Modal Emissions Model Research Articles

New and tractable formulations for the eco-driving and the eco-routing-and-driving problems

Eco-driving and eco-routing problems are both concerned with minimizing the fuel consumption of a single vehicle; the former does so by optimizing the vehicle’s speed profile on a given road segment, and the latter selects the best path for a vehicle from a given origin to a given destination. This paper studies a problem that combines the two, namely an eco-routing-and-driving problem (ERDP), in which the path and the speed profile on each link of the chosen path are optimized simultaneously. Using the comprehensive modal emissions model as the fuel consumption model, this paper describes new and tractable formulations for both the eco-driving and the eco-routing-and-driving problems through discretization, convexification and approximation, leading to a linear program for the former and a mixed-integer linear program for the latter. Computational results indicate that the new formulation of the eco-driving problem yields significant reductions in the solution time as compared to alternative formulations and algorithms, and that the new formulation of the eco-routing-and-driving problem affords further reductions in fuel consumption when compared with other methods.

Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

In this study, we formulate a freight vehicle path-planning model in the context of dynamic time-varying networks that aims to capture the spatial and temporal distribution characteristics inherent in the carbon dioxide emission trajectories of freight vehicles. Central to this model is the minimization of total carbon dioxide emissions from vehicle distribution, based on the comprehensive modal emission model (CMEM). Our model also employs the freight vehicle travel time discretization technique and the dynamic time-varying multi-path selection strategy. We then design an improved genetic algorithm to solve this complicated problem. Empirical results vividly illustrate the superior performance of our model over alternative objective function models. In addition, our observations highlight the central role of accurate period partitioning in time segmentation considerations. Finally, the experimental results underline that our multi-path model is able to detect the imprint of holiday-related effects on the spatial and temporal distribution of carbon dioxide emission trajectories, especially when compared to traditional single-path models.

Comparison of microscale traffic emission models for urban networks

Traffic-related air quality issues remain in urban areas worldwide. For this reason, there is an increasing need to estimate the contribution of road traffic to atmospheric emissions at local level with high temporal and spatial resolution. Modal models compute emission rates as a function of specific engine or vehicle operating conditions at the highest resolution (seconds). They can be applied for microscale studies being a cost-effective tool to emulate differences in emissions levels in road networks. Two modal emission models, the Australian PΔP (Power-delta-Power) and the simplified version of the European PHEM (Passenger Car and Heavy-duty Emission Model), PHEM-light model, have been used. Also, a comparison to the cycle-variable emission model VERSIT+micro (Netherlands organisation for applied scientific research state of the art traffic emission model) has been performed. For the comparison of both modal models, the main variables involved in traffic emission calculation were identified. 1 Hz speed-time profiles for individual vehicles were generated with the traffic microsimulation model VISSIM (Vehrkehr in Statden SIMulation) for different traffic conditions. To understand the response of modal models, detailed estimations of NO X emissions and fuel consumption were compared for different vehicle classes. Instantaneous emission profiles for individual driving patterns are highly sensitive to speed-acceleration profiles, vehicle mass, and road gradient, which are essential variables for the emission calculation. Although there are differences between European and Australian models, engine power and load were used to map vehicle classes for a more consistent comparison. It is essential to accurately define these parameters for each vehicle class in addition to detailed driving patterns to obtain high-resolution emissions estimates. In this sense, a larger number of vehicle classes included in the model provides more flexibility to develop representative emissions estimates. Emission predictions between modal models were reasonably consistent presenting larger differences with the cycle-variable model, despite both modal models being based on different on-road fleet measurements. In conclusion, analysing emission estimations for different traffic conditions demonstrates the importance of an accurate definition of the model parameters for a specific vehicle fleet.

Development of Driving Cycle for Passenger Cars and Estimation of Vehicular Exhaust Emission Factors

Driving pattern of the given vehicle in a city is represented by the driving cycle which is used by modal models for estimation of vehicular emissions. In this study, a driving cycle was developed for passenger cars in the city of Islamabad. A representative route for city was identified which included parts of highway, arteries, and streets. Speed-time data was collected along the selected route in both peak and off-peak hours. The on-board measurement method was adopted for data collection on highways, whereas on arteritis and streets, chase car method has opted for data collection. The developed cycle showed comparable values of average speed and acceleration with New European Driving Cycle (NEDC), but acceleration and deceleration were observed to be lower. The emission estimation and fuel consumption were calculated by the Comprehensive Modal Emission Model (CMEM) for 4 selected vehicle types. The average emissions for the selected vehicles were 204.4 g CO2 /km, 2.3 g CO /km, 0.09 g HCs /km, 0.31 g NOx /km emitted, and 65.7 g of fuel consumed /km. The developed cycle showed higher fuel consumption and CO2 emissions /km traveled than NEDC for all 4 passenger car types. However, HCs, CO, and NOx emissions were lower. The developed driving cycle can be used for estimation of the emissions from passenger cars in the city and this information can be useful for future emission projections and policy making.

