Intelligent Mobility

How the Confluence of Technology Enablement and Changing SocialExpectations are Causing a Revolution in Transportation Systems

Intelligent Mobility Solution: SPATIOWL

CONTENTS

Introduction: How the Confluence of Technology Improvements and Changing Consumer Expectations Force Change ..................................................................................................3

How New IT Capabilities, New Social Expectations, and Competition are Changing the Transportation Landscape ...........................................................................................................................3

Case Study: Change in the Office Environment ..............................................................................................................4

Acknowledge the needs of the masses or be bowled over by their momentum ...........................................4

Three Key Social Trends that have Dramatically Changed Transportations Expectations ..............................4

Cell Phone Usage Experiment – Tokyo .....................................................................................................................5

The Results: ......................................................................................................................................................................5

Four Technology Enablers that are Changing Transportation................................................................................8

Voice of Market: How Transport Managers Envision the Future: Four Expectations from Transportation Professionals ............................................................................................9

Introducing SPATIOWL .................................................................................................................................................11

The SPATIOWL Traffic Optimization Methodology ................................................................................................12

Application 1: Public Transport Visualization & Analysis ................................................................................................13

Case Study #1: Tramway Optimization for a European Transport Provider in North Africa ......13

Application 2: More Accurate Car Navigation ................................................................................................................14

Case Study #2: Car Traffic Flow for a Leading Global Automobile Company ................................14

Application 3: Road traffic condition monitoring with camera image analysis .......................................................15

Case Study #3: Road Traffic Condition Monitoring with Camera Image Analysis for a Chinese City ........................................................................................................................15

Application 4: Multimodal Transport Route Search ........................................................................................................16

Case Study 4: Multimodal Solution for European Tourist City ...........................................................16

Application 5: Human Flow Management ........................................................................................................................17

Case Study #5: Large Event Management in Singapore........................................................................17

Application 6: Unexpected Incident Management .........................................................................................................18

Case Study #6: Train Delay Prediction for Passengers in Tokyo .........................................................18


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INTRODUCTION: HOW THE CONFLUENCE OF TECHNOLOGY IMPROVEMENTS AND CHANGING CONSUMER EXPECTATIONS FORCE CHANGE

Frost & Sullivan, as a market research and consulting company, has tracked both industry and social trends for more than 50 years. Our recent research focuses on concepts of converge, disrupt, collapse.

At Frost & Sullivan, we ask questions like:

• IT Enablement – How will the onslaught of new IT capabilities enable or change business models?

• Resetting Social Baselines – How do consumer interactions with new technology reset baseline expectations for business interactions?

• Competition – How are competitive players in new spaces finding new opportunities and forcing business change?

How New IT Capabilities, New Social Expectations, and Competition are Changing the Transportation Landscape

At the bequest of Fujitsu, Frost & Sullivan began to dig deeply into many of the new business models and social trends that we track to understand which of these has the opportunity to change other business areas. We began our search with some areas that are believed to have historically been fairly inefficient and in some ways, protected by gatekeepers. Traffic, for instance, has been the bane of commuters’ existence since the mass move to the suburbs of the 1950s. Yet for all the grumblings, few cities have approached the traffic problem as an efficiency problem that hurts economies. We know, for instance, that city planners often set lights to “traffic calming” mode, which slows down traffic, rather than optimizes for efficiency.

By the same token, human transport companies like Amtrak, Greyhound, and others are notorious for their transportation not arriving at the specified time. And, the closer a company is to a monopoly, the longer this trend has lasted.

But, in this context, we’ve re-asked our three core questions: A) What core technologies are enabling changes to traffic or transportation flow, B) what expectations are changing that may auger on change, and C) what competitors have come along that may force change?

We believe that transportation systems are ripe for change. To see how the pattern might occur, we first looked at another industry with similar characteristics, the corporate office environment, and within that ERP and other computing systems. In this environment, as well, we found a setting protected by gate keepers, e.g., the CIOs and CTOs. And, like traffic, we found a situation that was optimized for safety and security, most clearly at the expense of efficiency or user experience. While we believe safety is important, the example of what has happened in offices is instructive.


Intelligent Mobility Solution: SPATIOWL Intelligent Mobility Solution: SPATIOWL


Case Study: Change in the Office Environment

In the office environment we’ve seen a mass revolt against clunky enterprise email and management systems as consumers have become accustomed to sleek interfaces. We see social networks enabling smoother communication. And individuals demand that their companies allow them to use their own computers, the concept of BYOD, “Bring your own device.”

