those microsimulation tools, we identify some best practices related with the ..... To assess the urban network, field tests must be conducted to collect vehicle ...
Transportation Research Part D 34 (2015) 293–306
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
How to combine different microsimulation tools to assess the environmental impacts of road traffic? Lessons and directions T. Fontes ⇑, S.R. Pereira, P. Fernandes, J.M. Bandeira, M.C. Coelho University of Aveiro, Centre for Mechanical Technology and Automation, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
a r t i c l e
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Keywords: Integrated simulation platform Road traffic Microscopic traffic models Emission Simulation scenarios
a b s t r a c t In the last decades, traffic microsimulation platforms have a growing complexity allowing a detailed description of vehicle traffic dynamics in a second-by-second basis. However, to project spatially their outputs, some precautions must be followed. Therefore, we analyze some variables used in the microscopic traffic models which have a high impact on further applications, especially when a spatial projection is required. To assess these objectives, a microsimulation framework which includes traffic and emission models was defined to characterize traffic flows and to evaluate vehicular emissions. This general methodology was then applied in a European medium sized city using two scenarios: (i) considering a Lagrangian approach and (ii) using an Eulerian approach of the simulation road traffic platform. The Lagrangian approach shows that if we have long links (some hundred meters, e.g. >500 m), we lose the spatial detail on emissions. On the other hand, using the Eulerian approach to define very small links (some few meters, e.g. 50 km/h). Nevertheless, this length depends on the model time step. If we consider 1 s of time step and a maximum speed recorded in a motorway of 140 km/h, the minimum link length will be about 39 meters to assure that all vehicles will be considered. Similarly, in highways and urban roads, where vehicles can achieve maximum speed values of 120 km/h and 70 km/h, the minimum length will be approximately 34 and 20 m respectively. These dimensions must be always taken into account in order to guarantee the collection of at least one point and thus to minimize the underestimation of the values. However, while in some cases with high traffic flow, the statistical significance can be satisfied, in other locals with lower levels of traffic flow such significance may not be guaranteed. In fact this error tends to be more significant as both link length and traffic volumes are lower. In such cases to collect significant statistic data, the determination of the link length must use a time step 10 times lower than the used in the simulation. Thus, if the time step of the simulation is 1 s and a maximum speed recorded is 120 km/h or 70 km/h, the minimum link length in those areas with less traffic flow will be about 4 and 2 m respectively. Another problem is related with the definition of the maximum length of links, especially in the representation of long routes (e.g. motorways or highways). Although the link characteristics are usually constant in some situations (e.g. maximum speed or the number of lanes), other variables such as the traffic congestion, local speed restrictions (e.g. caused by dangerous bend or a high number of pedestrians) and driver behavior parameters (lane change, car following) can vary along the link. In such cases, the road cross-section must be represented for a group of links and not only by one link (Fig. 1). Nevertheless, some of these variables can be very restricted to a particular time period. This is the case of congestion in short time periods (e.g. 15 or 30 min each day). In such cases, the link location in the network and the scope of coupled model must be together assessed. For instance, if an air quality model is used to analyze the impacts of human exposure to air
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40 km
Zone of cut-off Link Connector
(a)
(b)
(c)
Fig. 1. Examples of a correct representation of links usually occurred in highways and motorways: (a) a simple road bend; (b) a transition between a rural area to an urban congested area; and (c) a motorway with different number of lanes.