A novel modal emission modelling approach and its application with on-road emission measurements

Modal emission models describe vehicular emissions with stratified vehicle kinetic conditions or engine parameters. They are widely adopted in regulatory applications in North America and Europe to estimate emissions and energy consumption from mobile sources. However, challenges exist in the development of modal bins, especially that previous approaches rely on manual adjustment and tuning, which increase the propensity to emission misclassification. This study proposes a new approach to generate modal bins, which overcomes the limitations of previous studies. It uses a Greedy algorithm to define optimal mode boundaries and improve model robustness. The model is calibrated with emission data from a portable emission monitoring system and validated against an independent dataset. Our modelling approach can effectively reflect carbon dioxide (CO2) emissions in steady and aggressive driving conditions with errors lower than 7% at the trip level. The introduction of engine parameters is found to improve model prediction for carbon monoxide (CO) and nitrogen oxides (NOx) by about 30% compared with the models relying on external variables.

Measuring fuel consumption in vehicle routing: new estimation models using supervised learning

In this paper, we propose and assess the accuracy of new fuel consumption estimation models for vehicle routing. Based on real-world data consisting of instantaneous fuel consumption, time-varying speeds observations, and high-frequency traffic, we propose effective methods to estimate fuel consumption. By carrying out nonlinear regression analysis using supervised learning methods, namely Neural Networks, Support Vector Machines, Conditional Inference Trees, and Gradient Boosting Machines, we develop new models that provide better prediction accuracy than classical models. We correctly estimate consumption for time-dependent point-to-point routing under realistic conditions. Our methods provide a more precise alternative to classical regression methods used in the literature, as they are developed for a specific situation. Extensive computational experiments under realistic conditions show the effectiveness of the proposed machine learning consumption models, clearly outperforming macroscopic and microscopic consumption models such as the Comprehensive Modal Emissions Model (CMEM) and the Methodology for Estimating air pollutant Emissions from Transport (MEET). Based on sensitivity analyses we show that MEET underestimates real-world consumption by 24.94% and CMEM leads to an overestimation of consumption by 7.57% with optimised parameters. Our best machine learning model (Gradient Boosting Machines) exhibited superior estimation accuracy with a gap of only 1.70%.

Evaluation of vehicular pollution levels using line source model for hot spots in Muscat, Oman.

A detailed investigation was carried out to assess the concentration of near-road traffic-related air pollution (TRAP) using a dispersion model in Muscat. Two ambient air quality monitoring (AQM) stations were utilized separately at six locations near major roadways (each location for 2months) to monitor carbon monoxide (CO) and nitrogen oxides (NOx). The study aimed to measure the concentration of near-road TRAP in a city hot spots and develop a validated dispersion model via performance measures. The US Environmental Protection Agency (US EPA) Line Source Model was implemented in which the pollutant emission factors were obtained through Comprehensive Modal Emission Model (CMEM) and COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Traffic data of all vehicle categories under normal driving conditions including average vehicle speed limits and local meteorological conditions were included in the modeling study. The analysis of monitoring data showed that hourly (00:00 to 23:00) concentrations of CO were within the US EPA limits, while NOx concentration was exceeded in most locations. Also, the measured pollutant levels were consistent with hourly peak and off-peak traffic volumes. The overall primary statistical performance measures showed that COPERT model was better than CMEM due to the high sensitivity of CMEM to the local meteorological factors. The best fractional bias (0.47 and 0.39), normalized mean square error (0.44 and 0.50), correlation coefficient (0.64 and 0.70), geometric mean bias (1.07 and 1.57), and geometric variance (2.00 and 2.32) were obtained for CO and NOx, respectively. However, the bootstrap 95% CI estimates over normalized mean square error, fractional bias, and correlation coefficient for COPERT and CMEM were found to be statistically significant from 0 in the case of combined model comparison across all the traffic locations for both CO and NOx. In overall, certain roads showed weak performance mainly due to the terrain features and the lack of reliable background concentrations, which need to be considered in the future study.

