DataHax Launches "DENNOU Research" to Visualize Traffic Congestion Factors by Linking Traffic Survey Cameras and Facility Cameras

DataHax Inc. (Headquarters: Shibuya-ku, Tokyo; Representative Director: Heita Oyama) has launched "DENNOU Research," a traffic congestion visualization service that analyzes data by combining traffic survey cameras installed on roads and intersections with cameras within facilities such as commercial facilities, tourist facilities, and event venues, as well as parking lot cameras.

This service provides integrated visualization not only of vehicle flow on roads but also of entry and exit status, dwell times, and occupancy status within facility entrances/exits and parking lots. It supports the identification of factors causing congestion around facilities, the review of security deployment and guidance plans, the improvement of parking operations, and the verification of the effectiveness of implemented measures.

Background of the New Service "DENNOU Research"

Usefulness of AI Cameras in Addressing Traffic Issues

In recent years, with the recovery of inbound demand and the increase in large-scale events, traffic congestion during entry and exit has become a major issue around tourist destinations, commercial facilities, and event venues. Particularly in tourist areas and large commercial facilities, queues of vehicles waiting to enter often extend to surrounding roads, causing congestion. On the other hand, on-site operations such as traffic control and parking guidance rely heavily on human labor. With labor shortages and an aging workforce, traditional operational management is beginning to show its limitations.

Approaching the "Causes" of Congestion by Viewing Roads and Facilities Holistically

Traditionally, traffic volume surveys on the road side alone have had difficulty grasping the occupancy status within parking lots, entry/exit processing, and vehicle dwell times within facilities. Conversely, looking only at the parking lot and facility side made it difficult to understand from which direction surrounding road traffic was extending, or at what times road congestion was affecting parking operations, leading to a division of information between the road and facility sides. DENNOU Research combines traffic survey cameras with facility and parking cameras to visualize vehicle flow between roads and facilities in an integrated manner, supporting everything from identifying congestion causes to improving operations.

Traffic Volume Survey (Image is for illustration purposes) Vehicle Distribution Map by AI License Plate Recognition (Image is for illustration purposes)

Service Overview (What is DENNOU Research?)

Our company has utilized AI license plate recognition technology "AutoCode" to analyze over 10 million cumulative vehicle data points. We have engaged in traffic analysis and visitor analysis at approximately 50 locations, primarily in the Kanto and Kansai regions, including public facilities and expressways. By integrating with the AI smart parking system "DENNOU PARK," we now support the optimization of regional traffic as a whole, including visualization of traffic conditions and congestion mitigation not only for parking operations but also for surrounding roads.

*Acquired license plate information is managed appropriately for the purpose of traffic analysis and understanding congestion trends, and is not used to identify individuals.

Specific Examples of Acquired Data

Vehicle dwell times near facility entrances/exits

Time periods when entry queues occur

Peak analysis before and after event start/end

Correlation analysis between parking lot occupancy and surrounding road congestion

Effectiveness verification after changing guard deployment and guidance routes

Comparison of usage status between multiple parking lots

Visualization of vehicle flow for right-turn entry, left-turn entry, and exit

Effects Achieved with DENNOU Research

1. Visualization and Trend Analysis of Congestion

Understand congestion status based on hourly inflow and dwell data, supporting the grasp of congestion "causes and precursors."

2. Verification of Policy Effectiveness

Quantitatively verify the effects of measures taken to improve congestion on traffic jams and dwell times using data. Supports an improvement cycle that does not stop at implementation.

3. Labor Saving and Efficiency Improvement in Operations

The results of repeated policy implementation and verification can be utilized not only for alleviating congestion around facilities but also for optimizing personnel allocation and making decisions on equipment selection.

Message from the Representative

"We believe that connecting the two previously divided perspectives of roads and facilities is the first step in addressing urban traffic challenges. If the cause is visible, guidance, security, and operations can be changed. Through DENNOU Research, we will contribute to creating towns that are not dictated by vehicle flow."(Representative Director, Heita Oyama)

Track Record of AI License Plate Recognition Technology Implementation

1. Traffic volume survey operations using vehicle numbers. 22 cameras operated simultaneously, supporting 24-hour surveys on weekdays and holidays.

2. Digitalization of traffic volume surveys on "toll roads" has fully commenced. Over 20 AI cameras visualize vehicle passage times and speeds.

3. Provided AI vehicle number recognition camera technology to a national park.

Future Outlook

We will redefine parking lots not merely as revenue-generating facilities but as integral infrastructure supporting regional transportation. In the future, we aim to build a next-generation transportation management platform that integrates roads, parking lots, commercial facilities, and local governments, utilizing AI cameras and traffic data.

By visualizing "vehicle flow," we will contribute to the realization of more comfortable and sustainable urban and regional societies that are not disrupted by traffic jams and congestion.

DataHax Inc.

[Representative] Heita Oyama, Representative Director and President [Location] 3-27-15 Sakagami Bldg. 7F, Shibuya-ku, Tokyo [Business Activities] AI Parking Business, AI Vehicle Number Recognition and Analysis Business, AI Traffic Volume Survey Business [Official HP] https://datahax.jp/ [Service Page] https://www.dennou-park.com/

[Representative Profile]

Heita Oyama, Representative Director, CEO, Founder / Graduated from Ritsumeikan University, College of Social Sciences in 2010, and Waseda University Graduate School of Finance in 2014 (MBA). After working in corporate sales for small and medium-sized enterprises at Sumitomo Mitsui Banking Corporation, he was involved in back-office operations at a boutique financial advisory firm. He became an independent freelance software engineer and participated in multiple AI projects and web application businesses. Founded the company in 2019. At our company, he is responsible for software development, including AI and applications, as well as business development. Keywords: