A vehicle type-dependent visual imaging model for analysing the heterogeneous car-following dynamics

2015 ◽  
Vol 4 (1) ◽  
pp. 68-85 ◽  
Author(s):  
Liang Zheng ◽  
Peter J. Jin ◽  
Helai Huang ◽  
Mingyun Gao ◽  
Bin Ran
Keyword(s):  
Car Following ◽  
Vehicle Type ◽  
Imaging Model ◽  
Visual Imaging

Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection

2018 ◽  
Vol 32 (32) ◽  
pp. 1850396 ◽  
Author(s):  
Hongjun Cui ◽  
Jiangke Xing ◽  
Xia Li ◽  
Minqing Zhu
Keyword(s):  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersection ◽  
Environmental Benefit ◽  
Cruise Control ◽  
Car Following ◽  
Vehicle Type ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model ◽  
Fuel Consumption And Emissions

In this paper, the HDM car-following model, the IIDM car-following model and the IDM car-following model with a constant-acceleration heuristic is utilized to explore the effects of ACC/CACC on the fuel consumption and emissionsat the signalized intersection. Two simulation experiments are studied: (i) one with free road ahead and (ii) the second with a red light 300 m downstream at the second intersection. The numerical results show that CACC vehicle is the best vehicle type among the three vehicle types from the perspective of vehicle’s cumulative fuel consumptions and cumulative exhaust emissions. The results of this paper also suggest a very high environmental benefit of ACC/CACC at little or no cost in infrastructure.

scholarly journals A vehicle type-based approach to model car following behaviors in simulation programs (case study: Car-motorcycle following behavior)

2019 ◽  
Vol 43 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Ehsan Amini ◽  
Masuod Tabibi ◽  
Ehsan Ramezani Khansari ◽  
Mohammadreza Abhari
Keyword(s):  
Car Following ◽  
Vehicle Type

ANALYSIS OF CAR FOLLOWING HEADWAY ALONG MULTILANE HIGHWAY

2016 ◽  
Vol 78 (4) ◽  
Author(s):  
Mohd Erwan Sanik ◽  
Joewono Prasetijo ◽  
Ahmad Hakimi Mat Nor ◽  
Nor Baizura Hamid ◽  
Ismail Yusof ◽  
...  
Keyword(s):  
Regression Models ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Linear Regression Models ◽  
Car Following ◽  
Vehicle Type ◽  
Field Data Collection ◽  
Headway Distribution ◽  
Heavy Goods Vehicles ◽  
Linear Regressions

This study describes driver’s car following headway on multilane highways.  The aim of this study is to analyse the driver’s car following headway along multilane highway at four selected locations.  The objectives of this study were to determine car headway at Jalan Batu Pahat – Ayer Hitam multilane highway and to develop linear regression models to present the relationships between headway and speed.  Videotaping method was used in field data collection during peak hours.  Data were extracted from recorded video by using the image processing technique software.  The distance headways and associated vehicles speeds were classified into vehicle following category by vehicle type: car following car, car following heavy goods vehicle, heavy goods vehicle following heavy goods vehicle and heavy goods vehicle following car categories.  Linear regressions models were used to develop the relationships between headway and speed. Based on all headway distribution, it is found that patterns of the vehicle headways at four study locations were similar, which shown a significant number of the vehicles travel at headways less than 5 seconds.  Furthermore, it can be concluded that many drivers tend to follow the vehicles ahead closely on multilane highways.  The regression models were significantly reliable based on their R-square values which are ranging between 0.80 and 0.95.  From the analysis, cars were found to maintain larger headways when following heavy goods vehicles compare to when following other cars.

New Estimation Technique for Vehicle-Type-Specific Headway Distributions

1998 ◽  
Vol 1646 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Piet H. L. Bovy
Keyword(s):  
Real Life ◽  
Estimation Procedure ◽  
Distribution Model ◽  
Parameter Estimates ◽  
Passenger Cars ◽  
Car Following ◽  
Vehicle Type ◽  
Real Life Data ◽  
Headway Distribution ◽  
Parameter Values

