This document proposes integrating a macroscopic traffic flow model (METANET) with a microscopic dynamic emission and fuel consumption model (VT-Micro) to enable model-based dynamic traffic control. The control aims to reduce emissions, fuel consumption, and travel time using dynamic speed limit control. Simulation results indicate this approach can balance the conflicting objectives of reducing environmental impacts while improving traffic flow.
1) The document tracks changes over 20 years to the traffic service quality in downtown Fort Worth using the Two-Fluid model. It calibrates the model for 1990 and 2012 to compare the Two-Fluid parameters (Tm, n) over time.
2) Key network attributes like block length, number of lanes, and signal timing were also compared between 1990 and 2012. Changes to these attributes help explain changes to the Two-Fluid parameters.
3) The results show certain attributes like the fraction of one-way streets and signal density are major factors in determining traffic service quality as represented by the Two-Fluid parameters. Comparing the 1990 and 2012 calibrations indicates how the downtown network
6. Assessment of impact of speed limit reduction and traffic signalDr, Madhava Madireddy
1. The study examines the effects of reducing speed limits from 50 km/h to 30 km/h in a residential area of Antwerp, Belgium and implementing coordinated traffic signals along a major road using microscopic traffic simulation and an emissions model.
2. Reducing speed limits in the residential area was found to reduce CO2 and NOx emissions by about 25%. Implementing coordinated traffic signals was found to reduce emissions by about 10%.
3. The integrated model combines microscopic traffic simulation to model vehicle behavior with an emissions model to estimate pollutants based on vehicle speeds and accelerations from the simulation. This allows assessment of traffic management measures on local air pollution.
6. Assessment of impact of speed limit reduction and traffic signalDr, Madhava Madireddy
Reducing speed limits from 50 km/h to 30 km/h in a residential area of Antwerp and coordinating traffic lights along a major road were found to reduce vehicle emissions:
- Speed limit reduction led to around 25% lower CO2 and NOX emissions from smoother traffic flow.
- Coordinating traffic lights to create a "green wave" reduced emissions by about 10% along the arterial road.
- An integrated traffic simulation and emission model was used to assess the environmental impacts of these traffic management measures on a neighborhood in Antwerp, Belgium.
The document reviews optimal speed car-following models. It discusses macroscopic and microscopic traffic models, with a focus on microscopic optimal speed models. The optimal speed model defines a desired speed that is a function of headway distance and helps model traffic flow situations. The document also proposes enhancements to the optimal speed model, including a weighting factor dependent on relative speed and spacing to improve braking reactivity. In conclusion, it evaluates optimal speed models and their ability to realistically model traffic dynamics while avoiding collisions.
This document summarizes research on integrating traffic and emission models to simulate the impacts of traffic on emissions. It describes:
1) Developing an approach to combine traffic simulation and emission models in a distributed way.
2) Proposing a method to calibrate microscopic emission models using aggregate emission measures.
3) Applying the integrated models to evaluate how different traffic demands and signal controls impact emissions.
This study used micro-simulation traffic modeling (Paramics) coupled with an emissions prediction model (Versit+) to examine the impact of two traffic management schemes on vehicle emissions in Antwerp, Belgium. Reducing the network speed limit was found to decrease CO2 emissions by 23-41% and NOx and PM by 27-45%, while removing green wave traffic signal coordination increased emissions by around 10%. The models provided an effective way to evaluate potential traffic and air quality impacts of management strategies at a network level.
A Model For The Dynamic System Optimum Traffic Assignment ProblemSean Flores
This document describes a model for the dynamic system optimum traffic assignment problem. The model seeks to reduce total travel delays in a road network by routing drivers along routes with the lowest marginal delay. It is applicable to networks with many origin-destination pairs and bottlenecks. The model discretizes time and simulates vehicle movement to determine link flows and queues while respecting the first-in, first-out queue discipline at bottlenecks. Numerical results are provided for two test networks to demonstrate the model.
A Gradient Projection Algorithm For Side-Constrained Traffic AssignmentLori Moore
This document presents a new solution procedure for the side-constrained traffic assignment problem (SCTAP) based on combining the inner penalty function method with a path-based gradient projection algorithm. The SCTAP incorporates explicit capacity constraints into the standard traffic assignment framework to model bottlenecks and queues. The new algorithm finds solutions at both the path and link level while ensuring all intermediate solutions satisfy the side constraints. The procedure only checks constraints on links belonging to the shortest path, making it efficient.
1) The document tracks changes over 20 years to the traffic service quality in downtown Fort Worth using the Two-Fluid model. It calibrates the model for 1990 and 2012 to compare the Two-Fluid parameters (Tm, n) over time.
2) Key network attributes like block length, number of lanes, and signal timing were also compared between 1990 and 2012. Changes to these attributes help explain changes to the Two-Fluid parameters.
3) The results show certain attributes like the fraction of one-way streets and signal density are major factors in determining traffic service quality as represented by the Two-Fluid parameters. Comparing the 1990 and 2012 calibrations indicates how the downtown network
6. Assessment of impact of speed limit reduction and traffic signalDr, Madhava Madireddy
1. The study examines the effects of reducing speed limits from 50 km/h to 30 km/h in a residential area of Antwerp, Belgium and implementing coordinated traffic signals along a major road using microscopic traffic simulation and an emissions model.
2. Reducing speed limits in the residential area was found to reduce CO2 and NOx emissions by about 25%. Implementing coordinated traffic signals was found to reduce emissions by about 10%.
3. The integrated model combines microscopic traffic simulation to model vehicle behavior with an emissions model to estimate pollutants based on vehicle speeds and accelerations from the simulation. This allows assessment of traffic management measures on local air pollution.
6. Assessment of impact of speed limit reduction and traffic signalDr, Madhava Madireddy
Reducing speed limits from 50 km/h to 30 km/h in a residential area of Antwerp and coordinating traffic lights along a major road were found to reduce vehicle emissions:
- Speed limit reduction led to around 25% lower CO2 and NOX emissions from smoother traffic flow.
- Coordinating traffic lights to create a "green wave" reduced emissions by about 10% along the arterial road.
- An integrated traffic simulation and emission model was used to assess the environmental impacts of these traffic management measures on a neighborhood in Antwerp, Belgium.
The document reviews optimal speed car-following models. It discusses macroscopic and microscopic traffic models, with a focus on microscopic optimal speed models. The optimal speed model defines a desired speed that is a function of headway distance and helps model traffic flow situations. The document also proposes enhancements to the optimal speed model, including a weighting factor dependent on relative speed and spacing to improve braking reactivity. In conclusion, it evaluates optimal speed models and their ability to realistically model traffic dynamics while avoiding collisions.