Sleek and useful social networks like Facebook led to internal social networks like Yammer. At the same time, employees brought on file-sharing systems like Dropbox, even at the protestations of their bosses, because they had become accustomed to the efficiency that had been engendered in their own lives. And, finally, we’re beginning to see clunky legacy ERP systems being replaced with systems that have cleaner layouts. Again, employees who have become accustomed to the sharp look and feel of their home computing and smartphone interfaces simply rebel against the uncomfortableness of work environments.

Acknowledge the needs of the masses or be bowled over by their momentumIn all of these cases, IT managers and CIOs, who had typically worried only about security, eventually bowed to the pressure of the masses. They either did, or they were replaced by managers who acknowledged the need for efficiency and work comfort.

The message was clear: people have become accustomed to ease of use and expect efficiency. The baseline of expectations, which was set by personal computers and smartphones, has resulted in and will continue to result in changes in the office world. And in this scenario the gatekeepers (CIOs, COOs) were often bowled over by public opinion. Those who refused to provide changes that enabled their employees were often replaced by people who would.

Whether you’re transporting produce, commodities or humans, the need for efficiency has grown all the more important. The possibilities for efficiency have grown, as have the expectations of clients and end-users.

The pace of change has caused expectations of end users to increase manifold in just a few years. As a result, expectations of the role of logistics manager in public transport, municipalities, or logistics companies are being redefined.

THREE KEY SOCIAL TRENDS THAT HAVE DRAMATICALLY CHANGED TRANSPORTATIONS EXPECTATIONS

We looked at three key expectation changers and the affect that they’re having on resetting peoples’ expectations of transportation providers.

1. The Prevalence of Smart Phones – And expectations for end-to-end and immediate routing

There is an overwhelming amount of data about the proliferation of smart phones in our societies. Frost & Sullivan data notes that by 2020, smart phone penetration will be 80%. But this number can sometimes be misleading. Does it include existing phones? Does the phone that has been sitting unused in my drawer for five years count?

We recently conducted simple marketing experiments in Tokyo to see how many people actually use phones in any given situation, rather than how many people own phones; we know that it is nearly 100% in this society.



Cell Phone Usage Experiment – TokyoWe chose five common routes in downtown Tokyo and measured the number of users of cellphones on weekday and weekend evenings. We did experiments with passengers standing on platforms and with passengers seated and standing on subway cars. In all cases the results blew away our wildest expectations. They showed a much higher rate of cell phone usage at any given moment compared to both our expectations and our understanding of the situation even a few years ago.

The Results:Amongst seated passengers, an average of 77% of seated passengers were visually checking their cell phones, compared with only 8% sleeping or nothing, the next most common activity. Passengers standing on trains had similarly high rates of checking cell phones, at 57%. Even amongst those on the platforms, 41% of passengers were checking their cell phones, making it the most common activity.




Figure 1: Passengers Checking Cell Phone

Figure 2: Activities by Seat Passengers on Subways in Tokyo

The takeaway from this is not only that many people have cell phones–something we’ve known for years –but also that they are engaging with them constantly. This constant engagement with the smart phone becomes an enabler of many business models. It also sets new expectations amongst consumers.

In the world of mobility, it is clear that pedestrians will have greater expectations than ever before.

2. Uberfication – And expectations for on-time, immediate delivery and predictability

In 2006, the city of San Francisco recorded 200,000 cab rides and zero UBER rides. In 2015, it had 180,000 UBER rides and only 100,000 cab rides. This shows the ability for a new business model to disrupt an old one, but it also shows how a new model can expand a market. And as markets and business models are created, so are new expectations. Uber has a few attributes that help it solve transport issues better than taxis: A) clear time expectations because the Uber app can tell a user within a minute or two how soon the car will arrive and approximately how long the trip will take; B) Uber is able to deliver more cars in peak times and has decreased the wait time from 27 minutes to 9 minutes in the countryside; C) customer service is rated as high, partly as



a result of a competitive driving environment; and D) receipts and cash are unnecessary. These may seem like small changes, but they have had a massive impact on the changing of loyalties compared to established taxis.

