Vehicle Specific Power Research Articles

Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data

During the passing decade, taxi floating car data (FCD) has become an important tool to investigate urban trip choice behaviors and activities. The corresponding taxi exhaust reduction issue is also with rather significance for traffic emission mitigation in urban areas. By taking Shanghai as an empirical case, this paper analyzed the spatiotemporal characteristics of multimode travelers by combining the taxi FCD (from Qiangsheng Inc.), the metro smartcard data and the GPS trajectories of Mobike, one of the most popular shared bicycles in China, 2018). Binomial logit models (BNL) were proposed to estimate mode choices for both peak and off-peak periods by incorporating socio-economic, demographic, urban morphology, land use properties, and various trip-related variables. The choices between metro and taxi, Mobike and taxi were analyzed, respectively, with the corresponding influential factors identified. The results indicated that the percentage of residential and commercial land uses, the number of educational facilities have significant impacts on travel mode choice during peak hours, while the percentage of commercial land, the number of hospitals and bus lines are more prominent during off-peak periods. To quantify the emission reduction benefits, localized calculation of automobile exhaust was established according to the Vehicle Specific Power (VSP) based measurements obtained from the Portable Emission Measurement System (PEMS) experiments. Then, five corresponding emission mitigation schemes were proposed based on the model findings, and the cost-benefit of each countermeasure was further analyzed. Comparing with releasing the peak-hour crowdedness of metro stations, increasing Mobike supply, updating taxis into electric vehicles, and equipping taxis with catalytic converters, the scheme of removing non-motor vehicle restrictions was found with the shortest payback period and was consequently recommended as accordance with the proposal of urban eco and non-motorized transportation. Findings of this study is useful for transportation management in improving the mode share of metro and bicycles, thus to alleviate the congestion and auto emissions in urban areas.

Capturing the Variability in Instantaneous Vehicle Emissions Based on Field Test Data

Emission models are important tools for traffic emission and air quality estimates. Existing instantaneous emission models employ the steady-state “engine emissions map” to estimate emissions for individual vehicles. However, vehicle emissions vary significantly, even under the same driving conditions. Variability in the emissions at a specific driving condition depends on various influencing factors. It is important to gain insight into the effects of these factors, to enable detailed modeling of individual vehicle emissions. This study employs a portable emissions measurement system (PEMS), to collect vehicle emissions including the corresponding parameters of engine condition, vehicle activity, catalyst temperature, geography, and meteorology, to analyze the variability in emission rates as a function of those factors, across different vehicle specific power (VSP) categories. We observe that carbon dioxide, carbon monoxide, nitrogen oxides, and particle number emissions are strongly correlated with engine parameters (engine speed, torque, load, and air-fuel ratio) and vehicle activity parameters (vehicle speed and acceleration). In the same VSP bin, emissions per second on highways and ramps are higher than those on arterial roads, and the emissions when the vehicle is traveling downhill tend to be higher than the emissions during uphill traveling, because of higher observed speeds and accelerations. Morning emissions are higher than afternoon emissions, due to lower temperatures.

Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty Gasoline Vehicles.

Spatial variability in real-world on-road tailpipe light-duty gasoline vehicle nitrogen oxides, hydrocarbon, carbon monoxide, and carbon dioxide emission rates, the locations of emissions hotspots, and factors that explain spatial variability are quantified. A sample of 205 vehicles were measured on four predefined round-trip study routes using Portable Emission Measurement Systems. The trips on each route were divided into segments, averaging 1/4 mile in length. Segment-average emission rates were estimated based on measured 1 Hz emission rates. Emission hotspots are defined as segments with ≥90th percentile of segment-average emission rates. The hotspots have average emission rates 2-4 times greater, depending on the pollutant, than other segments. Hotspots are of heterogeneous characteristics including road attributes and vehicle activity metrics. For example, some hotspots were on arterial roads with an upstream signalized intersection and positive road grade, whereas some hotspots were on interstates with positive grade. Vehicle activity metrics, including average vehicle specific power and relative positive acceleration, help identify the hotspots. To reliably identify a fleet-average hotspot, data are needed for at least 36-130 vehicles, depending on the pollutant.