pollution, the congested links near schools must be assessed in detail. However the traffic congestion which occurs near to a market that occurs once a week during 3 h may not be so relevant. As such, the extra time spent with the network design, which can be double or more, must be balanced and carefully compared with the overall gains. In addition, when a hot spot of traffic congestion is observed in a road represented by only one link, some variables as speed and acceleration will be overestimated. Thus, in order to minimize the propagation of errors more than one link will be needed to represent the variability of traffic flow related to congestion and/or topography. If these best practices are not implemented, when the traffic information is allocated to the link, a constant value of the number of vehicles or average speed is observed. Although this is true for some variables as the number of vehicles, other variables, such as speed and acceleration vary along the link. These results highlighting the relevance to implement best practices during the coding of the road network in the traffic modeling. Different problems may arise when representing certain types of intersections such as roundabouts. These interrupted traffic facilities are usually represented in the models by two links. Nevertheless, the number of links depends on the total number of entry and exit legs of the roundabout. Specifically, if a roundabout with four entry and exit legs is represented by two links, an overestimation of the number of vehicles will be observed when the information is projected in the links. Note that traffic flows along the circulating area of the roundabout (or intersection) is always different between entry and exit legs. Thus, the number of links considered to represent the roundabout must be equal to the number of entry and exit legs, as exhibited in Fig. 2. A similar approach must be done at intersections (e.g. traffic signals or stop-controlled). In order to address the impact of these questions, a general methodology is proposed considering two steps: (1) traffic modeling and (2) emission modeling. Fig. 3 presents an overview of this simulation framework. Firstly, data related with road configuration and vehicle dynamics collected using GPS data-logger equipped vehicles must be used to validate traffic
(a)
(b)
(c)
Zone of cut-off Link Connector
Fig. 2. Examples of correct representation of links in traffic signals intersection (a) and roundabouts with two and four legs (b and c respectively).
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Fig. 3. Methodological simulation framework.
volumes estimated by a traffic model (e.g. VISSIM, PARAMICS, AIMSUM). Then, the outputs of this model must be used as inputs in an emission model (e.g. VSP – Vehicle Specific Power and/or EMEP/EEA – European Monitoring and Evaluation Programme/European Environment Agency) to quantify the emission amounts with high temporal and spatial resolution. To assess the urban network, field tests must be conducted to collect vehicle dynamics, traffic volumes and traffic signals timing. Field campaigns must be performed for the representative period. To reduce systematic errors, the tests must be conducted using different drivers and vehicles. The evaluation of the traffic model can be made in two main steps: calibration and validation. In the calibration the parameters related to the number of simulation runs, driver behavior parameters (car-following, lane-change and gapacceptance parameters), speed distributions and simulation resolution must be addressed. This step adjusts the aforementioned traffic model parameters according to the study domain characteristics. The validation step must be focused on the comparison between field data parameters and traffic model outputs. The validated parameters may include: (a) traffic volumes; (b) travel times; and (c) average speed. To obtain an accurate representation of the network traffic conditions, two goodness-of-fit measures can be used: Geoffrey E. Havers (GEH) and/or the Root Mean Square Percentage Error (RMSPE). The GEH compares observed and estimated traffic volumes (Dowling et al., 2005) while RMSPE quantifies the average magnitude of the error (Cambridge Systematics Inc, 2010). Case study The methodology proposed was applied to estimate the road traffic emissions of oxide nitrogen (NOx), carbon monoxide (CO), hydrocarbons (HC), and particulate matter (PM) in a European medium size city, Aveiro (Portugal). This city is a coastal urban area which covers an area of 3.9 4.5 km2, and acts as an important Iberian terminal where different roads cross the country. Fig. 4a depicts the simulation study domain and the main roads that were drawn in the traffic model. To perform this study, firstly, data related with road configuration and vehicle dynamics were collected using GPS datalogger equipped vehicles (see section ‘‘Data collection’’) and used as input in the simulation framework (see subsection ‘‘Modeling framework’’). Section ‘‘Scenarios’’ presents the scenarios and section ‘‘Results and discussion’’ the main results obtained and a discussion of the overall methodology presented. Data collection In order to analyses the peak periods of a typical working day, field campaigns were performed between 7 and 9 a.m., from Tuesdays to Thursdays. The data was collected under dry weather conditions during March 2011 to: (1) Vehicles dynamics: 10 different routes with heterogeneous traffic conditions across the study domain were covered using GPS data-logger equipped vehicles. The equipment was used to collect second-by-second vehicle dynamics. For each route, 15 trips were performed using a passenger car and different drivers. (2) Traffic volumes: traffic was counted in 56 strategic points of the study network (Fig. 4). Based on these data, origin/ destination matrices were defined for each intersection and assigned to the overall study domain. (3) Traffic signals timing: the cycle length and phasing was measured six times in the traffic lights across the domain.
