Path Choice Behavior Analysis Based on Vehicle Trajectory Data

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Zuyao Zhang ◽  
Li Tang ◽  
Yifeng Wang ◽  
Xuejun Zhang
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Trajectory Data ◽  
Vehicle Trajectory

scholarly journals Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yajuan Deng ◽  
Meiye Li ◽  
Qing Tang ◽  
Renjie He ◽  
Xianbiao Hu
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Stated Preference ◽  
Route Choice ◽  
Small Scale ◽  
Trajectory Data ◽  
Underlying Assumption ◽  
Route Choice Behavior ◽  
Mnl Model ◽  
Taxi Trajectory

Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters’ route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi’an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters’ route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters’ route choice behaviors, when compared with the traditional MNL model.

Car-Following Behavior Analysis from Microscopic Trajectory Data

2005 ◽  
Vol 1934 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Saskia Ossen ◽  
Serge P. Hoogendoorn
Keyword(s):  
Reaction Time ◽  
High Resolution ◽  
Behavior Analysis ◽  
High Frequency ◽  
Digital Images ◽  
Optimal Parameter ◽  
Trajectory Data ◽  
Car Following ◽  
Vehicle Trajectory ◽  
Parameter Values

The development of accurate and robust models in the field of car following has suffered greatly from the lack of appropriate microscopic data. Because of this lack, little is known about differences in car-following behavior between individual driver–vehicle combinations. This paper studies the car-following behaviors of individual drivers by making use of vehicle trajectory data extracted from high-resolution digital images collected at a high frequency from a helicopter. The analysis was performed by estimating the parameters of different specifications of the well-known Gazis–Herman–Rothery car-following rule for individual drivers. This analysis showed that a relation between the stimuli and the response could be established in 80% of the cases. The main contribution of this paper is that considerable differences between the car-following behaviors of individual drivers could be identified. These differences are expressed as different optimal parameter values for the reaction time and the sensitivity, as well as different car-following models that appear to be optimal on the basis of the data for individual drivers.

scholarly journals Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data

2021 ◽  
Vol 13 (16) ◽  
pp. 9278
Author(s):  
Ruoxi Jiang ◽  
Shunying Zhu ◽  
Hongguang Chang ◽  
Jingan Wu ◽  
Naikan Ding ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Traffic Safety ◽  
Pearson Correlation ◽  
Highway Safety ◽  
Lane Change ◽  
Trajectory Data ◽  
Average Value ◽  
Traffic Conflict ◽  
Vehicle Trajectory ◽  
Safety Estimation

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

Modeling Speed Disturbance Absorption following Current State–Control Action–Expected State Chains

2005 ◽  
Vol 1934 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Ruihua Tao ◽  
Heng Wei ◽  
Yinhai Wang ◽  
Virginia P. Sisiopiku
Keyword(s):  
Recovery Period ◽  
Control Action ◽  
State Control ◽  
Trajectory Data ◽  
Lane Changing ◽  
Car Following ◽  
Current State ◽  
Speed Advantage ◽  
Vehicle Trajectory ◽  
A Current

This paper explores driver behavior in a paired car-following mode in response to a speed disturbance from a front vehicle. A current state– control action–expected state (SAS) chain is developed to provide a framework for modeling of the hierarchy of expected actions incurred during the need for speed disturbance absorption. Three car-following scenarios and one lane-changing scenario are identified with defined perceptual informative variables to describe the process of speed disturbance absorption. Those variables include dynamic spacing versus the follower's speed, disturbance-effecting and -ending spacing, headway, acceleration– deceleration, speed recovery period, speed advantage, and lane-changing duration. A significant improvement in car-following modeling introduced in the paper is the integration of car-following and lane-changing behaviors in the SAS chain. Moreover, critical values of perceptual informative variables are statistically developed as a function of the follower's speed by using observed vehicle trajectory data. Furthermore, models that determine the probability of a lane change in response to a speed disturbance and models for acceptable lane-changing decision-making conditions at the adjacent lanes are developed on the basis of the analysis of observed vehicle trajectory data. The work presented in this paper provides an analysis of speed disturbance and speed absorption phenomena and car-following and lane-changing behaviors at the microscopic level. This work establishes the foundation for further research on multiple speed disturbance absorption and its impact on traffic stabilities at the macroscopic analysis level.

scholarly journals Improving Motorway Mobility and Environmental Performance via Vehicle Trajectory Data-Based Control

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 86862-86869 ◽  
Author(s):  
Yifei Zhao ◽  
Shunchao Yin ◽  
Duo Li ◽  
Qian Yu ◽  
Prakash Ranjitkar
Keyword(s):  
Environmental Performance ◽  
Trajectory Data ◽  
Vehicle Trajectory
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