How to develop intelligent transportation in the era of big data

The acceleration of urbanization and automobile popularization has intensified the contradiction between transportation supply and demand in major cities. Traffic safety, traffic congestion and environmental pollution have become three major problems that plague China's transportation sector. Statistics show that by the end of 2015, there were 279 million motor vehicles in the country, 12 provinces with more than 10 million motor vehicles, and 11 cities with more than 2 million cars in the city, including more than 450 in Beijing. Ten thousand vehicles. The number of bayonet points in the city of Beishangguang has more than 3,000, and the number of medium-sized cities is mostly around 1,000. At the same time, as the density of urban bayonet increases, the trend of bayonet networking and information sharing accelerates, and the amount of data will increase substantially, which will result in more massive traffic data.

The intelligent transportation industry needs to plan and design in various aspects such as video surveillance, bayonet management, electronic police, traffic signal control, traffic flow, and traffic guidance. In the past construction, it was often based on experience or some basic data for planning and design, which could not reach a satisfactory level after construction. Therefore, it is necessary to obtain traffic data in a timely, accurate and fast manner, and to construct a traffic big data platform, which has a large volume, a large data type (Variety), and a great value for traffic flow, traffic signals, vehicle big data, and the like ( Value), high-speed processing (Velocity) of big data for comprehensive integration and aggregation, and multi-angle accurate analysis, multi-level correlation processing, diversified reporting.

1. The development of the intelligent transportation industry

Summarizing the development of the intelligent transportation industry in the past 10 years, we find that the development of urban intelligent transportation is the epitome of informatization development. From informationization to systemization to intelligent development, it can be roughly classified into three stages:

Infrastructure stage

The development of urban intelligent transportation is in the first place necessary for the construction of transportation infrastructure. For example, the coverage of traffic signal automatic control equipment in the main roads of the downtown area, the construction of trunk coordination systems; the traffic video monitoring of major trunk roads; the traffic infrastructure of important intersections, the construction of digital law enforcement equipment for road sections, and the integration of such subsystems. The construction of the center backstage. Simple, small-scale system integration applications for individual subsystems, such as road monitoring and system integration of electronic police.

This stage is mainly to alleviate the contradiction between supply and demand of urban road network and vehicles, improve traffic capacity and reduce traffic load in urban central areas.

Promotion management stage

After the completion of the basic infrastructure, with the increasing urban traffic demand, the traffic management system must improve its management level as soon as possible. First, expand infrastructure construction, such as network coverage of multi-regional traffic signals, coordinated control of signal points in urban points, lines, and surface traffic sections; increase and improve information integration subsystems; then conduct large-scale integrated system integration applications, coordinated coordination of multiple subsystems, Thereby improving management efficiency and level. This stage is mainly to improve the various subsystems, improve the efficiency of each system, improve the level of business management, and alleviate the contradiction between urban people and vehicles.

Development service stage

After solving the comprehensive management and traffic safety of urban traffic, the construction of traffic information service will become a construction project that can effectively serve the public. At this stage, resources of various application areas need to be gathered and integrated to build an intelligent transportation big data platform. Excavate, share, integrate, and apply various types of traffic data, and improve the service system to improve the level of government services.

This stage is mainly to use the advanced technology, such as cloud computing, big data, Internet technology, etc. to serve the public after the construction of the intelligent transportation system.

Due to the complexity of the development of China's intelligent transportation industry, the characteristics of the three stages exist simultaneously. But in general, the prevailing situation is: the lack of potential laws and clues in the massive data mining through big data analysis technology, can not fully explore and analyze various types of road traffic information, lack of road dynamic traffic conditions, traffic accidents, traffic violations The ability of intelligent analysis cannot better use intelligent traffic big data for traffic management.

2. The impact of big data on intelligent transportation

What is big data, whether it is 4V or 5V, only proposes the concept of big data from the characteristic latitude of the data, and does not improve to the application level to explain big data. Big data is not only a collection of data for collection and integration, management analysis, and processing. It also adopts new technology process optimization, deep insight, and intelligent analysis and decision-making to adapt to massive, high-growth and diversified information assets.

What impact does big data have on intelligent transportation?

We can look at a more prominent problem first: in recent years, after large-scale traffic control systems in various places, although traffic violations have been contained, traffic congestion has not decreased. From the past experience, one of the main reasons is to "rebuild and use lightly." Therefore, to ease traffic congestion, we must start with management applications. One of the effective effects is the use of traffic big data.

Big data collection and storage

The scope, breadth and depth of traffic data collection increased sharply, and the amount of data continued to expand with the construction of intelligent transportation systems. Traffic big data is constructed by generating traffic flow monitoring data, video monitoring data, system data, and service data by coil, video detection, microwave, bayonet, GPS, floating car, and the like. Taking Shanghai data as an example, the city has access to more than 6,600 bayonet ports, with nearly 80 million daily traffic data, generating a large number of videos, pictures and traffic records. Using secondary recognition technology to analyze vehicle images and videos to form more accurate data resources.

Big data analysis and application

Efficient cloud computing capabilities, with the ability to retrieve seconds of back-to-back data, provide fast protection for big data analytics applications. An intelligent analysis algorithm based on deep learning provides a powerful tool for big data analysis applications. The analysis of traffic big data brings more effective support to traffic management, decision-making, planning, service and active security.

Utilizing big data technology, combined with high-definition surveillance video, bayonet data, coil micro-acquisition wave data, etc., and supplemented by intelligent research and judgment, it can basically realize the intersection adaptation and signal timing optimization. Through big data analysis, the comprehensive traffic capacity of multiple intersections in the region is obtained, which is used to optimize the timing of multi-gate traffic lights in the region to improve the traffic efficiency in a single intersection or region. For example, according to weekdays/holidays, morning and evening peaks/other periods, key road junctions/minor key intersections/general intersections, day/nighttime, etc., manual or system automatically sets different timings to achieve a significant increase in traffic within the area. ability.

The big data analysis and judgment function can also support the secondary identification of the card mouth data and video surveillance data, improve the accuracy of the vehicle information, and then use the big data to realize the functions of trajectory analysis, footing analysis, and hidden vehicle analysis. In-depth mining of vehicle big data, to achieve comprehensive monitoring of the situation beforehand, timely tracking in the event, accurate backtracking after the different scenarios. The vehicle big data platform built by Changzhou assists the relevant departments to automatically find more than 10 deck vehicles every day, and then quickly finds the deck vehicles for penalty management according to the trajectory analysis and the analysis of the landing points.

Combined with intelligent algorithms, secondary recognition and other functions, it can more accurately identify the license plate, body color, model, car logo, annual models and other features, and for sun visor detection, seat belt detection, call detection, driver face recognition, etc. Analyze.

Using the intelligent traffic management system, you can obtain road weather, construction conditions, accidents, combined with big data analysis, and provide travel drivers and traffic control departments with information on weather, road conditions, accident-prone locations, parking lots, etc., and based on vehicle destinations. Driving habits, recommended driving routes for road conditions.

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