How Comprehensive Transportation will evolve
How will transportation systems evolve in the future? New technologies such as 5G, AI, IoT, and big data make it possible for smart transportation enterprises to continuously expand the number of services they can provide.
At the end of its 13th Five-Year Plan in 2020, China's urbanization level had reached about 45%. With the growth of megalopolises such as the Yangtze River Delta, Greater Bay Area, and Beijing-Tianjin-Hebei economic zone, China's urbanization level is expected to be between 50% and 60% in 2025. Urban areas such as these accommodate numerous industries and vast populations, which means more active social and economic activities. In turn, different regions and cities will be more closely linked, and the number of vehicles will continue to rise, leading to severe challenges for the entire transportation system. In short, transportation planning modes that rely on deploying more infrastructure and conducting system-specific design based on the total capacity requirement will no longer meet the surging needs for transportation.
In these circumstances, China's Ministry of Transport issued the Outline for Building National Strength in Transportation. Unlike previous transportation plans — in the 12th and 13th Five-Year Plans — the Outline doesn't focus on the development objectives of a single transportation mode or a certain transportation element. Instead, it aims to establish 1-2-3 Transport Rings — in which commutes within a city take one hour, travel between city clusters is possible in two hours, and trips between the country's major cities can be made in three hours. It also intends to create 1-2-3 Logistics Circles. In these circles, delivery of goods within the country will take one day, delivery of goods to neighboring countries can be completed in two days, and delivery to major cities will be possible in three days. The ultimate goal is to build a comprehensive and systematic transportation system with advanced expressways, a complete backbone road network, and a nationwide basic road network.
A traditional urban planning system typically focuses on dividing lands into different functional areas, with transportation positioned as 'auxiliary facilities' that connect different functional areas of a city. When decision makers noticed the association between land use, the relationship between work sites and residential buildings, and transportation, they introduced a systematic engineering concept and created the four-phase model, describing and predicting the association quantitatively for the first time, which made transportation planning more systematic and engineering-oriented and significantly boosted industry development during the early stages of urbanization.
This model, however, is system-specific, which means studies on various transportation elements — such as public buses and railways, road facilities and road vehicles, and dynamic transportation and static transportation — are conducted separately. As a result, this model fails to take into account the association between sub-systems and different elements.
Meanwhile, Europe, North America, and Japan have a longer history of urbanization and correspondingly higher urbanization rates (over 75% in the Greater Tokyo Area, Greater London, and Singapore). To meet the extensive and intensive transport requirements of increasing urbanization, these cities and districts have developed comprehensive transportation systems in their own ways. In Tokyo, different metro line operators break the boundaries between cities through cross-line operation. Its Transit-Oriented Development (TOD) model is used for hub-based land use and transportation planning. Meanwhile, London uses the Electronic Road Pricing (ERP) system to coordinate the use of road infrastructures and modes of transportation. In Singapore, the urban rail transit is integrated with regular ground transport for unified road network planning and rail transit shuttle planning.
These measures have improved the entire transportation systems' capacity, optimized passenger experience, and elevated urban development.
Because their technologies aren't particularly mature, traditional smart transportation practitioners tend to set boundaries for themselves in terms of the services that they offer, so they don't prioritize matters such as infrastructure — roads, bridges, tunnels, and so on — operations of transportation enterprises, and passenger services. This has hindered the development of digital and intelligent transportation and led to a restricted knowledge of the solutions available in this field.
As we review the scope of smart transportation and the services involved, new technologies such as AI and big data bring unlimited possibilities.
Edge Computing and AI
The route planning for buses and subways is highly dependent on the spatial-temporal features — Origin-Destination matrix, or OD matrix — of current and future travel. Take the passenger flow analysis of the traditional public transportation system for example. In these scenarios, the data is mainly collected through swiping of transport cards. The usual technical path is a model in which data is collected when a passenger swipes his card, and vehicle operation data is uploaded, then traffic survey data is referenced for the model calibration. Because of various factors such as data quality, the difficulties of keeping traffic survey data up to date, and spatial-temporal resolution issues, it's difficult for the OD matrix to accurately reflect the actual status of traffic flows. Consequently, network planning based on such an OD matrix is insufficient.
In recent years, the rapid development of computer vision and Artificial Intelligence (AI) has significantly improved the resolution of cameras and the recognition capability of AI algorithms, and the computing cost has plummeted. This means technologies originally used in high-value service scenarios can now be widely adopted in the public transportation industry. The video AI-based head-shoulder recognition person re-identification technology (which matches people across disjointed camera views in a multi-camera system) on the hardware platform based on a combination of a low-cost vehicle-mounted camera and edge computing can be applied in public transportation for passenger flow recognition and precise passenger flow analysis at a low cost. It provides network planning with an OD matrix that covers all times of day and all passenger flows, which can greatly improve the precision of spatial-temporal matching of transport capacity and passenger flow distribution, and increase computing power without adding vehicles. In this way, the transportation network can accommodate more passengers.
