Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km². The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.     

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Τhis study demonstrates the combination of a microscopic traffic simulator (AIMSUN) with an instantaneous emissions model (AVL CRUISE) to investigate the impact of traffic congestion on fuel consumption on an urban arterial road. The micro traffic model was enhanced by an improved car-following law according to Morello et al. (2014) and was calibrated to replicate measured driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control (UTC). The method was implemented to study the impact of congestion on fuel consumption for the category of Euro 5 diesel <1.4 l passenger cars. Free flow and congested conditions led to respective consumption differences of −25.8% and 20.9% over normal traffic. COPERT 5 rather well predicted the impact of congestion but resulted to a much lower relative reduction in free flow conditions. Start and stop system was estimated to reduce consumption by 6% and 11.9% under normal and congested conditions, respectively. Using the same modelling approach, UTC was found to have a positive impact on CO₂ emissions of 8.1% and 4.5% for normal and congested conditions, respectively, considering the Turin vehicle fleet mix for the year 2013. Overall, the study demonstrates that the combination of detailed and validated micro traffic and emissions models offers a powerful combination to study traffic and powertrain impacts on greenhouse gas and fuel consumption of on road vehicles over a city network.     

Impacts of traffic-flows on vehicular-exhaust emissions at traffic junctions

This paper examines the impact of traffic-flow on CO, NO₂ and PM emissions at two distinct traffic junctions and evaluates the use of emission factors. The study includes three scenarios regarding pollutant emissions, which combine a field, experimental and semi-empirically estimated traffic parameters for free, interrupted and congested traffic-flow conditions. It evaluates the emission patterns for heterogeneity in traffic characteristics of both junctions. The results suggest the corrections to be made to emission factors at traffic junctions for better forecast of air quality.     

Variability in urban driving patterns

Although it is known that driving patterns strongly affect the emission of pollutants from vehicles, existing empirical knowledge about driving patterns is limited. The first-step in this project was to find relevant parameters for describing driving patterns. These served as a basis for investigating variations in such patterns. An experimental study was carried out to compare driving patterns between and within different street-types, drivers and traffic conditions. Data were analysed using general factorial analysis of variance. Driving patterns showed very significant differences between street type and driver, and these factors had significant impact on all the parameters employed. The effect of street type was generally higher than the driver effect. Average speed and deceleration levels were lower at peak hours compared to off-peak hours. Men had higher acceleration levels than women generally and specially on one street type. The study showed no major differences in average speed for gender except for one street type where men drove faster than women. The knowledge attained in this study may be a step towards a better knowledge of driving patterns and their variation, and may provide possibilities of changing driving patterns and thus exhaust emissions from vehicles. Knowledge about driving patterns is also an essential part in efforts to improve models to calculate emission from traffic in urban environment.     

Assessing energy consumption impacts of traffic shifts based on real-world driving data

Information and communication technologies used for on-board vehicle monitoring have been adopted as an additional tool to characterize mobility flows. Furthermore, traffic volumes are traditionally measured to understand cities traffic dynamics. This paper presents an innovative methodology that uses an extensive and complementary real-world dataset to make a scenario-based analysis allowing assessing energy consumption impacts of shifting traffic from peak to off-peak hours. In the specific case of the city of Lisbon, a sample of 40 drivers was monitored for a period of six months. The obtained data allowed testing the impacts of increasing the percentage of traffic shifting from peak to off-peak hours in energy consumption. Both average speed and energy consumption variations were quantified for each of the tested percentages, allowing concluding that for traffic shifts of up to 30% a positive impact in consumption can be observed. In terms of potential gains associated to shifting traffic from peak hours, reductions in energy consumption from 0.1% to 0.4% can be obtained for traffic volumes shifts from 5 to 30%. Overall, the maximum reduction in energy consumption is achieved for a 20% traffic shift. Average speed variation follows the same trend as energy consumption, but in the opposite direction, i.e. instead of decreasing, average speed increases. For the best case scenario, considering only the sections of roads with traffic sensors, a 1.4% reduction in trip time may be achieved, as well as savings of up to 6 l of fuel and 14.5 kg of avoided CO₂ emissions per day.     

