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Assessment of the impact of speed limit reduction and traffic signal coordination on vehicle emissions using an integrated approach

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This paper examines the effects of two traffic management measures, speed limit reduction and coordinated traffic lights, in an area of Antwerp, Belgium. An integrated model is deployed that combines the microscopic traffic simulation model Paramics with the CO(2) and NO emission model VERSIT+. On the one hand, reductions in CO(2) and NO emissions of about 25% were found if speed limits are lowered from 50 to 30 km/h in the residential part of the case study area. On the other hand, reductions in the order of 10% can be expected from the implementation of a green wave signal coordination scheme along an urban arterial road.
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Assessment of the impact of speed limit reduction and
trac signal coordination on vehicle emissions using an
integrated approach
Madhava Madireddya,b, Bert De Coensela,, Arnaud Cana, Bart Degraeuweb,
Bart Beusenb, Ina De Vliegerb, Dick Botteldoorena
aGhent University, Department of Information Technology, Acoustics research group,
St.-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
bFlemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium
This paper examines the eects of two trac management measures, speed limit reduc-
tion and coordinated trac lights, in a case study area in Antwerp, Belgium. For this
purpose, an integrated model that combines the microscopic trac simulation model
Paramics with the CO2and NOxemission model VERSIT+is constructed and vali-
dated. On the one hand, reductions in CO2and NOxemissions in the order of 25 %
were found if speed limits are lowered from 50 to 30 km/h in the residential part of the
case study area. On the other hand, reductions in the order of 10 % can be expected
from the implementation of a green wave signal coordination scheme along an urban
arterial road.
Keywords: Microscopic trac simulation, CO2, NOx, Speed limits, Trac light
synchronization, Green wave
1. Introduction1
With the increasing amount of road trac in urban areas in the last few decades,2
controlling congestion and vehicle related emissions have become major challenges for3
city planners. Congestion increases travel times and idling, and because of this, urban4
regions are facing increasing concentrations of air pollutants. Next to this, the rise of5
Corresponding author. Tel.: +32 9 264 9994; fax: +32 9 264 9969.
Email address: (Bert De Coensel)
Preprint submitted to Transportation Research Part D May 23, 2011
atmospheric carbon dioxide, which is a major greenhouse gas, has become a matter of6
concern. A number of trac management measures are therefore considered in vari-7
ous cities, such as diverting trac from peak hours to o-peak hours using congestion8
pricing, reducing speed limits, coordinating trac lights along major arterials, replac-9
ing signalized intersections with roundabouts, or even adding additional lanes where10
expanding the road network is feasible.11
It is widely acknowledged that if the number of acceleration and deceleration events,12
associated with stop-and-go trac, is reduced, fuel eciency increases and emissions13
are reduced (El-Shawarby et al.,2005;Int Panis et al.,2006). On the one hand, op-14
timized signal timing (Li et al.,2004;Pandian et al.,2009) and coordinated trac15
lights (Zito,2009) are increasingly applied along major arterials, in order to smoothen16
trac flow. Usually, systems are designed to create green waves along arterial roads17
facing high demands. On the other hand, speed reductions, such as through the in-18
troduction of zones with a 30 km/h speed limit, are becoming popular for protecting19
residential areas, as they provide benefits in terms of road safety, trac diversion, as20
well as smoother flows and reduced emissions (Int Panis et al.,2006).21
Because it is often infeasible to employ a trial-and-error method for assessing the22
environmental eects of trac management measures, microscopic simulation models23
are increasingly employed for this purpose; see e.g. De Coensel et al. (2007) for the24
case of noise emissions or Smit and McBroom (2009) for the case of air pollutant emis-25
sions. Microscopic trac models consider the behavior of individual vehicles, which26
are modelled to obey empirically based rules for car following, lane changing and27
overtaking (Helbing,2001). They allow to estimate the impact of detailed measures,28
because the influence of braking and acceleration is taken into account. However,29
they require a large amount of detail in input data (road layout, signal timings, trac30
