Microscopic Traffic-Emission Simulation and Case Study for
Evaluation of Traffic Control Strategies
CHEN Kun 1, , YU Lei 1, 2
(1 School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2 Texas Southern University, Houston 77004, China)
Abstract: Many studies have shown that the quantity of vehicle emissions are typically affected by vehicles’
instantaneous speeds and acceleration rates. In order to develop effective strategies for vehicle emission
controls, it is necessary to develop a microscopic simulation platform for estimating vehicle emissions that can
capture the vehicle’s instantaneous modal activities. This article is intended to develop an integrated
microscopic traffic-emissions simulation platform by using the microscopic traffic simulation model VISSIM
and the modal emissions model CMEM. A sub-network selected from the Haidian district of Beijing is used to
build a traffic simulation network, whose traffic and emission conditions are subsequently evaluated.
This
article first analyzes the relationship between the instantaneous emission / fuel consumption rate and the
instantaneous speed/acceleration. Then, it analyzes and calculates the emissions for a variety of vehicle types in
the network. Finally, it evaluates the impact of two alternative traffic control and management strategies using
the developed platform.
Key words: microscopic traffic simulation; emission estimation; VISSIM; CMEM (Comprehensive Modal
Emissions Model); traffic control
1 Introduction
With the rapid increase of the motor vehicles’ population in Beijing (China), in the recent years, the vehicle
exhaust emissions have become one of the most important sources that cause the continued worsening of the
city’s air quality,. Although, the traffic pollution problems have attracted many attentions, the primary focus of
studies in the field of transportation has been on the traffic congestion mitigation in stead of on the traffic
emission reduction[1]. Traffic control strategies are widely applied to mitigate traffic congestions, smooth traffic
flows, and reduce travel times, which are rarely considered for being used to reduce emissions[2–4]. However,
traffic emissions are largely influenced by the speed and acceleration of the motor vehicles.…….
2 Overview of state-of-the-art
3 Development of microscopic traffic emission simulation platform
3.1 Description of VISSIM
There are two kinds of data required for establishing a VISSIM network: (1) static data representing the
roadway infrastructure, which include links with start and end points, link length, width, grade, lane number,
and location of stop lines, and others.; and (2) dynamic data required for traffic simulation applications, which
includes: (a) traffic volumes for all links entering the network, traffic volumes entering, and for different turn
Corresponding author. E-mail: chen*****@gmail.com
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directions at each intersection[5,6], (b) public transport routing, departure times and dwell times[7], and (c)
priority rules and signal timing plans at intersections.
All of these data can be collected by the field
surveys. .……
3.2 Description of CMEM
.…… In CMEM, the second-by-second vehicle tailpipe emissions are modeled as the product of three
components: fuel rate (FR), engine-out emission index (gemission/gfuel), and time-dependent catalyst pass fraction:
(1)
where FR is fuel use rate in grams/s, which is a function of power demand, engine speed, and air/fuel
equivalence ratio; engine-out emission index (gemission/gfuel) is engine-out emissions in grams per gram of fuel
consumed, which equals to the ratio of engine-out emissions divided by fuel consumption; and CPF is the
catalyst pass fraction, which is defined as the ratio of tailpipe to engine-out emissions. Essentially, CPF is a
function primarily of fuel/air ratio and engine-out emissions. .……
The data required for predicting vehicles’ emissions in CMEM model include two categories[8]: (A) vehicle
operating variables, and (B) specific vehicle parameters, as shown in Fig. 1.
Fig. 1
CMEM model structure
.…….……
3.3 Interface between VISSIM and CMEM
.…… In the case study in this article, three typical categories of vehicles including car, Light Goods Vehicle
(LGV), and bus are selected in VISSIM by considering the actual situations in Beijing. Then, three suitable
vehicle categories are also selected from CMEM model based on the technical characteristics[9]. The mapping
relationship of vehicle categories between VISSIM model and CMEM model is shown in Table 1.
Table 1 Mapping relationship of vehicle categories between VISSIM and CMEM
Vehicle
categories
Car
LGV
Vehicle type defined
in
VISSIM model
1
Vehicle type defined
in
CMEM model
5
3-way catalyst, FI(fuel-injected), >50K miles
7
17
Tier 1, light delivery truck, loaded vehicle
Technical characteristics
Bus
8
40
Note: Tier 1 is a federal vehicle emissions standard in the United States.
Diesel-power, light delivery turck
.…… In this section, emissions of different vehicle categories are calculated for 15 minutes in AM peak
periods. The results indicate that the contributions of different vehicle categories to air pollution are different,
as shown in Table 2.
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Table 2 Emission volumes of different vehicle categories in the study network
Categories
CO(g)
HC(g)
NOX(g)
Car
96413.28
1619.41
1440.83
Bus
409.13
249.48
1711.64
LGV
994.15
9.26
13.77
.…….……
6 Conclusions
The purpose of this article is to develop an integrated microscopic traffic-emissions simulation platform for
estimating vehicle emissions, which can capture the instantaneous vehicles’ modal activities, and quantify the
relationship between motor vehicles’ exhaust emissions and vehicles’ operating modes. The case study
illustrates that vehicles’ emissions are strongly dependent on vehicles’ operating modes, especially during
acceleration. Further, different instantaneous speeds and accelerations result in different traffic emissions.
The impacts of different traffic control strategies on traffic emissions were analyzed. Setting up of bus
exclusive lane can improve the traffic operation of the roads in the study network, and reduce the emissions of
CO, HC, and NOx of buses respectively by 2.58%, 5.02%, and 2.67%, respectively. However, setting up of bus
exclusive lane increases the CO emissions of cars and LGVs, by 13.26% and 16.52%, respectively. The
optimization of signal timing plan can improve the traffic operations as well as reduce the traffic emissions.
In summary, some traffic control strategies, which are used to improve the traffic operations may increase
traffic emissions. Therefore, the implementation of traffic control and management strategies should consider
not only mitigating the traffic congestions, but also reducing the traffic emissions.
Acknowledgements
This research was funded by the National Natural Science Foundation of China (No. 7063****), the
National Basic Research Program of China (2006CB******).
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Author Biographies
Dr. *******,(1961-), male, is currently a full Professor in School of Traffic and Transportation, Beijing
Jiaotong University, China. He has a professional experience of about 20 years in teaching research and
consultancy in the area of urban traffic. Travel demand modeling and traffic flow modeling are his areas
of research interest. He has guided a number of doctoral degree students and has published more than
100 research papers in international and national journals and conference proceedings.
Mr. ******(1985-) is a Ph.D. School of Traffic and Transportation, Beijing Jiaotong University, China. His
doctoral research work is in the area of ‘Bus Priority Measures on Roads Carrying Heterogeneous
Traffic’. He has published more than twenty research papers in international and national journals and
conference proceedings. He is interested in teaching/research and wishes to pursue an academic career
on completion of his doctoral work.
Note:
The birthday, sex, Educational qualifications and research interest are essential items needed.
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