The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008 
594 
Contrarily, for the urban traffic monitoring, in order to ensure 
the system efficiency and derive the traffic flow information, a 
much broader area is needed to be covered by laser scanner 
surveying and multiple instances of vehicle object have to be 
recognized and located from there simultaneously. It requires 
more advanced algorithms to separate 3D vehicles laser points 
from complex clutter surroundings. Under this situation, some 
operations used for pose estimation or geometric inference are 
not crucial as semantic decision of whether a vehicle exists or 
not (vehicle counting). 
In this paper we will study the feasibility and characteristics of 
using ALS data to analyze vehicle activity in urban areas. Since 
urban areas usually characterize dense road networks, 
vegetation occlusion and anomalies (e.g. irregular structures 
like wire, pole or flowerbed), we try to find out the optimal 
laser data acquisition configuration for traffic monitoring in 
view of reliability and efficiency, and propose conceptual 
design of approach for vehicle detection and motion indication. 
In this work initial research efforts are made to explore the 
capability of solely using state-of-art commercial airborne laser 
scanner for the task. The general and boundary conditions of 
traffic analysis based on ALS are to be examined and outlined. 
The purposed concepts and algorithms methods will initially be 
assessed empirically in terms of accuracy and recognition rate. 
Different impact factors on the results should be studied. 
Moreover, an improved completeness of vehicle detection can 
be expected due to penetration of laser ray through tree 
canopies. The modeling of object under volume scatters is an 
important issue for the recognition task in the 3D laser data. 
The goal is to diagnose to what extent vehicles under trees can 
be hit and sampled by penetrating laser rays, and further be 
recognized and reconstructed by computer operations, even if 
human inspection also cannot. 
This paper is structured as follows: first, the configurations of 
laser data recording in view of urban traffic analysis are 
discussed and the vehicle models for stationary and moving 
ones are introduced; next, general approaches for detecting 
vehicle from urban laser data tending to derive traffic flow 
parameters are proposed and analyzed; and finally, the 
conclusions are presented. 
2. LASER DATA ACQUISITION FOR URBAN 
TRAFFIC ANALYSIS 
Usually, traffic monitoring using LiDAR, as mentioned here, 
refers to the direct collection of 3D information from airborne 
platform rather than from ground-based sensor. Deriving the 
traffic flow parameters statistically demands a certain spatial 
coverage of data acquisition. Currently, ALS systems show a 
great variability and flexibility concerning data acquisition 
strategies; we want to first compare and analyze different 
scanning configurations and attempt to qualitatively evaluate 
results on the traffic analysis depending on different factors. 
Generally, traffic - related information are expected to be 
extracted as add-ons of regular LiDAR mapping systems, 
together with topography and city models, so that current laser 
surveying systems could be adapted to the solution to traffic 
monitoring at no extra efforts. However, in the long term, one 
may also think of operational traffic monitoring systems based 
on ALS. 
Current ALS systems work almost solely in the pulse time-of- 
flight measurement principle for ranging, detecting a 
representative trigger signal for multiple echoes in real time 
using analogue detectors (Pfeifer & Briese, 2007). The direct 
objective of ALS is to reconstruct 3D geometric model of 
sensed environment as accurate as possible. Various system 
specifications and relations have been examined in order to 
clarify the scanning process and related impact factors on the 
range accuracy (Baltsavias, 1999). However, via taking a deep 
look into them, some parameters are also considered as being 
relevant and sensible for the traffic-related analysis using ALS 
data, which are listed as follows: 
1. View angle, namely the angle between the scan plane and the 
horizontal level 
2. Surface sampling capacity — Footprint size, which is affected 
by laser beam divergence and flight height and Point 
density, namely point spacing which can be decomposed 
into along-track and across-track components 
3. Field of view, namely swath width which is determined by 
flight height and range of scan angle 
4. Scan pattern and relation between flight path and vehicle 
queue 
5. Minimum detectable object/energy 
Being different from freeway and other open areas, such as 
rural areas, urban areas face a more complex situation 
concerning the traffic analysis from ALS due to dense road 
networks, numerous buildings and vegetations, anomaly 
structures. Any adjustment of sensor configurations can easily 
lead to change of data characteristics, which may be exploited 
for specific applications. 
2.1 View angle. Concerning the view angle of ALS, normally, 
it amounts to 90 degree, perpendicular to flight line, forming 
the most common scanning geometry: nadir-view; if not 
perpendicular, it then refers to forward - or backward looking 
ALS. In case of oblique view (Hebei & Stilla, 2007), a side of 
vertical structures such as building façade is recorded whereas 
another side would cast a big shadow causing loss of 
information about surrounding objects (Fig. lc, d). The oblique 
view of ALS can also lead to abnormal incidence angle of laser 
ray interacting with the illuminated surface, which has been 
proven to be adverse to laser backscattering mechanism. Most 
incident laser energy is scattered away in this case, especially 
for vehicle surfaces which are constituted of mental (Fig. la, b). 
Moreover, the travel path of emitted laser ray becomes longer 
due to inclination. It is crucial for detection of those vehicles 
beneath the vegetation, because the penetration rate of the laser 
ray decreases and we can receive even fewer laser pulses 
backscattered from the vehicle surface. Overall, to avoid the 
missing laser data and consider material properties related to 
laser incidence angle, the scan geometry of nadir-view is 
required. 
2.2 Surface sampling capacity. The footprint size and the 
point density seem to be two most relevant parameters among 
system configurations. But they are determined by independent 
factors which can be selected before flight. Nowadays, most 
commercial systems can achieve the point density of about 1-10 
pts/ m~ with a footprint diameter of up to 50cm increase per 
1000m distance. According to experience it seems to get better 
detection results when the laser point density increases. 
Normally only the object model, which is represented by laser 
point samples with certain level of detail, can be found and 
recognized. Furthermore, laser footprints should not be 
overlapped with each other in order to ensure that the captured 
surface information carried by each laser echo are not mixed.