Speed variation during peak and off-peak hours on urban arterials in Shanghai

Increased speed variation on urban arterials is associated with reductions in both operational performance and safety. Traffic flow, mean speed, traffic control parameters and geometric design features are known to affect speed variation. An exploratory study of the relationships among these variables could provide a foundation for improving the operational and safety performance of urban arterials, however, such a study has been hampered by problems in measuring speeds. The measurement of speed has traditionally been accomplished using spot speed collection methods such as radar, laser and loop detectors. These methods can cover only limited locations, and consequently are not able to capture speed distributions along an entire network, or even throughout any single road segment. In Shanghai, it is possible to acquire the speed distribution of any roadway segment, over any period of interest, by capturing data from Shanghai’s 50,000+ taxis equipped with Global Positional Systems (GPS). These data, hereafter called Floating Car Data, were used to calculate mean speed and speed variation on 234 road segments from eight urban arterials in downtown Shanghai. Hierarchical models with random variables were developed to account for spatial correlations among segments within each arterial and heterogeneities among arterials. Considering that traffic demand changes throughout the day, AM peak, Noon off-peak, and PM peak hours were studied separately. Results showed that increases in number of lanes and number of access points, the presence of bus stops and increases in mean speed were all associated with increased speed variation, and that increases in traffic volume and traffic signal green times were associated with reduced speed variation. These findings can be used by engineers to minimize speed differences during the road network planning stage and continuing through the traffic management phase.     

An empirical study to determine freight travel time at a major port

Abstract

This paper examines the reliability measures of freight travel time on urban arterials that provide access to an international seaport. The findings indicate that the reliability index calculated by the median of travel time, which is less sensitive to extreme values in a highly skewed distribution, is more appropriate. This paper also examines several statistical distributions of travel time to determine the best fit to the data of freight trips. The results of goodness-of-fit tests indicate that the log-logistic is the best statistical function for freight travel time during the midday off-peak period. However, the lognormal distribution represents a better fit to arterials with heavily congested traffic during peak periods. Additionally, travel time prediction models identify the relationships between travel time, speeds and other factors that affect travel time reliability. The analysis suggests that incident-induced delays and speed fluctuations primarily contributed to the unreliability of freight movement on the urban arterials.     

Carbon dioxide emissions from diesel and compressed natural gas buses during acceleration

Motor vehicle emission factors are generally derived from driving tests mimicking steady state conditions or transient drive cycles. Neither of these test conditions, however, completely represents real world driving conditions. In particular, they fail to determine emissions generated during the accelerating phase – a condition in which urban buses spend much of their time. We analyse and compare the results of time-dependant emission measurements conducted on diesel and compressed natural gas buses during an urban driving cycle on a chassis dynamometer and we derive power-law expressions relating carbon dioxide emission factors to the instantaneous speed while accelerating from rest. Emissions during acceleration are compared with that during steady speed operation.     

Experimental evaluation of Heavy Duty Vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions

Exhaust emissions and fuel consumption of Heavy Duty Vehicles (HDVs) in urban and port areas were evaluated through a dedicated investigation. The HDV fleet composition and traffic driving from highways to the maritime port of Genoa and crossing the city were analysed. Typical urban trips linking highway exits to port gates and HDV mission profiles within the port area were defined. A validation was performed through on-board instrumentation to record HDV instantaneous speeds in urban and port zones. A statistical procedure enabled the building-up of representative speed patterns. High contrasts and specific driving conditions were observed in the port area. Representative speed profiles were then used to simulate fuel consumption and emissions for HDVs, using the Passenger car and Heavy duty Emission Model (PHEM). Complementary estimations were derived from Copert and HBEFA methodologies, allowing the comparison of different calculation approaches and scales. Finally, PHEM was implemented to assess the performances of EGR or SCR systems for NO_X reduction in urban driving and at very low speeds.The method and results of the investigation are presented. Fuel consumption and pollutant emission estimation through different methodologies are discussed, as well as the necessity of characterizing very local driving conditions for appropriate assessment.     

Empirical modelling of the relationship between bus and car speeds on signalised urban networks

Vehicle speed is an important attribute for analysing the utility of a transport mode. The speed relationship between multiple modes of transport is of interest to traffic planners and operators. This paper quantifies the relationship between bus speed and average car speed by integrating Bluetooth data and transit signal priority data from the urban network in Brisbane, Australia. The method proposed in this paper is the first of its kind to relate bus speed and average car speed by integrating multi-source traffic data in a corridor-based method. Three transferable regression models relating not-in-service bus, in-service bus during peak periods and in-service bus during off-peak periods with average car speed are proposed. The models are cross-validated and the interrelationships are significant.     

