Modeling and Estimation of Headway Distributions

1997 ◽  
Vol 1591 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Hein Botma
Keyword(s):  
Estimation Method ◽  
Simulation Models ◽  
Independent Random Variables ◽  
Microscopic Simulation ◽  
Simple Analysis ◽  
Rural Roads ◽  
The Road ◽  
Road Capacity ◽  
Measurement Location ◽  
Fourier Series Analysis

A simple analysis to derive Branston’s generalized queueing model for (time-) headway distributions is presented. It is assumed that the total headway is the sum of two independent random variables: the empty zone and the free-flowing headway. The parameters of the model can be used to examine various characteristics of both the road (e.g., capacity) and driver-vehicle combinations (e.g., following behavior). Furthermore, the model can be applied to vehicle generation in microscopic simulation models and to safety analysis. To estimate the different parameters in the model, a new estimation method is proposed. This method, which was developed on the basis of Fourier-series analysis, was successfully applied to measurements collected on two-lane rural roads. The method was found to be both computationally less demanding and more robust than traditional parameter techniques procedures, such as maximum likelihood. In addition, the method provides more accurate results. Parameters in the model were examined with the developed estimation method. Estimates of these parameters at a specific period and a specific measurement location were to some extent transferable to other periods and locations. Application of the method to road capacity estimation is discussed.

scholarly journals Lane Change Behavior on Freeways: An Online Survey Using Video Clips

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
V. L. Knoop ◽  
M. Keyvan-Ekbatani ◽  
M. de Baat ◽  
H. Taale ◽  
S. P. Hoogendoorn
Keyword(s):  
Online Survey ◽  
Simulation Models ◽  
Lane Changes ◽  
The Road ◽  
Video Clips ◽  
Road Capacity ◽  
Change Behavior ◽  
Instrumented Vehicle ◽  
The Moment ◽  
The Right

Freeways form an important part of the road network. Yet, driving behavior on freeways, in particular lane changes and the relation with the choice of speed, is not well understood. To overcome this, an online survey has been carried out. Drivers were shown video clips, and after each clip they had to indicate what they would do after the moment the video stopped. A total of 1258 Dutch respondents completed the survey. The results show that most people have a strategy to choose a speed first and stick to that, which is the first strategy. A second, less often chosen, strategy is to choose a desired lane and adapt the speed based on the chosen lane. A third strategy, slightly less frequently chosen, is that drivers have a desired speed, but contrary to the first strategy, they increase this speed when they are in a different lane overtaking another driver. A small fraction have neither a desired speed nor a desired lane. Of the respondents 80% use the right lane if possible, and 80% avoid overtaking at the right. Also 80% give way to merging traffic. The survey was validated by 25 survey respondents also driving an instrumented vehicle. The strategies in this drive were similar to those in the survey. The findings of this work can be implemented in traffic simulation models, e.g., to determine road capacity and constraints in geometric design.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals Capacity Model of Exclusive Right-Turn Lane at Signalized Intersection considering Pedestrian-Vehicle Interaction

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Chengcheng Yang ◽  
Jiawen Wang ◽  
Jieshuang Dong
Keyword(s):  
Urban Areas ◽  
Great Influence ◽  
Estimation Method ◽  
Interaction Model ◽  
Arrival Rate ◽  
Microscopic Simulation ◽  
Road Capacity ◽  
Capacity Model ◽  
Pedestrian Impact ◽  
Exclusive Right

In high density urban areas, pedestrians have a great influence on the capacity of intersections. This paper studies the influence of pedestrians on road capacity and proposes an exclusive right-turn lane capacity model considering pedestrian-vehicle interaction (PV-RTC). Firstly, a pedestrian-vehicle interaction (PVI) model is proposed based on the logit model and static games theory of incomplete information. Through this model, the probability of 6 kinds of pedestrian-vehicle interaction situations (vehicles yield to pedestrians, pedestrians yield to vehicles, etc.) in the crosswalk can be obtained. Then, based on the basic idea of the stop line method and the probabilities of above situations, the PV-RTC model is established, and the sensitivity analysis of the important factors (pedestrian arrival rate, yielding rate, and green time ratio) affecting the model is carried out to clarify the mechanism of the proposed model. Finally, a pedestrian-vehicle interaction model of cellular automata for the exclusive right-turn lane is established and its simulation results are compared with the results of the PV-RTC model. The results show that the relative error between the microscopic simulation model and PV-RTC model is less than 15% overall, which verifies the validity of the PV-RTC model. This study provides references for a more precise estimation method of pedestrian impact on road capacity.

scholarly journals Road Roughness Estimation Based on the Vehicle Frequency Response Function

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 89
Author(s):  
Qingxia Zhang ◽  
Jilin Hou ◽  
Zhongdong Duan ◽  
Łukasz Jankowski ◽  
Xiaoyang Hu
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Gaussian White Noise ◽  
Estimation Method ◽  
Time Shift ◽  
Ride Quality ◽  
The Road ◽  
Road Roughness ◽  
Measured Response

