scholarly journals Estimation of the Change in Cumulative Flow over Probe Trajectories using Detector Data

2020 ◽  
Vol 2674 (11) ◽  
pp. 649-661
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Erik-Sander Smits ◽  
Chris Tampère ◽  
Serge P. Hoogendoorn
Keyword(s):  
Iterative Approach ◽  
Flow Conditions ◽  
Trajectory Data ◽  
Flow Curves ◽  
Traffic Conditions ◽  
Traffic State ◽  
Cumulative Error ◽  
Macroscopic States ◽  
Future Work ◽  
Cumulative Flow

Detector data can be used to construct cumulative flow curves, which in turn can be used to estimate the traffic state. However, this approach is subject to the cumulative error problem. Multiple studies propose to mitigate the cumulative error problem using probe trajectory data. These studies often assume “no overtaking” and thus that the cumulative flow is zero over probe trajectories. However, in multi-lane traffic this assumption is often violated. Therefore, we present an approach to estimate the change in cumulative flow along probe trajectories between detectors based on disaggregated detector data. The approach is tested with empirical data and in microsimulation. This shows that the approach is a clear improvement over assuming “no overtaking” in free-flow conditions. However, the benefits are not clear in varying traffic conditions. The approach can be applied in practice to mitigate the cumulative error problem and estimate the traffic state based on the resulting cumulative flow curves. As the performance of the approach depends on the changes in traffic conditions, it is suggested to use the probe speed observations between detectors to assign an uncertainty to the change in cumulative flow estimates. Furthermore, a potential option for future work is to use more elaborate schemes to estimate the probe relative flow between detectors, which may, for instance, combine probe speeds with estimates of the macroscopic states along the probe trajectory. If these macroscopic estimates are based on the cumulative flow curves at the detector locations, this would result in an iterative approach.

scholarly journals Effect of Enhanced ADAS Camera Capability on Traffic State Estimation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1996
Author(s):  
Hoe Kyoung Kim ◽  
Younshik Chung ◽  
Minjeong Kim
Keyword(s):  
State Estimation ◽  
Traffic Engineering ◽  
Flow Density ◽  
Microscopic Simulation ◽  
Traffic Demand ◽  
Traffic System ◽  
Traffic Conditions ◽  
Traffic State ◽  
Vehicle Identification ◽  
Traffic State Estimation

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

Study of the Response of PW Traffic Flow Model to a Bottleneck on a Circular Road and its Suitability for Heterogeneous Traffic Conditions

2021 ◽  
Vol 9 (4) ◽  
pp. 460-463
Keyword(s):  
Fluid Flow ◽  
Traffic Flow ◽  
Flow Model ◽  
Equation Model ◽  
Heterogeneous Traffic ◽  
Flow Conditions ◽  
Flow Models ◽  
Traffic Conditions ◽  
Model Response ◽  
Traffic Flow Models

The traffic flow conditions in developing countries are predominantly heterogeneous. The early developed traffic flow models have been derived from fluid flow to capture the behavior of the traffic. The very first two-equation model derived from fluid flow is known as the Payne-Whitham or PW Model. Along with the traffic flow, this model also captures the traffic acceleration. However, the PW model adopts a constant driver behavior which cannot be ignored, especially in the situation of heterogeneous traffic.This research focuses on testing the PW model and its suitability for heterogeneous traffic conditions by observing the model response to a bottleneck on a circular road. The PW model is mathematically approximated using the Roe Decomposition and then the performance of the model is observed using simulations.

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

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.

scholarly journals Biobjective Emergency Logistics Scheduling Model Based on Uncertain Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Youqiang Sun ◽  
Yeqing Ren ◽  
Xingjuan Cai
Keyword(s):  
Cloud Model ◽  
Traffic Volume ◽  
One Dimension ◽  
Trajectory Data ◽  
Inference Procedure ◽  
Traffic Conditions ◽  
Mutation Operators ◽  
Scheduling Model ◽  
Emergency Logistics ◽  
Proposed Model

Emergency logistics scheduling appears more and more important in modern society because of frequent occurrence of unpredictable disasters. Most of the existing studies consider a certain emergency logistics scheduling model, and most of them are based on an ideal scenario. Considering the uncertain traffic condition and the real road condition, a biobjective emergency logistics scheduling model is proposed, which includes two objectives: transportation time and transportation cost. The uncertainty of the proposed model is reflected in two aspects: the occurrence time of emergencies and the traffic volume predicted by the cloud model. The numerical characteristics of traffic information are abstracted from the spatial-temporal trajectory data by the reverse cloud model, and the inference procedure of the one-dimension cloud model further predicts the uncertain traffic volume using the numerical characteristics. In addition, the crossover and mutation operators of multiobjective evolutionary algorithms are modified to solve the model. The experimental results show that the inference procedure of one-dimension cloud model can accurately predict the traffic volume at the departure time; and the proposed model is more reasonable than the existing scheduling models; at the same time, the improved NSGA-II can also provide superior schemes in different departure times and traffic conditions for decision makers.