Characterization of incidents on an urban arterial road

The early warning of incidents on urban arterial roads in a congested city can reduce delay, accidents and pollutant emission. Freeway incident detection systems implemented in recent years may not be suitable for arterial incidents. Arterial incident detection is more difficult. The traffic flow on an arterial road is not conserved from the upstream end of a road link to the downstream end because urban traffic does turn in and out of side‐streets, car‐parks and local residences. Roadside friction such as kerbside parking and shopping traffic also tends to create apparent incidents which are in fact frequent and normal events. This paper develops a definition for an arterial incident and describes a case study on an arterial road in Melbourne, Australia. The study shows that detectors upstream of an incident are more useful for incident detection than downstream detectors. It also identifies occupancy and speed as the appropriate parameters to characterise and detect arterial incidents.     

Effect of roadway network aggregation levels on modeling of traffic-induced emission inventories in Beirut

Traffic-induced emissions pose a serious threat to air quality in heavily congested urban centers. While air quality can be characterized through field measurements and continuous monitoring, forecasting future conditions depends largely on estimating vehicle-emission factors coupled with mathematical modeling. Traffic and environmental planners have relied on overall average network speed in conjunction with speed-based emission factor models to estimate traffic emissions. This paper investigates the effect of three levels of roadway network aggregation, macro-scale (overall network basis), meso-scale (roadway functional class basis) and micro-scale (link-by-link basis) on emission inventories. A traffic model and an emission factor model were integrated to determine total emissions in the future Beirut Central District area for these three modeling approaches.     

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions     

Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads

The purpose of our study is to develop a “corrected average emission model,” i.e., an improved average speed model that accurately calculates CO₂ emissions on the road. When emissions from the central roads of a city are calculated, the existing average speed model only reflects the driving behavior of a vehicle that accelerates and decelerates due to signals and traffic. Therefore, we verified the accuracy of the average speed model, analyzed the causes of errors based on the instantaneous model utilizing second-by-second data from driving in a city center, and then developed a corrected model that can improve the accuracy. We collected GPS data from probe vehicles, and calculated and analyzed the average emissions and instantaneous emissions per link unit. Our results showed that the average speed model underestimated CO₂ emissions with an increase in acceleration and idle time for a speed range of 20 km/h and below, which is the speed range for traffic congestion. Based on these results, we analyzed the relationship between average emissions and instantaneous emissions according to the average speed per link unit, and we developed a model that performed better with an improved accuracy of calculated CO₂ emissions for 20 km/h and below.     

Independent driving pattern factors and their influence on fuel-use and exhaust emission factors

This study is aimed at finding independent measures to describe the dimensions of urban driving patterns and to investigate which properties have main effect on emissions and fuel-use. 62 driving pattern parameters were calculated for each of 19 230 driving patterns collected in real traffic. These included traditional driving pattern parameters of speed and acceleration and new parameters of engine speed and gear-changing behaviour. By using factorial analysis the initial 62 parameters were reduced to 16 independent driving pattern factors. Fuel-use and emission factors were estimated for a subset of 5217 cases using two different mechanistic instantaneous emission models. Regression analysis on the relation between driving pattern factors and fuel-use and emission factors showed that nine of the driving pattern factors had considerable environmental effects. Four of these are associated with different aspects of power demand and acceleration, three describe aspects of gear-changing behaviour and two factors describe the effect of certain speed intervals.     

Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

ABSTRACT

In recent years, there has been considerable research interest in short-term traffic flow forecasting. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g. 1 or 5?min) and lane level are still rare. In this study, a combination of genetic algorithm, neural network and locally weighted regression is used to achieve optimal prediction under various input and traffic settings. The genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) models are developed and tested, with the former forecasting traffic flow every 5-min within a 30-min period and the latter for forecasting traffic flow of a particular 5-min period of each for four lanes of an urban arterial road in Beijing, China. In particular, for morning peak and off-peak traffic flow prediction, the GA-ANN 5-min traffic flow model results in average errors of 3–5% and most 95th percentile errors of 7–14% for each of the four lanes; for the peak and off-peak time traffic flow predictions, the GA-LWR 5-min traffic flow model results in average errors of 2–4% and most 95th percentile errors are lower than 10% for each of the four lanes. When compared to previous models that usually offer average errors greater than 6–15%, such empirical findings should be of interest to and instrumental for transportation authorities to incorporate in their city- or state-wide Advanced Traveller Information Systems (ATIS).     

