Trends in traffic congestion
Congestion loss time on Expressway※1The trend continued to decrease after peaking in 1997, and in 2008, it decreased to about 50% of the peak.
After that, after an increase due to special holiday discounts since 2009 (5 discounts in local areas, upper limit of 1,000 yen, etc.), an increase due to reconstruction demand from the Great East Japan Earthquake since 2011, etc. The downward trend continued.
However, since 2017, the traffic volume within our jurisdiction has increased with the development of the network near the Tokyo metropolitan area, and traffic congestion has started to increase again.
* 1: An index that indicates the degree of congestion. Calculated by the product of the loss time due to traffic jams and the number of units affected.
Cause of congestion
About 70% of the traffic jams that occur in the jurisdiction during the year are due to traffic concentration. (2018)
As the breakdown of these congestion occurrence point, In-bound slope and sag portion※2Accounted for the majority of about 70%.
※ 2:Out-bound from the hill In-bound is called a sag part a recess approaches the slope. For details on the mechanism of traffic jams and traffic jam countermeasuresHere
Causes of traffic congestion
Places where traffic concentration occurs
Traffic congestion mitigation measures
In order to ensure safe and smooth road traffic for customers, we will implement both hard and soft measures to reduce traffic congestion.
Measures against hardware
Measures for software
Installation of additional lanes and extension of acceleration / deceleration lanes are conducted at major traffic congestion points.
Kan-Etsu Expressway(Out-bound) Hanazono IC Exit Lane Extension Status
Before the spread of ETC, the toll department was the worst place for traffic jams, but now it is almost eliminated.
By flashing so that the light moves in the direction of travel, it is effective in suppressing speed reduction and supporting speed recovery.
PML operation status near the aqualine (In-bound) aqua tunnel
Signs are installed at major traffic jam locations to promote conscious speed recovery.
Operational status of traffic congestion point signs
Operation status of speed recovery display board
In response to changes in traffic conditions, we are making improvements to optimal lane operation.
Kan-Etsu Expressway(In-bound) Oizumi JCT Lane Operation Change Status
By avoiding traffic jams, traffic demand will be dispersed and the traffic jam scale can be reduced.
We are promoting the development of technology such as traffic jam prediction using AI, and are working to further improve prediction accuracy and convenience.
Long-term congestion forecast
NEXCO EAST and Grid Co., Ltd. have developed a technology that makes use of AI to predict traffic jams that have been conducted by traffic forecasters several months ahead. The AI traffic jam prediction model is designed to learn past factor data that is likely to have a significant impact on traffic jams and to predict whether traffic jams will occur at a certain date and time in the future.
AI Prediction of congestion
NTT DOCOMO, Inc. (hereinafter referred to as “DOCOMO”) uses real-time demographics created using the mobile phone network mechanism and past traffic records and technical knowledge on traffic flow held by NEXCO EAST We are conducting demonstration experiments to predict traffic jams at home on certain routes using “AI traffic jam prediction” developed using intelligence (AI) technology. In the experiment, information such as the required travel time every 30 minutes is distributed on NEXCO EAST's website “ DraPla ”.
![[CA] AI traffic jam prediction on the Tokyo Wan Aqua-Line Expressway In-bound Line](/activity/safety/images/safety07-13.jpg)