The tides turned so suddenly that an astronaut taking even a short trip to the space center would have been surprised on his return. This massive switch happened despite huge protestations by the city’s taxi drivers, legal challenges, and lawsuits. It is clear that UBER broke laws and that the mass of public opinion that valued the benefits offered by UBER caused lawmakers, in many cases, to let UBER get away with these infringements. The fact that consumer expectations can influence lawmakers and law enforcement is further suggestion that public opinion plays a huge part in causing change.

The new UBER paradigm is indeed transformational. It sets up a whole generation of users who are so impatient that they want to (and can) order a car to pick them up with the flip of a phone. We are like Batman calling our Batmobiles. Today, millions of users have a new expectation that cars should be available to pick them up at the flip of a switch, within just a few minutes, and at a reasonable price.

This sets up a world in which transportation operators also will be expected to deliver quickly, predictably, and give clear data about the time their trains, busses, or boats will arrive. A survey, in fact, shows that predictability of travel time is now one of the key expectations of mass transport users.

3. Access Over Ownership: Car and Bike Sharing

Large companies, have commented on the “millennial” (those born between 1981 and 1999) preference for access over ownership. They suggest that millennial prefer to save money over own assets. Our research suggests that this generation prefers the benefits of access to the burdens of ownership.

The market for shared bicycles is booming. It started in the late 1990s and has seen 450% growth from 2008 to 2013. There are now 650 bike sharing programs around the world, including in 88 cities in the US, catering to 12 million users.

Users of bike sharing programs point out the differences from riding your own bicycle. With your own bicycle you have the burden of ownership (taking care of it). You need to find a place to park it, remember where it is, and go get it again. Bike sharing on the other hand, gives “access” to thousands of bikes you can ride and time. You can drop them off virtually anywhere. And you don’t have to worry about taking care of them, including servicing them.

The experience then is 180 degrees different and the benefits are seen to far exceed the loss (feeling of pride in your “own” bike) in the eyes of many millennials.

The IT Enabler: To enable the bike sharing programs, companies needed to be able to track a large number of bicycles, which required GPS prices to come down. A sufficient number of consumers needed access to smart phones to find bicycle locations for pick-up and drop-off.

The Social Enabler: Having now accessed the bicycle sharing paradigm, this generation will now demand better bicycle routing, which is being installed in cities across the country. At the same time, citizens envision how the bicycles fit into their entire transportation needs. They are now demanding the integration of bike sharing programs into a multimodal transport system that has already included trains, busses, trollies, and ferries in some cities. They expect not only a one-pass payment system, but require routing programs that can tell them the fastest route. With so many moving parts, they will also demand that these systems not give theoretical times, but up-to-date, real-time predictability. This means that integrators of mapping services need access to the actual stock of bicycles in any situation.



FOUR TECHNOLOGY ENABLERS THAT ARE CHANGING TRANSPORTATION

Consumer expectations change, we said, partly as a result of being exposed to new business models in disparate parts of their lives. But it is worth remembering that these ideas could not change without enabling technologies. Many of the key technologies did not exist until a few years ago, or they did not exist at a low enough price point. Others will continue to evolve.

We have considered four technologies that we feel have been instrumental in engendering change within transportation systems.

1. Cell Phone Access / High Accuracy Mapping Programs – From a perspective of mass market access, many of the transportation applications that consumers now expect could not have been possible without consumers’ frequent access to cell phones. The explosion and improvement of these phones have enabled expectations to grow. Google Maps, which has long offered the ability to guide a car to a location, now provides walking directions, and in many cases train or bus connections. As mapping abilities improve one of the many competitors will become enabled to integrate mapping across multiple modes of transport and in real time. Another enabling aspect of the proliferation of cell phones is the ability for mapping companies to gather data on current locations. This, of course, allows mapping companies to spot traffic jams and adjust expected times.

2. Sensors Proliferation and Connectability – Sensors have long been used in traffic planning and in traffic direction. The most basic sensor is also the most used - the under pavement coil. A simple electrically charged wire is coiled in at least four loops in a circle or square a few centimeters under the pavement. This creates a magnetic inductive charge. As metal automobiles drive over the pavement, they cause a change in the magnetic inductance, which allows traffic managers to be aware of the presence of cars. This presence meter can be used for traffic flow counting, car parking, and most commonly, for delay-cycle traffic signals. In this use, when a car approaches the non-main route of a traffic intersection, it triggers a cycle to prioritize this non-main route at the next possible safe interval.

