scholarly journals The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections

2020 ◽  
Author(s):  
Julian Bock ◽  
Robert Krajewski ◽  
Tobias Moers ◽  
Steffen Runde ◽  
Lennart Vater ◽  
...  
Keyword(s):  
Road User

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

scholarly journals Contrasting Perspectives on the Comfort and Safety of Pedestrians Interacting with Other Road Users

2021 ◽  
Vol 2675 (3) ◽  
pp. 33-43
Author(s):  
Alexander Bigazzi ◽  
Gurdiljot Gill ◽  
Meghan Winters
Keyword(s):  
Traffic Safety ◽  
Injury Risk ◽  
Road User ◽  
Motor Vehicles ◽  
Advisory Group ◽  
User Interactions ◽  
The Public ◽  
Road Users ◽  
Expert Assessments ◽  
Sample Representation

Assessments of interactions between road users are crucial to understanding comfort and safety. However, observers may vary in their perceptions and ratings of road user interactions. The objective of this paper is to examine how perceptions of yielding, comfort, and safety for pedestrian interactions vary among observers, ranging from members of the public to road safety experts. Video clips of pedestrian interactions with motor vehicles and bicycles were collected from 11 crosswalks and shown to three groups of participants (traffic safety experts, an engaged citizen advisory group, and members of the general public) along with questions about yielding, comfort, and risk of injury. Experts had similar views of yielding and comfort to the other two groups, but a consistently lower assessment of injury risk for pedestrians in the study. Respondent socio-demographics did not relate to perceptions of yielding, comfort, or risk, but self-reported travel habits did. Respondents who reported walking more frequently rated pedestrian comfort as lower, and respondents who reported cycling more frequently rated risk as lower for pedestrian interactions with both motor vehicles and bicycles. Findings suggest small groups of engaged citizens can provide useful information about public perspectives on safety that likely diverge from expert assessments of risk, and that sample representation should be assessed in relation to travel habits rather than socio-demographics.

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

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