counts, etc.), and are therefore mainly useful to study trac management measures31
within small to medium sized areas, such as a part of a city. Next to this, compu-32
tational models for estimating pollutant emissions that return realistic results for the33
stop-and-go behavior of vehicles in urban environment have not been available until34
In the present paper, the eects of trac management measures on the CO2and36
NOxemissions in a part of the city of Antwerp, Belgium, are evaluated. For this37
purpose, a microscopic trac model in combination with a state-of-the-art air pollutant38
emission model is employed. In Section 2, the construction of the trac network model39
for the case study area is presented, together with a validation of the integrated model.40
In Section 3, the eects of a speed limit reduction and of trac signal coordination41
on emissions are presented. The approach presented could be of inspiration for the42
construction of guidance tools for urban planning practice.43
2. Methodology and validation44
2.1. Case study area45
The case study area, called “Zurenborg”, is located in the southeastern part of the46
19th century city belt of Antwerp, Belgium. Figure 1shows a map of the region. In47
the east, the area is bounded by the R1 freeway, on which a speed limit of 100 km/h48
holds, and a major road (the R10 or “Singel”), with a speed limit of 70 km/h. In the49
southwest, the area is bounded by a railway track. In the north, the area is bounded by50
a major arterial road (the N184 or “Plantin en Moretuslei”), which connects the city of51
Antwerp (situated at the west side of the area) with suburban areas in the east. This52
road has 2 lanes in each direction, and implements trac signal coordination. More53
in particular, during morning rush hour, all signals along this road operate at the same54
cycle time (60 s to 90 s, depending on the presence of pedestrians or buses), and the55
temporal oset of the cycle of each intersection is set such that vehicles travelling from56
east to west encounter only green lights, when driving at the desired speed of 50 km/h.57
A similar trac signal setting is applied in the reverse direction during the evening rush58
hour. Trac intensity during morning rush hour, from east to west, varies between 70059
and 1000 vehicles/hour, depending on the segment that is considered (vehicles also60
enter along the side streets). The triangular area within the eastern, southwestern and61
northern borders is mainly residential, with an overall speed limit of 50 km/h.62
2.2. Microscopic trac simulation model63
In this work, Quadstone Paramics, a commercially available microscopic trac64
simulation tool, is used as the modelling software. A simulation network of the tri-65
0 100 200 300 400m
Figure 1: Map of the case study area “Zurenborg” in Antwerp, Belgium. The triangular area bounded by the
R1, the N184 and the railway forms the outline of the trac simulation network. The circles along the N184
mark signalized intersections with coordinated trac lights.
angular case study area was constructed on the basis of GIS (Geographic Information66
System) data and aerial photographs, which supplied the detailed positions of all roads67
and buildings in the study area. Network wide trac demands were calibrated for the68
morning rush-hour, based on trac counts made available by the Flemish Department69
of Mobility and Public Works. Trac signal parameters (cycle times, signal osets70
between intersections etc.) were set according to the actual situation, based on data71
obtained from the Antwerp police department. Two types of vehicles (light and heavy72
duty) were considered, which were linked to the respective emission classes of the73
emission model (see Section 2.3). The railway passing through the study area was not74
modelled. The simulation time considered was 1 h, with a timestep of 0.5 s. Vehicles75
are loaded onto the network at the edge roads along the sides of the network, according76
to the trac demands. During simulation, the position, speed and acceleration of each77
vehicle is recorded at each timestep, for subsequent calculation of emissions.78
It should be noted that, although the microscopic trac model is able to take into79
account a wide range of vehicle driving behavior, a number of factors that have an80
influence on vehicle speeds and accelerations cannot be (fully) taken into account.81
Among those are the influence of pedestrians crossing the street, cars slowing down82
to park or cars leaving a parking spot, or the full extent of the stochastic component83
in driver’s behavior. Next to this, the trac counts used to calibrate the model reflect84