This document summarizes research on integrating traffic and emission models to simulate the impacts of traffic on emissions. It describes:
1) Developing an approach to combine traffic simulation and emission models in a distributed way.
2) Proposing a method to calibrate microscopic emission models using aggregate emission measures.
3) Applying the integrated models to evaluate how different traffic demands and signal controls impact emissions.
This study used micro-simulation traffic modeling (Paramics) coupled with an emissions prediction model (Versit+) to examine the impact of two traffic management schemes on vehicle emissions in Antwerp, Belgium. Reducing the network speed limit was found to decrease CO2 emissions by 23-41% and NOx and PM by 27-45%, while removing green wave traffic signal coordination increased emissions by around 10%. The models provided an effective way to evaluate potential traffic and air quality impacts of management strategies at a network level.
A Model For The Dynamic System Optimum Traffic Assignment ProblemSean Flores
This document describes a model for the dynamic system optimum traffic assignment problem. The model seeks to reduce total travel delays in a road network by routing drivers along routes with the lowest marginal delay. It is applicable to networks with many origin-destination pairs and bottlenecks. The model discretizes time and simulates vehicle movement to determine link flows and queues while respecting the first-in, first-out queue discipline at bottlenecks. Numerical results are provided for two test networks to demonstrate the model.
A Gradient Projection Algorithm For Side-Constrained Traffic AssignmentLori Moore
This document presents a new solution procedure for the side-constrained traffic assignment problem (SCTAP) based on combining the inner penalty function method with a path-based gradient projection algorithm. The SCTAP incorporates explicit capacity constraints into the standard traffic assignment framework to model bottlenecks and queues. The new algorithm finds solutions at both the path and link level while ensuring all intermediate solutions satisfy the side constraints. The procedure only checks constraints on links belonging to the shortest path, making it efficient.
A Robust Algorithm To Solve The Signal Setting Problem Considering Different ...Joshua Gorinson
This paper presents an algorithm to optimize traffic signal settings that considers the interaction between signal timing and traffic assignment. The algorithm iteratively updates signal timings based on fixed traffic flows, and then updates traffic flows based on the new signal timings, with the goal of minimizing total delay. Two different traffic assignment approaches are considered: user equilibrium assignment and a platoon simulation model. The proposed algorithm is compared to other optimization methods on a real traffic network, demonstrating its robustness in handling different assignment approaches.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
An Alternative Genetic Algorithm to Optimize OSPF WeightsEM Legacy
This document presents a genetic algorithm approach to optimize OSPF routing weights. The algorithm aims to minimize maximum and average link utilization directly, unlike previous methods that minimized a convex cost function. It can find weights for both single and multiple shortest path routing. The genetic algorithm uses a chromosome encoding of link weights. It selects parents using rank selection and produces offspring using a reproduction strategy combining crossover and mutation. Additional mutation is applied to offspring not meeting certain conditions. The algorithm is tested on small networks and compared to MIP-based methods, showing results for larger networks with increasing traffic demands.
This document discusses the application of a lattice-Boltzmann computational fluid dynamics (CFD) code for automobile and motorcycle aerodynamics simulations at BMW. It begins by explaining how CFD is used alongside wind tunnel testing to analyze vehicle designs earlier in the development process. It then provides details on the lattice-Boltzmann method, including describing the mesoscopic approach, kinetic theory, and the concepts of the lattice model. The document explains how macroscopic fluid properties emerge from microscopic particle distributions and collisions in the model.
A novel k-means powered algorithm for an efficient clustering in vehicular ad...IJECEIAES
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Speed profile optimization of an electrified train in cat linh-ha dong metro ...IJECEIAES
An urban railway is a complex technical system that consumes large amounts of energy, but this means of transportation still has been obtained more and more popularity in densely populated cities because of its features of highcapacity transportation capability, high speed, security, punctuality, lower emission, reduction of traffic congestion. The improved energy consumption and environment are two of the main objectives for future transportation. Electrified trains can meet these objectives by the recuperation and reuse of regenerative braking energy and by the energy - efficient operation. Two methods are to enhance energy efficiency: one is to improve technology (e.g., using energy storage system, reversible or active substations to recuperate regenerative braking energy, replacing traction electric motors by energy-efficient traction system as permanent magnet electrical motors; train's mass reduction by lightweight material mass...); the other is to improve operational procedures (e.g. energy efficient driving including: ecodriving; speed profile optimization; Driving Advice System (DAS); Automatic Train Operation (ATO); traffic management optimization...). Among a lot of above solutions for saving energy, which one is suitable for current conditions of metro lines in Vietnam. The paper proposes the optimization method based on Pontryagin's Maximum Principle (PMP) to find the optimal speed profile for electrified train of Cat Linh-Ha Dong metro line, Vietnam in an effort to minimize the train operation energy consumption.
Validation of Experimental and Numerical Techniques for Flow Analysis over an...IJERA Editor
The impact of improvement in vehicle aerodynamics mainly reflects in lower fuel consumption and lower carbon dioxide emissions into the atmosphere. The governments of many countries support continuous aerodynamics’ improvement programs as a way of mitigating the energy crisis and atmospheric pollution. This work has the main goal to validate experimental and numerical techniques for application in road vehicles. The experimental results were obtained through the analysis of the flow around a standard body with simple geometry called Ahmed Body, using hot wire anemometry from experiments in wind tunnel. It was also proposed a computational validation using a commercial software (Star CCM +) to further analyze the flow and to corroborate the experimental results. Both results were compared and allowed characterizing the flow around the vehicle. The results obtained analyzing the Ahmed Body aimed further application on aerodynamics of heavyduty vehicles, which is an ongoing research being developed at the Experimental Aerodynamics Research Center – CPAERO, in Brazil.
Traffic assignment models are used to estimate traffic flows on a transportation network based on origin-destination flows and the network's topology, link characteristics, and performance functions. Traffic is assigned to paths between origin-destination pairs based on travel time or impedance. Traffic assignment is a key part of travel demand forecasting and is used to predict future network flows and performance under different planning scenarios. Common traffic assignment methods include all-or-nothing assignment, user equilibrium assignment, and system optimum assignment.
