Estimation of carbon dioxide emissions per urban center link unit using data collected by the Advanced Traffic Information System in Daejeon, Korea

Highlights

• The subject was estimation of CO₂ emissions in urban networks in Daejeon, Korea.

• This study uses real-time traffic data collected from ATIS to estimate CO₂ emissions.

• The ANN model was used for estimating the traffic volume per link unit.

• This study proves that real-time estimated CO₂ emissions in terms of the link unit.

Abstract

CO₂ emissions on roads in urban centers substantially affect global warming. It is important to quantify CO₂ emissions in terms of the link unit in order to reduce these emissions on the roads. Therefore, in this study, we utilized real-time traffic data and attempted to develop a methodology for estimating CO₂ emissions per link unit. Because of the recent development of the vehicle-to-infrastructure (V2I) communication technology, data from probe vehicles (PVs) can be collected and speed per link unit can be calculated. Among the existing emission calculation methodologies, mesoscale modeling, which is a representative modeling measurement technique, requires speed and traffic data per link unit. As it is not feasible to install fixed detectors at every link for traffic data collection, in this study, we developed a model for traffic volume estimation by utilizing the number of PVs that can be additionally collected when the PV data are collected. Multiple linear regression and an artificial neural network (ANN) were used for estimating the traffic volume. The independent variables and input data for each model are the number of PVs, travel time index (TTI), the number of lanes, and time slots. The result from the traffic volume estimate model shows that the mean absolute percentage error (MAPE) of the ANN is 18.67%, thus proving that it is more effective. The ANN-based traffic volume estimation served as the basis for the calculation of emissions per link unit. The daily average emissions for Daejeon, where this study was based, were 2210.19 ton/day. By vehicle type, passenger cars accounted for 71.28% of the total emissions. By road, Gyeryongro emitted 125.48 ton/day, accounting for 5.68% of the total emission, the highest percentage of all roads. In terms of emissions per kilometer, Hanbatdaero had the highest emission volume, with 7.26 ton/day/km on average. This study proves that real-time traffic data allow an emissions estimate in terms of the link unit. Furthermore, an analysis of CO₂ emissions can support traffic management to make decisions related to the reduction of carbon emissions.

Introduction

The continuous increase in greenhouse gases has brought about global warming (IPCC, 2007). In particular, carbon dioxide (CO₂) is a major cause of global warming (IPCC, 2007). In various urban activities, CO₂ is emitted as a by-product, and the transportation sector is one of the major sources of CO₂ emission (Zegras, 2007, Christen et al., 2011, Vicente et al., 2013). In 2008, road emissions accounted for 80% of the total emission in the transportation sector, which has since then increased continuously (Li et al., 2013).

To resolve this problem, Intelligent Transportation Systems (ITS) were introduced for reducing CO₂ emissions by improving road efficiency and mitigating traffic congestion (Lu et al., 2007). For the US, the United States Department of Transportation (USDOT) undertook Connected Vehicle Research, which focuses on “improving systematic productivity and individual mobility and reducing the negative environmental impacts of the surface transportation system (Glassco et al., 2011).” Applications for the Environment: Real-time Information Synthesis (AERIS), a sub-project of the Connected Vehicle Research, uses vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies to collect real-time traffic data and to support ITS services that can reduce CO₂ emissions by using the collected data (Miller et al., 2011).

Glassco et al. (2011) claimed that an exact estimation of CO₂ road emissions is required before executing ITS services for having certain desired environmental effects and presented modeling measurement, which is the most widely used technology for measuring the CO₂ emission. Modeling measurement calculates emission by vehicle movement and is then divisible into microscale and mesoscale models. The microscale model (e.g. PHEM, CMEM) uses the driving cycles of vehicles, vehicle data, road gradient, etc., for calculating the amount of instantaneous emission (Smit et al., 2010, Franco et al., 2013). The advantage of this model is the use of detailed input data for the estimation of the exact amount of instantaneous emission. However, its major drawback is the impossibility of collecting the driving cycle data of all of vehicles on the road for calculating the amount of CO₂ emission. The mesoscale model (e.g. COPERT, MOBILE, EMFAC) uses emission factors (EFs) and vehicle kilometers traveled (VKT) per average speed of different vehicle types for calculating the amount of emission per link unit (Smit et al., 2010). The advantage of this model is its capability of calculating the amount of emission per average speed of the total traffic volume within the given link, but the use of the average speed as the calculation basis has a drawback in that it cannot reflect driver behaviors and road characteristics.