the average situation during morning rush hour. Therefore, trac counts and speed85
distributions measured at a single instant in time within the simulated region could86
significantly dier from those that are simulated. Nevertheless, as only average trends87
are usually considered, microscopic trac simulation models are increasingly being88
applied for estimating the emissions from trac flows. Earlier work has shown that,89
for emission modelling purposes, a reasonably good agreement between simulated and90
measured speeds and accelerations can be achieved (De Coensel et al.,2005).91
2.3. Emission model92
The instantaneous CO2and NOxemission of each vehicle in the simulation is cal-93
culated using the VERSIT+vehicle exhaust emission model, based on the speeds and94
accelerations extracted from the trac model. The VERSIT+model, developed by95
TNO (Smit et al.,2007), is based on more than 12,500 measurements on vehicles of96
a wide range of makes and models, fuel types, Euro class, fuel injection technology,97
types of transmission etc. The model uses multivariate regression techniques to de-98
termine emission factors for dierent vehicle classes. As the model requires actual99
driving pattern data as input, it is fully capable of accounting for the eects of con-100
gestion on emission. A derived model was recently developed by TNO (Ligterink and101
De Lange,2009), specifically targeted at a coupling with microscopic trac simula-102
tion models. For this, emission parameters of dierent vehicles (with varying age, fuel103
type etc.) were aggregated into a prototypical vehicle emission model representing the104
average emission of the Dutch vehicle fleet. While there may be dierences between105
individual vehicles, the model is aimed at predicting measurement results aggregated106
over a suciently large number of vehicles sampled from the Dutch vehicle fleet. In107
this work, the VERSIT+light and heavy duty vehicle classes representing the fleet in108
Dutch urban environments during the year 2009 was used. Finally, it has to be noted109
that only emissions are considered in this paper; the dispersion of air pollutants is not110
A small-scale validation of the dynamic properties of the emission model was112
carried out using VOEM, VITO’s on-road emission and energy measurement sys-113
tem (De Vlieger,1997). Measurements of instantaneous speed, acceleration, CO2
and NOxemissions were carried out using 4 dierent diesel vehicles subjected to the115
MOL30 driving cycle, which is based on real driving behavior in urban, suburban and116
freeway trac situations. Subsequently, the emission model was used to estimate the117
CO2and NOxemissions based on measured speeds and accelerations. Finally, both118
measured and estimated emission time series were compared. In general, a good dy-119
namic agreement was found, with temporal correlation factors r2=0.90 ±0.03 for120
CO2and r2=0.72 ±0.10 for NOxfor all test vehicles, indicating that the model is able121
to capture the dependencies on speed and acceleration well. The somewhat lower cor-122
relations for NOxmay be explained by the presence of an Exhaust Gas Recirculation123
(EGR) system in some of the vehicles considered. More details on this validation can124
be found in Trachet et al. (2010).125
2.4. Validation of the integrated model126
The accuracy of the integrated model (combination of trac and emission models)127
concerning the estimation of emissions is examined using data from a series of actual128
vehicle trips through the case study area. On the one hand, a vehicle equipped with129
data logging devices was driven several times along the N184 on a typical working130
day. Instantaneous speed, throttle position and fuel consumption were gathered through131
the CAN-bus interface of the vehicle on a second-by-second basis, while the vehicle132
location was logged using a GPS device. On the other hand, trip data for all (light133
duty) vehicles driving along the N184 was extracted from the microsimulation model.134
In both cases, only the part of the trip along the N184 was considered. Subsequently,135
instantaneous emissions were calculated using the emission model, for both measured136
and simulated vehicle trips. Figure 2shows the normalized distribution of calculated137
CO2and NOxemissions per km, for the measured and simulated vehicle trips. In138
general, a good agreement was found between both, suggesting that the accuracy of the139
0.00 012345678
Fraction [normalized]
NO [g/km]
simulated trips
measured trips
0.00 0 250 500 750 1000 1250 1500
Fraction [normalized]
CO [g/km]
simulated trips
measured trips
Figure 2: Normalized distributions of CO2and NOxemissions per km, for both measured and simulated
vehicle trips along the N184.