Optimizing Data Plane Resources for Multipath FlowsIRJET Journal
This document discusses optimizing data plane resources for multipath flows. It introduces the concepts of routing with minimum overhead (RMO) and decomposition with minimum overhead (DMO) to minimize forwarding costs when splitting traffic flows across multiple paths.
The key ideas are:
1) Dividing a traffic flow across multiple paths improves bandwidth utilization but incurs higher forwarding costs due to additional network resources used.
2) RMO and DMO problems are defined to minimize these forwarding costs by reducing the number of paths or nodes used. Efficient algorithms are presented to solve the problems.
3) Simulation results show that algorithms which prefer smaller paths generally perform better at reducing the number of nodes traveled, though they may increase
Newton-raphson method to solve systems of non-linear equations in VANET perfo...journalBEEI
This document discusses using the Newton-Raphson method to solve systems of non-linear equations to optimize VANET performance. It selects three MAC protocol parameters - transmission power, bitrate, and contention window - as variables. Simulations are run with different values of these parameters. The results for energy consumption, packet delivery ratio, and delay are used to derive three non-linear equations. The Newton-Raphson method is then applied to find the coefficients a, b, and c to optimize the objective function of minimizing energy*delay/PDR. Tables 4-6 show the objective function results for different parameter configurations.
REVIEW OF OPTIMAL SPEED TRAFFIC FLOW MODELBashir Abdu
The document discusses optimal speed traffic flow models, which aim to more realistically model driver behavior compared to previous car following models. It describes several generations of optimal speed models that have been developed over time to address limitations. The models incorporate factors like desired optimal speed that is independent of the leader's speed, safe distance between vehicles, and asymmetric acceleration and deceleration behavior. The latest models presented in the document aim to produce realistic traffic dynamics like spontaneous jam formation and recover better delay time and kinematic wave properties.
A Review on Road Traffic Models for Intelligent Transportation System (ITS)IJSRD
Traffic flow models seek to describe the interaction of vehicles with their drivers and the infrastructure. Almost all the models directly or indirectly characterize the relationship among the traffic variables: the position, the speed, the flow, and the density of vehicles. These relationships can be based on either the behavior of individual vehicles in a traffic network in relation to the dynamics of other vehicles, the overall characteristics of the flow of vehicles in a traffic network, or a combination of the behavior of individual vehicles in a traffic network and the overall traffic flow characteristics. This paper describes the different models for automatic Traffic control system.
A novel method for determining fixed running time in operating electric train...IJECEIAES
This document proposes a novel method for determining fixed running time when electric trains operate using an optimal speed profile to reduce energy consumption. The method uses numerical-analytical calculations to determine accelerating, coasting, and braking times based on holding and braking speeds from the optimal profile, with the constraint that the running time equals the scheduled time. Simulation results show energy savings of up to 8.7% for a fixed running time and 11.96% for a running time one second longer.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Model predictive-fuzzy-control-of-air-ratio-for-automotive-enginespace130557
Automotive engine air-ratio plays an important role of
emissions and fuel consumption reduction while maintains
satisfactory engine power among all of the engine control variables.
Internet Traffic Engineering for Partially Uncertain DemandsEM Legacy
This document summarizes an academic paper about modeling and solving the problem of routing network traffic with both fixed and uncertain demands. It considers routing strategies that use link metrics to determine shortest paths in IP networks. The proposed model represents uncertain demands using a hose model that specifies maximum aggregate traffic amounts. It calculates link loads by combining loads from fixed demands, maximum outbound uncertain demands, and maximum inbound uncertain demands. Computational results are presented for both multiple and unique shortest path routing strategies to demonstrate the benefits of considering both fixed and uncertain traffic demands.
A Dynamic Vehicular Traffic Control Using Ant Colony And Traffic Light Optimi...Kristen Carter
This document proposes a dynamic vehicular traffic control system using ant colony optimization and optimized traffic lights. It aims to reduce traffic congestion in urban areas. The system divides the road network into cells and uses artificial ants to guide vehicles along the least congested paths within each cell. It also proposes a new method for optimizing traffic light timing at intersections based on real-time vehicle count data collected from vehicles and traffic lights using VANET technology. Simulation results using the DIVERT simulator show that the proposed traffic light optimization method improves average vehicle speed and reduces waiting times and stopped vehicles at intersections compared to a system with usual fixed-duration traffic lights.
A MULTIPURPOSE MATRICES METHODOLOGY FOR TRANSMISSION USAGE, LOSS AND RELIABIL...ecij
In the era of power system restructuring there is a need of simplified method which provides a complete allocation of usage, transmission losses and transmission reliability margin. In this paper, authors presents a combined multipurpose matrices methodology for Transmission usage, transmission loss and transmission reliability margin allocation. Proposed methodology is simple and easy to implement on large power system. A modified Kirchhoff matrix is used for allocation purpose. A sample 6 bus system is used to demonstrate the feasibility of proposed methodology.
This article describes a method for developing a high-resolution vehicular emission inventory by integrating an emission model with a traffic model. The method uses portable emissions monitoring system data to categorize vehicle driving conditions into discrete speed and vehicle-specific power bins. Average emission rates and time spent in each bin are used to calculate total trip emissions and emission factors under specific average link speeds. The model was validated and found to predict emissions within 20% of measured data. This approach allows integration of emission and traffic models to better evaluate traffic emission reduction measures.
This document presents a bottom-up methodology to estimate vehicle emissions in Beijing, China at the grid level. The methodology combines vehicle emission factors based on speed from the MOBILE5B-China model with vehicle activity data from a travel demand model. Applying this approach, total emissions of HC, CO and NOx in Beijing's urban area in 2005 were estimated to be 13.33×104, 100.02×104 and 7.55×104 tons respectively. The grid-based estimates provide a more accurate spatial distribution of emissions compared to typical macro-scale approaches used in China.
A Robust Algorithm To Solve The Signal Setting Problem Considering Different ...Joshua Gorinson
This paper presents an algorithm to optimize traffic signal settings that considers the interaction between signal timing and traffic assignment. The algorithm iteratively updates signal timings based on fixed traffic flows, and then updates traffic flows based on the new signal timings, with the goal of minimizing total delay. Two different traffic assignment approaches are considered: user equilibrium assignment and a platoon simulation model. The proposed algorithm is compared to other optimization methods on a real traffic network, demonstrating its robustness in handling different assignment approaches.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
An Alternative Genetic Algorithm to Optimize OSPF WeightsEM Legacy
This document presents a genetic algorithm approach to optimize OSPF routing weights. The algorithm aims to minimize maximum and average link utilization directly, unlike previous methods that minimized a convex cost function. It can find weights for both single and multiple shortest path routing. The genetic algorithm uses a chromosome encoding of link weights. It selects parents using rank selection and produces offspring using a reproduction strategy combining crossover and mutation. Additional mutation is applied to offspring not meeting certain conditions. The algorithm is tested on small networks and compared to MIP-based methods, showing results for larger networks with increasing traffic demands.