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.

scholarly journals A Model-Driven-to-Sample-Driven Method for Rural Road Extraction

2021 ◽  
Vol 13 (8) ◽  
pp. 1417
Author(s):  
Jiguang Dai ◽  
Rongchen Ma ◽  
Litao Gong ◽  
Zimo Shen ◽  
Jialin Wu
Keyword(s):  
Rural Areas ◽  
Template Matching ◽  
Color Space ◽  
Recall Rate ◽  
Road Extraction ◽  
Rural Roads ◽  
Model Driven ◽  
The Road ◽  
Center Point ◽  
Manual Input

Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

Modeling Passenger Behavior in Nonpayment Areas at Rail Transit Stations

2015 ◽  
Vol 2534 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Mingjun Liao ◽  
Gang Liu
Keyword(s):  
Choice Model ◽  
Transition Probability ◽  
Simulation Models ◽  
Critical Issue ◽  
Microscopic Simulation ◽  
Movement Model ◽  
New Ideas ◽  
Path Navigation ◽  
Pass Through ◽  
Passenger Behavior

The nonpayment area at urban transit stations in China usually becomes extremely crowded during peak hours because many passengers queue to buy tickets and pass through the fare gates. How to evaluate the performance of these activities is a critical issue for the design and management of the nonpayment area. This study used microscopic simulation models to investigate passenger behavior in the nonpayment area. The study developed a queue choice model, a passenger movement model, and a path navigation model. Some new ideas were involved. First, the study introduced the concepts of dynamic queue length and dynamic distance between the current passenger and alternative queues into the queue choice model. Second, a new factor, called direction of goal, was proposed to navigate a passenger through the dynamic end of a queue or other goals. This factor was used to construct the transition probability function of a cellular automata model. Finally, the proposed models were calibrated and verified on the basis of a field survey and sensitivity analysis. The results show that the proposed models can capture passenger behaviors in the nonpayment area and perform well for queue estimation.

Parameter Sensitivity Analysis of a Cooperative Dynamic Bus Lane System With Connected Vehicles

2021 ◽  
pp. 036119812110357
Author(s):  
Meng Xie ◽  
Michael Winsor ◽  
Tao Ma ◽  
Andreas Rau ◽  
Fritz Busch ◽  
...  
Keyword(s):  
Travel Time ◽  
Transition Period ◽  
Penetration Rate ◽  
Simulation Models ◽  
Driving Behavior ◽  
Connected Vehicles ◽  
Microscopic Simulation ◽  
Communication Range ◽  
Bus Lane ◽  
The Impact

This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.

scholarly journals Macroscopic dynamics and the collapse of urban traffic

2018 ◽  
Vol 115 (50) ◽  
pp. 12654-12661 ◽  
Author(s):  
Luis E. Olmos ◽  
Serdar Çolak ◽  
Sajjad Shafiei ◽  
Meead Saberi ◽  
Marta C. González
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Spatial Dynamics ◽  
Urban Traffic ◽  
Nonequilibrium Phase Transition ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Road Capacity ◽  
Total Vehicle ◽  
The Individual

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

Prediction of Design Hourly Volume on Rural Roads

2020 ◽  
pp. 036119812096556
Author(s):  
Miloš Petković ◽  
Vladan Tubić ◽  
Nemanja Stepanović
Keyword(s):  
Coefficient Of Determination ◽  
Rural Roads ◽  
The Road ◽  
Annual Average Daily Traffic ◽  
Proposed Model ◽  
Traffic Volumes ◽  
Implementation Costs ◽  
On The Road ◽  
Predicted Values ◽  
Design Hourly Volume

Design hourly volume (DHV) represents one of the most significant parameters in the procedures of developing and evaluating road designs. DHV values can be accurately and precisely calculated only on the road sections with the implemented automatic traffic counters (ATCs) which constantly monitor the traffic volume. Unfortunately, many road sections do not contain ATCs primarily because of the implementation costs. Consequently, for many years, the DHV values have been defined on the basis of occasional counting and the factors related to traffic flow variability over time. However, it has been determined that this approach has significant limitations and that the predicted values considerably deviate from the actual values. Therefore, the main objective of this paper is to develop a model which will enable DHV prediction on rural roads in cases of insufficient data. The suggested model is based on the correlation between DHVs and the parameters defining the characteristics of traffic flows, that is, the relationship between the traffic volumes on design working days and non-working days, and annual average daily traffic. The results of the conducted research indicate that the application of the proposed model enables the prediction of DHV values with a significant level of data accuracy and reliability. The coefficient of determination (R2) shows that more than 98% of the variance of the calculated DHVs was explained by the observed DHV values, while the mean error ranged from 4.86% to 7.84% depending on the number of hours for which DHV was predicted.

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