Estimating Light-Duty Vehicle Emission Factors using Random Forest Regression Model with Pavement Roughness

Pavement roughness would affect the running of vehicle movement, and thus possibly impact fuel consumption and vehicle emissions, the numerical relationships and analytical steps of which are, however, not yet well studied. The major objective of this paper is to quantify vehicular emission factors—hydrocarbons (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2)—and fuel consumption as a function of pavement roughness (the International Roughness Index [IRI]) and other factors. Within each operating mode identification (OMID) bins of vehicle operational status, a random forest regression model (RFRM) was identified to estimate emission factors and fuel consumption. The field test data, with a total length of 1,067.41 mi driving and 323,075 data pairs from one test vehicle, were used to train and validate models. The portable emissions measurement system (PEMS) and a smartphone application for IRI were employed for the tests in Texas, U.S., roadways. Results show that the optimum roughness conditions for lower emissions and fuel consumption are in categories B and C with moderate roughness. The root-mean-square error (RMSE) during training, testing, and validation processes of the RFRM are within 6.4%, implying a good fit of resulted models. IRI has the most OMID bins as number one predictor, followed by vehicle specific power (VSP) and speed. Through separated modeling for each OMID, the impacts of IRI are successfully grasped. It is recommended conducting more field measurements with more vehicle types. This would help with possible incorporation of vehicle emissions, fuel consumption, and other environmental factors into the pavement design, maintenance, and retrofitting process.

Evaluation of the Precision and Accuracy of Cycle-Average Light Duty Gasoline Vehicles Tailpipe Emission Rates Predicted by Modal Models

A vehicle specific power (VSP) modal model and the MOtor Vehicle Emission Simulator (MOVES) Operating Mode (OpMode) model have been used to evaluate and quantify the fuel use and emission rates (FUERs) for on-road vehicles. These models bin second-by-second FUERs based on factors such as VSP, speed, and others. The validity of binning approaches depends on their precision and accuracy in predicting variability in cycle-average emission rates (CAERs). The objective is to quantify the precision and accuracy of the two modeling methods. Since 2008, North Carolina State University has used portable emission measurement systems to measure tailpipe emission rates for 214 light duty gasoline vehicles on 1,677 driving cycles, including 839 outbound cycles and 838 inbound cycles on the same routes. These vehicles represent a wide range of characteristics and emission standards. For each vehicle, the models were calibrated based on outbound cycles and were validated based on inbound cycles. The goodness-of-fit of the calibrated models was assessed using linear least squares regression without intercept between model-predicted versus empirical CAERs for individual vehicles. Based on model calibration and validation, the coefficients of determination ( R2) typically range from 0.60 to 0.97 depending on the vehicle group and pollutant, indicating moderate to high precision, with precision typically higher for higher-emitting vehicle groups. The slopes of parity plots for each vehicle group and all vehicles typically range from 0.90 to 1.10, indicating good accuracy. The two modeling approaches are similar to each other at the microscopic and macroscopic levels.

Distance-based emission factors from vehicle emission remote sensing measurements

Vehicle emission remote sensing has the potential to provide detailed emissions information at a highly disaggregated level owing to the ability to measure thousands of vehicles in a single day. Fundamentally, vehicle emission remote sensing provides a direct measure of the molar volume ratio of a pollutant to carbon dioxide, from which fuel-based emissions factors can readily be calculated. However, vehicle emissions are more commonly expressed in emission per unit distance travelled e.g. grams per km or mile. To express vehicle emission remote sensing data in this way requires an estimate of the fuel consumption at the time of the emission measurement. In this paper, an approach is developed based on vehicle specific power that uses commonly measured or easily obtainable vehicle information such as vehicle speed, acceleration and mass. We test the approach against 55 independent comprehensive PEMS measurements for Euro 5 and 6 gasoline and diesel vehicles over a wide range of driving conditions and find good agreement between the method and PEMS data. The method is applied to individual vehicle model types to quantify distance-based emission factors. The method will be appropriate for application to larger vehicle emission remote sensing databases, thus extending real-world distance-based vehicle emissions information.

Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade

Past research showed on-road emissions patterns unique to hybrid electric vehicles (HEVs), indicating the need to account for them in emissions models as projected HEV sales increase over the coming decades. This work defines and characterizes a variable that quantifies HEV operating behavior to inform future development of new HEV emissions models based on current knowledge of conventional vehicle (CV) emissions patterns. Instantaneous hybridization factor (IHF), was quantified using on-road data collected from a 2010 Toyota Camry HEV. IHF is the ratio of electric system power to total system power and accounts for energy storage in the high voltage battery (IHF ranges from −1 to +1). Relationships between IHF and vehicle specific power (VSP), road type and road grade were examined. Negative VSP resulted in regenerative braking operation (IHF = −0.01 to −1) 90% of the time. IHF identified the VSP range where HEV operation was highly variable (VSP = −1 to 8 kW/ton) when driving at speeds below the ICE-off threshold (42 mph). VSP and IHF together account for 76–86% of the variability in HEV CO2 emissions in this study. CO2 model results using VSP computed with the measured real-world road grade (R2 = 0.86) gave improved fits over the no-grade VSP model (R2 = 0.69). This study establishes one framework for calculating the instantaneous HEV power split, confirms the need to include road grade in VSP for accurate modeling of vehicle emissions, and identified the need for significant improvements in on-board diagnostic (OBD) scantool measurement requirements for HEVs in three areas: (1) temporal resolution (sub-second to capture transient events such as ICE restarts); (2) simultaneous data logging capability for multiple controller area networks (i.e., engine and HEV parameters together); and (3) improved data precision.

Determining the set of representative variables of real-world driving cycle of bus: a case study of Hanoi

This paper analysed the real-world driving data to determine the representative parameters of driving cycle for the purpose of the typical driving cycle development of bus in Hanoi. The real-world driving data of bus in Hanoi were collected by using the Global positioning system technique with 1Hz data update rate. The real-world driving data of fifteen bus routes in the inner city were collected continuously, on weekdays as well as at weekends. The data, then, were used to calculate 33 kinematics parameters reflecting the realistic driving characteristics, including vehicle-specific power. The hierarchical agglomerative clustering method was used to determine a minimal set of representative variables from the 33 kinematics parameters. The 14 representative parameters of the real-world driving cycle of bus in Hanoi were determined.

Real-world assessment of vehicle air pollutant emissions subset by vehicle type, fuel and EURO class: New findings from the recent UK EDAR field campaigns, and implications for emissions restricted zones

This paper reports upon and analyses vehicle emissions measured by the Emissions Detecting and Reporting (EDAR) system, a Vehicle Emissions Remote Sensing System (VERSS) type device, used in five UK based field campaigns in 2016 and 2017. In total 94,940 measurements were made of 75,622 individual vehicles during the five campaigns. The measurements are subset into vehicle type (bus, car, HGV, minibus, motorcycle, other, plant, taxi, van, and unknown), fuel type for car (petrol and diesel), and EURO class, and particulate matter (PM), nitric oxide (NO) and nitrogen dioxide (NO2) are reported. In terms of recent EURO class emission trends, NO and NOx emissions decrease from EURO 5 to EURO 6 for nearly all vehicle categories. Interestingly, taxis show a marked increase in NO2 emissions from EURO 5 to EURO 6. Perhaps most concerningly is a marked increase in PM emissions from EURO 5 to EURO 6 for HGVs. Another noteworthy observation was that vans, buses and HGVs of unknown EURO class were often the dirtiest vehicles in their classes, suggesting that where counts of such vehicles are high, they will likely make a significant contribution to local emissions. Using Vehicle Specific Power (VSP) weighting we provide an indication of the magnitude of the on-site VERSS bias and also a closer estimate of the regulatory test/on-road emissions differences. Finally, a new ‘EURO Updating Potential’ (EUP) factor is introduced, to assess the effect of a range of air pollutant emissions restricted zones either currently in use or marked for future introduction. In particular, the effects of the London based Low Emission Zone (LEZ) and Ultra-Low Emissions Zone (ULEZ), and the proposed Birmingham based Clean Air Zone (CAZ) are estimated. With the current vehicle fleet, the impacts of the ULEZ and CAZ will be far more significant than the LEZ, which was introduced in 2008.