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A25
links
N109
(a)
connector
(b)
(47) (9)
Fig. 4. Study domain and data collection points (a) and links and connectors representation (b).
As illustrated in Fig. 4, 47 points were used for calibration while the remaining nine points were used to validate the traffic volumes estimated by the traffic model (see subsection ‘‘Modeling framework’’). A detailed description of the field work can be found in Bandeira et al. (2013). Modeling framework Data collected were used to validate traffic volumes estimated by the traffic model applying the VISSIM model. Then, the outputs of this model were used as inputs in the emission methodologies (VSP and EMEP/EEA). These methodologies were used to quantify the emissions amounts with high temporal and spatial resolution. The procedure follows the general rules defined on section ‘‘Modeling framework’’. Traffic modeling As pointed out by Kokkinogenis et al. (2011) each traffic simulator has advanced features, for instance, VISSIM has parameters and function flexibility, PARAMICS adapts to use all the distributed machine resource available, AIMSUM provides different forms to extend it, MITSIM has various types of controllers available for use, SUMO has a flexible architecture, and MAS-T2er Lab and ITSUMO are originally agent-oriented. In order to compare all of these microsimulation road traffic models, different studies have been conducted that highlight the main advantages and disadvantages. Ratrout and Rahman (2009) present a comparative list of these studies. As demonstrated by the literature review, the VISSIM model is one of the most used traffic microsimulation models in research (Table 1), and, thus, it was selected for this work. VISSIM 5.30 model can simulate individual vehicle movements. Furthermore, it allows exporting different vehicles performance parameters such as desired maximum braking and acceleration per vehicle and class (PTV, 2011). Such microscopic model is widely recognized as a powerful tool for management strategies analysis in the real world case studies (e.g. Abou-Senna et al., 2013; Fontes et al., 2014; Zhang et al., 2009) since it can be calibrated to match deterministic capacity relationships and reliable vehicle dynamics outputs. Each model has specific limitations in the representation of the road network. In the representation of the links, VISSIM has many adjustable parameters in the interface, nevertheless some disadvantages can be also pointed out. As opposed to some models as PARAMICS, it is difficult in VISSIM to design a bend and the model does not include roundabout functions
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Fig. 5. VSP modes distribution of a generic light passenger vehicle for a road grade of 0%.
Table 2 Cross-sections characteristics. Cross-section ID
Length (m)
Corridor type Urban
Interurban
Intersection Motorway
N
Type Roundabouts (no. legs)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 * **
3804 687 7270 1011 4317 2181 1989 2485 428 3063 1715 1078 2842 2812 1081 2460 2193 1541 4817 272
X X X X X X X X X X X X X X X X X X X X
7 1 10, 11** 1 3 0 2 2 1 3 2 2 2, 3** 6, 7** 1 2 1 0 6, 7** 1
Traffic lights (no. of legs)
Others* 7
1 (5) 1 (4) 1 (4)
9, 10** 3
1 (3) 1 (5) 1 (4)
1 (4) 2 (3, 4)
2 (3, 4) 2 (3) 4 (2,3)
2 1 1
0, 1** 2, 3**
1 (4) 2 1 (2) 2 (4) 1 (3)
1 (4)
3, 4**
Note: Each entry and exit at the selected cross-section was considered as an intersection. Note: Number intersections considered according with the driving direction.
(as in the GETRAM model). Moreover, although VISSIM can be adjustable yield sign location and users can see the link length directly in the desktop, in other models the interface is not so friendly. Other drawbacks of this model are the impossibility of visualization of the midlines of the links, direct definition of link’s length, and users can only define turning as one lane to one lane between links. A detailed explanation of both calibration and validation of the traffic model for the study domain can be found in Fontes et al. (2014). Emission modeling VSP and/or EMEP/EEA methodologies can be used to estimate the road traffic emissions of NOx, CO, HC and PM. To perform these estimates, statistical data to represent the vehicle characteristics such as the fuel type, age and gross weight distribution must be used. Thus, to represent the typical vehicle characteristics considering such variables the data from ACAP (2006, 2011) and ISP (2012) were used. Due to the flat terrain the effect of road grade is negligible.