Comparison of platoon formations using a departure time coordination heuristic

A platoon is a set of virtually linked vehicles that drive closely behind one another. Truck platooning is a fast-emerging application of connected and autonomous vehicles to help tackle the traditional problems of the transportation industry and to reduce cost and minimise emissions. In this study, we present a departure time coordination-based heuristic solution for platoon formations. We also use a comprehensive modal emission model to understand the influence of velocity and size of the platoon on fuel savings potential. We compare the heuristic's performance on the different variants of the platooning problem in the literature with a central platoon coordinator and analyse the impact of the type of vehicle, speed, load and size of the platoon on the fuel savings potential of platooning. Numerical analysis shows a fuel savings potential of 6.4% to 8% for a platoon size of three with heavy duty vehicles at 80 kmph for different configurations.

The green location-routing problem

This paper introduces the Green Location-Routing Problem (GLRP), a combination of the classical Location-Routing Problem (LRP) and the Pollution-Routing Problem (PRP). The GLRP consists of (i) locating depots on a subset of a discrete set of points, from where vehicles of limited capacity will be dispatched to serve a number of customers with service requirements, (ii) routing the vehicles by determining the order of customers served by each vehicle and (iii) setting the speed on each leg of the journey such that customers are served within their respective time windows. The objective of the GLRP is to minimize a cost function comprising the fixed cost of operating depots, as well as the costs of the fuel and CO2 emissions. The amount of fuel consumption and emissions is measured by a widely used comprehensive modal emission model. The paper presents a mixed integer programming formulation and a set of preprocessing rules and valid inequalities to strengthen the formulation. Two solution approaches; an integer programming based algorithm and an iterated local search algorithm are also presented. Computational analyses are carried out using adaptations of literature instances to the GLRP in order to analyze the effects of a number parameters on location and routing decisions in terms of cost, fuel consumption and emission. The performance of the heuristic algorithms are also evaluated.

Study on the vehicle routing problem considering congestion and emission factors

To meet the requirement of greening transportation in poor traffic condition, vehicle routing problem (VRP) with consideration of fuel consumption and congestion is studied. We formulated a time-dependent green vehicle routing problem (TD-GVRP) model with minimised total cost as the objective function which includes fuel consumption cost, and the measurement of fuel consumption is based on the Comprehensive Modal Emissions Model (CMEM). In the model, the situation of waiting at customer nodes to avoid bad traffic is defined. To solve this model, a Response Surface Method (RSM)-based hybrid algorithm (HA) that combines genetic algorithm (GA) and particle swarm optimisation (PSO) is constructed. Finally, using instances from PRPLIB database, the following experiments are carried out and the corresponding conclusions are drawn. (i) Comparison of the proposed objective and traditional VRP objectives shows that fuel consumption can be greatly reduced by introducing fuel consumption factor into the objective function. (ii) Sensitivity analysis of congestion duration provides the influence of congestion duration on fuel consumption and travel time. (iii) Experiments based on different waiting time reveal that the optimisation of departure time can reduce fuel consumption and total cost to some extent.

Simulation Analyses of Two On-Ramp Lane Arrangements

Ramps are vital pieces of infrastructure connecting city traffic networks to freeways. The performance of a ramp is to some extent determined by the on-ramp lane arrangement. In this paper, our primary aim is to evaluate the performance in terms of travel time and vehicle emissions for two on-ramp lane arrangements: added lane and zip merging. We estimate the travel time and CO2 emissions on the basis of the speed, and acceleration of vehicles in accordance with the improved comprehensive modal emission model (CMEM), and then analyse the impacts of traffic volume and heavy goods vehicles (HGVs) on travel time and emissions. The impacts of main road traffic flow on travel time and emissions for the two on-ramp lane arrangements are analysed under scenarios with traffic volumes of 800, 1 000, 1 200, 1 400, 1 600 and 1 800 vehs/h/lane. Meanwhile, the relationships between travel time, emissions and various proportions of HGVs (2%, 4%, 6%, 8% and 10%) for both on-ramp lane arrangements are evaluated as well. We eventually present emission contour charts for the two on-ramp lane arrangements based on the possible combinations of traffic volumes and HGV percentages.

Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

AbstractIn this research, by taking advantage of dynamic fuel consumption–speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well‐recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT‐Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy‐efficient driving behaviors in the soon‐to‐come era of connected and automated vehicles.