This document discusses the application of a lattice-Boltzmann computational fluid dynamics (CFD) code for automobile and motorcycle aerodynamics simulations at BMW. It begins by explaining how CFD is used alongside wind tunnel testing to analyze vehicle designs earlier in the development process. It then provides details on the lattice-Boltzmann method, including describing the mesoscopic approach, kinetic theory, and the concepts of the lattice model. The document explains how macroscopic fluid properties emerge from microscopic particle distributions and collisions in the model.
A novel k-means powered algorithm for an efficient clustering in vehicular ad...IJECEIAES
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
Speed profile optimization of an electrified train in cat linh-ha dong metro ...IJECEIAES
An urban railway is a complex technical system that consumes large amounts of energy, but this means of transportation still has been obtained more and more popularity in densely populated cities because of its features of highcapacity transportation capability, high speed, security, punctuality, lower emission, reduction of traffic congestion. The improved energy consumption and environment are two of the main objectives for future transportation. Electrified trains can meet these objectives by the recuperation and reuse of regenerative braking energy and by the energy - efficient operation. Two methods are to enhance energy efficiency: one is to improve technology (e.g., using energy storage system, reversible or active substations to recuperate regenerative braking energy, replacing traction electric motors by energy-efficient traction system as permanent magnet electrical motors; train's mass reduction by lightweight material mass...); the other is to improve operational procedures (e.g. energy efficient driving including: ecodriving; speed profile optimization; Driving Advice System (DAS); Automatic Train Operation (ATO); traffic management optimization...). Among a lot of above solutions for saving energy, which one is suitable for current conditions of metro lines in Vietnam. The paper proposes the optimization method based on Pontryagin's Maximum Principle (PMP) to find the optimal speed profile for electrified train of Cat Linh-Ha Dong metro line, Vietnam in an effort to minimize the train operation energy consumption.
Validation of Experimental and Numerical Techniques for Flow Analysis over an...IJERA Editor
The impact of improvement in vehicle aerodynamics mainly reflects in lower fuel consumption and lower carbon dioxide emissions into the atmosphere. The governments of many countries support continuous aerodynamics’ improvement programs as a way of mitigating the energy crisis and atmospheric pollution. This work has the main goal to validate experimental and numerical techniques for application in road vehicles. The experimental results were obtained through the analysis of the flow around a standard body with simple geometry called Ahmed Body, using hot wire anemometry from experiments in wind tunnel. It was also proposed a computational validation using a commercial software (Star CCM +) to further analyze the flow and to corroborate the experimental results. Both results were compared and allowed characterizing the flow around the vehicle. The results obtained analyzing the Ahmed Body aimed further application on aerodynamics of heavyduty vehicles, which is an ongoing research being developed at the Experimental Aerodynamics Research Center – CPAERO, in Brazil.
Traffic assignment models are used to estimate traffic flows on a transportation network based on origin-destination flows and the network's topology, link characteristics, and performance functions. Traffic is assigned to paths between origin-destination pairs based on travel time or impedance. Traffic assignment is a key part of travel demand forecasting and is used to predict future network flows and performance under different planning scenarios. Common traffic assignment methods include all-or-nothing assignment, user equilibrium assignment, and system optimum assignment.
Optimizing Data Plane Resources for Multipath FlowsIRJET Journal
This document discusses optimizing data plane resources for multipath flows. It introduces the concepts of routing with minimum overhead (RMO) and decomposition with minimum overhead (DMO) to minimize forwarding costs when splitting traffic flows across multiple paths.
The key ideas are:
1) Dividing a traffic flow across multiple paths improves bandwidth utilization but incurs higher forwarding costs due to additional network resources used.
2) RMO and DMO problems are defined to minimize these forwarding costs by reducing the number of paths or nodes used. Efficient algorithms are presented to solve the problems.
3) Simulation results show that algorithms which prefer smaller paths generally perform better at reducing the number of nodes traveled, though they may increase
Newton-raphson method to solve systems of non-linear equations in VANET perfo...journalBEEI
This document discusses using the Newton-Raphson method to solve systems of non-linear equations to optimize VANET performance. It selects three MAC protocol parameters - transmission power, bitrate, and contention window - as variables. Simulations are run with different values of these parameters. The results for energy consumption, packet delivery ratio, and delay are used to derive three non-linear equations. The Newton-Raphson method is then applied to find the coefficients a, b, and c to optimize the objective function of minimizing energy*delay/PDR. Tables 4-6 show the objective function results for different parameter configurations.
REVIEW OF OPTIMAL SPEED TRAFFIC FLOW MODELBashir Abdu
The document discusses optimal speed traffic flow models, which aim to more realistically model driver behavior compared to previous car following models. It describes several generations of optimal speed models that have been developed over time to address limitations. The models incorporate factors like desired optimal speed that is independent of the leader's speed, safe distance between vehicles, and asymmetric acceleration and deceleration behavior. The latest models presented in the document aim to produce realistic traffic dynamics like spontaneous jam formation and recover better delay time and kinematic wave properties.
A Review on Road Traffic Models for Intelligent Transportation System (ITS)IJSRD
Traffic flow models seek to describe the interaction of vehicles with their drivers and the infrastructure. Almost all the models directly or indirectly characterize the relationship among the traffic variables: the position, the speed, the flow, and the density of vehicles. These relationships can be based on either the behavior of individual vehicles in a traffic network in relation to the dynamics of other vehicles, the overall characteristics of the flow of vehicles in a traffic network, or a combination of the behavior of individual vehicles in a traffic network and the overall traffic flow characteristics. This paper describes the different models for automatic Traffic control system.
A novel method for determining fixed running time in operating electric train...IJECEIAES
This document proposes a novel method for determining fixed running time when electric trains operate using an optimal speed profile to reduce energy consumption. The method uses numerical-analytical calculations to determine accelerating, coasting, and braking times based on holding and braking speeds from the optimal profile, with the constraint that the running time equals the scheduled time. Simulation results show energy savings of up to 8.7% for a fixed running time and 11.96% for a running time one second longer.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Model predictive-fuzzy-control-of-air-ratio-for-automotive-enginespace130557
Automotive engine air-ratio plays an important role of
emissions and fuel consumption reduction while maintains
satisfactory engine power among all of the engine control variables.