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18
1
19
20
A25 (highway)
16
17
14
15
2
N109 (interurban)
13 10
6
12
11 9
3
7
4
8
5
500 m Fig. 6. Location and identification of the cross-sections analyzed in the study domain of Aveiro.
Table 3 Results by scenario for the traffic and emission model obtained for the study domain of Aveiro in 2011.
Number of links Number of vehicles (veh/km) Average speed (km/h) Emissions (kg/kmday)
NOx CO HC PM
Scenario 1 (S1)
Scenario 2 (S2)
1043 626 44 4627.1 8029.9 1285.7 77.7
1155 543 48 4406.0 6652.6 1476.0 152.2
% S2/S1 10.7 13.3 9.1 4.8 17.2 14.8 95.9
To apply the VSP methodology (Coelho et al., 2009; Frey et al., 2008; Haibo et al., 2006; USEPA, 2002), detailed information about vehicle’s operation and vehicle’s dynamics (second-by-second speed, acceleration and road grade) is required. The direct physical interpretation and strong statistical correlations with vehicle emissions, allows that VSP has to be a widely recognized approach for emission modeling. Such methodology has been used from both gasoline (Frey et al., 2008; USEPA, 2002) and diesel (Coelho et al., 2009) passenger cars, as well as for diesel buses (Haibo et al., 2006). VSP values are usually categorized in 14 modes so that each mode generates an average emission rate. Emissions by segment can be derived based
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on the time spent in each VSP mode multiplied by its respective emission factor. Fig. 5 exemplifies the VSP modes distribution for a generic light passenger vehicle according with instantaneous speed and acceleration for a road grade of 0%. Both emissions rates and the VSP equation variable can be found elsewhere (Frey et al., 2008; USEPA, 2002). Another option is the EMEP/EEA methodology (EEA, 2013). This is an average speed model based on speed, slope and load factor. The emission factor equations are available for several types of vehicle categories, as well as, depending on the age and motor of each vehicle category.
(a)
(b)
?: Identification of links with problems of representation. Fig. 7. Number of vehicles, by link, at 9 a.m. in the city of Aveiro by scenario: (a) scenario 1; (b) scenario 2.
Fig. 8. Number of vehicles (vph), average speed (km/h) and average acceleration (m/s2) by ID cross-section recorded at 9 a.m. in the city of Aveiro.
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In this work, the VSP methodology was applied to estimate de emissions for all vehicle categories expect for heavy duty vehicles and motorcycles, and for PM emissions. For these cases there is a lack of VSP emission factors adapted to the European fleet, therefore, the EMEP/EEA methodology was applied (EEA, 2013). Scenarios Traffic engineers are usually focused on track the movement of vehicles in a two-dimensional grid without any concern about their three-dimensional representation. However, in some cases, projection of the traffic information is required (e.g. air quality models, noise models). If any concern is followed, some negative impacts can be obtained. Therefore, in order to demonstrate the main differences between both approaches, the simulation framework previously presented on section ‘‘Modeling framework’’, was used to assess two scenarios. The main differences between these scenarios are related to the way information is represented: – Scenario 1: Lagrangian approach – in such scenario any concerns were taken into account in the representation of the links defined in the traffic model. The main objective is to track the movement of vehicles in a two-dimensional grid, subject to stochastic movement of every individual vehicle; – Scenario 2: Eulerian approach – in such scenario, best practices were taken into account in the representation of links defined in the traffic model. The practices were related with the representation of intersections with traffic signals, roundabouts, stop-controlled intersections, ramps and traffic congestion zones (detailed described on section ‘‘Modeling framework’’). The main objective of this approach is to assess a correct representation of traffic allocated to a specific link in a delimited period of time, i.e. using a three-dimensional representation. To assess both scenarios, 20 cross-sections were selected in the study domain. These sections combine characteristics by different corridor types and a different number and type of intersections. Table 2 presents the characteristics of these crosssections and Fig. 6 their location. Results and discussion Table 3 presents the results, by scenario, obtained with the traffic model and the emission model for the domain of Aveiro. In the analysis of these results is important to emphasize a key point. Even the road network size is the same and the traffic flow and vehicle dynamics equivalent in both scenarios, when the link representation changes, the results also change. This occurs due the modifications of the spatial projection (e.g. one simple intersection is initially represented using two links and then represented by three links connected in the center of the intersection). With the improvement of the spatial design of the road network, the number of links used to represent the road network in S2 increased almost 11%. As a consequence, several differences are obtained when both scenarios are compared. Regarding the traffic characteristics, it is observed that in S2 the average traffic flow decreases, but average vehicle speed raises ( 13% and 9%, respectively). This is possible due to the fact that some acceleration and deceleration events and vehicles are not recorded in small links. As result, a general overview shows that, while in S1 (Lagrangian approach) the NOx and CO emissions are overestimated, leading to a reduction of 5% and 17%, respectively, on S2 both HC and PM emissions were underestimated, leading to an increase of 15% and 96%, respectively. A detailed analysis of the emission maps shows several spatial differences. In fact, when the link representation is performed considering the implementation of best practices, the traffic characteristics, when projected, tend to be improved. Fig. 7 shows the results of traffic flow obtained with and without the implementation of best practices defined on section ‘‘Modeling framework’’. The only differences between both scenarios are in the link representation. The remaining parameters were not changed. Note that some intersection ramps recorded traffic flow values near to the half obtained in precedent or subsequent links when these values should be very near. By performing a deeper analysis of the differences across scenarios, Fig. 8 exhibits some vehicle characteristics used in the emission prediction, in particular the average number of vehicles, average speed and acceleration obtained for each one of the 20 cross-sections defined previously for the study domain (see subsection ‘‘Modeling framework’’). Generally, cross-sections with higher average acceleration tend to overestimate traffic flows. Although some exceptions, these are mostly the cases of intersections with roundabouts (e.g. cross-sections 2, 9 and 20). In such cases, only one link and one connector were firstly used in S1 to represent each intersection. Furthermore, Fig. 8 shows an increase of the average speed in all cross-sections with the exception of the biggest corridor analyzed (cross-section 3). This is possible due to the deceleration events that are not recorded in S1. Since the dimension of some links is very small, the values are underestimated. The changes of the road network were performed with the main purpose of: (i) defining links to collect traffic congestion events (which typically occur near roundabouts, traffic lights or parking lots); and (ii) redefining links to avoid the duplication and/or the absence of traffic (as exemplified in Fig. 7). Therefore, although in some cross-sections the number of links does not change, their design was improved (e.g. 6, 7, 17 and 18). Table 4 lists the length, the percentage of links used to design the network and the NOx, CO, HC and PM emissions produced by cross-section in the S2 in relation to S1. Regarding the number of links used in both scenarios to represent the road network, several differences can be observed. In the S2 case, this number increases in 80% of the cross-sections analyzed (N = 16), when compared with S1, while the
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Fig. 9. Correlation between traffic performance (number of links, traffic volume, average speed and average acceleration) and emissions (CO, HC, NOx and PM) by cross-section.
number of links recorded in the remaining cases still constant (N = 4). The number of links increases in cross-sections which include mostly intersections and/or roundabouts corridors. In addition, an inverse correlation (r2 = 78%) is observed between such variable and the number of vehicles (Fig. 9). Any clear pattern is observed between the number of links used and emissions.
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Table 4 Emission results by cross-section obtained for the study domain.
