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Measures for Traffic Congestion in Urban Areas
Simulate in a digital space to optimize urban transportation
Solving the Problem of Mobility in Cities and Realizing a Sustainable Society

Challenge in Society

challenges in society
Chronic traffic congestion in urban areas causes economic losses, environmental pollution, and stress for people. Although transportation companies and local governments aim to introduce new transportation services to eliminate traffic congestion, it is difficult to accurately predict traffic conditions, and it takes time and cost to solve the problem.

If you're dealing with traffic congestion in urban areas

If you're dealing with traffic congestion in urban areas,
Fujitsu's digital rehearsal digitally reproduces traffic conditions and verifies the effectiveness of measures in advance. Models that take into account people's behavior and the impact on society are combined to reproduce the impact on traffic and predict the effect. We will help you optimize urban transportation by grasping the effects and selecting the most appropriate measures before introduction.

The benefits of Measures for Traffic Congestion in Urban Areas

  1. Objective verification of the effects of measures
    • You can simulate various transportation measures in a digital space and verify the effects based on objective data.
  2. Selection of optimal measures
    • Pre-validation enables you to select the most appropriate measures and reduce wasteful investments.
  3. Building consensus among the parties concerned
    • By sharing simulation results, we promote consensus among stakeholders and support smooth implementation of measures.
  4. Contribution to urban sustainability
    • We contribute to urban sustainability by alleviating traffic congestion and reducing CO2 emissions.

Demo
App Click here to try the demo app

API Click here to try the Web API

Technical overview

Target Industry/Users

  • Local governments and consulting companies in charge of transportation policies and urban planning
  • Transport operators such as railways, buses, taxis and shared mobility

Challenges in Target Industry and Operations

  • Local governments and consulting firms: Eliminating traffic congestion, revitalizing public transportation, and reducing environmental impact
  • Transportation business: Improving convenience for users, reducing costs, and reducing environmental impact

Technical Challenges

  • The introduction of new transport services and policies requires considerable cost and time, so it is necessary to examine the effects in advance and formulate optimal measures.
  • Conventionally, it has been difficult to accurately model real-world traffic phenomena and predict the effects of measures because individual human behavior and social impacts have not been sufficiently considered.

Solutions

  • Real-world traffic phenomena are digitally reproduced, and the effects of various measures can be verified in advance. This makes it possible to quantitatively assess the effects of measures before they are introduced and to select the most appropriate measures.
  • By building models that take into account people's behavior and the impact on society, and combining them, we can reproduce the impact on people's movements and traffic phenomena and more accurately predict the effects of measures.

Fujitsu's Technological Advantage

  • Fujitsu's proprietary digital rehearsal combines big data analysis by AI with knowledge of behavioral economics to accurately reproduce people's behavior as it changes according to circumstances, predict changes in people's behavior due to measures, and verify the effects and impacts of measures in advance.
  • The behavior of people can be reproduced with high precision by our original "Behavior Choice Model (*1)" which combines prospect theory, one of the representative theories of behavioral economics, and machine learning. It can express the differences in the choices made by people while having the behavioral characteristics common to many people.
    (*1) Behavior Choice Model is model for selecting human behavior.

Take a moment to reflect on your daily activities. Isn't it a series of choices about when to wake up and what to eat?
Fujitsu developed "Behavior Choice Model" to reproduce such human behavior on the digital twin.
Advantages of this model include the ability to explain the rationale behind the selections made, as well as that the predicted results match well with the actual selection results. In the demo application, you can see the results of recreating a person's movement using "Behavior Choice Model" that selects the mode and route of travel.

Use Cases

  • End User (Transport operators, municipal officials, consultants, etc.):
    • When considering measures to solve urban transportation issues, we examine the effects of various measures, such as introducing new transportation services or changing existing transportation policies, in advance, compare travel time, costs, and CO2 emissions, and search for the most appropriate measures. Based on data, we select and implement effective measures to realize a sustainable urban transportation system.
  • App developers:
    • When developing an application to verify new measures and services in advance, we use a behavior choice model to predict what transportation method users and citizens will choose under what circumstances. Based on the prediction of end-user behavior, this model can lead to more effective measures, information provision, and service improvement.

Case Studies

  • There are various measures to eliminate traffic congestion in urban areas, but they can be broadly classified into two approaches: expansion of traffic capacity and adjustment of traffic demand.
  • Expansion of traffic capacity
    It is an approach to increase the overall traffic capacity by expanding the road itself, which is prone to traffic jams, and by increasing detours.
    • Developing road networks:
      We will increase the capacity of our roads and realize smooth traffic flow by increasing the number of lanes, securing the width of our roads, developing bypasses, and developing ring roads.
    • Bottleneck mitigation:
      Measures will be taken at places where traffic jams are likely to occur, such as the expansion and multi-level construction of parking lots and multi-level construction of railroad crossings and intersections.

      In the demo application, you can change the location and price of the parking lot in the "park & ride" measures.

  • Adjustment of traffic demand
    It is an approach to alleviate congestion by controlling the traffic volume itself. We consider countermeasures from both a constant demand adjustment and a sudden demand adjustment such as an event such as a fireworks display or a disaster.
    • Traffic Demand Management:
      We will introduce measures to adjust and manage traffic volumes and reduce congestion during peak periods. For example, it regulates traffic flow through measures such as promoting the use of shared mobility, road closures and road pricing.

      In the demo application, you can try out "road closure", "road pricing" and "park & ride" measures.
      *"Optimal Shared Mobility Deployment" application for shared mobility operators allows users to experiment with changes in initial deployment volumes, locations, and discounts.

    • Promoting multimodality:
      By combining multiple modes of transportation, such as buses and bicycles, we increase the diversity of transportation and reduce the use of cars.
      For information on designing local public transportation, refer to "Redesign Assistance for Public Transportation"

Technical Trial

Documents

  • Demo App
Document Title Explanation
API Operation Manual Explains how to use the APIs provided by the demo application
  • "Optimal Shared Mobility Deployment" Demo App
Document Title Explanation
Concept&Navigation The concept of Social Digital Twin, the world we want to realize and the features of core technologies as a means to realize it
"Optimal Shared Mobility Deployment" Demo App Introduction Introducing the demo application
"Optimal Shared Mobility Deployment" Demo App Operation Manual How to use the demo application
"Optimal Shared Mobility Deployment" Data Generation Manual Explaining the file upload function of the demo application and how to create data to be used for data registration using API
"Optimal Shared Mobility Deployment" Restrictions Demo application restrictions

Demo
App Click here to try the demo app

API Click here to try the Web API