Measures for congestion on Expressway

Changes in traffic congestion

Congestion loss time on Expressway ^{* 1} Has been on a downward trend since peaking in 1997, and has decreased to about 50% of its peak in 2008.

After that, due to the increase in special holiday discounts (5 discounts for rural areas, maximum 1,000 yen, etc.) since 2009 and the reconstruction demand from the Great East Japan Earthquake since 2011, etc. The declining trend continued.

However, since 2017, the traffic volume in our jurisdiction has increased due to the network development in the suburbs of the Tokyo metropolitan area, and the traffic congestion has started to increase again.

* 1: An index that indicates the degree of congestion. Calculated by multiplying the time lost due to traffic jam and the number of affected vehicles.

Causes of traffic congestion

About 70% of the traffic jams that occur in the jurisdiction of the year are due to traffic congestion. (2018)

As the breakdown of these congestion occurrence point, In-bound slope and sag portion ^{* 2} Account for 70% of the total.

※ 2: Out-bound from the hill In-bound is called a sag part a recess approaches the slope. For more information on traffic congestion mechanisms and traffic congestion countermeasures, Here

Traffic congestion mitigation measures

In order to secure safe and smooth road traffic for our customers, we will implement both hardware and software measures to reduce traffic congestion.

• Hardware measures

  

• Soft measures

  

[Installation of additional lanes] [Extension of acceleration / deceleration lanes]

We are installing additional lanes and extending acceleration / deceleration lanes at major traffic congestion areas.

Click here for more details

• Extension of Kan-Etsu Expressway (Out-bound) Hanazono IC exit lane

  

[Development and dissemination of ETC]

Before the spread of ETC, the toll booth was the worst place where traffic jams occurred, but it is now almost eliminated.

[Traffic capacity Large pacemaker lights]

By blinking the light so that it moves in the direction of travel, it is effective in suppressing speed reduction and supporting speed recovery.

• PML operation status near the aqua line (In-bound) aqua tunnel

  

[Providing speed recovery information through traffic congestion point signs, etc.]

Signs are installed at major traffic congestion points to promote conscious speed recovery.

[Change of lane operation]

We are improving the lane operation so that it can respond to changes in traffic conditions.

• Lane change status of Kan-Etsu Expressway (In-bound) Oizumi JCT

  

[Providing traffic congestion prediction information]

By avoiding traffic congestion, traffic demand will be dispersed, and you can expect a reduction in traffic congestion.

NEXCO EAST Official Congestion Forecaster Congestion Forecast & Outing Guide

[Information provision using AI]

We are promoting technological development 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 utilized AI to develop a technology that enables traffic forecasters to predict traffic congestion several months ahead. The traffic jam prediction model using AI learns past factor data that may greatly affect traffic jam occurrence, and predicts whether or not traffic jam will occur at a certain date and time in the future.

  NEXCO EAST and Grid Succeeded in developing Japan's first Expressway company's first AI-based traffic congestion prediction technology during traffic congestion!

  

• AI traffic jam prediction

  Real-time demographics created by NTT Docomo, Inc. (hereinafter referred to as “docomo”) using the mechanism of the mobile phone network are combined with NEXCO EAST 's past traffic jams and technical knowledge about traffic flow, etc. We are conducting a verification test to predict traffic jams during the return time on some routes using "AI traffic jam prediction", which was developed using intelligence (AI) technology. In the experiment, information such as transit time every 30 minutes is distributed on the NEXCO EAST customer website “DraPla”.

  • [CA] Prediction of AI congestion on the Tokyo Wan Aqua-Line Expressway In-bound line
  • NEXCO EAST and NTT Docomo, [CA] Tokyo Wan Aqua-Line Expressway starts traffic jam prediction demonstration experiment by "AI traffic jam prediction"
  • [CA] Tokyo Wan Aqua-Line Expressway 's "AI Congestion Prediction" will provide transit time every 30 minutes