Big Data and Cloud Computing
Highways, especially expressways, are the lifeline of social and economic operation. For many years, freight transport on highways accounted for more than 70% of China's total freight transport. That proportion has continued to rise in recent years and is now 78%. The trunk highways are all fully loaded, or even overloaded, which is a great challenge to the highway system. To tackle this issue, the usual approach in the industry is to build new roads, or reconstruct or expand existing expressways — expanding four-lane roads into six-lane ones and eight-lane ones, for example. The drawbacks of projects like these are that they're very costly and they require vast areas of land that is typically used as farmland.
Based on cloud computing and big data, for complex computing tasks — such as holographic perception of a single road section, situational awareness of the road network in an entire area, minute-level short-term prediction, and management and control simulation and deduction in complex scenarios — expressway operation staff can now implement proactive and refined management and control measures, such as ramp control, flow division and merging guidance, section-based rate limiting, and temporary opening of the emergency lane. In this way, minor traffic accidents won't interrupt traffic flow, soft capacity expansion is achieved without any need for significant physical reconstruction or expansion, and the vehicle passing efficiency can be boosted with only small reconstruction or expansion.
Different modes of transport (bus, subway, and private car), different transportation scenarios (urban transportation system and inter-city transportation system), and different service flows (supervision flows of transportation bureaus and operation flows of transportation enterprises) are like cold and warm ocean currents, which can bring about great changes when they meet. The joining points of modes, scenarios, and flows (for example, traffic hubs such as high-speed railway stations and airports, and planning and optimization of the integrated public transportation line network as the joining point of service supervision flow and operation flow of public transportation) are similar to those of cold and warm currents. The digitalization and intelligence of these joining points can really make a difference.
Smart transportation practitioners should gradually abandon single-domain digitalization and strive to remove process breakpoints, implement multi-mode collaborative operations, and achieve cross-scenario in-depth development, developing solutions that can maximize business value for customers.
An overview of a comprehensive transportation system
Urban rail transit is the main focus of China's transportation construction in recent years. As the most important public transport mode in cities, urban rail transit bears most of the public transportation traffic. Meanwhile, the development of traditional ground public transportation also faces great challenges. However, because of the split management and lack of technologies, ground public transportation and urban rail transit are hard to coordinate. In some regions, they are even competing with each other, resulting in the waste of public resources and inefficient operations.
Different transportation modes in urban rail transit
Urban rail transit is designed to provide long-distance travel services across urban functional areas. Shuttle services are required to take passengers to and away from metro stations, and buses can meet this need. Based on the cloud and big data technology, the integrated OD analysis and transportation capacity matching and dispatch as well as AI-based video analysis can apply the respective strengths of buses and subways, build a complete industry chain that covers all passengers, and provide one-stop, seamless travel services, achieving the goal of increasing transportation capacity with computing power.
Airports, wharfs, and high-speed railway stations are hubs of urban transportation and the windows to display the city image and the operation efficiency of the transportation system. They connect inter-city and intra-city transportation scenarios, transporting most of the passengers of a city. These hubs need to gather the departing passengers from various places in the city efficiently and conveniently. They also need to provide safe, convenient, and swift access for arriving passengers to enter the urban transportation system (bus, rail, and taxi), so transportation hubs must be able to accurately predict passenger flows, and predict and dispatch the transportation capacity between multiple transportation modes.
The process includes streamlining arrival hall security check (Stage 2) and shuttle station entry (Stage 3) for quick passing through hubs; connecting getting on or off the vehicle (Stage 1) and urban rail, bus, or taxi (Stage 5) for accurate passenger flow forecasting and transportation capacity matching; linking Stage 1 and hub entrance and exit (Stage 6) for emergency response in case of huge traffic volumes.
Process of urban transportation hubs
These core capabilities need to be designed and developed based on digitalization of single domains and comprehensive AI and big data to accurately match the transportation capacity with the passenger flow and quickly, securely, and conveniently gather and divert passengers in the hub.
Technical requirements of urban transportation hubs
The continuous improvement of digital and information-based technologies in the transportation domain boosts the efficiency and security of the transportation system, reduces the carbon emission of the entire transportation system, and enhances the transportation experience for passengers.
New technologies such as 5G, AI, IoT, and big data enable smart transportation practitioners to consider how to implement the digitalization and informatization of transportation infrastructure from a deeper and more integrated perspective. This way, they can continuously expand smart transportation's service boundaries and gradually shift from infrastructure digitalization to service process digitalization. Ultimately, this will enable them to plan and design innovative and smart comprehensive transportation solutions that span across different facilities, transportation modes, scenarios, and service processes.