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

16-5338

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