The identification of truck-related greenhouse gas emissions and critical impact factors in an urban logistics network

Abstract Trucking activities in urban logistics networks (ULNs) are a major source of greenhouse gas (GHG) emissions. Diversified logistics demand leads to a variety of truck trip purpose, to study truck related emissions by trip purpose is necessary. This study aims to analyze the characteristics of trucking activities in a ULN by trip purpose and to investigate the relationships between trucks' trip emissions and critical influential factors from a ULN perspective, with a particularly focuses on the effects of the Euclidean distance of a trip, the vehicle curb weight, as well as the population density at a trip's origin/destination (OD). By combining a large set of empirical GPS trucking data and analytical information of ULN properties from Shenzhen, China, an imputation matrix approach is first developed to classify the truck data, based on trip purposes. Then, the trucking characteristics in terms of each trip purpose are extracted from the processed data. The GHG emissions associated with the trucking activities are estimated using a variant of the comprehensive modal emissions model (CMEM). A multivariate regression analysis is conducted to independently and quantitatively identify whether the critical factors vary in terms of a trip's purpose and to establish how such factors impact on GHG emissions. These results suggesting that designing emission management measures should take such purposes into consideration. The significant of OD Euclidean distance and the vehicle curb weight may vary by trip purpose, while the OD population density could also be regarded as an underlying determinant of most trip purposes. The results can be extended to other cities with similar classifications of trip purposes in their ULNs, thereby providing a decision-support tool for governmental policies and regulations, the locations of logistics facilities, and operational plans for trucks.

Optimizing the green open vehicle routing problem with time windows by minimizing comprehensive routing cost

With the rapid development of the sharing economy, outsourcing logistics operations to third party logistics has become an efficient way of reducing costs in freight transportation. It can be modeled as a variant of the open vehicle routing problem (OVRP), where the vehicles do not return to the depot after servicing customers. However, very few papers have studied fuel consumption in the context of third party logistics. In this work, the mathematical model of the green open vehicle routing problem with time windows (GOVRPTW) was described based on the comprehensive modal emission model (CMEM). A hybrid tabu search algorithm involving several neighborhood search strategies was designed to solve this problem. Computational experiments were performed on realistic instances based on the real road conditions of Beijing, China. The effect of empty kilometers is analyzed through comparing different cost components. Compared with closed routes, the open routes reduced the total cost by 20% with both the fuel emissions costs and the CO2 emissions cost down by nearly 30%. For the experiments with congested nodes, the fuel and emissions cost rose by 12.3%, and the driver cost even increased by 31.3%.

Fuzzy green vehicle routing problem with simultaneous pickup – delivery and time windows

In this paper, we propose a fuzzy green vehicle routing problem with simultaneous pickup and delivery and time windows (F-GVRP-SPDTW), in which the amounts of fuel consumption and emission are estimated by a comprehensive modal emission model. A mixed integer nonlinear programming model is proposed to minimize the cost of fuel consumption and emissions of vehicles. Moreover, the fuzzy approach with credibility measure is applied under conditions in which both pickup and delivery demands are uncertain. To solve the problem, we have proposed an adaptive large neighborhood search heuristic by applying new removal and insertion operators. Finally, computational experiments are conducted on a set of benchmark instances from the literature to evaluate the efficiency of the proposed solution technique. The results indicate that the proposed solution method is capable of finding high quality solutions in most of the instances.

Fuel consumption model for heavy duty diesel trucks: Model development and testing

A simple, efficient, and realistic fuel consumption model is essential to support the development of effective eco-freight strategies, including eco-routing and eco-driving systems. The majority of the existing heavy duty truck (HDT) fuel consumption models, however, would recommend that drivers accelerate at full throttle or brake at full braking to minimize their fuel consumption levels, which is obviously not realistic. To overcome this shortcoming, the paper applies the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) framework to develop a new model that is calibrated and validated using field data collected using a mobile emissions research laboratory (MERL). The results demonstrate that the model accurately predicts fuel consumption levels consistent with field observations and outperforms the comprehensive modal emissions model (CMEM) and the motor vehicle emissions simulator (MOVES) model. Using the model it is demonstrated that the optimum fuel economy cruise speed ranges between 32 and 52km/h with steeper roads and heavier trucks resulting in lower optimum cruise speeds. The results also demonstrate that the model generates accurate CO2 emission estimates that are consistent with field measurements. Finally, the model can be easily calibrated using data collected using non-engine instrumentation (e.g. Global Positioning System) and readily implemented in traffic simulation software, smartphone applications and eco-freight programs.