Today, of course, there are many more complicated types of sensors. And they are now often connected in real time via GPS, Wi-Fi, or cellular technologies. Cities like Santander, Spain, track car and people movements, plus temperature and precipitation, through thousands of sensors throughout the city. London, England, and many other cities use sensors in each of their busses to locate them throughout the city. This allows them to offer predictability to end users about when a bus will arrive. It also allows them to adjust traffic lights to optimize for bus movement. The examples are endless.

The bottom line is that the proliferation of these sensors and their real-time connection to municipal data systems are enabling cities and transportation providers to predict, provide information, and optimize in real time to meet the new increased expectations of consumers.

3. Algorithms and Big Data – Algorithms and big data are applying statistical analysis to a huge array of new industries. Within transportation, the next step is for companies to be able to predict travel times. Current applications are able to predict short ride time travel, but have more trouble when a longer trip is planned. Using a program like Google maps, the variability in time can change by a factor of 100% or more as new traffic patterns emerge. But future algorithms that take into account changing weather patterns, rush-hour expectations, and event ending times will be able to predict travel times 60 to 90 minutes into the future. The next step would be to create predictive algorithms for travel times at a time in the distant future, a day or week later.



4. Artificial Intelligence (AI) – Artificial intelligence has been in existence in various forms since the use of the term at the Dartmouth Conference in 1956. However, the past five to 10 years have brought major breakthroughs in computers’ ability to take thousands or tens of thousands or more data inputs in a process of “Deep Learning” and recognize and predict patterns. It has been applied to areas from criminology to healthcare (mental and rare diseases, factory visualization, and agriculture optimization). Artificial intelligence is uniquely suited to traffic management in its ability to predict traffic flow patterns. It has and will be used for city planners trying to understand the real expected impact of long-term building projects or event activity, as well as accurate real-time prediction of the time it takes to get across town on any given day, or the affect a traffic accident will have on car, bus, or train commute times.

VOICE OF MARKET: HOW TRANSPORT MANAGERS ENVISION THE FUTURE: FOUR EXPECTATIONS FROM TRANSPORTATION PROFESSIONALS

Over the course of our research, Frost & Sullivan interviewed a wide range of transportation professionals, from those in charge of traffic management at municipalities, to mass transit systems, mapping companies, new model transport companies (e.g., UBER and bicycle sharing), as well as product transportation companies.

We wanted to understand these professionals’ understanding of current technologies and their expectations of technology. We also wanted to gauge their awareness of end-user needs and to learn from them, in their own words, how the expectations of people in their roles have changed in recent years. What pressures do they feel from their bosses? What KPIs are they being judged on? What aspirations do they have to keep their organizations on the cutting edge and keep being praised by society?

While results varied to some degree, we found many points of agreement between their perspectives. We found four key themes, which we’ve translated into the “new expectations of technology providers.”

1. Real Time and Multi-modes a Prerequisite for Mapping and End-to-end Delivery

We found that municipal transportation officers were by and large very much aware of the new transportation- user expectations. Many expressed, for instance, the perspective that transport providers are becoming more diverse users of transportation. They referenced data suggesting that many commuters use more than one transportation mode in a day. Said one municipal traffic planner at a large city in the Northwest, “I want a program that will integrate more than one mode of transportation—walking, biking, driving, transit.”

Municipal traffic planners were also very aware that users are gathering the majority of their transportation information via smartphone mapping systems. They expressed the need to use real-time data to help users find the best route across town, and in some cases to improve that route. Said the municipal planner, “I want the ability to take real-time streaming data that would help with optimal routing.”

I want a program that will integrate more than one mode of transportation – walking, biking, driving, transit.

2. A World of Real-time Sensing – And Traffic Planner as Chief Efficiency Officer

Professionals expressed that they were sometimes hamstringed by outdated solutions that didn’t provide enough real-time data. They understood that users wanted immediate insights for transport, but saw their sensing system as outdated. In particular they admitted that traffic patterns were often set well in advance and were aware that their current traffic systems were very fallible to changes in daily traffic flow. For instance, they



knew that major events at popular locations could knock their systems out of optimal traffic patterns. They were particularly attuned to these real-time needs for unusual or emergency situations.

Said one planner, “Visualization and diagnosing of patterns is something that is of interest to us…. Basically, putting sensors on corridors to assess travel times, particularly around events like construction or big projects or emergency events.”

Time and again, interviewees expressed that the spending cycle has now begun, and they believed they could and must receive budgets for the right types of analysis to optimize their routing.

“We want to assess how things are operating, and we want a software platform that could provide that analysis,” said the planner.