Average time gaps in congested freeway flow

Characteristics of time gaps (that is, the time separation between the rear of the lead vehicle and the front of the following vehicle) in congested freeway flow provide an important link between microscopic and macroscopic traffic flow. Although individual time gaps are a microscopic phenomenon, average time gaps can easily be determined from commonly collected macroscopic traffic flow data. Data from San Diego freeways and the Queen Elizabeth Way in Ontario, Canada are analyzed to show that average time gaps in congested flow are essentially constant with respect to speed; that they vary considerably between lanes at a single location and, for the same lane, from site to site; that they display considerable scatter; and that at some sites there is a distinct increase in average time gaps in the median lane in the transition to congested flow but at others there is no change or a slight reduction. The variability of average time gaps is not easily explained, although differences in driver populations may partly explain differences among different sites. Hysteresis due to acceleration and deceleration does not appear to be an explanation for the high degree of scatter in average time gaps, since no positive correlation was found between speed changes and average time gaps.     

The impact of speed post-processing methods on regional mobile emissions estimation

Average roadway segment travel speeds play an important role in estimating stabilized running vehicle emissions. Currently stabilized, or hot, running emissions are computed based on speeds produced during the travel demand modeling process. Speed data from the travel forecasting models are widely recognized as being insufficiently accurate for air quality purposes. Frequently post-processing techniques are seen as the most cost-effective means of improving the accuracy of the speed estimates. Using the Sacramento Metropolitan area, this paper focuses on the impacts of different speed post-processors on regional peak period emissions inventories. The results indicated that most post-processed speeds produce consistently and significantly higher running emissions, particularly in locations with heavy traffic. The observed differences in emissions between different types of post-processed speeds vary with congestion level, pollutant type and the underlying approach encapsulated in the speed post-processor calculations. The Sacramento case study suggests that the post-processor used to develop speeds for the purposes of calculating on-road emissions inventories can significantly influence the emissions inventories.     

Incorporating work zone configuration factors into speed‐flow and capacity models

This study aims to develop work zone speed‐flow and capacity models, which incorporate work zone configuration factors including the number of work zones, geometrical alignment, work zone speed limit, and work zone length. On the basis of the traffic data from six work zone sites with various work zone configurations, two nonlinear traffic speed and flow models including work zone configuration factors are developed for the uncongested and congested traffic conditions, respectively. A work zone capacity model is proposed on the basis of the two models. The three models can further be used to examine the effects of work zone configuration factors on the speed‐flow relationship and capacity at work zones. Results show that traffic speed, traffic flow, and work zone capacity increase with the posted speed limit. Traffic speed under uncongested conditions decreases with the geometric alignment, the number of work zones, work zone length, and heavy vehicle percentage. Under congested conditions, the increase of the number of work zones is found to exhibit a larger negative impact on the traffic flow than the increase of geometric alignment. The number of work zones is also found to have the largest negative impacts on work zone capacity, followed by the geometric alignment. Short work zone length exhibits a relatively minor contribution to increasing work zone capacity. Copyright © 2014 John Wiley & Sons, Ltd.     

Acceleration noise and level of service of urban roads - A case study

Capacity measurement of roads under mixed traffic conditions as prevailing in India is ambiguous as it varies with time, composition of traffic and roadway encroachments. High incidence of slow moving vehicles and tricycles adds to the problem. Volume - capacity ratio appears to be an inadequate measure of defining level of service under mixed traffic situations. An attempt is made in this paper to explore the possibility of presenting unconventional parameters like standard deviation of speed, co-efficient of variation of speed and acceleration noise as possible measures of level of service. Tentative ranges of acceleration noise are proposed in association with flow and speed to explain level of service of urban roads catering to mixed traffic. The results are based on a study conducted in Madras, a major metropolitan city of India.