This paper discusses using taxi GPS data to classify urban land use into categories. It finds pickup and drop-off patterns from taxi data correspond to land use types. Using six features of these patterns, it can recognize land use categories with 95% accuracy. The results also identify areas that changed land use and reveal unusual social events. Pickup/drop-off dynamics further reflect the extent an area is used for a particular land use class.

1. 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

2. 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:
GPS
MICROCONTROLLER
ZIGBEE
ZIGBEE
ARM
MICROCONTROLLER
PC
(VB.NET)
GPRS Wireless
Transmission Module
GPRS Wireless
Transmission
Module

3. 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