LAND-USE CLASSIFICATION USING TAXI GPS TRACES

Abstract:

Detailed land use, which is difficult to obtain, is an integral part of urban planning.

Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility

and activity information, which can be closely related to the land use of a region. This paper

discusses the potential use of taxi traces for urban land-use classification, particularly for

recognizing the social function of urban land by using one year’s trace data from 4000 taxis.

First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear

patterns corresponding to the land-use classes of these regions. Second, with six features

designed to characterize the pick-up/set-down pattern, land-use classes of regions could be

recognized. Classification results using the best combination of features achieved a

recognition accuracy of 95%. Third, the classification results also highlighted regions that

changed land-use class from one to another and such land-use class transition dynamics of

regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics

could further reflect to what extent each region is used as a certain class.

EXISTING SYSTEM:

LAND-USE classification is an important aspect of urban planning. It is defined as

the recognized human use of land in a city. The granularity of land area in land-use

classification ranges from buildings to administrative zones. The concept of land use has been

evolving for tens of years from ecological vegetation to urban land use and from coarse

classes to detailed classes. Early research on land-use classification attempted to recognize

different ecological vegetation such as forests and wetlands. Such land-use classification has

broad applications in ecology, studies on the relationship between urbanization and

deforestation, and farmland changes. Later studies classified urban land into built-up and

non-built-up lands to delineate urban region and model urban growth.

PROPOSED SYSTEM:

In this project, we are implementing the Real-time GPS Mapping in Google Map,

Land-Use Classification Using Taxi GPS Traces. First, we cannot address regions that have

few taxi passengers. The taxi passenger flow is only a small part of the whole human flow

and these results in some regions having fewer passengers. However, if the trace data of

PC(VB.NET)

GPRS Wireless Transmission

Module

ARMMICROCONTROLLER

ZIGBEE

GPRS Wireless Transmission Module

MICROCONTROLLER

GPS

ZIGBEE

personal cars are available, our method can be easily applied to complementary trace data to

handle more regions. Second, our work currently only addresses regions with pure land use.

We do not consider regions with multiple land-use classes, which will be focus of future

work.

BLOCK DIAGRAM:

HARDWARE REQURIMENT:

1. ARM-MICROCONTROLLER

2. ZIGBEE

3. GPS

4. GPRS Wireless Transmission Module

5. PC

6. POWER SUPPLY

SOFTWARE REQURIMENT:

1. KIEL IDE

2. FLASH MAGIC

3. EMBEDDED-C