qing trb poster 16
TRANSCRIPT
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City-Wide Hourly Traffic Emission Estimation Using Cellular Activity Data
Qing Li, Yang Cheng, Fan Ding, Xia Wan, Bin Ran
Wisconsin Traffic Operations and Safety (TOPS) Laboratory
Department of Civil and Environmental Engineering
University of Wisconsin-Madison
Estimating vehicle emissions can help provide valuable information to thepublic and authorities for better planning and decision making. Highcosts of directly measuring emission have restricted governmentagencies to obtain accurate and timely information. Cellular activity datais cellphone communication records with cellular towers, generatedduring phone calls, texting, user data exchange activities, and all othercellular network system communication. This paper presents aninnovative Hierarchical Clustering and Grid Mapping (H-G) approach fortraffic and vehicle emission estimation using cellular activity data. Thisapproach can reveal city-scale traffic dynamics and therefore to estimatetraffic emissions. The proposed method was tested using a data from amidsize city in China. The results demonstrated the effectiveness and therationality of the proposed model in traffic and emissions estimation.
Abstract
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IntroductionMany countries are suffering from air pollution, causing significanthealth hazards.
Due to the difficulties of measuring traffic emissions directly, trafficdemand/volume data is usually used as an indirect approach.
Cellular probe technologies use cellphones as traffic probes to collectand estimate dynamic traffic information, which could be used as inputsfor vehicle emission models.
Advantage: big data. Disadvantage: noisy data, inaccurate locations.
In this paper, a Hierarchical Clustering and Grid Mapping (H-G)approach is proposed to use cellular activity data for hourly trafficemission estimation in a city scale.
Conclusion This paper presents an innovative H-G approach for traffic emission estimation using cellular activity data.
This approach is able to reveal city scale traffic dynamics and emissions. Different from traditional vehicle emission models which can only detect a few emissions in some fixed points, the proposed model can estimate hourly vehicle emissions for the whole city.
Future work includes further investigation of the uncertainty and noise in the cellular activity data to obtain higher spatial and temporal resolutions.
Conclusion and Future Work
Methodology
The proposed H-G approach comprises five modules: data preprocessing,grid generation, hierarchical clustering, map matching, and emissionmodel. Road Network Information
City Area
Generate Grid Map
and Cost Matrix
Raw Cellular Data
Cell Tower LocationsGPS-Labeled Cellular
Data
Time Window
Data
Preprocessing
Grid
Generation
GPS-Labeled Cellular
Data
Hierarchical
Clustering
Map
Matching
Grid-Labeled
Movements
Vehicle Information
Emission
ModelGrid-Based Emissions
Data Preprocessing Cellular data with each record is defined as vector ri = {id, timestamp, celltower_id}, and Each cell tower is defined as CTP = {celltower_id, lat, lon}. Use following equation to generate a sequence of GPS-labeled records GRi = {id, timestamp, lat, lon}
Grid Generation Gird mapping method is proposed to simplify map matching problem
due to the noisy cellular data.
Define the number of directed connections from Gridi,j to its adjacent grid Gridi,J (Gridi,J out(i. j)) as DCGridi,j -> Gridi,J. Define transition cost as:
Hierarchical Clustering Due to the noise of cell tower transitions and data quality issue, cell
towers within a certain distance should be considered as the same location.
Complete Linkage algorithm is applied to generate centroids of a sequence of records generated by each cellular user.
Map Matching Shortest path algorithm is applied. Movements are calculated for each
grid.
_i i celltower id pGR r CT
, ,
, , , ,
, ( , )
, ( , )
Grid Gridi j I J
i j I J Grid Gridi j m n
m n out i j
DC
Grid Grid I J out i jDC
Grid
eTC Grid
e
, ,( )i j k i jGrid TW Length of Grid Total Movement inGrid
Vehicle Miles Traveled
PM2.5 Results
DiscussionUrban area generated more PM2.5 than rural area due to its higher traffic. There were lots of PM2.5 in state and national highways. Moreover, in the northwest region of Taicang, a major road also contributed many PM2.5. After obtaining the source of PM2.5 or other vehicle emissions, it can be used for urban planning and environment protection.
,
,
( )i j ki j k
VMT
Grid TW , ,
( ) ( )i jGrid i j k
EM TWi Grid TW EF
Experiments
Emission Model Due to the difficulty of revealing travel modes by cellular probe data,
assume there is a fixed percentage of motor vehicles among all travel modes except pedestrian, which is excluded when applying the clustering algorithm, independent of time.
Data Collection
Taicang, a midsize city in China, was selected as the test bed. The cellular data was collected from a major cellphone carrier in China from 01:00:00 17th September, 2014 to 23:59:59 17th September, 2014. There were more than 1,000,000 cellphone users and each user generated 22.4 records a day on average.
Gird Generation Hierarchical Clustering Map Matching
17:00:00-17:59:59
17:00:00-17:59:59