Enables inexperienced personnel to work efficiently and simplifies calculation of best logistics routes in new territories


NTT DOCOMO, INC. announced it has launched the Sharing Operation Optimization System, which uses AI to optimize operations for maintaining effective allocations of shared micromobility vehicles and replacing depleted batteries used by these vehicles.

The system was adopted by DOCOMO BIKE SHARE, INC., the provider of a bicycle-sharing service that allows users to reserve electric-assist bikes at the most convenient bike station, ride comfortably around the city, and then freely choose their preferred return station. Hereafter, the system will be gradually deployed throughout Tokyo to manage the companys shared-bike fleet.

Micromobility-sharing services allow users to easily rent bicycles and other small, lightweight vehicles and then conveniently return them to any station operated by the service. As the number of sharing-service users continues to grow globally, the corresponding increases in vehicles and renting/returning stations is making it difficult to ensure that vehicles in each fleet are optimally allocated and equipped with charged batteries at all times.

DOCOMOs new system uses AI to generate optimized plans for collecting and reallocating vehicles and replacing spent batteries. The system uses machine learning to simulate vehicle movements in order to predict the availability of vehicles and charged batteries at each station. Maintenance personnel can then use tablets or other mobile devices to view precisely which vehicles need to be trucked to other stations and which batteries need to be replaced for maximum operational efficiency.

The system forecasts rental/return trends based on a variety of data, such as in-use and returned vehicles, weather forecasts, date and time, travelling distances between stations, and each trucks storage capacity as well as quantities of vehicles and batteries on board any truck at any time. Using this information, the system generates an optimized reallocation plan, including the best transport routes. The system enables personnel with less experience to function as efficiently as experienced staffers. It is also expected to help operators to develop efficient operating routes in new territories.

Going forward, DOCOMO will continue to assess the systems performance, including its forecasting accuracy and the effectiveness of its recommended routes for bike reallocation, based on which the company expects to further upgrade the system and adapt the technology for supply-and-demand optimization in various other fields.

AppendixSystem Overview

DOCOMOs Sharing Operation Optimization System consists of three technologies: demand forecasting, simulations and reallocation planning.

1. Demand Forecasting Technology

Demand forecasting technology makes hourly predictions of how many vehicles will be in use and how many will be available at each station over the next 24 hours. In addition to real-time data, the system takes into account other data such as weather forecasts and dates and times. It has been shown to have the capacity to accurately forecast changing variations in vehicle demand.

2. Simulation Technology

This technology uses Multi-Agent Simulations*1 to project the precise movements among stations by the shared vehicles and their logistical-support trucks. The movement of each vehicle is forecasted using real-time data and statistics, including the probability of vehicles moving between specific stations and predicted demand at each station. Using these input values, the system forecasts the number of vehicles at each station and the remaining charge of each vehicles battery. Simulations are run every 10 minutes to enable the reallocation plan to be continuously updated.

3. Reallocation Planning Technology

Reallocation planning technology uses the simulation results to generate joint optimization plans*2 for vehicle collection/reallocation and battery replacement at each station, including the order in which trucks should visit stations and which transport routes to take. Forecasts of conditions in coming hours support the prioritization of battery replacements at the busiest stations, collections at stations where vehicle returns are expected to spike, etc., helping inexperienced personnel to work more efficiently and operators to develop efficient operating routes in new territories.

  1. Replicates the behavior of people interacting with others and their surrounding environments, such as when growing numbers of people avoid an increasingly crowded road, thereby changing the overall flow of people.
  2. Computational process to maximize or minimize evaluation values calculated from the combination of several different indicators by repeatedly performing appropriate actions on functions that are controllable by the system.


NTT DOCOMO, INC., Japans leading mobile operator with over 86 million subscriptions, is one of the worlds foremost contributors to 3G, 4G and 5G mobile network technologies. Beyond core communications services, DOCOMO is challenging new frontiers in collaboration with a growing number of entities (+d partners), creating exciting and convenient value-added services that change the way people live and work. Under a medium-term plan toward 2020 and beyond, DOCOMO is pioneering a leading-edge 5G network to facilitate innovative services that will amaze and inspire customers beyond their expectations.