MODELING DRIVER BEHAVIOR ON URBAN STREETS

Traffic flow on straight roads is the most common traffic phenomenon in urban road traffic networks. In this paper, a realistic cellular automaton (CA) model is proposed to investigate driver behavior on urban straight roads based on our field observations. Two types of driver behavior, free and car-following, are simulated. Free driving behavior is modeled by a novel five-stage speeding model (two acceleration stages, one steady stage and two deceleration stages). Car-following processes are simulated by using 1.5-s as the average headway (1.5-s rule), which is observed in local urban networks. Vehicular mechanical restrictions (acceleration and deceleration capabilities) are appropriately reflected by a five-stage speeding model, which has the dual-regimes of acceleration and deceleration. A fine grid (the length of each cell corresponds to 1 m) is used. Our simulation results demonstrate that the introduction of the dual-regimes of acceleration and deceleration, 1.5-s rule and fine grid matches actual driver behavior well on urban straight roads.

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Car following and lane changing behavior using NGSIM and China data

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v8.i1.pp14-25 ◽

2019 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Md Mijanoor Rahman ◽

Mohd. Tahir Ismail ◽

Majid Majahar Ali

Keyword(s):

Reproducing Kernel ◽

Road Traffic ◽

Reproducing Kernel Hilbert Space ◽

Driver Behavior ◽

Trajectory Data ◽

Maximum Acceleration ◽

Lane Changing ◽

Traffic Jam ◽

Car Following ◽

Stopping Distance

Road safety is imperative theme because increasing road fatalities deaths in world. Besides road fatalities, traffic jam is increasing, human is frustrated for uncomfortable journey. The roads safety and passengers comfortable of the roadway system are vastly depended on the Car following (CF) and Lane Changing (LC) features of drivers. CF and LC theory describe the driver behavior by following paths in a traffic stream. In this research, researchers have compared to US-101 Next-Generation-Simulation (NGSIM) data with Beijing forth ring road, China freeways real trajectory data by CF and LC models. The CF data has been calibrated with Genetic Algorithm (GA). Reproducing Kernel Hilbert Space (RKHS) is generated the LC beginning and finishing points. Findings revealed that the CF parameters as maximum acceleration, minimum deceleration, free speed, minimum headway and stopping distance percentages of Chinese data are 74.71%, 79.95%, 66.57%, 0.018% and 65.65% respectively of NGSIM data. After completing the comparison, researchers have been found out optimization safety and comfortable acceleration-deceleration and LC beginning-finishing points of driver behavior. Here this analysis generates the driver behavior at real traffic network on the express highways of specific two roads US-101 (NGSIM) data and Chinese freeways data. Since NGSIM data is well simulated so road traffic is more safety and comfortable for journey.

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Influence ranking of road segments in urban road traffic networks

Computing ◽

10.1007/s00607-020-00839-0 ◽

2020 ◽

Vol 102 (11) ◽

pp. 2333-2360

Author(s):

Tarique Anwar ◽

Chengfei Liu ◽

Hai L. Vu ◽

Md. Saiful Islam ◽

Dongjin Yu ◽

...

Keyword(s):

Road Traffic ◽

Traffic Networks ◽

Urban Road ◽

Road Segments ◽

Road Traffic Networks

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Modeling the Vehicle-Bicycle Collision - The Analysis of the Projection Distance and Acceleration at the Cyclist's Head

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.880.177 ◽

2018 ◽

Vol 880 ◽

pp. 177-182 ◽

Cited By ~ 2

Author(s):

Oana Victoria Oţăt ◽

Ilie Dumitru ◽

Victor Oţăt ◽

Lucian Matei

Keyword(s):