Internet Traffic Engineering for Partially Uncertain DemandsEM Legacy
This document summarizes an academic paper about modeling and solving the problem of routing network traffic with both fixed and uncertain demands. It considers routing strategies that use link metrics to determine shortest paths in IP networks. The proposed model represents uncertain demands using a hose model that specifies maximum aggregate traffic amounts. It calculates link loads by combining loads from fixed demands, maximum outbound uncertain demands, and maximum inbound uncertain demands. Computational results are presented for both multiple and unique shortest path routing strategies to demonstrate the benefits of considering both fixed and uncertain traffic demands.
A Dynamic Vehicular Traffic Control Using Ant Colony And Traffic Light Optimi...Kristen Carter
This document proposes a dynamic vehicular traffic control system using ant colony optimization and optimized traffic lights. It aims to reduce traffic congestion in urban areas. The system divides the road network into cells and uses artificial ants to guide vehicles along the least congested paths within each cell. It also proposes a new method for optimizing traffic light timing at intersections based on real-time vehicle count data collected from vehicles and traffic lights using VANET technology. Simulation results using the DIVERT simulator show that the proposed traffic light optimization method improves average vehicle speed and reduces waiting times and stopped vehicles at intersections compared to a system with usual fixed-duration traffic lights.
A MULTIPURPOSE MATRICES METHODOLOGY FOR TRANSMISSION USAGE, LOSS AND RELIABIL...ecij
In the era of power system restructuring there is a need of simplified method which provides a complete allocation of usage, transmission losses and transmission reliability margin. In this paper, authors presents a combined multipurpose matrices methodology for Transmission usage, transmission loss and transmission reliability margin allocation. Proposed methodology is simple and easy to implement on large power system. A modified Kirchhoff matrix is used for allocation purpose. A sample 6 bus system is used to demonstrate the feasibility of proposed methodology.
This article describes a method for developing a high-resolution vehicular emission inventory by integrating an emission model with a traffic model. The method uses portable emissions monitoring system data to categorize vehicle driving conditions into discrete speed and vehicle-specific power bins. Average emission rates and time spent in each bin are used to calculate total trip emissions and emission factors under specific average link speeds. The model was validated and found to predict emissions within 20% of measured data. This approach allows integration of emission and traffic models to better evaluate traffic emission reduction measures.
This document presents a bottom-up methodology to estimate vehicle emissions in Beijing, China at the grid level. The methodology combines vehicle emission factors based on speed from the MOBILE5B-China model with vehicle activity data from a travel demand model. Applying this approach, total emissions of HC, CO and NOx in Beijing's urban area in 2005 were estimated to be 13.33×104, 100.02×104 and 7.55×104 tons respectively. The grid-based estimates provide a more accurate spatial distribution of emissions compared to typical macro-scale approaches used in China.
This document discusses opportunities to reduce air pollution and greenhouse gas emissions from the road transportation sector in Durban, South Africa through a co-benefits approach. It develops an emissions inventory for the road transport sector in Durban and explores intervention opportunities that could simultaneously reduce air pollution and greenhouse gas emissions. The greatest potential for co-benefits was found to come from reducing vehicle kilometres travelled by privately-owned vehicles and improving the efficiency of road freight transport.
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2. 2. MODELS
2.1 Traffic flow models
We consider METANET [Messmer and Papageorgiou, 1990] to
simulate the traffic flow of a freeway. METANET is a macro-
scopic traffic model that describes the average behavior of ve-
hicles in a traffic network. In this modeling technique, a link
(a homogeneous freeway) is divided into a number of segments
where the traffic behavior in each segment is described by a
set of dynamic equations. These dynamic equations describe
the density, flow, and average speed of the traffic flow in each
segment.
The equations used to calculate the traffic variables for every
segment i of a link are given by 2 :
qi(k) = λρi(k)vi(k) (1)
ρi(k +1) = ρi(k)+
T
Lλ
[qi−1(k)−qi(k)] (2)
vi(k +1) = vi(k)+
T
τ
[V[ρi(k)]−vi(k)]
+
Tvi(k)[vi−1(k)−vi(k)]
L
−
Tη [ρi+1(k)−ρi(k)]
τL(ρi(k)+κ)
(3)
V[ρi(k)] = vfree exp
−
1
b
ρi(k)
ρcr
b
(4)
where qi, ρi, and vi, denote the flow, density, and space-mean
speed of segment i of the link, T denotes the simulation time, L
denotes the length of the segments of the link, and λ denotes
the number of lanes of the link. Furthermore, vfree the free-
flow speed, ρcr the critical density, τ a time constant, η the
anticipation constant, b the parameter 3 of the fundamental
diagram, and κ is the model parameter. METANET can also
include lane drops, merging lanes, on-ramps, and so on [Hegyi,
2004, Kotsialos et al., 2002, Messmer and Papageorgiou, 1990].
Shock waves are fundamental characteristic of a traffic system.
In METANET this phenomenon can be modeled by considering
two different values of the anticipation constant η [Hegyi,
2004].
The dynamic equations described above describe traffic flow
under uncontrolled speed limits. However, in our study we
impose dynamic speed limit control on some of the segments
of the link. Therefore, on the controlled segments of the link
the desired speed V[ρ(·)] in (4) is replaced by [Hegyi, 2004]:
V[ρi(k)] = min
vfree exp
−
1
b
ρi(k)
ρcr
b
, (1+α)vlim,i(k)
(5)
where vlim,i(k) is the speed limit control input for segment i at
time step k, and (1+α) is the compliance factor.
Since the demand at the origin of a link (or freeway) can
exceed the capacity or the number of vehicles that can enter
2 In general all variables have also a subscript for their link index m, but since
we consider only one link in our case study we drop the index m for simplicity
of notation.
3 In the original METANET model the parameter b is denoted by a. But to
avoid confusion with the acceleration (which will be indicated with a in this
paper) we chose to use b.
the freeway, a queue may develop. The dynamics of the queue
wo is modeled as:
wo(k +1) = wo(k)+T(do(k)−qo(k)) (6)
where do is the demand at the origin and qo is the outflow of the
origin. The outflow qo is dependent on the number of vehicles
that want to enter to the origin of the freeway, the capacity of
the origin, and the number of vehicle that can enter the origin.
