NTT West Japan Demonstration experiment using traffic prediction model started at “Yumeshima”, planned site for Osaka/Kansai Expo

NTT West Japan
Demonstration Experiment Utilizing Traffic Prediction Model Begins at Yumeshima, Planned Site for Osaka/Kansai Expo
-Assuming an increase in traffic volume due to large-scale
construction, confirmed the effects of reducing traffic congestion and reducing CO2 emissions-

Nippon Telegraph and Telephone West Corporation (hereafter, NTT West), Chuo Fukken Consultants Co., Ltd. (hereafter, Chuo Fukken Consultants) and Obayashi Corporation (hereafter, Obayashi Corporation) are working together to reduce traffic congestion, which is an important urban issue. , we have been working on the creation of a traffic volume prediction model. Now that we have the prospect of creating a prediction model, we will start a demonstration experiment from November 2022 at Yumeshima, the planned site for the Osaka-Kansai Expo, assuming an increase in the traffic volume of construction vehicles during large-scale construction.
1. background
Traffic congestion not only hinders comfortable movement in cities, but also hinders the distribution of goods in various industries, leading to a decline in productivity and, in turn, a significant impact on CO2 emissions. Therefore, in recent years, efforts have been made to improve the efficiency of transportation by combining the development of various urban transportation networks such as LRT*1 and BRT*2 (hard aspects) with measures such as MaaS*3 and park-and-ride*4 (soft aspects). is flourishing. However, even during urban development work to develop the infrastructure to support these, a large number of construction vehicles are concentrated, causing congestion on the surrounding roads. Therefore, there is still a great need for traffic congestion control based on existing transportation systems. Therefore, in April 2022, NTT West, Chuo Fukken Consultants, and Obayashi Corporation agreed to cooperate on reducing traffic congestion to alleviate congestion and avoid construction delays. As an area of ​​concern, we have been considering the area around Yumeshima, which is the planned site for the 2025 Osaka-Kansai Expo, as an example.
As a first step, Obayashi will develop the necessary indicators and required accuracy for construction vehicle management based on surrounding traffic information analyzed and predicted using different approaches by NTT West Chuo Fukken Consultants, which is capable of analyzing and predicting traffic volume. and created a traffic forecast model.
As a second step, we will use the created traffic volume prediction model to confirm the effect through a simulation of changes in traffic volume based on the number of construction vehicles generated at construction sites around Yumeshima.
[Image 1

Fig. 1 About the phases of this demonstration
2. Demonstration outline
Jointly started confirming the effects of a traffic volume prediction model aimed at reducing urban traffic congestion by analyzing traffic volume using probe car data*5 and event information from nearby facilities, as well as big data analysis of traffic volume. Did. In particular, assuming an increase in the number of construction vehicles during large-scale development work, by adding the amount of construction vehicles generated to the actual measured traffic volume, traffic congestion due to changes in the time and route of
construction vehicles based on a traffic volume prediction model Check the effect of control and CO2 reduction.
[Image 2

Fig. 2 Image of traffic congestion control using traffic volume prediction results
3. Role of each company
NTT West Japan
Establish a congestion prediction model by analyzing probe car data, etc., and examine control such as optimization of vehicle allocation plans for loading and unloading of construction work vehicles to reduce traffic congestion.
(1) Traffic volume forecast
Using past traffic volume and event information of nearby facilities, etc., a model was created to predict the volume of traffic flowing into and out of Yumeshima.
(2) Maximum traffic volume calculation
In addition to the possible traffic capacity based on traffic engineering, congestion is determined based on the intersection demand rate and past probe car data on single roads, and the upper limit of traffic volume is calculated.
(3) Prediction of the number of vehicles that can be dispatched Predict the number of vehicles that can be allocated from the difference between the upper limit traffic volume calculation result and the traffic volume prediction result.
Chuo Fukken Consultants
Analysis of start and end points of area traffic using big data analysis and medium- to long-term traffic volume prediction based on traffic engineering.
(1) Detailed understanding of traffic characteristics through big data analysis By continuously observing OD traffic volume*6 over a long period of time, we can gain a detailed understanding of traffic characteristics that are affected by various fluctuating factors.
(2) Refinement of traffic volume forecast
Based on the analysis of (1), refine the traffic volume forecast by estimating the future traffic volume targeting a specific date and time.
(3) Establishment of methods for refining traffic volume forecasts By verifying the necessary data and generalizing the method, we will establish a method for refining future traffic volume forecasts. Obayashi Corporation
Assuming vehicle management in large-scale construction, we confirmed the requirements of the prediction method and the effect of improving accuracy, and examined the possibility of future system cooperation. (1) Prediction method requirement definition and effect confirmation Assuming the number of construction vehicles and management operations in large-scale construction, we defined requirements such as the output data items and prediction accuracy of the prediction method, and confirmed the utilization effect of the prediction results. (2) Examination of cooperation with on-site systems
We examined cooperation with on-site systems such as the
already-developed construction vehicle management system “FUTRAL(R)*7” and extracted technical issues.
[Image 3

Fig. 3 Main roles of the three companies
Four. period
Scheduled from November 2022 to March 2023
Five. Demonstration area
Konohana Ward, Suminoe Ward, Minato Ward, Osaka City (road around Yumeshima area)
[Image 4

Figure 4 Yumeshima Area Surroundings
6. Future prospects
Utilizing the traffic volume prediction model created in the first stage, if we can confirm a certain effect in reducing traffic congestion and reducing CO2 emissions by changing the time and route of construction vehicles in the second stage, we will be able to use it at construction sites that will be in operation from fiscal 2023 onwards. While proceeding with the demonstration, we will establish the service with the aim of horizontally expanding it to large-scale development projects nationwide.
*1 Abbreviation for Light Rail Transit, a next-generation train with excellent features such as ease of getting on and off, punctuality, speed, comfort, etc., by utilizing low-floor vehicles (LRV) and improving tracks and tram stops. model tram system
*2 Abbreviation for Bus Rapid Transit, which has achieved higher performance than conventional buses in terms of speediness,
punctuality, and transportation capacity by applying various ideas to the running space, vehicles, operation management, etc. A
next-generation bus system that provides users with a high level of convenience, such as by enhancing connectivity with public
transportation
*3 An abbreviation for Mobility as a Service, which is an abbreviation for search, reservation, payment, etc., that optimally combines multiple public transportation and other transportation services in response to the transportation needs of local residents and travelers on a trip-by-trip basis. service in bulk
*4 A transportation method that uses a car from home to the nearest station, parks in a parking lot close to the station, transfers to public transportation, and moves to the destination.
*5 Various information (position, speed, etc.) obtained through communication networks, etc. from a large number of running vehicles, each car being regarded as a sensor.
*6 Traffic volume between origin and destination from a certain origin (Origin) to a destination (Destination)
*7 Construction vehicle management system “FUTRAL”
https://www.obayashi.co.jp/news/detail/news20220606_1.html
[Inquiries regarding this matter]
NIPPON TELEGRAPH AND TELEPHONE WEST CORPORATION Business Sales Headquarters Enterprise Business Sales Department
Phone: 06-6469-4118
Chuo Fukken Consultants Co., Ltd. Planning Division Transportation Planning Group
Phone: 06-6160-4140
Obayashi Corporation Corporate Communication Office Public Relations Section E-mail: press@ml.obayashi.co.jp
Details about this release:
https://prtimes.jp/main/html/rd/p/000000325.000032702.html


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