Concerning the analysis of the other vehicle characteristics, different patterns between pollutants are found. Such trends confirm some of the results achieved on Table 3 to the overall domain. The analysis of emissions obtained on S2 by crosssection confirm that. While NOx, CO and HC emissions are mostly overestimated (in 55% (N = 16), 30% (N = 4) and 45% (N = 9) of the cross-sections analyzed respectively), the PM emissions are underestimated in all cross-sections analyzed. The comparison of both scenarios in terms of traffic flow shows significant differences. The highest decrease is recorded in roundabouts installed at isolated intersections (cross-sections 2, 4, 9 and 20). In these intersections the values reach, on S2, less 52% of road traffic. If this leads to a general decrease in all pollutants, the increase of average speed in more than 10% in these type of intersections change this tendency, increasing the emissions of some pollutants and cross-sections. This is particular true to HC and PM (e.g. for cross-sections 2, 4 and 20 an increase of 19–52% and 44–87%, respectively is achieved). However, some cross-sections including arterials with roundabouts (12, 13 and 17) yielded lower HC emissions in S2 than in S1. Such pattern means that in the mid-blocks segments, located between adjacent intersections, some acceleration and deceleration events and vehicle are not recorded. This occurs since on those areas vehicle speed is relatively constant. The lower number of vehicles and acceleration/deceleration values obtained in S1 confirm these findings (see Fig. 8). While in motorway corridors a similar trend between both groups of pollutants is noted (with cross-sections 16 and 18), in interurban and urban corridors the emission pattern changes according the number and type of intersections. In urban corridors with a few number of intersections, only one, NOx emissions were in S1 overestimated. Therefore, in those cases a decreasing of emissions in S2 were observed: 54.5% and 44.8% in the cross-sections 6 and 7 respectively. Nevertheless, in these sections, CO emissions were underestimated in S1 leading to an inverse trend: 20.1% and 1.6% in cross-sections 6 and 7 respectively increase their values on S2. In contrast, in S1, an underestimation of NOx emissions was observed in some urban corridors with three or more intersections with traffic lights (e.g. cross-section 14). This is possible due to the situations in which vehicles decelerated until stopping at red signal and further acceleration back to desired speed are not detailed recorded since long links are used to represent the overall corridor. About 87.5% (N = 14) of the cross-sections which have in average, decelerations (N = 16), have also a simultaneous overestimation of NOx. Nonetheless, no clear pattern was found between the cross-sections analyzed and acceleration. The highest correlations were found between CO emissions and acceleration (r2 = 45.6%) (see Fig. 9). Regarding the other pollutants, the statistical correlation is higher between HC emissions and the number of links (r2 = 47.0%). No significant correlations were found to the remaining pollutants (Fig. 9).
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Conclusions This paper has addressed different problems related to the representation of road traffic networks based on microscopic traffic models when a trustworthy spatial map showing the traffic distribution is required as an output. These problems are particularly critical in interactions with external modeling platforms of emissions, air quality and noise. A case study based on a microscopic traffic model (VISSIM) and emission methodologies (VSP and EMEP/EEA) were linked and applied to characterize traffic flows, and to evaluate the traffic emissions with high temporal and spatial resolution. This general methodology was then applied in a European medium sized city (Aveiro, Portugal) using two scenarios: (i) considering a Lagrangian approach and (ii) using an Eulerian approach of the simulation road traffic platform. In the Eulerian approach, an increase of 11% in the number of links used to represent the road network was recorded. This change leads to a decrease of the average traffic flow and a raise of the average speed, changing the emission pattern previously observed in the Lagrangian approach. While in the Lagrangian approach the NOx ( 5%) and CO ( 17%) emissions were overestimated on the Eulerian approach, HC (+15%) and PM (+96%) emissions were underestimated. The majority of the cross-sections which have negative accelerations have also a simultaneous overestimation of NOx (87.5% of the cross-sections). Although in the analyzed cross-sections no clear pattern was found between the emission and acceleration, the cross-sections with higher average acceleration tend to overestimate traffic flows. With some exceptions, these are mostly the cases of intersections with roundabouts. In these cases, in the Eulerian approach HC and PM emissions tend to increase in some isolated intersections of some arterials with roundabouts while HC emissions decreased. This occurs since some acceleration and deceleration events are not recorded in the Lagrangian approach. While in motorway corridors a similar emission trend is noted, in interurban and urban corridors the pattern changes according to the number and type of intersections. The findings suggest that the link representation must be done carefully in the traffic model. Although microscopic traffic models can represent the reality with high detail using a ‘‘simple’’ representation, if some details are dismissed, when the data is projected, some parts of the information can be lost. In these cases an underestimation or overestimation of the results can be observed, especially in the case of intersections where acceleration/decelerations events are crucial. If we have a long link (some hundred meters, e.g. >500 m), we lose the spatial detail on emissions. On the other hand, if we define small links (some few meters, e.g.