This is expressed as:
qo(k) = min
do(k)+
wo(k)
T
, Co, Co
ρjam −ρ1(k)
ρjam −ρcr
(7)
with Co is the capacity flow of the origin and ρjam is the
maximum density of the link. Moreover, in the METANET
model the speed of the origin of the link is set equal to the
speed of the first segment of the link. Mathematically,
vo =
⎧
⎨
⎩
min{vlim,1(k),v1(k)} if segment 1
is controlled
v1(k) otherwise
. (8)
The downstream boundary condition is mostly assumed to be
congestion-free. But if a downstream density ρd is defined, the
virtual downstream density is taken to be:
ρN+1(k) = min{ρd(k), ρN(k)} (9)
where N is the number of segments in the link.
2.2 Emission and fuel consumption models
Traffic emission and fuel consumption models calculate the
emissions produced and fuel consumed by vehicles based on
the operating conditions of the vehicles. Both emissions and
fuel consumption of a vehicle are influenced by the vehicle
technology, vehicle status (such as age, maintenance, etc.), ve-
hicle operating conditions, the characteristics of the infrastruc-
ture, and external environment conditions. For a given vehicle
technology and status of a vehicle, emission and fuel consump-
tion models can be calibrated for every vehicle, or for homoge-
neous vehicle categories. The main inputs to the models are the
operating conditions of the vehicle (such as speed, acceleration,
engine load) [Heywood, 1988].
In general, emission and fuel consumption models are cali-
brated based on the operating conditions of the vehicle in a
traffic flow. These models can be either average-speed-based or
dynamic. Average-speed-based emission and fuel consumption
models estimate or predict traffic emission and fuel consump-
tion based on the trip-based average speed of traffic flow [Ntzi-
achristos and Samaras, 2000]. These models can also be used
with local speeds to take some of the variation of the speeds
into account [Boulter et al., 2002]. On the contrary, dynamic (or
also called microscopic) emission and fuel consumption mod-
els use second-by-second speed and acceleration of individual
vehicles to estimate or predict emission and fuel consumption.
Such model provide better accuracy than average-speed-based
models.
VT-micro [Ahn et al., 1999] is a microscopic dynamic emission
and fuel consumption model that yields emissions and fuel
consumption of one individual vehicle using second-by-second
speed and acceleration. The model has the form:
Jx(k) = e(ṽT (k)Pxã(k)) (10)
where Jx is the estimate or prediction of variable x ∈ {emission
gases, fuel consumption}, v(k) and a(k) are respectively the
speed and acceleration with the operator ˜
· defined for z ∈ R
12th IFAC CTS (CTS 2009)
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150
3. Fuel
consumption
[l/km]
0
0 20 40 60 80 100 120
a = −0.5
a = 0
a = 0.5
0.1
0.2
0.3
Speed [km/h]
(a) Fuel consumption
CO
2
emissions
[kg/km]
0
0 20 40 60 80 100 120
a = −0.5
a = 0
a = 0.5
0.1
0.2
0.3
0.4
0.5
0.6
Speed [km/h]
(b) CO2 emissions for diesel fuel
Fig. 1. CO emission and fuel consumption curves of vehi-
cles as a function of the speed for accelerations a ∈
{−0.5, 0, 0.5} m/s.
as z̃ = [1 z z2 z3]T , and Px the model parameter matrix for the
variable x. The value of Px can be found in [Ahn et al., 1999].
Fig.1(a) depicts the fuel consumption versus the vehicle speed
for three acceleration values generated from the VT-micro
model. In our study we chose this model because it is simple
and it takes the acceleration of the vehicle into account.
2.3 Integrating METANET with VT-micro
The VT-micro model is a microscopic traffic emission and
fuel consumption model while METANET is a macroscopic
traffic flow model. Thus, these two different models have to be
integrated in such a way that the VT-micro model can get speed
and acceleration inputs of the traffic flow from the METANET
model at every simulation time step. The speed of the traffic
flow can be easily obtained from (3). However, the computation
of the acceleration is not as straightforward. In the sequel
we show how to obtain the acceleration from the METANET
model.
Consider a segment of a link as in Fig. 2. The figure shows the
traffic flow at time step k and k + 1. At the time k the number
of vehicles in segment i is equal to Lλρi(k) and the number
of vehicles going from segment i to segment i + 1 in the time
step k to k+1 is Tqi(k). Therefore, the number of vehicles that
stayed in segment i in the period from time step k to time step
k + 1, i.e. [kT, (k + 1)T] is equal to Lλρi(k) − Tqi(k). From
segment i−1 segment i segment i+1
Tqi−1(k) Lλρi(k)−Tqi(k)
Tqi(k)
at k
at k +1
Fig. 2. Illustration of traffic flow in METANET
time step k to k+1 the acceleration is not only due to the change
in speed of the vehicles within the segment i, but also there is
an acceleration for the vehicles flowing from segment i − 1 to
segment i. Hence we have the following accelerations:
aΔi(k) =
vi(k +1)−vi−1(k)
T
(11)
ai(k) =
vi(k +1)−vi(k)
T
(12)
where aΔi denotes the acceleration of the vehicles flowing from
segment i − 1 at k into segment i at k + 1 and ai denotes
acceleration of the vehicles that are staying in segment i during
the period [kT, (k +1)T].
Therefore, at time step k + 1, we provide the VT-micro
model with two accelerations and two speeds along with
their corresponding number of vehicles, i.e., we have the pair
(vi−1(k), aΔi(k)) with Tqi−1(k) being the number of vehicles
and the pair (vi(k), ai(k)) with Lλρi(k)−Tqi(k) as the number
of vehicles.
With the input pairs discussed above, the VT-micro model of
(10) model gets changed to:
¯
Jx,i(k) =(Lλρi(k)−Tqi(k))e(ṽi(k)PxãT
i (k)) +
Tqi−1(k)e(ṽi−1(k)PxãT
Δi(k)) (13)
where the ˜
· operator is defined as in (10). We call this model
the “VT-macro” emission and fuel consumption model. This
can be interpreted as the total emission and fuel consumption
of a segment from time step k to time step k +1.
