Smarter cities, smarter tourism
New interfaces for tourism in China
We designed new technologies that will substantially improve tourists’ user experiences in Chinese cities. Our approach to smart tourism is technology-aware, culturally relevant and user-centered.
We want to improve tourists’ user experiences by designing new tools that they can access, while using the technology they already have. We chose to focus on three key areas: mobile, big data and augmented reality. Big data allow to analyze user-generated content to create a unique personal experience and to use algorithms for real-time and historical crowd data to predict and disperse crowds before they occur. Augmented reality allow to enhance touristic moments to personalize the experience through contextualized rich media and to expands spaces available for tourism for a better use of spaces. We considered culturally relevant cities: Beijing, Shanghai, Shenzhen and Guangzhou as tier one cities. Wuxi, Wuhan and Zhengzhou as tier two cities. Anshun and Xinxian as tier three cities.
Travelers posted thousands of messages to the Chinese travel sites Mafengwo and Ctrip, as well as to English language travel review sites like TripAdvisor.
How can we mine these data for clues and needs?
We perform an English-language review of a Beijing hotel that was posted on the user-generated review site TripAdvisor and the researchers collected nearly 150 sample social reviews in English and Mandarin Chinese. Reviews were collected in six key topical vertical areas: eating, entertainment, shopping, living, travel, transportation.
What key problems do tourists have when traveling in China and how can design thinking and smart tech solve these problems? Researchers created 18 problem scenarios, derived from review data. These scenarios became our design challenges.
To solve the problem of constant crowds in restaurants, with no way to make an easy online reservation, we developed a smart dining solution that involve big data and location mapping with mobile technology combined to create personalized entertainment and reservation experiences. It's a smarter, hassle-free dining for tourists and local restaurant.
A mobile phone-based restaurant reservation app reduces crowds at restaurants while encouraging exploration:
- Pull up the homepage of restaurant where the tourist is standing.
- Pull current reservation data from restaurant.
- Display current wait time data to tourist.
- Display historical wait time data to tourist.
- Solicit tourist’s information.
- Server communicates tourist data to restaurant.
- Match tourist’s current location.
- Sort/find local attractions.
- Display attraction listing and directions.
Once identified the problem of visitors that spend hours waiting in line for popular attractions, leading many to avoid popular destinations altogether, we developed a contextualized multimedia solution that combines location-specific multimedia access with mobile technology for a personalized, contextualized multimedia experience that makes waiting part of the attraction.
A sophisticated app that adds context-specific rich media to the tourism experience, offering tourists material to enjoy the time while waiting in lines:
- WiFi-enabled phone syncs with front office to start a personalized wait time countdown for each new guest who joins the line.
-Tourists can scroll through histories and timelines, adding a level of personalized interaction to information delivery.
-The app recognizes when tourists are on the local WiFi, and makes exclusive video content available to them so the wait feels more enjoyable and part of the touristic experience.
When we discovered that the tourists face enormous crowds while shopping, making it a stressful and unpleasant experience, we developed a smart shopping solution that combine ocular scanning and recognition technology with online cataloguing to enable personalized digital try-ons and clothing selection
A seamless way for users to explore and discover shopping in a new city, while simultaneously cutting down on time spent fighting crowds in fitting rooms and malls:
- A combination of these two features allows customers to be directed to stores that are less crowded, as well as near them.
- Leverages the existing camera on a smartphone and matches incoming data against a 3D map of the body.
- The app sorts a large set of garment information and maps it to the user’s body and preferences, thereby personalizing the experience of shopping.
To solve the problem of alternatives like hostels that are difficult to find compared to high-end hotels and which have lots of online reviews, making it hard to sort signal from noise, we create a large backend database of alternative options and combine this data with mobile technology to make options wider and more easily accessible. Sort reviews by keywords to make it easier for tourists to find relevant reviews.
An elegant, inviting app that eases the processes of finding diverse forms of accommodation in a new city, based on users’ preferences as well as careful filtering of review data to match their preferences to those of travelers whose preferences are similar to theirs:
- Use of keywords allows users to search an enormous corpus of user-generated content for material that is relevant to their specific needs, thereby ensuring personalization.
- Because of the app’s clever way of sorting data, customers have a huge array of factors that they can easily use to filter hotels and accommodations, including by amenities like pools and restaurants.
- System links with reservation systems on hotel websites, allowing consumers to make seamless reservations within the app and allowing hotels to see new customer data.
Facing the problem of high volume of crowds and difficulty navigation around crowds, which increase during holiday season. We figured out that there are no way to get reliable advance information about crowds, or to disperse crowds before they occur, so we combined real-time and historical crowd data in an easily read interface so tourists can avoid crowded spaces.
A simple app that uses location sensing to show tourists the sites nearest them, and recommends the best times to visit in order to avoid crowds:
- GPS link on the backend identifies user’s location and serves relevant results.
- Backend system accesses historical crowd data on popular attractions and displays it in an easily visualized form for tourists, offering recommendations on the best times to visit.
- The backend system also incorporates evolving data, and can be made responsible to real time data to reflect changes in crowd composition and size as more users employ the app.
Concerning the problem of Heavy traffic congestion if traveling by car. We figured out that rush hours are nearly unbearable, and it is difficult to ride in the subway and tourists are not familiar with rush hour timings and can’t avoid them, and don’t know alternate routes. They cannot optimize their time, so we decided to provide real-time and historical transit data to tourists in an interface that combines locations of local monuments and syncs with their offline calendar, enabling seamless travel and tourism.
An app that visualizes the best way to travel between any two points in the city, tracks users’ calendar obligations to help prevent them from getting lost and missing once in-a-lifetime appointments, and directs them to the least crowded routes and modes of transit:
- GPS enables the server to identify and find attractions that are nearby.
- The app uses a mixture of real-time and historical data to estimate how long it will take to make future trips, especially ones between calendar destinations.
- The app filters real-time traffic data in order to build realistic estimates of how long it will take to travel via different modes of transit.
-The backend also links with most common calendar apps, providing smooth information about upcoming appointments.