Estimating impacts of emission specific characteristics on vehicle operation for quantifying air pollutant emissions and energy use

Abstract This paper proposes and illustrates a methodology to predict the fraction of time motor vehicles spend in different operating conditions from readily observable variables called emission specific characteristics (ESC). ESC describe salient characteristics of vehicles, roadway geometry, the roadside environment, traffic, and driving style (aggressive, normal, and calm). The information generated by our methodology can then be entered in vehicular emission models that rely on vehicle specific power, i.e., comprehensive modal emissions model (CMEM), international vehicle emissions (IVE), or motor vehicle emission simulator (MOVES) to compute energy consumption and vehicular emissions for various air pollutants. After generating second-by-second vehicle trajectories from a calibrated micro-simulation model, the authors estimated structural equation models to examine the influence of link ESC on vehicle operation. Authors' results show that 67% of the link speed variance is explained by ESC. Overall, the roadway geometry exerts a greater influence on link speed than traffic characteristics, the roadside environment, and driving style. Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. Better understanding the impact on vehicle operation of ESC could help metropolitan planning organizations (MPOs) and regional transportation authorities predict vehicle operations and reduce the environmental footprint of motor vehicles.

The accuracy of carbon emission and fuel consumption computations in green vehicle routing

Many recent green vehicle routing papers have presented mathematical models designed to minimize fuel consumption and the environmentally damaging carbon emissions in the routing decisions related to route choice, the load on the vehicle, and the speed. A popular model for computing such impact is the Comprehensive Modal Emissions Model (CMEM) that can compute fuel consumption and carbon emissions for a vehicle with a given speed and load. However, the CMEM requires that input parameters are specified for every second of a haul. To avoid this, computations are used in which the vehicle is assumed to travel at a fixed speed. This paper investigates the extent to which such fixed speed computations in the CMEM are suitable for actual driving conditions with fluctuating speeds. In our numerical experiments, we find that the CMEM results under fixed speeds are sometimes less than half of those computed under realistic driving conditions, depending on the type of conditions. This indicates that we cannot take for granted that fixed speed computations are sufficiently accurate to be used in green routing and validation of these computations is necessary.

Convex Fuel Consumption Model for Diesel and Hybrid Buses

The concave fuel consumption model may generate unrealistic driving recommendations in a control system; for instance, the model may recommend higher cruise speed to achieve lower fuel consumption levels on steeper roads. To improve the model performance with regard to driving control, the study developed a convex second-order polynomial fuel consumption model for conventional diesel and hybrid-electric buses. The model simultaneously circumvents the bang-bang type of control that implies that drivers would have to accelerate at full throttle or brake at full braking to minimize their fuel consumption levels. Six bus series (four diesel series and two hybrid series), covering a wide range of bus properties, were modeled. The model was developed on the basis of the Virginia Tech comprehensive power fuel-based model (VT-CPFM) framework and, given a lack of readily available data, calibrated by conducting empirical measurements. The model was validated by comparing its estimates against in-field measurements and predictions from the comprehensive modal emissions model, the Motor Vehicle Emissions Simulator model, and the concave VT-CPFM model. The results demonstrate that the convex model generates estimates consistent with field measurements and the predictions of the other models and can provide realistic driving recommendations without significantly sacrificing accuracy relative to the concave model. Optimum fuel economy cruise speed ranges from 39 to 47 km/h for all tested buses on grades ranging from 0% to 8% and decreases with the increase of grade and vehicle load.

Influence of driver characteristics on emissions and fuel consumption

Abstract Fuel consumption and atmospheric pollution emissions of vehicles depend on driving conditions, the characteristics of the driver and the car. The influence of driving style on the environmental aspects of a car journey has been investigated. Driver characteristics were determined by a Driver Behaviour Questionnaire and observed acceleration and deceleration behaviour. That results in four types of drivers with similar characteristics within a type group. We measured 56 trajectories of 28 drivers using GPS devices. The measurements were done on a route of 8.4 km in an urban environment in Chengdu (PR China). From the trajectories, the emissions and fuel consumption were determined with the Comprehensive Modal Emissions Model. The results were related to the traffic control along the journey resulting in fuel consumption and emissions per stop and per second idling. There are significant differences in saturation flow, emissions and fuel consumption between different driver types. Cautious, novice drivers have the lowest emission and fuel consumption and give the lowest saturation flow and have the lowest cruise speed; experienced smooth driving drivers give a high saturation flow while keeping fuel consumption and emissions also low. Aggressive experienced drivers have a high saturation flow and fuel consumption / emissions. Therefore, microscopic traffic models that simulate emissions and fuel consumption should take the differences between driver types into account.