integrated model is sucient for estimating the eects of trac management measures140
on emissions.141
3. Simulation results142
3.1. Eect of reduced speed limits143
As a first trac management measure, the eect of a speed limit reduction is stud-144
ied. Based on potential measures that are currently being considered by the trac plan-145
ning authorities of the city of Antwerp, speed limits are reduced from 100 to 70 km/h146
on the freeway, from 70 to 50 km/h on the Singel, and from 50 to 30km/h on the other147
residential roads and the N184. For the latter, the trac signal coordination was re-148
calibrated for the lower speed limit, in order to have a green wave as in the original149
scenario. It should be noted that the microscopic trac simulation model applies dy-150
namic trac assignment: routes are chosen according to the instantaneous congestion151
conditions. Trac demands were kept constant.152
Changes in the distribution of instantaneous speeds and accelerations for vehicles153
driving within the residential part of the network (excluding the N184, R10 and R1)154
are presented in Figure 3. It can be seen that, next to a reduction in average speeds, the155
speed distribution becomes more narrow, coupled with a reduction in the occurrence of156
maximum acceleration events. Hence, the speed limit reduction resulted in a smoother157
trac flow in the residential area. Note that maximum speeds are about 10 % above158
-2 -1 0 1 2
Fraction [normalized]
Acceleration [m/s ]
original scenario
reduced speed limits
0.0 0 102030405060
Fraction [normalized]
Speed [km/h]
original scenario
reduced speed limits
Figure 3: Normalized distributions of instantaneous speed and acceleration, for vehicles driving within the
residential part of the network.
0.0 0.5 1.0 1.5 2.0
Fraction [normalized]
NO [g/km]
original scenario
reduced speed limits
0.0 0 250 500 750 1000 1250 1500
Fraction [normalized]
CO [g/km]
original scenario
reduced speed limits
Figure 4: Normalized distributions of CO2and NOxemissions per km, for vehicles driving within the
residential part of the network.
the speed limits, as the trac model also accounts for speeding, in order to resemble159
the actual situation as close as possible. Figure 4shows the corresponding change in160
distribution of instantaneous distance-based emissions for the light duty vehicles; the161
results for heavy duty vehicles show a similar trend. The total distance travelled by162
all vehicles within the residential area reduced by 14.1 % because of trac rerouting.163
However, total CO2and NOxemissions reduced by resp. 26.8 % and 26.7 %. Con-164
sequently, also a reduction in distance based emissions was found, as can be seen in165
Figure 4. For the vehicles driving along the N184, similar results are found. Although166
the total distance travelled by all vehicles along the N184 was reduced only slightly167
by 0.2 %, still, a reduction in CO2and NOxemissions by resp. 9.9 % and 10.4 % was168
0.0 0.5 1.0 1.5 2.0 2.5
Number of trips
NO [g]
original scenario
without green wave
00200 400 600 800 1000
Number of trips
CO [g]
original scenario
without green wave
Figure 5: Distributions of total CO2and NOxemissions, for (light duty) vehicle trips along the N184.
