01556891

Component

https://doi.org/10.3141/2491-01

Models of driving behavior (e.g., car following and lane changing) describe the longitudinal and lateral movements of vehicles in the traffic stream. Calibration and validation of these models require detailed vehicle trajectory data. Trajectory data about traffic in cities in the developing world are not publicly available. These cities are characterized by a heterogeneous mix of vehicle types and by a lack of lane discipline. This paper reports on an effort to create a data set of vehicle trajectory data in mixed traffic and on the first results of analysis of these data. The data were collected through video photography in an urban midblock road section in Chennai, India. The trajectory data were extracted from the video sequences with specialized software, and the locally weighted regression method was used to process the data to reduce measurement errors and obtain continuous position, speed, and acceleration functions. The collected data were freely available at http://toledo.net.technion.ac.il/downloads. The traffic flow characteristics of these trajectories, such as speed, acceleration and deceleration, and longitudinal spacing, were investigated. The results show statistically significant differences between the various vehicle types in travel speeds, accelerations, distance keeping, and selection of lateral positions on the roadway. The results further indicate that vehicles, particularly motorcycles, move substantially in the lateral direction and that in a substantial fraction of the observations, drivers are not strictly following their leaders. The results suggest directions for development of a driving behavior model for mixed traffic streams.

01586813

15-2036

English

pp 1–11

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369275

Print

Figures (9) ; Photos; References (33) ; Tables (2)

Data files; Motorcycles; Traffic data; Traffic flow; Vehicle mix; Vehicle trajectories

Chennai (India)

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 30 2014 12:44PM

• Analysis of Network Exit Functions for Various Urban Grid Network Configurations
• Capacity Drop: Relationship Between Speed in Congestion and the Queue Discharge Rate
• Comparative Analysis of Traffic State Estimation: Cumulative Counts-Based and Trajectory-Based Methods
• Developing a Large-Scale Hybrid Simulation Model: Lessons Learned
• Driving Type Categorizing and Microscopic Simulation Model Calibration
• Empirical Study of Scooter–Vehicle Mixed Traffic Propagation on Urban Arterials
• Multiscale Traffic Flow Model Based on the Mesoscopic Lighthill–Whitham and Richards Models
• On Traffic Relaxation, Anticipation, and Hysteresis
• Real-Time Traffic Network State Prediction for Proactive Traffic Management: Simulation Experiments and Sensitivity Analysis
• Spatiotemporal Clustering of Urban Networks: Real Case Scenario in London
• Use of Microsimulation for Examination of Macroscopic Fundamental Diagram Hysteresis Patterns for Hierarchical Urban Street Networks
• Vehicle Dynamics Model for Estimating Typical Vehicle Accelerations