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- Title
Analysis of Traffic Volume and Travel-Time Relationship Using Continuous One-Hour Values on Urban Expressway.
- Authors
Fujita, Motohiro; Aratani, Atsuki; Yamada, Shinji
- Abstract
To make more efficient use of the expanded freeway and urban expressway networks, various measures such as bottleneck management and wide-area congestion pricing based on traffic data obtained from traffic detectors, including traffic volume and travel time, have been considered. Generally, the congestion status of the data varies from day to day. This study proposes a method for analyzing a graph of traffic volume and travel time to visually and intuitively grasp the change in the daily traffic situation using continuous one-hour values. These values are continuously generated hourly values obtained by shifting data every minute. Twenty-four hours 1 minute data for 128 days on 32 segments with detectors in the Nagoya Expressway Network in Japan were used to draw a continuous one-hour value graph. A number of graphs showed loops of continuous one-hour values with congestion and a smooth variation characteristic of values over time. These graphs provide an accurate estimate of the daily maximum one-hour traffic volumes and facilitate a sequential understanding of the congestion pattern changes on successive route segments. Hourly travel-time prediction models were constructed to macroscopically examine congestion measures over a range of several hours. These models were fabricated with high accuracy using multiple regression analysis based on the characteristics of continuous one-hour values. Exploratory predictive analysis of hourly travel-time models has allowed us to study and discuss various congestion factors in road structures and traffic flows, and it has been found to be easy to grasp the phenomenon and ensure accuracy and operability.
- Subjects
JAPAN; TRAFFIC flow; EXPRESS highways; CITY traffic; TRAVEL time (Traffic engineering); VEHICLE detectors; MULTIPLE regression analysis; CONGESTION pricing
- Publication
Journal of Advanced Transportation, 2023, p1
- ISSN
0197-6729
- Publication type
Article
- DOI
10.1155/2023/6866060