3.2. Eect of trac light coordination170
As a second trac management measure, the eect of trac signal coordination171
along the N184 is studied. The original situation, with implementation of a green wave172
from east to west, is compared to the scenario in which the coordination is removed.173
In order to desynchronize the trac signals, a small but random number of seconds174
(2 s) is added to or subtracted from the cycle times of all trac lights along the175
N184. This way, a wide range of waiting times and queue lengths at each intersection is176
encountered over the course of the simulation run. The results for this desynchronized177
scheme will thus represent the average over the results for all possible schemes in178
which there is no signal coordination. Again, trac demands were kept constant.179
Figure 5shows the changes in the distribution of total trip emissions for all light180
duty vehicles that drove along the N184, and that completed their trip during the sim-181
ulation run (only the part of the trip along the N184 is considered). It was found that182
CO2and NOxemissions increased by resp. 9.5 % and 8.7 % when the signal coor-183
dination was removed (light and heavy duty vehicles combined). Consequently, the184
implementation of trac signal coordination along the N184 resulted in a reduction of185
air pollutant emissions because of a smoother trac flow.186
4. Conclusions187
An integrated approach to assess the impact of trac management measures on188
CO2and NOxemissions was presented. The methodology consists of coupling a mi-189
croscopic trac simulation model with a state-of-the-art instantaneous air pollutant190
emission model. The latter was validated using a set of vehicles equipped with on-191
board measurement tools, and a good agreement was found between measurements and192
simulations. The above described approach diers from earlier work in that modelling193
results are representative for a complete vehicle fleet (in this case the Dutch fleet), and194
that this is accomplished through a well-calibrated emission model, instead of using a195
wide range of dierent vehicle categories in the trac simulation model, which makes196
for an easier calibration of the latter.197
This study rearms the environmental benefits of reducing speed limits in residen-198
tial areas, which is caused by the combination of trac rerouting and a smoother trac199
flow at lower average speed. Reductions in CO2and NOxemissions in the order of200
25 % were found if speed limits are lowered from 50 to 30 km/h in residential area,201
on top of the increased road safety that is expected from lower vehicle speeds. The202
present study also concludes that a reduction in the order of 10 % in CO2and NOx
emissions can be expected from the implementation of a green wave signal coordina-204
tion scheme. However, it has to be noted that trac signal coordination also decreases205
travel times, and the eect of facilitating trac flow may, in the long term, induce206
additional trac (Kitamura,2009). This side eect potentially osets the beneficial207
environmental consequences of signal coordination, or could even make the situation208
worse (Stathopoulos and Noland,2003).209
The authors are grateful to the Flemish Department of Mobility and Public works211
for providing trac counts, and to the Antwerp police department for providing traf-212
fic light timings for the case study area. The authors would also like to thank Stijn213
Vernaillen for gathering real-time speed profiles which were used to validate the trac214
model. This study was performed within the framework of Steunpunt Mobiliteit, which215
is supported by the Flemish Government. Bert De Coensel is a postdoctoral fellow,216
and Arnaud Can is a visiting postdoctoral fellow of the Research Foundation–Flanders217
(FWO–Vlaanderen); the support of this organization is also gratefully acknowledged.218
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Traffic signals strongly influence traffic in cities – in terms of delay but also in terms of the emissions emitted by traffic. This work analyzes the effect of different traffic control strategies on emissions like NOx and PMx. Simulations of an inner-city arterial show that reducing the speed limit leads to a lower production of NOx and PMx, but to an increase in delay. The impact of the traffic signal control on the emissions is less distinct: A carefully designed co-ordination reduces emissions, while a simple actuated control cannot improve traffic flow and therefore increases pollutant production.
Vehicular pollution is considered as a bigger issue especially in urban cities. Vehicular emissions contribute substantially to the air pollution with increase in pollutant levels of various organic compounds. Traffic characteristics like speed, acceleration, traffic volume and congestion patterns of vehicles have a major impact and influence on the rate of emissions. As vehicular volume and congestion increases, vehicular emission levels also increases and exposure to the pollutants causes many chronic health impacts for road users and pedestrians. This paper reviews various research articles on traffic characteristics and its high influence on the pollutant emission levels.
Driving behavior and speed enforcement are both important to road safety and affect vehicle exhaust emissions. Relationships between driving characteristics and safety or emissions have been assessed in multiple studies. However, there is scant information on whether safe driving also reduces emissions and how this relationship changes across urban areas. This study makes use of two similar GPS datasets collected in the metropolitan areas of Toronto and Beijing to conduct a comparative analysis of driving characteristics, speed limit violations, and emissions. Emissions for all trips were computed using the same emission rate database derived from a Portable Emissions Monitoring System (PEMS). We observe that the average speeds in the two cities are close to 25 km/h. In Toronto, the fraction of time spent at speeds over 80 km/h on expressways is 40% higher than in Beijing. We also note a higher level of accelerations in Toronto. The trips in Beijing have approximately 14%, 57%, 14%, and 21% lower emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and particle number (PN), respectively. Drivers in Toronto violate speed limits in 93% of their trips for 21% of trip travel time while the numbers for Beijing are 43% and 4%. These differences are not necessarily due to driving behavior, but rather to driving characteristics, which encompass the effects of behavior, road network design, traffic congestion, trip patterns, and speed enforcement. A scenario was evaluated by reconstructing drive-cycles to assess the effects of speed limit enforcement for trips where violations were detected. In Toronto, if obeying the speed limit, the mean trip travel time was estimated to increase by 1.8 min. In contrast, trip emissions of CO2, CO, NOx, and PN were found to decrease, on average, by 5.2%, 19.1%, 5.2%, and 2.9%, respectively. Speed limit enforcement can result in lower emissions, by reducing aggressive accelerations.
