How Big Data Can Solve Traffic Congestion

On May 1 this year, the 55-kilometer-long traffic jam in the Beijing-Tibet Highway once again shocked the world. The problem of traffic congestion in China has been given unprecedented attention. Is there really no solution to this congestion?

Big Data has become a hot word in 2014, and the big data method accompanied by the highly developed Internet is hailed as a panacea for solving all problems. As a result, all walks of life are closely following the footsteps of big data. The traffic area is an example.

To be sure, the application of big data is a very good tool for the traffic management department. It will also increase the efficiency and capability of the management department. However, the big data approach is just that. The majority of transparent big data applications are used in the public. All will only become toys.

At present, the application of big data in transportation mainly includes the following methods:

1. The use of a card issued by the public transport department is used in large quantities, and thus a large amount of data for passengers to travel is accumulated. This is also a type of big data. Therefore, the public transport department will calculate the traffic trip parameters of sub-periods, sub-sections, and sub-populations, even Create public transport models, and take targeted measures to formulate response plans in advance in various situations and scientifically allocate capacity.

2. The traffic management department pre-embedded or presets the IoT sensors on the roads to collect traffic flow and passenger flow information in real time, combined with various road monitoring facilities and traffic police command and control system data, thereby forming a smart traffic management system that is conducive to traffic Management departments improve road management capabilities, formulate evacuation and control measures, advance warnings and clear traffic.

3. Analyze the traffic conditions of urban roads through satellite map data, and obtain real-time data of road traffic. These data can be used by traffic management departments, and can also be published at various digital terminals for travel personnel to determine their own driving directions. And road planning.

4. Taxi is the largest user of urban roads. Through the real-time data provided by its on-board terminals or data acquisition systems, the traffic conditions of almost all major roads can be known at any time. The long-term accumulation of such data forms the urban area. The “heat map” of traffic can then be analyzed to determine what areas of the road are heavily crowded and provide a reference for travel.

5, smart phones are very popular, most smart phones will use the map application, so always open the GPS or Beidou positioning system, map providers will collect the data for big data analysis, which can be analyzed real-time road traffic congestion Status, travel flow trends, or the level of people gathered in a particular area. These data will be provided for reference after publication.

According to media reports, using car networking as an example, a rational analysis of experts tells us that if a city is fully linked with vehicles, cars, and roads, it can theoretically increase 270% of the city's road capacity.

These are the applications of big data in traffic management, which will help improve the transparency of road traffic information and help relieve traffic users. However, if we think that big data can solve the problem of traffic congestion, it is not correct. .

The core of traffic congestion is the mismatch between traffic capacity and traffic demand. It may be that the normal road resources are not enough, or the inconsistencies caused by instantaneous traffic flow peaks, but in general, most urban or suburban road congestion cannot pass. Big data traffic information is released to ease.

During this period of time, Beijing will expand the scope of Beijing-style permits and shorten the period of validity of Beijing-style permits. As a result, the queuing conditions for entering Beijing-bound passes will be unprecedented. According to many drivers, under normal circumstances, A Beijing-entry permit takes 2-3 hours, which means that if you drive from Tianjin to Beijing, the journey takes only one hour, and it takes 3 hours to process the Beijing-entry permit. However, as we all know, the length of time for queuing for entering Beijing is not fixed, and there are occasional occasions when the driver completes half an hour. Many drivers are predicting when there will be less people to handle. There are drivers who choose to do it at 1 midnight. The result is that they line up for 3 and a half hours because too many people have common ideas with him. As a result, people are congested.

In other areas of transportation, the opening of big data traffic information will bring about the transparency of traffic flow, and the same choice will lead to the emergence of the next traffic jam. The heat map of the sights only represents the present, if everyone gets the same Information, the result cold spots will quickly become hot spots. Of course, the hotel may be an exception. If you publish a restaurant with a large number of queues, it is likely that this hotel will have more queues.

Some people say that the travel of a single individual is random and uncontrollable, and once each traffic participant is “connected” in a certain way to form a large group that can share traffic information in real time, then this group has a certain “ "Intelligence", through mutual influence to achieve self-tuning and self-optimization, and the results will certainly move toward reducing congestion. Indeed, in the modern mobile Internet state, each terminal user is both the creator of traffic information and the provider of traffic information, thereby connecting and interacting with each other via the Internet, but this type of traffic intelligence does not provide relief for traffic congestion. How much effect.

The application of big data in transportation is not turning traffic big data into “public knowledge”. Traffic information in the state of public knowledge will lead to chaos in travel games, and thus will not help to alleviate traffic conditions. Big data is useful for traffic management, but such big data is applied at a small-scale management center. Once it becomes shared by all people, it will become a travel game.