Impact of V2V Communication on Eco-Route Choice

Abstract Usually, route choice is performed considering a single objective like considering a single object among travel time, emission, and travel distance. In this article, a methodology has been developed to find the eco-friendly route considering multi-objective-travel time, emission and travel distance. Pareto optimality and weighted product model are used for multi-objective optimization and route choice is done by Dijkstra’s shortest path algorithm. Simulation software, AIMSUN is used to perform micro-simulation for collecting second by second vehicle speed and acceleration profile. Vehicle Specific Power (VSP) model is used to estimate emission. Emission at the traffic network can be significantly reduced by using the vehicle to vehicle communication. Using the V2V communication system, CO2, NOx, CO, HC can be reduced up to 5.34%, 9.57%, 25.84%, and 3.67% respectively in route choice considering budget travel time and travel distance. The difference in route choice pattern has also found considering without and with V2V communication.

Variability in Measured Real-World Operational Energy Use and Emission Rates of a Plug-In Hybrid Electric Vehicle

Compared to comparably sized conventional light duty gasoline vehicles (CLDGVs), plug-in hybrid electric vehicles (PHEVs) may offer benefits of improved energy economy, reduced emissions, and the flexibility to use electricity as an energy source. PHEVs operate in either charge depleting (CD) or charge sustaining (CS) mode; the engine has the ability to turn on and off; and the engine can have multiple cold starts. A method is demonstrated for quantifying the real-world activity, energy use, and emissions of PHEVs, taking into account these operational characteristics and differences in electricity generation resource mix. A 2013 Toyota Prius plug-in was measured using a portable emission measurement system. Vehicle specific power (VSP) based modal average energy use and emission rates are inferred to assess trends in energy use and emissions with respect to engine load and for comparisons of engine on versus engine off, and cold start versus hot stabilized running. The results show that, compared to CLDGVs, the PHEV operating in CD mode has improved energy efficiency and lower CO2, CO, HC, NOx, and PM2.5 emission rates for a wide range of power generation fuel mixes. However, PHEV energy use and emission rates are highly variable, with periods of relatively high on-road emission rates related to cold starts.

Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China

There is a growing recognition that optimizing urban form can reduce road transport-related carbon emissions, particularly in carbon emissions by daily travel. In this study, we proposed an improved bottom-up approach combining Vehicle Specific Power (VSP) model and Enhanced Two-Step Floating Catchment Area (E2SFCA) method to estimate the carbon emissions by daily travel. Then, a geographical weighted regression (GWR) model considering the urban residential density (RD) and land use mix level (LML) of urban form as independent variables was employed to explore the relationship between urban form and carbon emissions. A typical working day (June 5th of 2017) of Hangzhou was chosen as a case study in this paper. The results showed that (1) per hour carbon emission in the morning peak period of 6:30 a.m.-9:30 a.m. was higher than that in the evening peak of 22:00–24:00 by 33.34%; and distribution of the highest carbon emissions was in the eastern, northeastern and northwestern parts of Hangzhou; (2) The RD and LML were positively and negatively associated with carbon emissions. and the coefficients for Ln (RD) and (LML) ranged from 0.29 to 0.70 and from −9.01 to −6.06, respectively. (3) The spatial distribution of coefficients demonstrated that the highest effects of RD on carbon emissions were observed in the central parts of Hangzhou, and those of LML on carbon emissions were observed in southern Hangzhou featured by industrial parks. This study may provide insights to mitigate carbon emissions from daily travel with multiple public policies including mixed land-use policies, urban density control and spatial planning.

Analysis of emissions from various driving cycles based on real driving measurements obtained in a high-altitude city

Vehicle emissions standards and regulations remain weak in high-altitude regions. In this study, vehicle emissions from both the New European Driving Cycle and the Worldwide harmonized Light-duty driving Test Cycle were analyzed by employing on-road test data collected from typical roads in a high-altitude city. On-road measurements were conducted on five light-duty vehicles using a portable emissions measurement system. The certification cycle parameters were synthesized from real-world driving data using the vehicle specific power methodology. The analysis revealed that under real-world driving conditions, all emissions were generally higher than the estimated values for both the New European Driving Cycle and Worldwide harmonized Light-duty driving Test Cycle. Concerning emissions standards, more CO, NOx, and hydrocarbons were emitted by China 3 vehicles than by China 4 vehicles, whereas the CO2 emissions exhibited interesting trends with vehicle displacement and emissions standards. These results have potential implications for policymakers in regard to vehicle emissions management and control strategies aimed at emissions reduction, fleet inspection, and maintenance programs.

Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management

Abstract This paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.

