hello everyone my name is Zhihan Fang it's my great honor to present our work
Urban Map Inference by Pervasive Vehicular Sensing Systems with Complementary Mobility
this is a joint work of Rutgers and siat shenzhen institute of advanced technology in china
this is the outline of my presentation today I will give a short introduction first and then
design evaluation and conclusion as we know digit roadmap is of great importance for many real world
applications such as navigations location-based services however creating and maintaining
digital map requires high human energy and cost for example a digital map company
HERE reports millions of updates to their global database every day
in recent years with the ubiquity of location sensing devices on vehicles a large amount of gps
sources of vehicles are collected this creates a great opportunity for automatic roadmap inference
most existing works are focused on now influence based on single types of vehicle fleets which
ignores the diversity and complementary mobility patterns among different types of vehicles
for example i will show you in the two hidden maps above the left heatmap is gps diversity of trucks
and right one is the gps direct density of taxis a lighter car that indicates a higher density
based on heat maps we find trucks are mostly concentrated on primary roads or main roads and
the taxis are everywhere residential various business areas so how can we use the difference
and this diversity is our target in this paper many works has been done on mapping inference we can
use a two-dimensional taxonomy to categorize all the works one dimension is equivalent for either
single types of vehicles or multiples types of fleets the other dimension is dependency either relying
on or independent from existing maps so after we categorize all the existing words in this two
dimensional taxonomy we find that there is an empty spot which is the combination of multiple phase and demand
map inference we can uniquely position our work here
so our target improvement problem is to infer road maps based on complementary mobility features
of multiple vehicle affiliates so let's talk more about the design detail this is the data
set we use including both vehicle fleets and road maps the details is given as follows
the first key component is map sketching so as shown in these two figures there are two
roads one is primary road A the other one is residential road B there are much more trucks on the
primary road A and much more personal vehicles on the residential road B so how can
we use this diversity or difference in map sketching we design our map
sketching with two steps first step is to utilize this CMF complementary mobility features to
identify the road points so we assign different weights to GPS points for example we give high weights
to the point with the same vehicle types but lower weights with different vehicle types
the detail of the weighting function is given as follows
so in the second step we want to utilize the CMF for a road map segment linking
we utilize the entropy of CMF because we know that in an intersection the real type the vehicle type
will be more random than more different kind of type of vehicles so we are identified in action by
the entropy of the CMF the vehicle types then we link the road segments by the identified real point
that is road intersections the second component is map painting the idea is
very straightforward even though i don't give the detail here so the first step is the trip
construction we link the gps trips and we match the trip to roads on these three maps
by map matching then we extract the features from the trips on the roads and link these features
to a road segment then we train a clustering or a classification learning model to
learn the relationship relation between road types and the road features so next is our evaluation
for map sketching we use a precision and record for the metrics we use two baselines
kde kernel that's the estimation method and also trajectory c trajectory clustering method
there are two SOA methods so we achieve about a higher precision and record
on the compared with the two methods for map painting we use accuracy as
evaluation metrics have the same and we have two baselines process rule research and combat minus
we have a higher accuracy compared the two baseline methodsand comparing single
and multiple fleets we found that multiple fleets can boost the accuracy last is our conclusion
we start with gps records and we have two key components one is road structure inference
we call that map sketching the second one is the road type inference we call it man painting
so that's my presentation thank you