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Two scenarios for improving traffic flow are simulated and analyzed using the VISSIM microsimulation model and the Comprehensive Modal Emissions Model. Short-run and long-run emissions of CO, HC, NO x and CO 2 and fuel consumption are estimated. In the short run, with traffic volumes held constant, results demonstrate that the smoothing of traffic flow will result in reduced emissions. Long-run emissions are simulated by synthetically generating new trips into the simulated networks to represent potential induced travel. This is done until a "break-even" level of emissions for each pollutant and fuel consumption is reached that is equivalent to the base level before the traffic flow improvement was added. By also calculating short-run changes in travel time from the improvement, the travel time elasticity equivalents for each pollutant are calculated. These values are compared with travel time elasticities in the literature to evaluate whether long-run emissions benefits are likely to endure. Simulations are conducted using different assumptions of vehicle soak time to simulate cold-start and hot-stabilized operating modes. Results indicate that, in most cases, long-run emissions reductions are unlikely to be achieved under the two scenarios evaluated.
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Urban noise mapping traditionally involves the use of a traffic simulation model, which is often based on the estimation of macroscopic traffic flows. However, intersections and other local traffic management measures are not always modeled correctly. It is well known that the specific deceleration and acceleration dynamics of traffic at junctions can influence local noise emission. Finding the best strategy for using traffic modeling results in noise mapping is a current topic of research in the IMAGINE project. In this paper, a case study is presented, consisting of a large set of microscopic traffic simulations and associated noise emission calculations, which provides some insight into the specific dynamics of the noise emission near different types of intersections. It will be shown that it is possible to refine current traffic noise prediction models, based on macroscopic traffic simulation, using a correction on the average vehicle emission, aggregated in lane segments. A spatial approach should be used, in which inbound and outbound lanes are divided into deceleration, queuing, stopline and acceleration zones. Results from regression analysis on the numerical simulations indicate that meaningful relations between noise corrections and traffic flow parameters such as traffic intensity and composition can be deduced.
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Realistic emission and fuel consumption rates of petrol-driven cars were determined by on-the-road experiments in 1995. A validated, in-house developed, on-board measuring system was used. Six three-way catalyst (TWC) cars and one carburetted non-catalyst car were measured. The effects of road type, driving behaviour and cold start on CO, HC and NOx emissions and fuel consumption were analysed. In real traffic situations, emissions for TWC cars were found to be at least 70% lower than for the non-catalyst car. For TWC cars, emissions decreased across the board from city to rural and motorway traffic. Without a catalyst, motorway traffic resulted in the highest NOx emissions. Compared to normal driving, aggressive driving gave emissions which were up to four times higher. Except for NOx, calm driving resulted in lower emissions still. Comparable fuel consumption rates were obtained from normal and calm driving. Those from aggressive driving were higher, by as much as 40% in city traffic. Cold starts resulted in significantly higher CO and HC emission values than hot starts. These differences were less pronounced for NOx. Emissions from TWC cars were higher than generally expected, compared to the European emission limit values (91/441/EEC) and the emission factors used in Flanders and the Netherlands (Klein,1993) for the national emission inventories. Low-emitting cars during the emission test on a chassis dynamometer, as prescribed by the 91/441/EEC directive, did not necessarily give low emissions in real traffic situations.
In the proposed signal timing model, a performance index function for optimization is defined to reduce vehicle delays, fuel consumption and emissions at intersections. The model optimizes the signal cycle length and green time by considering the constraint of a minimum green time to allow pedestrians to cross. The data used in a case study is from an intersection in Nanjing city. The relationships between the signal cycle length and vehicle delay, fuel consumption, emission, and performance index function are analyzed.