Real-Road Driving and Fuel Consumption Characteristics of Public Buses in Southern China

In this paper, the annual data of 40 public buses fueled with diesel in four cities in southern China have been analyzed for investigating the driving and fuel consumption characteristics. Results showed that the best traffic condition and smoothest driving style were obtained in Fuzhou, which was exact opposite to the results in Wuhan. And the driving characteristics of each city was weakly affected by the season’s variation. The fuel consumption characteristics of the public buses in different cities were studied by the method based on VSP (Vehicle Specific Power) model, and the fuel consumption factors in different cities and seasons have been given. The main factors influenced the fuel consumption characteristics could be summarized by the variations of driving characteristics and air conditioning using. The high fuel consumption was concentrated in region with low vehicle velocity range of 0 ∼ 10 km/h, and the proportion of this vehicle range directly affected the average fuel consumption level. The effect of the air conditioning using on fuel consumption was especially obvious in summer and winter, and the using of air conditioning would increase about 10% of the total vehicle fuel consumption.

Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration

This study is the first of a two-part paper. The overall study presents a new methodology to improve the accuracy of hybrid vehicles’ energy-consumption model over conventional transportation modeling methods. The first paper attempts to improve an equation for vehicles’ driving-power estimation to be more realistic and specific for a particular vehicle model or fleet. The second paper adopts the driving-power equation to estimate the requested driving power. Then, the data are utilized to construct the hybrid-vehicle energy-consumption model, namely, the traction-force–speed-based energy-consumption model (TFS model). The main concept of the first paper is to utilize the power-split hybrid powertrain’s accessible on-board diagnostics (OBD) dataset, and its dynamic model to estimate the total propulsion power. Then, propulsion power was applied as the main parameter for driving-power equation development and vehicle-specific coefficient calibration. For coefficient calibration, this study implemented the stepwise multiple regression method to select and calibrate an optimal set of coefficients. Results showed that conventional driving-power equations Vehicle-Specific Power (VSP) LDV 1999 and VSP Prius3Spec provide low prediction fidelity, especially under high-speed (>80 km/h) and heavy-load driving (≥50 kW). In contrast, D r v P w P r i u s 3 , proposed in this study, effectively improved prediction to become more accurate and reliable through all driving conditions and speed ranges. It dramatically helped to reduce prediction discrepancy over the conventional equations at heavy-load driving, from an R-square of 0.79 and 0.78 to 0.96. D r v P w P r i u s 3 also the prediction error at high-speed driving from the maximal error of approximately −20 to −5 kW. This study also discovered that aerodynamics and rolling resistance were the primary factors that caused the prediction error of conventional VSP equations. In addition, results in this study showed that both of the approaches used to establish the P P T d r v and D r v P w P r i u s 3 equations were valid for a power-split hybrid vehicle’s driving-power estimation. For the coefficient-calibration part, the stepwise and multiple regression method is low-cost and simple, allowing to calibrate an appropriate set of optimal coefficients for a specific vehicle model or fleet.

Transient Characterization of Automotive Exhaust Emission from Different Vehicle Types Based on On-Road Measurements

Previous works on real-world vehicle emission characteristics have mainly focused on the influences of fuel, speed, vehicle type, elevation, and other factors on vehicle emission quantity and components. However, few studies have investigated the transient trend of automotive exhaust emissions through on-road measurements. The key objective of the present paper was to examine the transient characteristics of exhaust emissions from different vehicle types on the roads of Tianjin. To achieve the goal, a portable emission measurement system (PEMS) was employed to monitor emissions from selected test vehicles—private cars, passenger vehicles, and cargo vehicles. It was found that the high-emission points of test vehicles were mainly distributed in two regions: the high-speed region (speed > 70–90 km/h, vehicle-specific power (VSP) > 0 kW/t) and the medium-speed–acceleration region (20–30 km/h < speed < 60–90 km/h, 0 kW/t <VSP < 12 kW/t). The CO, hydrocarbon (HC), NOx, and particulate number (PN) average emission rates in the high-emission points could be 3.15–14.93 times, 1.93–24.89 times, 3.23–6.03 times, and 3.22–30.27 times of those of average emission rates. The HC, NOx, and PN average emission rates of China IV vehicles in the high-emission points were 2.46–4.92 times, 3.56–6.03 times, and 3.22–13.21 times of those of average emission rates, not less than those of China III (1.93–2.52 times, 2.75–3.90 times, and 9.98–22.34 times). Test vehicles mainly emitted nucleation-mode and Aitken-mode particles, and the increase of the PN concentration emission rate in low-speed and high-speed regions was higher than that in the medium-speed region. The exhaust gas recirculation (EGR) + diesel particulate filter (DPF) could effectively inhibit the Aitken output caused by turbocharged intercooler (CIC). The selective catalytic reduction (SCR) might cause more nucleation-mode particles.