3. Integration Key: Piecemeal Solutions No Longer Acceptable

Regarding current traffic management solutions, planners expressed an overall disappointment with integrated capabilities. While managers at the largest cities felt their needs were being met well by large integration organizations, those in mid-sized cities felt that too many solutions were piecemeal. They were often based on legacy systems and had weak compatibility with each other.

Current traffic management solutions, for the most part, are piecemeal and lack the capacity for interoperability. Traffic management officials consistently remark that they must manage a variety of different software and manually analyze different outputs, creating a cumbersome and inefficient process.

We don’t have a good way to easily tie in physical infrastructure in the field that can gather information … and display it to us in real time.

Said one operator, “We need to be able to assess how things are operating, see whether we need to change our traffic plan, notify the public, things like that….The gap that we have right now is that we don’t have a good way to easily tie in physical infrastructure in the field that can gather information about travel times along different corridors, and then have an easily deployable system that can gather that information back and display it to us in real time.”

I want to be able to integrate a solution into our existing software, but I don’t want to have to run it myself. I would want it to be used as a software as a service.

4. External Operation Expected: Software as a Service with KPIs

Lastly, traffic managers, particularly at mid-sized cities, have expressed that they require traffic solutions to be provided as a service rather than managing the service and the hardware to support it themselves. Many of the managers acknowledge that they come from generalist management backgrounds and prefer that technology providers deliver solutions and also operate them.

Budgets exist but traffic managers want service providers to meet tangible KPIs that can be shown to have created improvements in efficiency, safety, etc.

“I want to be able to integrate a solution into our existing software, but I don’t want to have to run it myself. I would want it to be used as a software as a service…. What we want is to know exactly what we’re going to get out of these new systems. We’re expecting a certain percentage improvement in delays.”

The technology and managing of it has come along quickly, but many of the company and municipal managers are not technologists. Their goals are to set the KPIs and have external companies fulfill them.



INTRODUCING SPATIOWL

Many technology companies are providing solutions to cities and other transportation players.

Globally the Fujitsu Group has been a major player for over 30 years. It has worked with cities in Japan and around the world, and with organizations from automobile companies to mass transit systems, as well as taxi companies.

As proof of its expertise and experience, it has top share of the Japan taxi reservation and management service business. In the late 1990s, it worked with the telematics division of a top-three global automobile company to gain a wealth of skill and knowledge about telematics and to optimize navigation data. In 2005 it began operation of a project under the auspices of Japan’s Ministry of Economy Trade and Industry (METI) to use probe data to track traffic information and visualize traffic flow in Tokyo, the world’s most populous urban area.

Based on this wealth of project experience it launched SPATIOWL in 2011. SPATIOWL is a traffic and transport management service that improves efficiency for municipalities and train, bus, and ride-sharing companies.

The solution captures location data from diverse sources, aggregates it, and uses analytics to generate the best real- time transport, as well as short and long-term planning decisions.

This allows public transport operators and city traffic planners to improve daily passenger satisfaction, optimize long- term traffic flow, and create new business models.

SPATIOWL brings a wealth of traffic management experience and includes many of the most requested features by traffic professionals. It is a real-time solution that takes input from public and private sensors. It offers multimodal optimization both for transportation users (in a potential app format) and for transportation providers themselves. In particular, SPATIOWL’s predictive capabilities have received acclaim, having been chosen as the best methodology by a leading Japanese carmaker. The SPATIOWL product is installed by skilled implementers that have experience looking at previous traffic cases. They are able to role the product out in a modified Software-as-a-Service methodology, meeting KPIs and diminishing the need for onsite planners to make decisions.



Below is the methodology behind the SPATIOWL system, as well as case studies that show how it has been successfully used globally.

THE SPATIOWL TRAFFIC OPTIMIZATION METHODOLOGY

SPATIOWL traffic optimization executes in four main areas: data collection and aggregation, data management and analysis, optimization, novel service creation.

Data Collection and Aggregation: The system collects spatial and temporal data about moving objects such as automobiles and pedestrians. Collection and aggregation is via multiple inputs, from roadside sensors to GPS probes, and may be sourced from cities, transport companies, or Fujitsu’s own sensors. At the same time, information on weather conditions, holidays, local events, and the like are gathered for use in analysis.