The paper examines the effects of coordinated traffic lights on CO and C6H6 roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C6H6 roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time.
(added to the original) The addition of transportation capacity affects potentially all attributes of trips made by urban residents: time of day, destination, mode, route, and linking of trips. In the long run, added capacity may influence a household’s automobile ownership decision, residence, and job location choice, as well as firms’ location decisions. Neither primary growth effects nor the secondary trip effects of added capacity are thoroughly understood—determining the effect of added capacity is not at all a trivial task because it is concerned with intricately and dynamically interrelated system components: transportation supply system, land use, accessibility, and travel demand. This paper presents a review of theoretical and empirical results in the literature that shed light on the effect of added transportation capacity. Tentative findings include the following: Using existing origin–destination data appears to be a very cost-effective and expeditious approach to addressing the added capacity issue, but it can be better used with more elaborate statistical methods to test behavioral theories. There is no empirical indication that added capacity generates a significant volume of induced traffic. The standard sequential procedure is capable, in principle, of forecasting diverted, transferred, and shifted traffic, although actual practice may be less than ideal. Abbreviated application of the procedure, unwarranted attempts to transfer models and extrapolation of the models to inapplicable options are unfortunately present. A better understanding of trip timing decisions and trip chaining behavior is needed. Impacts on car ownership, residential and job location choice, and land use need to be better understood and incorporated into the forecasting procedure. More widespread use of panel surveys is encouraged.
The development and validation of a model for dynamic traffic noise prediction is presented. The model is composed of a GIS-based traffic microsimulation part coupled with an emission model, and a beamtrace-based 2.5D propagation part, which takes into account multiple reflections and diffractions. The model can be used to analyze the influence of real urban traffic situations (e.g., traffic flow management, road saturation) in the usual equivalent sound level maps. However, it also allows to calculate and visualize statistical noise levels and indicators derived from them. Novel descriptors based on the power spectrum of noise level fluctuations can be obtained. A part of Gentbrugge, Belgium, is taken as a validation area; different traffic demand scenarios are simulated.
The development and validation of a model for dynamic traffic noise prediction is presented. The model is composed of a GIS-based traffic microsimulation part coupled with an emission model, and a beamtrace-based 2.5D propagation part, which takes into account multiple reflections and diffractions. The model can be used to analyze the influence of real urban traffic situations (e.g., traffic flow management, road saturation) in the usual equivalent sound level maps. However. it also allows to calculate and visualize statistical noise levels and indicators derived from them. Novel descriptors based on the power spectrum of noise level fluctuations can be obtained. A part of Gentbrugge. Belgium. is taken as a vatidation area: different traffic demand scenarios are simulated. (C) 2004 Elsevier Ltd. All rights reserved.
The main objectives of this paper are two fold. First, the paper evaluates the impact of vehicle cruise speed and acceleration levels on vehicle fuel-consumption and emission rates using field data gathered under real-world driving conditions. Second, it validates the VT-Micro model for the modeling of real-world conditions. Specifically, an on-board emission-measurement device was used to collect emissions of oxides of nitrogen, hydrocarbons, carbon monoxide, and carbon dioxide using a light-duty test vehicle. The analysis demonstrates that vehicle fuel-consumption and emission rates per-unit distance are optimum in the range of 60–90 km/h, with considerable increase outside this optimum range. The study demonstrates that as the level of aggressiveness for acceleration maneuvers increases, the fuel-consumption and emission rates per maneuver decrease because the vehicle spends less time accelerating. However, when emissions are gathered over a sufficiently long fixed distance, fuel-consumption and mobile-source emission rates per-unit distance increase as the level of acceleration increases because of the history effects that accompany rich-mode engine operations. In addition, the paper demonstrates the validity of the VT-Micro framework for modeling steady-state vehicle fuel-consumption and emission behavior. Finally, the research demonstrates that the VT-Micro framework requires further refinement to capture non-steady-state history behavior when the engine operates in rich mode.