Traffic Accidents ◽

Road Traffic ◽

Road Accidents ◽

Speed Variation ◽

Close Analysis ◽

Collision Type ◽

Projection Distance ◽

Negative Effect ◽

Head Area ◽

Road Traffic Networks

The ever-growing demand for transportation and the need to carry both people and goods has led to increased congestions of road traffic networks. Subsequently, the main negative effect is the multiplication of serious road accidents. Of the total number of serious road accidents, a significant increase has been registered among cyclists, with 13.9% in 2014 of total vehicles involved in traffic accidents, compared to 6.6% in 2010. The present paper underpins a close analysis of the kinematic and dynamic parameters in the event of a vehicle - bicycle – cyclist assembly – collision type. To study the vehicle-bicycle-collision type, we carried out a comparative analysis with regard to the distance the cyclist is thrown away following the collision, the speed variation of the vehicle and of the bicycle, and the speed variation in the cyclist’s head area, as well as the variation of the acceleration recorded on the vehicle, the bicycle and the cyclist’s head area. Hence, we modelled and simulated the vehicle – bicycle collision for two distinct instances, i.e. a frontal vehicle – rear bicycle collision and a frontal vehicle - frontal bicycle collision.

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Car-following model of multispecies systems of road traffic

Physical Review E ◽

10.1103/physreve.55.2203 ◽

1997 ◽

Vol 55 (3) ◽

pp. 2203-2214 ◽

Cited By ~ 52

Author(s):

Anthony D. Mason ◽

Andrew W. Woods

Keyword(s):

Road Traffic ◽

Car Following ◽

Car Following Model ◽

Multispecies Systems

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Methods for division of road traffic network for distributed simulation performed on heterogeneous clusters

Computer Science and Information Systems ◽

10.2298/csis120601006p ◽

2013 ◽

Vol 10 (1) ◽

pp. 321-348 ◽

Cited By ~ 3

Author(s):

Tomas Potuzak

Keyword(s):

Road Traffic ◽

Distributed Simulation ◽

Computation Time ◽

Traffic Network ◽

Traffic Networks ◽

Computing Environment ◽

Heterogeneous Cluster ◽

Heterogeneous Clusters ◽

The Road ◽

Road Traffic Networks

The computer simulation of road traffic is an important tool for control and analysis of road traffic networks. Due to their requirements for computation time (especially for large road traffic networks), many simulators of the road traffic has been adapted for distributed computing environment where combined power of multiple interconnected computers (nodes) is utilized. In this case, the road traffic network is divided into required number of sub-networks, whose simulation is then performed on particular nodes of the distributed computer. The distributed computer can be a homogenous (with nodes of the same computational power) or a heterogeneous cluster (with nodes of various powers). In this paper, we present two methods for road traffic network division for heterogeneous clusters. These methods consider the different computational powers of the particular nodes determined using a benchmark during the road traffic network division.

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ANALYSIS OF DRIVER BEHAVIOR IN VEHICLE FOLLOWING AND LANE CHANGING PARAMETERS ON URBAN MID BLOCK SECTIONS

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i2.470 ◽

2021 ◽

Vol 9 (2) ◽

pp. 1169-1177

Author(s):

Sowjanya, Et. al.

Keyword(s):

Road Traffic ◽

Driver Behavior ◽

Video Data ◽

Heterogeneous Traffic ◽

Heterogeneous Environments ◽

Trajectory Data ◽

Lateral Direction ◽

Lane Changing ◽

Traffic Conditions ◽

Road Section

In mixed traffic situations, there is weak or no lane behavior of the driver much more complicated where vehicle and driver behavior show a huge difference between them. Road traffic driving behavior on urban midblock sections is one of the most complex phenomena to be examined particularly in heterogeneous traffic conditions. This is often attributed to the capacity of the road section and the traffic flow features at the macroscopic and microscopic level of a road section. Very few researchers have attempted to investigate these features in heterogeneous environments because of the lack of adequate information gathering methods and the amount of complexity involved. In this background, an access controlled mid block road section was selected for video data collection. The main objectives of this study include developing vehicular trajectory data and analyzing the lane changing and vehicle following behavior of driver on the mid block section considering the relative velocities and relative spacing between various types of vehicles under heterogeneous traffic conditions. The videos were collected from urban roadway in the Kurnool district of Andhra Pradesh. The length of the stretch is 120m and the width is 7.0 m. The data was extracted to know the variations in terms of longitudinal and lateral speeds, velocities, vehicle following and lane changing behavior of the drivers. The data extracted was smoothened by moving average method to minimize the human errors. Lateral amplitude of the vehicles of various types was analyzed. The study revealed that vehicles in the mixed stream, in general and in particular, Bikes and Autos particularly move substantially in the lateral direction.

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