People living in the city every day produce a large amount of data, both structured and unstructured, some closely related to transportation, while some seem to have nothing to do with transportation, these data are distributed in different administrative departments, Internet companies or traditional operating companies.　　For example, with the smart city construction boom, many cities have been covered with sensing equipment (intersection inlet road geomagnetic, electronic police, chokepoints, etc.), through the geomagnetic can be collected to a certain interval of the intersection inlet road traffic flow, speed and occupancy; through the electronic police or chokepoints can be accessed in real time through the chokepoints of the vehicle license plate number, through the time, and the location of the vehicle speed, the data basically converge in the local traffic police department. These data are basically converged in the local traffic police department. Internet companies provide instant messaging, navigation, and *** enjoyment services to city residents, and can obtain their location in real time through the positioning of customer terminals. The scope of traditional operating companies is also very wide, including bus companies and passenger transportation companies, cab companies, communication operators, etc. Bus companies and passenger transportation companies converge passenger flow data (IC cards, third-party payment and zero tickets), vehicle positioning data, etc., cab companies converge cab positioning data, and communication operators can converge customer cell phone MAC addresses. The data listed above can provide data support for urban transportation planning, policy making, design and management. In the future, the author will combine his more than 10 years of theoretical research and transportation engineering experience to explain the future application scenarios and potential value of each type of data.

The analysis of urban transportation system is a complex system, especially in the conflict between supply and demand of transportation, how to improve the efficiency of the entire transportation system, to enhance the quality of the residents' travel is a challenge to every transportation manager, researcher, engineer. Since its birth, transportation science has been inseparable from data, which is an engineering science based on statistics.

Internet companies were the first to recognize the value of data application in the field of transportation, but also greatly promote the application of cloud computing, big data and other new generation of information technology in the field of transportation. Gaode, drip congestion ranking, Ali city brain is the Internet company with its own data resources to carry out the exploration of big data applications in the field of transportation.

Internet companies to enter the traditional intelligent transportation industry, on the one hand, the Internet company frequently released a variety of reports based on big data analysis, on the other hand, also began to produce a variety of voices of doubt. The current urban transportation has one foot into the big data era, while the other foot needs to be driven by the effective integration of traditional transportation theory and mobile Internet data. Dial the current transportation big data industry's prosperous camouflage, we look at it with a cool eye, see the current there are still many problems, today will talk about one or two:

First, the so-called traffic big data is still basically a single source of data for analysis, the analysis of the precision needs to be further improved, the application scene needs to be further enriched. Most of the research focuses on vehicle GPS data and video data based on the extraction of vehicle description information, traffic flow status information, research congestion indicators and traffic police law enforcement applications;

Secondly, the layout of the urban traffic sensing equipment has not been optimized from the perspective of traffic big data analysis. Urban intelligent transportation system planning an important task is to study the urban traffic collection equipment layout scheme, at present, fewer people from the height of urban transportation planning and management think tank top-level design, the distribution of detectors to study. In addition, improving the applicability as well as stability of sensing equipment is also an important means to effectively improve the current data quality.

Third, urban transportation big data lack of unified data standards. Previously also discussed the current data can be used for traffic system analysis, these data come from different sources, in order to be able to make use of the above data in the future, break the data barriers, the formation of urban transportation data pools, it is necessary to *** with the exploration of the data *** enjoyment mechanism, and the development of a unified data standards; In addition, the formation of the city's data pools, the urban transportation data governance will be a complex and arduous task.

Fourth, the innovation of urban transportation theory in the era of big data faces great challenges. Traditional transportation theory is basically based on statistics, that is, based on the sample to carry out research, and the arrival of the big data era has changed the transportation theory data sources, so that the data from the sample to the full sample, data from the targeted survey to extract useful information from big data. Therefore, traffic demand forecasting, traffic capacity analysis, traffic control and other basic theories will produce a huge change, transportation scholars should not only look up to the sky but also down-to-earth, in the basic field of research to invest more energy, should not be the current clouds cover the eyes.

The integration of urban transportation system theory and big data technology has a long way to go, and we look forward to discussing and advancing with the majority of transportation engineers and researchers***.