop-and-go conditions make this model suitable to ITS, especially to autonomous intelligent cruise-control systems. The car-following algorithm is validated at microscopic and macroscopic levels by using field data. Simulated versus field trajectories and statistical tests show very strong agreement between simulation results and field data.

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.

Sensor Fault Diagnosis of Connected Vehicles Under Imperfect Communication Network

2016 ◽  
Author(s):  
Zoleikha Abdollahi Biron ◽  
Satadru Dey ◽  
Pierluigi Pisu
Keyword(s):  
Communication Network ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Cruise Control ◽  
Vehicle System ◽  
Safety And Reliability ◽  
The Individual ◽  
Control Management

Connected vehicles are one of the promising technologies for future Intelligent Transportation Systems (ITS). Despite being the potentially beneficial in creating an efficient, sustainable and green transportation system, connected vehicles presents a set of specific challenges from safety and reliability standpoint. The first challenge arises from the information lost due to unreliable communication network which affects the control/management system of the individual vehicles and the overall system. Secondly, faulty sensors can affect the individual vehicle’s safe operation and in turn will create a potentially unsafe node in the vehicular network. Therefore, it is of utmost importance to take these issues into consideration while designing the control/management algorithms of the individual vehicles as a part of connected vehicle system. In this paper, we consider a connected vehicle system under Co-operative Adaptive Cruise Control (CACC) and propose a diagnostic scheme that deals with these aforementioned challenges. The effectiveness of the overall diagnostic scheme is verified via simulation studies.

scholarly journals An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity

2020 ◽  
Vol 54 (2) ◽  
pp. 59-73
Author(s):  
Yang Wang ◽  
Yu Xiao ◽  
Jianhui Lai ◽  
Yanyan Chen
Keyword(s):  
Missing Data ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Domain Knowledge ◽  
Rapid Development ◽  
Transportation Systems ◽  
Nearest Neighbour ◽  
Flow Data ◽  
Fundamental Parameters ◽  
Determination Of Parameters

Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.

scholarly journals VEHICLE TO EVERYTHING (V2X) COMMUNICATIONS TECHNOLOGY FOR SMART MOBILITY IN MALAYSIA: A COMPREHENSIVE REVIEW

2021 ◽  
Vol 56 (4) ◽  
pp. 534-563
Author(s):  
Sumendra Yogarayan ◽  
Siti Fatimah Abdul Razak ◽  
Afizan Azman ◽  
Mohd. Fikri Azli Abdullah
Keyword(s):  
Cellular Networks ◽  
Communication Technology ◽  
Intelligent Transportation Systems ◽  
Hybrid Methods ◽  
Substantial Effect ◽  
Transportation Systems ◽  
Vehicular Communication ◽  
Dedicated Short Range Communications ◽  
Vehicle To Vehicle ◽  
Technology Platforms

Vehicle to Everything (V2X) communication technology assesses the potential as the new phenomenon for Intelligent Transportation Systems (ITS) in the context of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N). Dedicated Short-Range Communications (DSRC) is the conventional vehicular communication standard for ITS. The cellular network based on 4G/5G for ITS deployment has become a competitor to DSRC. Recent advancements in technologies have motivated the research community to develop a hybrid DSRC, and cellular networks approach to support reliable ITS applications. Nevertheless, as new techniques come forward, the technical and regulatory challenges may also vary across countries. Given that the existing comparative studies have not been covered as a whole, we evaluated the V2X communication technology to classify the adaptability of DSRC, cellular networks, and hybrid methods. The study also includes available V2X technology platforms and products. In addition, the challenges of deployments are also depicted in this study. The outcome indicates that many automotive industries and telecommunication providers recognize V2X substantial effect on ITS. The work is underway to decide which capabilities will be added since this is a long-term benefit for our future transportation.

Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

2021 ◽  
pp. 036119812110357
Author(s):  
Xue Liu ◽  
Tangtao Yang ◽  
Haiyang Chen ◽  
Tony Z. Qiu
Keyword(s):  
Traffic Safety ◽  
Rapid Development ◽  
Transportation Systems ◽  
Bayesian Filtering ◽  
Connected Vehicle ◽  
Positioning Accuracy ◽  
Position Information ◽  
Cooperative Positioning ◽  
Roadside Unit ◽  
Positioning Method

With the rapid development of intelligent transportation systems and connected vehicle (CV) technology, vehicle-to-infrastructure communication technologies have provided new solutions to traditional traffic safety and efficiency issues. However, the current intelligent CVs often provide positioning services only through a single GPS. These modules’ positioning accuracy can be insufficient to support the safety and reliability of security applications. The question arises of how to enhance GPS positioning accuracy in a CV environment without adding additional equipment and using only the information that existing CV devices can access. This paper proposes a roadside unit (RSU)-assisted GPS-RSS (received signal strength) cooperative positioning method for a CV environment. The rough position information from GPS is combined with RSS ranging and dead reckoning to obtain preliminary position estimated coordinates of the CV. Bayesian filtering is performed to obtain a more accurate preliminary position estimate. The final position estimated coordinates, obtained after data fusion, are combined with the high-precision map data (MAP) sent by the RSU to match the lane where the vehicle is located. Simulation and field tests verify each other, and the results show that the lane positioning accuracy of GPS can be improved by 21% within the range from the RSU to the CV’s on-board unit.

scholarly journals A Hybrid Intelligent Multisensor Positioning Methodology for Reliable Vehicle Navigation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xu Li ◽  
Qimin Xu ◽  
Chingyao Chan ◽  
Bin Li ◽  
Wei Chen ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Rapid Development ◽  
Transportation Systems ◽  
Least Square ◽  
Real Field ◽  
Integration Scheme ◽  
Support Vector ◽  
Filter Model ◽  
Hybrid Positioning

With the rapid development of intelligent transportation systems worldwide, it becomes more important to realize accurate and reliable vehicle positioning in various environments whether GPS is available or not. This paper proposes a hybrid intelligent multisensor positioning methodology fusing the information from low-cost sensors including GPS, MEMS-based strapdown inertial navigation system (SINS) and electronic compass, and velocity constraint, which can achieve a significant performance improvement over the integration scheme only including GPS and MEMS-based SINS. First, the filter model of SINS aided by multiple sensors is presented in detail and then an improved Kalman filter with sequential measurement-update processing is developed to realize the filtering fusion. Further, a least square support vector machine- (LS SVM-) based intelligent module is designed and augmented with the improved KF to constitute the hybrid positioning system. In case of GPS outages, the LS SVM-based intelligent module trained recently is used to predict the position error to achieve more accurate positioning performance. Finally, the proposed hybrid positioning method is evaluated and compared with traditional methods through real field test data. The experimental results validate the feasibility and effectiveness of the proposed method.

scholarly journals Experimental Analysis of Car-Following Dynamics and Traffic Stability

2005 ◽  
Vol 1934 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Prakash Ranjitkar ◽  
Takashi Nakatsuji ◽  
Akira Kawamura
Keyword(s):  
Intelligent Transportation Systems ◽  
Optimization Technique ◽  
Model Performance ◽  
Transportation Systems ◽  
Model Parameters ◽  
Cruise Control ◽  
Position Measurement ◽  
Car Following ◽  
The Past ◽  
Safety Research

The study of car-following dynamics is useful for capacity analysis, safety research, and traffic simulation. There is also growing interest in its applications in intelligent transportation systems, such as advanced vehicle control and safety systems and autonomous cruise control systems. A large number of car-following models have been developed in the past five decades. Some of them were investigated and validated against experimental data; nevertheless, the results were not that consistent for some models, e.g., those for the General Motors (GM) model. As a part of the problem, the data acquisition and calibration techniques were not advanced then. The past few decades have seen remarkable advancements in these techniques, e.g., the use of the differential Global Positioning System (GPS) for position measurement, the use of Doppler's principle for speed measurements, and the use of genetic algorithms for optimization. It might be useful to reassess some outstanding issues in car-following dynamics in light of the latest technological advancements. This paper attempts to investigate car-following dynamics on the basis of the real-time kinematic GPS data collected from test track experiments. The GM model was evaluated along with some well-known simulation models, including the Gipps model and the Leutzbach and Wiedemann model. A genetic algorithm-based optimization technique was adapted for calibration. The sensitivities of drivers to their speeds and spacings from the vehicle ahead were found to vary among drivers. The interpersonal variations in model performance were significant. The GM model parameters were identified with improved reliability. The stability of traffic flow was analyzed experimentally.

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