Traffic emission estimation as calculated by modeling measurement can be used for supporting decision making (e.g. reducing personal mobility, restructuring urban areas and switching to low carbon vehicle) for greenhouse gas reduction (Carmichael et al., 2008, Escobedo et al., 2008, Mensink and Cosemans, 2008, Nejadkoorki et al., 2008). Greenhouse gas emissions on a regional scale refer to the total emission from traffic, and mesoscale modeling is the most appropriate emission estimation technique for these. The existing studies used simulations (e.g. SATURN, EMME/2, TransCAD, TRAEMS) for calculating emissions via average speed and VKT (Nejadkoorki et al., 2008, Affum et al., 2003). Emissions on the roads are estimated by predicting the traffic flow and the average speed per link unit as the output data on the basis of the input data regarding a trip (or journey) matrix and road traffic networks, etc., and the output data are used for calculating the emission volumes. However, such simulations can reflect subjective viewpoints of planners. As emissions are calculated on the basis of the input data, it is impossible for ever-changing real-time traffic situations to be reflected in the calculation. Accordingly, this study uses real-time traffic data collected from Advanced Traffic Information System (ATIS) to estimate CO₂ emissions per link unit in central urban areas. ATIS, which has recently been introduced to provide drivers with real-time traffic information, uses a V2I wireless technology (Kianfar and Edara, 2013). It directly collects data from PVs that pass urban links to estimate a space-mean speed (Li et al., 2011, Miwa et al., 2012). For Daejeon metropolitan city, where this study is based, the IDs and travel-time information of PVs, which are equipped with an on-board unit (OBU) and pass within the communication range of roadside equipment (RSE), are collected via dedicated short-range communication (DSRC) and transmitted to the center. The traffic center calculates the differences between RSEs, whereupon the link travel time of each PV is calculated. When the mesoscale model is applied to the calculation of CO₂ emissions in central urban links, VKT information is required, which in turn requires the collection of passing traffic volume per link unit. However, it is impossible to install fixed detectors (loop, video, radar and infrared etc.) at every link to collect the traffic volume, considering the implementation and operational costs that doing so would involve (Zhang et al., 2007, Zhou et al., 2011). In this study, therefore, traffic volume is estimated on the basis of the data collected from PVs. As PVs, Daejeon uses OBU, which enables the use of the electronic toll collection system (ETCS) attached to a vehicle. The distribution rate of OBU in Daejeon is currently 10.6%. Therefore, the number of OBUs collected per unit time data can be determined as a sample extracted from a specific ratio of the entire traffic population that passes the links. Of the PV data collected to estimate traffic speeds at links, the number of PVs per unit time is additionally collected. Accordingly, in this study, we first use the number of PVs and the data collected from ATIS and estimate the total population that passes the links. The objective of this study is to estimate CO₂ emissions from the road networks by utilizing VKT calculated from the traffic data collected from link units, and the average speed.

This rest of this paper is organized as follows: In Section 2, the analysis of the structure of road networks of Daejeon, the current status of ATIS, and the characteristics of the collected data are described. In Section 3, the application of a multiple linear regression model and an artificial neural network model (ANN) to suggest ways to estimate the traffic volume is discussed. In Section 4, the use of the estimated traffic volume for the calculation of the CO₂ emission in urban road networks is described, and in the Section 5, conclusions and future study directions are discussed.