Figure 3: SPATIOWL’s Multi-layered Data Management Approach

Data Management and Analysis: Data is arranged into layers for easy visualization by traffic planners, transport operators, or researchers. A unique feature compared to competitive products is SPATIOWL’s use of location- based management that stores data with key values of latitude, longitude, and time stamps. This is more effective in tracking moving objects than competitor solutions which predominantly use only relational databases. It also utilizes the multi-layering capabilities to sort and provide information to the user. SPATIOWL also employs Artificial Intelligence for some of its analyses to improve its algorithm and modeling (using machine-learning)

Optimization: SPATIOWL has real-time data processing capabilities such as real-time visualization of moving objects, as well as analytic capabilities includng statistical analysis, predictive analysis, modeling and simulation. The SPATIOWL team utilizes its proprietary algorithm to provide insights, optimize routing for individual rides in real time, and provide the best routing plan for an area for long-term planning. The SPATIOWL team does not simply leave the work to the algorithm, but consults with the client during this phase of development.

Novel Service Creation: Based on data processing capabilities, the SPATIOWL team is able to develop innovative navigation services for passengers, transport operators, and road congestion managers. These may be as simple as smartphone interfaces or may include new optimization schemes and revenue-generating business models for the operator.



It has a wealth of services including accurate routing for drivers, traffic condition visualization for road traffic managers and public transport operators, multimodal route navigation for travelers, demand-forecast-basis taxi dispatching operation for taxi companies, and the like.

Below are some applications of usages of SPATIOWL.

Application 1: Public Transport Visualization & Analysis

SPATIOWL has the ability to visualize real-time public transport operation and statistical analyses based on KPIs such as train delay, congestion, passenger’s waiting time. These are displayed on a single viewer for public transport operators to take actions to improve their operation efficiency and effectiveness.

Case Study #1: Tramway Optimization for a European Transport Provider in North Africa

PROBLEM A large European transport provider had been contracted to provide tramway transport in a growing North African city. It found that the town had unacceptable delays in its tramway system. Further, in some areas, heavy congestion caused riders to wait for two or more trains to board. This was causing a financial burden for the operator, because the operation contract specified penalties for late service.

Figure 4: Visualizing Sources of Downtime

SPATIOWL SOLUTION Fujitsu’s SPATIOWL team installed software and recorded data from inputs provided by the city, tramway company, and its own initiatives. The team found extreme congestion delays at the terminus station in the evenings after beach days in August. It believed these delays could have a ripple effect for hours, causing unexpected traffic patterns for the trams and roads. The team developed an easy-to-use tramway operation viewer that acts as a single integrated dashboard to visualize tramway operations. The operator can now better understand service delays, passenger waiting times, and the long-term effects of temporary stoppages.

The operator has been able to gain insights into operations so that it can adjust tramway schedules. In the future it will be able to deploy fast-response planning to deal with incidents that had previously resulted in long-lasting congestion and delays.



Application 2: More Accurate Car Navigation

SPATIOWL analyzes and provides real-time and next-two-hour road traffic congestion information improve automobile navigation accuracy. This is based on inputs such as probe data (e.g. car navigation systems, dedicated probe sensors) and road side sensors (e.g. loop coil vehicle detector). The information is provided to car navigation service providers such as automobile companies and telematics service providers.

Case Study #2: Car Traffic Flow for a Leading Global Automobile Company

A leading Japanese-global automobile company came to Fujitsu with the goal of optimizing its car navigation service to win over customers from competitors. While many car navigation services existed, it realized that currently displayed expected travel times often showed a significant disparity with actual travel times.

Figure 5: An Optimized Driving Interface

Fujitsu’s SPATIOWL team worked with the automaker’s telematics teams to gather more data points for travel than typical real-time location inputs. It utilized road sensors, in-vehicle probes, and integrated inputs such as time of day, day of the week, holidays, and other predictive data. The team used its proprietary algorithm to create congestion prediction models. Based on real-time information and prediction models, drivers are able to predict their commuting times 10-20% more accurately than before, meaning they can plan their schedules more carefully and accomplish more.



Application 3: Road traffic condition monitoring with camera image analysis

SPATIOWL detects road traffic conditions and incidents such as congestion, illegal parking, pavement distress, based on camera streaming images. This is provided to road traffic managers to monitor traffic conditions from a distance. It can be adaptable to adverse condition such as fog, smoke, low visibility.