Assessing the emission impacts of autonomous vehicles on metropolitan freeways

While recent studies demonstrate the societal and economic benefits of driverless vehicles, little is known about the emission impacts of autonomous vehicles (AVs) in the context of mixed traffic. This paper explores the environmental impacts of AVs along an urban freeway corridor in a metropolitan area using Vehicle Specific Power (VSP) and EMEP/EEA emission methodologies paired with VISSIM traffic model. Three different AV penetration rates were implemented for through traffic along a freeway corridor in the city of Porto (Portugal) by considering long-term market predictions (10%, 20% and 30%). Afterwards, these scenarios were compared to current situation in terms of carbon dioxide, carbon monoxide, nitrogen oxides and hydrocarbon emissions, and travel time and stop-and-go situations. The emissions and traffic performance of each scenario were evaluated on three levels: a) overall study domain; b) corridor; c) impact of AVs on conventional vehicles (CVs). AVs yielded small savings in emissions in the overall study domain for automation levels below 30% (differences in traffic performance and emissions were not statistically significant). Corridor-level analysis showed decreases of 5% in emissions can be expected with AVs technology, but it penalizes travel time up to 13% for both AV and CV, when compared to the existing situation.

Impact of Road Geometry on Vehicle Energy Consumption and CO2 Emissions: An Energy-Efficiency Rating Methodology

This study presents a methodology for classifying road traffic energy efficiency. The indicators defined discriminate the impact of the road vertical and horizontal alignments upon energy consumption, disclosing the improvement potential of the road as a function of the traffic origin–destination matrix. The methodologic approach is based on basic physical principals, thus guarantying its generality, as opposed to the usual empirical mesoscale approaches. A simplified algebraic procedure is also proposed, resorting to simplified driving cycles and a constant speed assumption (CSA), thus avoiding the intricacy of microscale/microsimulation models. The simplified methodology was validated against field data acquired on the Portuguese highway A25. A microscale vehicle specific power analysis combined with detailed fuel models is compared against CSA results. The findings demonstrate its adequacy for free-flow traffic conditions and the importance of classifying road traffic energy-efficiency. For the case studied, it was found that 49.5% of the round trip propulsive energy expended by a 37-ton truck on the A25, a modern road, was degraded as heat through braking. The difference found between the microscale analysis and CSA approach is 0.8%, despite the speed unevenness, varying between 32 and 96 km/h, with a standard deviation of 24% of the average speed.

Evaluating the Effects of One-Way Traffic Management on Different Vehicle Exhaust Emissions Using an Integrated Approach

One-way traffic management is a recognized traffic organization to improve traffic efficiency and safety, but its effects on different traffic emissions remains unclear. This paper aims to investigate the impacts of one-way traffic management on three typical vehicle exhaust emissions including Carbonic Oxide (CO), Hydrocarbon Compounds (HC), and Nitrogen Oxides (NOx) in a traffic system using an integrated approach. Field experiment was conducted to collect the vehicular emission data under different traffic conditions using the onboard portable emission measurement system. An instantaneous emission model (i.e., Vehicle Specific Power) is calibrated using the collected field emission data and is incorporated into the microscopic traffic simulation tool VISSIM for quantifying the emissions before and after one-way traffic management through simulation. Two scenarios based on real networks and traffic demands of peak hours in part areas of Shanghai are developed for simulation and evaluation. The results show that in the intersections, the emission rates of COHC, NOx after one-way traffic management is significantly reduced by 20.46%, 21.29% and 21.06%, respectively. In the road sections, the emission rates of CO, HC, NOx in the road sections decrease by 23.38% and 26.29%. The overall CO, HC, NOx emissions in the studied network reduce by 21.34%, 22.29% and 23.77% separately due to one-way traffic management. The results provide insights into the derivative effects of one-way traffic management on traffic emissions in the intersections, road sections and network levels, and thus support scientific traffic management for promoting the sustainability of transport system.