From an analysis of brain function data, it is confirmed that auditory information improves the cognitive effect and based on this a voice warning system using speakers has been developed for use in tunnels to mitigate traffic congestion. This was the first time in Japan that speakers were used to provide voice guidance on speed recovery information during traffic congestion, which is traditionally carried out with visual information. This study will present an outline of this system and will show how it was applied in the Kobotoke Tunnel and will verify using data from vehicle detectors and ETC 2.0 probe data whether the voice information was effective. For example, results showed that the breakdown flow rate increased by about 8% with the system, and the average speed in the queue increased by about 10%, showing the possibility of reduced congestion. An upstream effect could also be inferred, with the average speed near bottlenecks increasing due to voice guidance.
In this study, using the master data of the past two household travel surveys conducted in the Kumamoto metropolitan area, the moving and parking time of all cars, the actual number of inflows and outflows by zone, and their changes were clarified. The appropriate parking capacity in the central area of Kumamoto city is calculated by our own developed parking simulation model and some kind of queuing theory. In addition, we try to calculate the appropriate parking capacity for all vehicles after the spread of Shared Autonomous Vehicle service (SAVs), which is expected to significantly reduce the parking time. As a result, it was revealed that the number of parked cars has decreased in many areas in Kumamoto city, and even now, that the parking lot is over-supplied in the central area of the city. With the spread of SAVs, the parking lot will become even more excessive. These results give useful knowledge for parking policies which consist not only of quantitative supply but also of the position and location.
Day-long origin-destination (OD) demand for transportation prediction is advantageous in terms of accuracy and reliability because it is not affected by hourly variation of OD distribution. However, hourly traffic prediction is important for transportation analysis. We examined and improved the basic time coefficient estimation (TCoE) mODel that estimates the time coefficients for OD demand from observed link flows given a proven day-long OD demand, which is based on a bi-level formulation of the generalized least square and the semi-dynamic traffic assignment. In this paper, we proposed the TCoE formulation to restrict the excessive variations that slightly increase in low demand pairs while the number of subareas increases.
The mega-shopping complex “SAKURA MACHI Kumamoto” consisting of a bus terminal, commercial facilities, a hotel, and a hall in the center of Kumamoto city opened on September 14, 2019. Along with the opening, a “Kumamoto Bus and Train Free Day” social experiment was conducted to make public transportation in the prefecture free all day. The SAKURA MACH DATA Project which consist with educational-industrial-governmental cooperation was also organized in accordance with this social experiment . In this paper, we report the results of measuring the impact of increasing the number of public transport users, expanding excursion behavior in central city areas, reducing road traffic congestion, and spreading to the local economy using various data sources.