Road Gradient Research Articles

A Chaotic Genetic Algorithm with Variable Neighborhood Search for Solving Time-Dependent Green VRPTW with Fuzzy Demand

Aiming at the time-dependent green vehicle routing problem with fuzzy demand, this paper comprehensively considers the dispatch costs, time window penalty costs, fuel costs, and the effects of vehicle travel speed, road gradient, and vehicle load on fuel consumption, a mixed integer programming model is formulated based on pre-optimization and re-optimization strategies. The traditional vehicle routing problems are modeled based on a symmetric graph. In this paper, considering the influence of time-dependent networks on route optimization, modeling is based on an asymmetric graph, which increases the complexity of the problem. In the pre-optimization stage, a pre-optimization scheme is generated based on the credibility measure theory; in the re-optimization stage, a new re-optimization strategy was used to deal with the service failure node In order to solve this problem, we developed a chaotic genetic algorithm with variable neighborhood search, pseudo-randomness of chaos was introduced to ensure the diversity of initial solutions, and adaptive neighborhood search times strategy and inferior solution acceptance mechanism were proposed to improve the performance of the algorithm. The numerical results show that the model and algorithm we proposed are effective.

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.

Analysis of MATSim Modeling of Road Infrastructure in Cyclists’ Choices in the Case of a Hilly Relief

For too long, many refined transportation models have focused solely on private and public transportation, assuming that bicycles only require simple models, such as bird flight distance or trips on horizontal tracks at a constant speed. This paper aims to study the impact of the road characteristics, such as road gradient, type of road and pavement surface of the road, on cyclists’ behavior using dedicated modules of MATSim. For that, we compare two approaches: a standard approach which does not consider the road characteristics, and a second approach that uses MATSim bicycle extension of Ziemke et al. The two approaches are analyzed over a sub-regional area around a district, focusing on a suburban city with an undulating relief made of average-to-steep hills. The focus is on the bicycle transportation model because the catchment area has a particularly challenging altitude profile and a large variety of roads, whether in type—from residential to national highway—or in pavement surface due to the number of green areas, such as parks and forests. This area is defined as a rather large 7 × 12 km, including five suburban cities in the South of Paris, France. A synthetic population of 126,000 agents was generated at a regional scale, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, with activity-time choice, location choice and modal choice. We wanted to know how accurately a standard model of bicycle travels can be made with a 2D flat Earth assumption by comparing it to an algorithm extension that explicitly considers road characteristics in cyclists’ route choices. Our finding is that the MATSim bicycle extension model impacts mainly the long trips. Otherwise, the differences are minimal between the two models in terms of travel time and travel distance.

Polycyclic Aromatic Hydrocarbons in Araucaria heterophylla Needles in Urban Areas: Evaluation of Sources and Road Characteristics.

Araucaria heterophylla needles were collected in urban areas of the city of Quito, Ecuador, to analyze the relationship between the concentration of polycyclic aromatic hydrocarbons (PAHs) with different emission sources and road characteristics. The PAHs were analyzed by high-performance liquid chromatography (HPLC) and included naphthalene (Naph), benzo[a]anthracene (BaA), chrysene (Chry), and benzo[a]pyrene (BaP), which are related to the sources considered in this work. The results indicated that some streets with moderate and low traffic intensity had higher total concentrations of PAHs than streets with high traffic intensity, showing the importance of non-traffic related emission sources and road characteristics on PAH emissions. All the studied PAHs were associated with traffic emissions, although Naph and BaP were more associated with acceleration and braking activities, while BaA and Chry also seemed to come from restaurant emissions. The presence of gas stations was also important in the emission of PAHs. Road capacity seems to have a higher effect on pollutant emission than road gradient and urban forms. The outcomes of this study are expected to facilitate the diagnostics of the concentration of PAHs in urban areas, which contribute to the design of strategies for the mitigation of pollution by PAHs in urban environment.

Real-time deployable and robust cooperative control strategy for a platoon of connected and autonomous vehicles by factoring uncertain vehicle dynamics

In real-world driving environments, a platoon of connected and autonomous vehicles (CAVs) will be subject to many disturbances (e.g., aerodynamic drag, dynamic road gradients) that can prevent them from achieving desired control accurately. These disturbances can cause uncertainties in the vehicle dynamics, reducing platoon safety and mobility. To address this problem, this study proposes a robust cooperative control (RCC) strategy, developed as a minimax problem, to ensure the safe and efficient maneuvering of a CAV platoon in the worst-case situation due to uncertainties in the vehicle dynamics. The maximization subproblem of the minimax problem determines the inputs of the uncertainties to maximize the cost function (i.e., the platoon performance indicator), while the minimization subproblem seeks to optimize the control decisions for all following vehicles in the platoon to minimize the cost function using inputs of the uncertainties from the maximization subproblem. A novel partition-based method of feasible direction is proposed to solve the minimax problem. It is globally convergent and computationally efficient, enabling the RCC strategy to be deployed in real time. The conditions for robust stability of the CAV platoon are also analyzed. Results from numerical studies show that compared to a control strategy that ignores uncertainties in vehicle dynamics, the RCC strategy can substantially improve the platoon performance under disturbances. Hence, it can be used to enable the safe and efficient maneuvering of CAV platoons in a real-world driving environment.