The VT-micro emission model of Ahn et al. [1999] does not
yield estimates of CO2 emissions. But, since there is almost an
affine relationship between fuel consumption and CO2 emis-
sions [Oliver-Hoyo and Pinto, 2008], we can compute the CO2
emissions as:
¯
JCO2,i(k) = δ1 +δ2 ¯
Jfuel,i(k) (14)
where δ1 and δ2 are the model parameters. The values of δ1
and δ2 can be found in [Oliver-Hoyo and Pinto, 2008] when
the fuel input ¯
Jfuel,i(k) in (14) is in kg/100 km. But, since the
fuel output of the VT-macro model in (13) is given in l/m,
the values of the parameters are changed to δ1 = 1.17 × 10−5
and δ2 = 2.65 for diesel-fuel car after dimensional convertion.
The model then provides the CO2 emissions in kg/m. The CO2
emission curves for diesel-fuel vehicle are plotted in Fig. 1(b).
The figure shows the variation of the emission values for three
different accelerations.
12th IFAC CTS (CTS 2009)
Redondo Beach, CA, USA, September 2-4, 2009
151
4. Traffic
system
Control
Control
inputs
inputs
MPC controller
Optimization
Model
Measurements
Prediction
Objective,
Constraints
Fig. 3. Conceptual representation of model predictive control.
3. MPC FOR TRAFFIC FLOW
Model predictive control (MPC) [Maciejowski, 2002, Camacho
and Bordons, 1995] is a dynamic control approach that uses
optimization of the control inputs based on prediction and a
moving horizon approach. The basic concept can be explained
with the help of Fig. 3 as follows. The MPC controller incor-
porates models of the traffic flow, emission, and fuel consump-
tion. At control time step kc (corresponding to the time instant
t = kc ·Tc where Tc is the control sampling time), the controller
collects measurements (such as density, flow, emission, etc.) of
the traffic system through sensors. Based on the obtained or
estimated current states and using the models, the controller
predicts the future evolution of the traffic states up to time step
kc + Np, where Np denotes the prediction horizon. Using on-
line optimization techniques the controller generates a sequence
of traffic control inputs that minimize the defined objective
function into the future. But, only the first control input is
applied to the traffic system. At the next control time step
kc + 1, the controller again collects the newly changed traffic
states, and it does the same operations as before. In this way,
the controller continuously updates the control inputs based on
the continuously changing demand and traffic dynamics of the
system.
MPC for traffic control (or similar approach) has already been
applied in literature [Bellemans et al., 2006, Gartner, 1984,
Hegyi et al., 2005, Kotsialos et al., 2002]. Its main advantages
are that it can handle constraints (such as maximum emission
levels), it can be applied to nonlinear models (e.g. nonlinear
traffic models), and it can also be used to address multi-criteria
optimization (such as emissions, fuel consumptions, and travel
time). The main disadvantage of MPC emanates from the
computation time required for on-line optimizations. However,
there are different methods to reduce the computational time.
One possible solution is to define a control horizon Nc, where
after kc + Nc − 1, the control input is made constant. Another
solution is to reduce Np and increase Tc, or to use blocking
Maciejowski [2002].
Since one of the goals in increasing the efficiency of a vehicle
engine or the purity of fuel is to maximize the conversion of
the fuel to CO2, it may have adverse effect on the overall
global warming. Thus, as a performace measure we consider
an objective function Jobj that incorporates CO2 emissions ¯
JCO2 ,
fuel consumption ¯
Jfuel, and total time spent TTS. It is described
as:
Jobj(kc) =
θ1
¯
JCO2,nom
M(kc+Np)−1
∑
k=Mkc
N
∑
i=1
¯
JCO2,i(k)
+
θ2
¯
Jfuel,nom
M(kc+Np)−1
∑
k=Mkc
N
∑
i=1
¯
Jfuel,i(k)
+θ3
TTS(kc)
TTSnom
(15)
where
TTS(kc) =
M(kc+Np)−1
∑
k=Mkc
N
∑
i=1
Lλρi(k)+wo(k)
T (16)
is the total time spent in the freeway and in the origin queue,
θi for i ∈ {1,2,3} are the weighting factors of the constituent
elements of the objective function, N is the number of segments
of a link, and ¯
JCO2,nom, ¯
Jfuel,nom, and TTSnom are the “nomi-
nal” values of respectively the CO2 emissions, fuel consump-
tion, and total time spent. For example in our case study (see
Section 5) the nominal values correspond to an uncontrolled
scenario with an average speed of 80 km/h.
However, since both fuel consumption and CO2 emissions are
related by an affine relationship given in (14), we get the
relation:
M(kc+Np)−1
∑
k=Mkc
N
∑
i=1
¯
JCO2,i(k) = NMNpδ1 +δ2
M(kc+Np)−1
∑
k=Mkc
N
∑
i=1
¯
Jfuel,i(k).
(17)
This means that the last two terms in the objective function suf-
fice to represent reduction of fuel consumption, CO2 emissions,
and travel time.
In the case study of Section 5 we use a macroscopic traffic
flow model METANET [Messmer and Papageorgiou, 1990]
integrated with a microscopic emission and fuel consumption
model [Ahn et al., 1999] described in Section 2. Note however
that the MPC approach is generic and can also accommodate
other, more complex traffic flow, emission, and fuel consump-
tion models.
4. OPTIMIZATION PROBLEM
The control input vlim,c,i(kc) in segment i at control time step kc
is related to the speed limit vlim,i(k) in segment i at simulation
time step k through a zero-order-hold operation, i.e.,
vlim,i(k) = vlim,c,i
k
M
(18)
where · denotes the floor operation. So, the objective function
Jobj(kc) in (15) depends on the state variables density ρi(Mkc +
j), flow qi(Mkc + j), space-mean speed vi(Mkc + j), and queue
length wo(Mkc + j) for j = 0,1,··· ,MNp −1 and on the control
inputs vlim,c,i(kc), vlim,c,i(kc +1), ··· , vlim,c,i(kc +Nc −1). This
is because each element of the objective function is dependent
on one or more of the state or input variables (see (13), (14),
(16), and the traffic flow model (1)–(4)). Then, if we collect the
above states into a big vector x̃(kc), and the control inputs into
a big vector ṽlim,c(kc) we can write the objective function
Jobj(kc) = f(x̃(kc),ṽlim,c(kc)). (19)
Then, the optimization problem of the MPC controller at time
step kc is
min
ṽlim,c(kc)
f(x̃(kc),ṽlim,c(kc)) (20)
12th IFAC CTS (CTS 2009)
Redondo Beach, CA, USA, September 2-4, 2009
152
5. demand
density
demand
[veh/h]
density
[veh/km]
time [h]
0
0
0 0.2
0.2 0.4
0.4 0.6
0.6 0.8
0.8 1
1 1.2
1.2 1.4
1.4 1.6
1.6 1.8
1.8 2
2
50
100
2000
4000
6000
Fig. 4. Demand do at the origin and density ρd at the end of the
freeway segments.
s.t. x̃(kc) = M (x̃o(kc),ṽlim,i(kc), ˜
d(kc)) (21)
x̃(kc) ≥ 0 (22)
vlow ≤ ṽlim,c(kc) ≤ vup (23)
vlim,c,i(kc +Nc + j) = vlim,c,i(kc +Nc −1)
for all controlled segments i (24)
where x̃o(kc) is the state of the network at time t = kcTc, ˜
d(k)
contains the evolution of the demand over the period [kcTc,(kc +
Np)Tc), j = 0,...,Np −Nc −1, M (·) is the state update equation
of the system, vlow denotes the lower speed limit, and vup
denotes the upper speed limit.