scholarly journals Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Serge P. Hoogendoorn
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Estimation Error ◽  
Ground Truth ◽  
Traffic Information ◽  
Estimation Errors ◽  
Sensing Data ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

Analysis of Vehicle-Following Behavior in Mixed Traffic Conditions using Vehicle Trajectory Data

2020 ◽  
Vol 2674 (11) ◽  
pp. 842-855
Author(s):  
Madhuri Kashyap N. R. ◽  
Bhargava Rama Chilukuri ◽  
Karthik K. Srinivasan ◽  
Gowri Asaithambi
Keyword(s):  
Mixed Traffic ◽  
Relative Speed ◽  
Linear Regression Models ◽  
Trajectory Data ◽  
Flow Models ◽  
Traffic Conditions ◽  
Traffic Flow Models ◽  
Vehicle Trajectory ◽  
New Variables ◽  
The Relationship

In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers using trajectory data. This paper proposes a novel use of vehicle trajectory data to identify car–car and auto–car pairs in the following regime and the regime duration, classify pairs as strict and staggered following, and investigate the factors influencing the following vehicle’s speed under different regimes in mixed traffic. Oblique trajectories and relative speed hysteresis plots are used to identify vehicle pairs in the steady-state following regime. Two new variables, oblique spacing (R) and the angle between the leader and the follower (θ), are proposed. Multiple linear regression models for the follower speed in strict and staggered following regimes are developed. The results show that cars exhibit following behavior more often than other vehicles. Also, while car–car pairs display both left and right staggered following, auto–car pairs predominantly demonstrate left staggered following. Regression analysis shows that the relationship between R and the speed of the following vehicle differs significantly when θ is close to 90° than when it deviates from 90°. The speed of followers is affected by leader and relative speeds. However, the relative speed has a smaller influence in both right and left staggered cases than strict follower cases. Finally, this study provides empirical evidence of qualitative and quantitative differences among following behaviors that can help in developing better microscopic traffic flow models for mixed traffic conditions.

Traffic state classification and prediction based on trajectory data

2021 ◽  
pp. 1-15
Author(s):  
Yu Yuan ◽  
Wenbo Zhang ◽  
Xun Yang ◽  
Yang Liu ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
Trajectory Data ◽  
Traffic State ◽  
State Classification

scholarly journals Fitting Volume Delay Functions under interrupted and uninterrupted flow conditions at Greek urban roads

2021 ◽  
pp. 1-16
Author(s):  
Roza E. Barka
Keyword(s):  
Urban Area ◽  
Flow Conditions ◽  
City Center ◽  
Pertinent Literature ◽  
Ring Road ◽  
Traffic Conditions ◽  
Loop Detectors ◽  
Video Recordings ◽  
The City ◽  
Derived Data

This paper presents the calibration of the most commonly used Volume Delay Functions (VDF): BPR, Conical, Akcelik and Modified Davidson, for an urban area populated by over 1 million inhabitants, the city of Thessaloniki in Greece. The estimation of the unknown coefficients was carried out for a typical freeway, the ring road of the city, and selected arterial and collected roads of the city center, through recent data of hourly observed vehicle speeds and volumes obtained from video recordings and loop detectors. The BPR function yielded the highest accuracy across all the examined road sections and was characterized as the most suitable to simulate and interpret the existing traffic conditions. The estimated coefficients differed significantly from the values proposed in the pertinent literature, which highlights the importance of using locally derived data for the calibration of the VDFs.

scholarly journals Pedestrian flow characteristics through bends: Effects of angle and desired speed

2020 ◽  
Vol 5 ◽  
pp. A89
Author(s):  
Jiahua Zhang ◽  
Charitha Dias ◽  
Majid Sarvi ◽  
Miho Iryo Asano
Keyword(s):  
Negative Impact ◽  
Flow Characteristics ◽  
Maximum Flow ◽  
Pedestrian Flow ◽  
Flow Conditions ◽  
Trajectory Data ◽  
Turning Angle ◽  
Flow Through ◽  
A Minor ◽  
Moving Speed

This study quantitatively described how the desired speed, which may reflect the emergency level, and the angle of bend affect the pedestrian flow by comparing fundamental diagrams derived from trajectory data collected through laboratory experiments. Results showed that the slow running (≈ 2.8 m/s speed) can increase the maximum flow through a bend by around 60 % compared to normal walking (≈ 1.4 m/s speed) regardless of the turning angle. Further, it was found that the turning angle of the bend has a stronger negative impact on the moving speed of crowds under running conditions. Compared to the turning angle, congestion level seemed to have a minor impact on the average moving speed through the bends. On the other hand, for 90° and 180° bends, the variations of the speed were observed to decrease with the increase of density which indicated that although congestion level deteriorated the flow conditions at bends, it homogenized the collective moving speed of pedestrians.

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