Designing a Model of the Early Warning System on the Road Curvature to Prevent the Traffic Accidents

Road traffic safety in developing countries is a complex problem involving many factors such as humans, vehicles, structures, and the roads' environments. This study focuses on reducing traffic accidents for passenger busses, aiming to identify the causes and create a simple computer model to warn in areas like EWS using geometric road data. The survey was conducted at accident-prone locations and simulated the travel speed, road geometric, and traffic composition data. Results can be concluded that the rate is largely influenced by the radius of curvature, visibility, road gradient, and weather conditions. The concept of the EWS computer model was developed using web-based technology that can use to record the data instantly. The model had tested in another location to validate the field parameter and geometric road improvement.

Real-world emission factors for SUVs using on-board emission testing and geo-computation

A Portable Emissions Measurement System (PEMS) was used to measure emissions of five sports utility vehicles (SUVs) in a wide range of real-world driving conditions. The program included testing of fuel quality, coast-down and emissions in start, hot running and extended idling conditions. Geo-computation methods were used to add critical information (road gradient) to the PEMS data. Results from this study are generally in good agreement with international PEMS data. Hot running NOx emission factors are on average seven times higher than the type-approval limit for diesel SUVs, and they reach about 2100 and 400 mg/km in urban conditions for NOx and NO2, respectively. They are 7 (NOx) and 4 (NO2) times higher than current emission factors in COPERT Australia. COPERT Australia emission algorithms for CO2 are well behaved and the PEMS data suggest an update is not required. COPERT Australia emission algorithms should be revised for diesel SUVs (NOx, NO2) and petrol SUVs (CO, THC, NO2) to ensure accurate estimation of vehicle emissions at fleet level. Inclusion of logistic regression is proposed for future COPERT updates.

Impact of Road Grade on the Risk Profile of Driver Behavior

Data generated from telemetry devices, such as acceleration and speed, are used in a variety of industries to determine the risk profile of a driver. This paper considers the addition of road gradient as a contextual variable to a driving behavior model to determine specifically if the risk of a driver is influenced by different road grades. This behavioral risk is demonstrated by comparing the performance of heavy vehicle drivers with and without the addition of road grade as a variable. This is done by using accelerometer data of 48 heavy goods vehicles over an entire month, and appending elevation data from digital elevation model (DEM) data, specifically that of the Shuttle Radar Topography Mission (SRTM), to the vehicles’ GPS traces. The elevation values are used to calculate road grade values which are then categorized in five grade levels. The results show that there is a clear influence of gradient on the behavior of the drivers studied. There is also evidence that steeper sections on a road can cause a change in an individual’s performance when compared with the group.

System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability

Sag is a road section where a downhill changes into an uphill, and is a highway bottleneck. We consider a system in which all vehicles are connected, and run on a single-lane road with a sag. We propose a simple strategy for removing each traffic jam caused by the sag. Our strategy assigns a vehicle upstream of the jam front to perform the jam-absorption driving (JAD): running toward the predicted goal, and finally removing the jam. We use a microscopic car-following model possessing the traffic instability, an acceleration model against the road gradient of a sag, and an instantaneous fuel consumption model. Our main goal is to elucidate the influence of the system size (the number of vehicles in the system) on our strategy. By increasing the system size from 500 to 10000 vehicles, we have found the following results for the average total travel time per vehicle, and the average total fuel consumption per vehicle. Our strategy can reduce the former with a slightly increasing rate of reduction. Our strategy can reduce the latter with a rate of reduction which decreases and becomes roughly constant. Optimal spatiotemporal scales of JAD for the former and the latter become roughly constant, respectively. Minimizing the former and the latter simultaneously is not possible. Not only vehicular traffic flow, but also the collective dynamics of other self-driven particles (such as ships, swarm robots, and pedestrians) lined up in a single column, and passing through a bottleneck can be modeled using our strategy.