Since the defined objective function is nonlinear and nonconvex
function, we make use of multi-start sequential quadratic pro-
gramming (SQP) [Pardalos and Resende, 2002] to numerically
solve (20)–(24).
5. SIMULATION AND RESULTS
To demonstrate the approach we consider a 12 km two-lane
freeway. The freeway is divided into 12 segments, where only
the middle 6 segments are controlled with dynamic speed
limits. In Fig. 4 we have presented the demand profile at the
origin and a model of a shock wave at the end of the freeway.
These two profiles provide one particular example of a traffic
scenario where there is a shock wave that can cause traffic jams
and a dynamic demand with a peak during the rush hour.
We consider a simulation period of 2 hours and implement
the aforementioned traffic controller for 4 different scenarios,
in particular, we investigate the following cases and objective
functions:
S1. uncontrolled
S2. controlled, total fuel consumption (or total CO2 emission)
S3. controlled, total time spent , and
S4. controlled, total fuel consumption and total time spent
The simulation results are listed in Table 1. Under the consid-
ered traffic conditions, the results indicate that when the objec-
tive of the MPC controller is to reduce fuel consumption or CO2
emission (S2), both the CO2 emissions and fuel consumption
are reduced by 3.07%. But the total time spent is reduced
only by 0.56%. This indicates that a control strategy that only
focuses on fuel consumption or CO2 emissions may not reduce
the travel time significantly. However, when the objective of the
controller is to reduce the travel time (S3), the MPC controller
ρ
[veh/km]
segments [km] time [h]
0
0
0.5
1
1.5
2
2
4
6
8
10
10
12
20
30
40
50
60
70
80
(a) S1 uncontrolled case
ρ
[veh/km]
segments [km] time [h]
0
0
0.5
1
1.5
2
2
4
6
8
10
10
12
20
30
40
50
60
70
80
(b) S3 TTS controlled case
Fig. 5. Traffic density distribution
reduces the total time spent, the fuel consumption, and the CO2
emission by 20.01%, 14.15%, and 14.15% respectively. On the
other hand, when the objective of the controller is set to be the
weighted sum of fuel consumption (or CO2 emissions) and total
time spent (S4), the controller results in a slight improvement
of the emissions and fuel consumption from scenario S3, and a
better improvement of the travel time than in scenario S2. These
results indicate that under the given traffic conditions, reducing
travel time can also have a positive effect on the reduction of
the fuel consumption and there by the CO2 emissions.
Since the improvement of the total time spent under scenario S3
is significant, we depicted the traffic densities of the whole link
in Fig. 5(b) in order to analyze the traffic phenomena in there.
In order to compare the density profile of the TTS controlled
case (S3) with the uncontrolled case (S1), we have also plotted
the density profile of the link for the uncontrolled case in
Fig. 5(a). Under the uncontrolled scenario (S1), the shock wave
created around the time t = 0.25 h propagates through the entire
link upstream from segment 12 to segment 1 (see Fig. 5(a)).
However, when an MPC controller is implemented to reduce
the travel time (S3) the shock wave is reduced and dissolves in
time as it propagates upstream (see Fig. 5(b)), i.e. the controller
creates relatively smooth traffic flow. Moreover, the peak of the
shock wave at segment 12 of case S3 is less than that of case S1.
This shows that the proposed MPC controller is able to either
reduce or avoid the appearance of shock waves.
12th IFAC CTS (CTS 2009)
Redondo Beach, CA, USA, September 2-4, 2009
153
6. Table 1. Simulation results
Control objectives
Variables S1:Uncontrolled S2: Fuel (g%) S3: TTS (g%) S4: TTS + Fuel (g%)
TTS (veh·h) 2142.3 2130.2 (-0.56) 1713.6 (-20.01) 1749.1 (-18.35)
CO2 (kg) 28368 27497 (-3.07) 24354 (-14.15) 24347 (-14.17)
Fuel (l) 10705.0 10376.0 (-3.07) 9190.2 (-14.15) 9187.6 (-14.17)
(g%) denotes the % change of the variables with respect to scenario S1
The results above show that under the presence of chock wave
and dynamic demand with peak during rush hour, MPC can still
offer a balanced trade-off between reducing fuel consumption,
emissions, and the travel time. The trade-off can be adjusted
by changing the weight of the fuel consumption, emission, and
total time spent terms in (15).
6. CONCLUSIONS
In this paper we have derived VT-macro, an integrated model
for describing traffic flows, emissions, and fuel consumption.
The integrated model can be used for on-line traffic control
purposes and it is based on a combination of the macroscopic
METANET traffic flow model and the microscopic emission
and fuel consumption model VT-micro. The interface between
the two submodels has been obtained by deriving equations to
extract average accelerations from the METANET model and
by adapting the VT-micro model to include these accelerations.
We have also described how the new VT-macro model can
be used in a model-based predictive control approach, and we
have illustrated the resulting control approach with simulation
experiments. The simulation study confirms that model-based
traffic control with a multi-criteria objective function can be
used to address the multi-faceted problem of reducing fuel
consumption, emissions, and travel time. The experiments also
show that the proposed approach can be used to obtain a
balanced trade-off between the different performance criteria.
In our future work, we will validate, compare, and assess
the performance of the VT-macro model. Moreover, we will
consider different flow or emission models, implement other
traffic control measures (such as ramp metering and route
guidance), and study other more complex case studies.
ACKNOWLEDGEMENTS
Research supported by the Shell/TU Delft Sustainable Mobil-
ity program, the BSIK project “Transition towards Sustain-
able Mobility (TRANSUMO)”, the Transport Research Center
Delft, and the European COST Action TU0702.
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