# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 3 Earlier Solution to Automatic TAZ Definition  
The definition of TAZs needs deep local knowledge, although the need for automatic TAZ definition was already realised in the last century. Due to the lack of computer resources, the methods were not successful enough, so the number of published solutions is rather limited. There are some examples for public transport, but for car traffic and general multi-modal transport TAZ definitions are very rare. [PERSON] [3,4] suggested a comprehensive method to produce TAZs for public transport, based on the daily timeline of the boarding data set. The method is able to produce TAZs fully automatically; the resulting TAZ shapes were almost identical to the shapes of the TAZs defined manually. In addition, a limited range of methods have been proposed for defining traffic analysis zones (TAZs). [PERSON] [5] introduced a new algorithm that uses a smoothed density surface of travel demand data to minimize information loss and improve the precision of the estimates of the origin-destination (OD) matrix. [PERSON] [6] combined a traffic volume distribution model with traffic simulation, using Voronoi diagrams for the TAZ partition, to accurately predict macro and micro traffic flow. [PERSON] [7] focused on pedestrian TAZs, proposing a division method based on pedestrian-attracting/producing points and a method to estimate the intensity of pedestrian travel. [PERSON] [8] also used Voronoi diagrams for the TAZ partition and applied a multiplicative competitive interaction model to estimate the new OD matrix, demonstrating the validity and precision of the method through a numerical test. However, some researchers started their activities from data sets provided by the GIS system. One of the early solutions comes from [PERSON] [9]. [PERSON] suggested in his work, in 1998, a method to define TAZs based on a GIS data set. Figure 2: Model and data error as a function of the level of detail. Similarly to the previous work, [PERSON] [10] built a method to generate TAZs from Geographic Information System (GIS) for Transportation (GIS-T) data. The paper of [PERSON] [11] presents a method for dividing urban areas into small zones for connectivity and accessibility analysis using the local depth value in space syntax theory, which was found to align with people's cognition of regions when applied in Shanghai. The study solved the problem of dividing urban areas into small zones for the analysis of connectivity and accessibility using the topological structure of the road network. The local depth value in space syntax theory was identified as suitable for assisting in zone division, and the zones divided by this value aligned well with people's cognition of regions. The study of [PERSON] [12] focused on the automated division of traffic zones in urban areas using spatial statistical analysis, with an application to Shanghai's transport network. The study proposed a method of spatial statistical analysis for the automated division of traffic zones, developed a model of spatial cluster analysis algorithm, and successfully achieved the automated division of traffic zones in Shanghai. The paper by [PERSON] [13] proposed the use of the semantic concept of traffic to extract the OD information of commuters from the CDR data from the mobile phone CDR. Based on the use of the extracted data, a traffic zone division was proposed. A K-means clustering algorithm was used to classify cell areas and tag them with four relevant attributes. Another K-means method was suggested by [PERSON] et al. [14]. Their aim was to provide a method for traffic zone division, the results of which are clear, accurate, and reduce the cost of division. The paper proposed a novel grid-based K-Means cluster method for traffic zone division by using GPS data. The classification method is effective and gives a good reference value for the analysis of the flow and trends of city traffic. Another approach was presented by [PERSON] [15] and [PERSON] [16].