Case Study #3: Road Traffic Condition Monitoring with Camera Image Analysis for a Chinese City

PROBLEM A Chinese city authority was building a new road and they planned to install a road monitoring system to secure its safety with low cost and human resources. Although they intended to install cameras to monitor the road, in the area sometimes they had adverse conditions such as low visibility due to fog, smoke or low brightness at night and the system needed to be secured against such conditions.

Figure 6: CCTV Cameras for Input

SPATIOWL SOLUTION Fujitsu’s SPATIOWL team installed camera streaming image analysis software into its monitoring system in the city. The software can detect road traffic conditions and road traffic managers are able to monitor incidents, illegal parking, degree of congestion, pavement distress etc.. The system is able to function even in severe environments like low visibility or low brightness situations.



Application 4: Multimodal Transport Route Search

SPATIOWL can search and recommend multimodal transport routes on smartphone application or personal computer. Options for the interface include train, subway, bus, walk and bicycle for passengers. These can be produced for the transport authority app. Information is not based purely on pre-registered information like a time table but on real-time information including train delays and closed stations.

Case Study 4: Multimodal Solution for European Tourist City

PROBLEM A major European city was looking to improve its rail, bus, and other transport systems. It was concerned that it took too long for citizens to get across town. This was hurting productivity, costing the local carriers excess money, causing ongoing complaints from the citizens, and was creating a bad image of the city for tourists.

Figure 7: A Multimodal Transportation User Interface

SPATIOWL SOLUTION Fujitsu’s team worked with the city, bank, and transport providers to create a card for seamless transport across train and bus, with future options for bike-share and car-share applications. Fujitsu then created a user-friendly app that will tell users the fastest route across town based on real-time inputs. In the future it plans to implement a predictive algorithm that will show the best route a few hours or weeks in advance.

This will make travel smoother, faster, and will improve riders’ approval ratings of the transport providers.



Application 5: Human Flow Management

SPATIOWL can induce people’s behavior to mitigate congestion around highly congested areas such as sports stadiums by offering incentives via smartphone applications. Coupons distributed for shopping or eating, can change transport users’ behavior, thus reducing congestion. The system uses AI-based modeling to model the expected changes.

Case Study #5: Large Event Management in Singapore

Problem The Singapore government and Fujitsu are working together with the goal of understanding and mitigating the effects of the completion of large events such as a soccer game on the entire transportation infrastructure, including automobiles and the subway. Not only did they want to understand the problem, but they wished for Fujitsu to propose solutions that could mitigate the influx of people into the system.

SPATIOWL SOLUTION Fujitsu studied the problem along with the city government and proposed a multi-part solution through its current proof of concept. The first stage was to give individuals access to a free smart phone app that could give them up-to-date information about congestion levels following or prior to an event, including expected levels after a period of 30 or 60 minutes. They could use this to make their own best transportation decisions. In stage two, Fujitsu will provide real-time flash coupons to app users. Users who choose to utilize the coupons will temporarily decrease the number of people actively using the transportation systems, easing demand for the transport services. Fujitsu and Singapore plan to test more solutions that are applicable in similar cities around the world.

Figure 8: Event Management After a Sporting Event



Application 6: Unexpected Incident Management

SPATIOWL can predict changes in congestion levels, train delay and the like, resulting from accidents and other major traffic events. The system uses AI-based modeling and real-time data (location, weather, etc.) to predict recovery times and suggest new routing options.

Case Study #6: Train Delay Prediction for Passengers in Tokyo

PROBLEM Transport operators needed to take quick action to recover smooth operations after unexpected events from accidents, flash rains, or other slowdowns. However, oftentimes transport operators act reactively rather than proactively to events, relying on the acumen of the on-call traffic directors and not always fully utilizing the wealth of past experiences built up within the organization. This can lead to the wrong decisions being made or slow response times in re-directing resources. Customer satisfaction falls and sometimes penalties are levied on the operators for their slow response.

SPATIOWL SOLUTION Fujitsu’s team utilizes an AI-based predictive engine for unexpected incident influence along with the Stanford Research Institute (SRI) to help transport operators become aware of how unexpected incidents influence train service delays, affected areas (stations and lines) and time to recovery of normal operations. Fujitsu’s AI solutions will allow it to provide predictive information on the expected time of delay and optimal routing. In partnership with the leading mapping service Jorudan Inc., Fujitsu is currently conducting a proof of concept in Japan to include train delay predictive capabilities into public transport routing applications. The predictive engine is also going to be implemented into SPATIOWL multimodal routing application in the near future.

Figure 9: Control Center for Train Management




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