Recently, a new statistical procedure was developed that enables fast, accurate, and robust estimation of composite headway distributions, such as Branston’s generalized queueing model (GQM). Until now, the new procedure had only been applied to aggregate vehicular flow. In this paper, the estimation procedure is extended to headway observations segregated according to vehicle type and period of the day. Consequently, the parameters of a new mixed-vehicle-type headway distribution model based on Branston’s headway model can be estimated. Distinction of vehicle type and sample periods provides additional insight into the plausibility of the headway distributions and parameter values, as well as into the car-following behavior of the distinct vehicle classes varying across the different periods. The estimation procedure was applied to traffic data collected on a two-lane rural road in the Netherlands. Comparison of the estimated headway distributions with real-life data shows that headway distributions can be realistically replicated with the Pearson-III-based mixed-vehicle-type GQM. Inter-pretable differences between the morning, noon, and evening sample periods and between passenger cars, unarticulated trucks, and articulated trucks are found. In addition, passenger-car equivalents for both articulated trucks and unarticulated trucks were determined from the parameter estimates.

scholarly journals A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 453 ◽  
Author(s):  
Ping Wu ◽  
Feng Gao ◽  
Keqiang Li
Keyword(s):  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Driver Model ◽  
Relative Distance ◽  
Car Following ◽  
Vehicle Type ◽  
Vehicle Velocity ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Naturalistic Driving

In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions.

scholarly journals Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Yang ◽  
Ruey Long Cheu ◽  
Xiucheng Guo ◽  
Alicia Romo
Keyword(s):  
Relative Velocity ◽  
Data Sets ◽  
Feature Maps ◽  
Passenger Cars ◽  
Passenger Car ◽  
Feature Map ◽  
Car Following ◽  
Vehicle Type ◽  
Vehicle Trajectories ◽  
Self Organizing

A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower’s velocity, relative velocity, and gap) while the output signals represented the response (the follower’s acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

A new car following model from the perspective of visual imaging

2015 ◽  
Vol 26 (08) ◽  
pp. 1550090 ◽  
Author(s):  
Liang Zheng ◽  
Zhengbing He
Keyword(s):  
Traffic Flow ◽  
Visual Angle ◽  
Apparent Size ◽  
Stable Region ◽  
Trajectory Data ◽  
Perception Threshold ◽  
Car Following ◽  
Car Following Model ◽  
Visual Imaging ◽  
Weber Ratio

The paper proposes a car following model from the perspective of visual imaging (VIM), where the visual imaging size of the preceding vehicle on a driver's retina is considered as the stimuli and determines the driving behaviors. NGSIM trajectory data are applied to calibrate and validate the VIM under two scenarios, i.e. following the car and following the truck, whose fitting performance outperforms that of visual angle car following model (VAM). Through linear stability analyses for VIM, it can be drawn that the asymmetry in traffic flow is preserved; the larger vehicle width, vehicle length and vehicle apparent size all benefit enlarging the traffic flow stable region; the traffic flow unstable region when following the car tends to fall in the relatively small distance headway range compared with that when following the truck. After that, numerical experiments demonstrate that the visual imaging information applied in VIM is more contributive to the traffic flow stability than the visual angle information in VAM when following the truck in the relatively large distance headway or involving the driver's perception threshold, i.e. Weber ratio; introducing Weber ratio would break the originally stable traffic flow or deteriorate the traffic fluctuation, which however can be alleviated by increasing drivers' sensitivity, e.g., decreasing Weber ratio. Finally, VIM is verified to be able to satisfy the consistency criteria well from the theoretical aspect.

A spring-mass-damper system dynamics-based driver-vehicle integrated model for representing heterogeneous traffic

2018 ◽  
Vol 32 (11) ◽  
pp. 1850135 ◽  
Author(s):  
Caleb Ronald Munigety
Keyword(s):  
Simulation Models ◽  
Integrated Model ◽  
Heterogeneous Traffic ◽  
Microscopic Simulation ◽  
Car Following ◽  
Vehicle Type ◽  
Traffic Conditions ◽  
Proposed Model ◽  
Mass Damper ◽  
The Stability

The traditional traffic microscopic simulation models consider driver and vehicle as a single unit to represent the movements of drivers in a traffic stream. Due to this very fact, the traditional car-following models have the driver behavior related parameters, but ignore the vehicle related aspects. This approach is appropriate for homogeneous traffic conditions where car is the major vehicle type. However, in heterogeneous traffic conditions where multiple vehicle types are present, it becomes important to incorporate the vehicle related parameters exclusively to account for the varying dynamic and static characteristics. Thus, this paper presents a driver-vehicle integrated model hinged on the principles involved in physics-based spring-mass-damper mechanical system. While the spring constant represents the driver’s aggressiveness, the damping constant and the mass component take care of the stability and size/weight related aspects, respectively. The proposed model when tested, behaved pragmatically in representing the vehicle-type dependent longitudinal movements of vehicles.

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