Eco-Co-Optimization strategy for connected and automated fuel cell hybrid vehicles in dynamic urban traffic settings

In urban traffic settings, the dynamic changes of the preceding and rear vehicles state, road gradient, road coefficient as well as the possible traffic congestion at signal intersections contribute to the difficulty of real-time optimal energy management for connected and automated fuel cell hybrid vehicles. To address this problem, an eco-co-optimization strategy is developed to achieve velocity planning and the promotion of energy management in this paper. First, gradient‑based model predictive control based on the fast projection gradient method is employed to obtain the real-time safe and optimal velocity according to the future information of driving conditions and signal lights state. Meanwhile, to achieve desirable velocity tracking and preferable power splitting, an energy management strategy based on model predictive control is designed, where a multi-objective performance function is leveraged to minimize the total cost, hydrogen consumption and extend battery service life. Additionally, an energy recovery strategy based on fuzzy logic control is executed to improve energy efficiency. The simulation results reveal that the developed strategy can obtain a real-time safe and optimal velocity sequence and enable the CAFCHV efficiently passes through the continuous signalized intersections. Simultaneously, compared with adaptive cruise control, the hydrogen consumption, SOC, global cost and battery degradation are reduced by 3.13%, 4.76%, 3.37%, and 14.48% in the planning state, respectively.

Sustainable vehicle routing problem on real-time roads: the restrictive inheritance-based heuristic algorithm

• This article aims to present an efficient vehicle routing algorithm for a real-time routing solution. • The recursive routing process reflects the vehicle's energy consumption mechanism and the effect of vehicle speeds on CO2 emissions. • The restrictive inheritance strategy and loop termination condition prevent high memory usage, redundancy, and infinite negative loop, leading to high computational efficiency and a Pareto-optimal solution. • The suggested method contributes to reducing energy consumption and CO2 emission and shows robustness in reflecting ever-changing road conditions. Road transportation is a substantial contributor to urban greenhouse gas (GHG) emissions. This article focuses on the energy-efficient vehicle routing problem, given the relationship between a vehicle's energy consumption and CO2 emission. Extensive studies have established the energy dissipation model considering road gradients, traffic flow, and road congestion, and the route searching process entails algorithm application. However, despite the efforts to overcome the trade-off between computational efficiency and solution quality, the probabilistic and randomized-based routing algorithms would be trapped into the local optima or compromise computational efficiency. Hence, this article aims to propose an efficient vehicle routing algorithm in the sustainable transportation domain. The method derives a Pareto-optimal solution in an acceptable execution time; furthermore, the embedded cost function includes energy dissipation and recuperation mechanism in hilly regions. For a real-world application, this article refers to road gradients and a real-time traffic API. The findings showed the amelioration of CO2 emissions and computational loads, thus corroborating the pragmatic aspect. The eco-oriented route decision connotes a practical foundation of sustainability and contributes to future studies in this promising field.

A green dynamic TSP with detailed road gradient dependent fuel consumption estimation

The rapid transition to the digitalization of firms directs companies to speed up their decision-making processes and make technology-based decisions. The increasing environmental concerns and fossil-fuel scarcity force companies to employ new logistical aims, such as reducing fuel consumption and greenhouse gas emission in addition to the traditional cost minimization and profit maximization objectives. Traditional assumptions in fuel consumption calculation have been left not only to reach the objectives of the company and make consistent delivery plans but also to increase the solution quality of real-life problems. From this point of view, this study contributes to the related literature by developing a decision support model for a Travelling Salesman Problem that considers dynamic customer requests and realistic road gradients of the entire road network while calculating fuel consumption from transportation operations. The applicability of the developed model has been shown with a real-life case study in which laboratory samples have been collected and transferred from local family clinics to a central laboratory. Several objective functions are employed to demonstrate the benefits of considering realistic road gradients. The results of the numerical analyses on static and dynamic instances illustrate the benefits of respecting realistic road gradients in fuel consumption and addressing dynamic requests during delivery operations.

Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach.

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

Effects of road slope on emission characteristics for a heavy-duty diesel vehicle based on engine-in-the-loop methodology

A dump truck with a maximum designed total mass of 24500 kg was selected to measure the emission characteristics of pollutants and carbon dioxide (CO2) under fully-loaded and unloaded conditions at the same simulated road with different road slope, same driver and vehicle model by using the engine-inthe- loop methodology. The results show that driving at gradient road will result in an increase in power and total emissions. The brake specific emissions of gaseous pollutant and CO2 are lower at gradient road, while the brake emission of particle number are higher at gradient road. Road slope affects exhaust temperature so as to affect nitrogen oxide (NOx) emissions. Under unloaded conditions, long-slope conditions are more likely to cause an increase in NOx emissions.

Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short sighted traffic and road geometry information can optimize their speed profile to increase energy efficiency, depending on the powertrain types. In particular, the developed speed planner reducing the battery output power through the energy-efficiency improvement systems in electrified vehicles. Consequently, the CAV that is aware of the existence of the upcoming road gradient increases the speed on the uphill, and decreases the speed on the downhill to minimize the battery output power, which is different from the natural behaviors of human-driven vehicles on sloped roads. To consider the constraints, the model predictive control-based speed planner is developed, and its effectiveness is verified under various driving conditions. Simulation results show that our approach significantly outperforms the alternative speed profiles in terms of battery energy-saving, achieving about 27.21% of the energy efficiency improvement.

Heavy Road Vehicle Platoon Control Considering Brake Fade With Adaptive Mass and Road Gradient Estimation

Heavy commercial road vehicle (HCRV) platoons are viable logistic solutions to freight movement. During long haul platoon operation, it is common to encounter roads of different gradients. This paper investigates the effect of brake fade phenomenon, which happens due to the continuous application of brake during downgrade operation on the string stability of HCRV platoons. A brake actuator model incorporating temperature effects during braking and characterizing brake fade has been used. A Sliding Mode Control (SMC) based string stable controller, which compensates for brake fade, has been designed. Since the brake fade factor and hence platoon stability directly depend upon the road gradient and vehicle mass, which are not directly measurable quantities, an algorithm that adaptively estimates the same has been integrated with the controller design. The algorithm could estimate the mass and gradient values with less than 2% mean absolute percentage error. The stability of the proposed fade compensated controller has been analyzed and its efficacy has been tested for various road conditions and for homogeneous and heterogeneous (overloaded cases) platoon operations. The proposed approach was seen to ensure string stability for all the considered test scenarios.

Developing Electric Bus Driving Cycles with Significant Road Gradient Changes: A Case Study in Hong Kong

Developing Electric Bus Driving Cycles with Significant Road Gradient Changes: A Case Study in Hong Kong

The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Modeling research of city bus fuel consumption for different driving cycles

Simulation studies can be used to determine the fuel consumption and carbon dioxide emissions of city buses. The operating conditions of such vehicles are characterised by a very high variability of vehicle speed due to the large number of stops along the route of the bus. During vehicle testing, driving cycles are used to replicate the real-world conditions and to achieve repeatable test conditions. Such a driving cycle is a profile of speed represented as a function of time or as a function of distance. The speed profile over time can be an advantageous determinant, based on laboratory tests, for estimating fuel consumption and pollutant emissions of city buses. The research subject of this paper was the simulation of bus driving under simulated urban traffic conditions, carried out by means of the VECTO software. VECTO is a tool designed to perform the calculations of fuel consumption and carbon dioxide emissions of vehicles. It enables to model the powertrain of trucks and buses and to carry out simulations on various routes defined by driving cycles. The test object was a mega class bus, equipped with a 225 kW engine. The bus has three axles, including the rear drive axle. The scope of research included four cycles: urban, interurban, urbandelivery and interurban. Each of these was analysed in terms of speed and road gradient. The aim of this work was to perform a simulation study of the effect of the vehicle traffic conditions on the amount of CO2 emitted and fuel consumption. The obtained results were analysed.

A Methodology to Investigate Powered Two-Wheeler Rider’s Comfort over Road Sections with Skew Superelevation

The proper surface water drainage not only affects vehicle movement dynamics but also increases the likelihood of an accident since inadequate drainage is associated with potential hydroplaning and splash and spray driving conditions. Nine solutions have been proposed to address hydroplaning in sections with inadequate drainage e.g. augmented superelevation and longitudinal slope, reduction of runoff length, and skew superelevation. The latter has been extensively implemented in highways recently, enhancing the safety level in the applied road segments regarding the effective drainage of the rainwater. However, the concept of the skew superelevation has raised concerns regarding the level of driver’s comfort when traveling over skew superelevation sections particularly with high speeds. These concerns were alleviated through the concept of the round-up skew superelevation which reduces both the lateral and the vertical acceleration imposed on the drivers and hence, improves comfort and traffic safety. The present study investigates the behaviour of power two-wheeler riders since they are susceptible to any changes on the pavement surface and therefore a comparison between the traditional superelevation practice and the skew superelevation concept is of paramount importance. The methodology is based on the utilization of sophisticated software to design the model of the road for several values of longitudinal slopes. Based on the values of the slopes and the use of mathematical equations, the accelerations imposed on the wheel of the motorcycle were calculated. Since the final aim of the study is the influence of the skew superelevation to the rider, it was deemed necessary to convey the calculated accelerations from the wheel to the rider. That was accomplished by implementing the quarter car suspension model adjusted to the features of two-wheeler vehicles. Finally, the accelerations derived from this process evaluated according to specific thresholds based on the literature which correspond to certain levels of comfort. The most important conclusion drawn is that the comfort of the riders is not dependent to a great extent on the form of the road gradient because the vertical acceleration imposed on the riders took similar values regardless of the value of the longitudinal slope.