Emission-based static traffic assignment models

Traffic assignment models can be classified according to the behavioral assumption governing route choice: the User Equilibrium (UE) and System Optimum (SO) traffic assignment. The first model refers to the “user-optimal” model, that is the users' least cost model, while the latter is based on overall cost minimization. The paradox of benefit between UE and SO makes it impractical. In addition, the dynamic and stochastic characteristic of traffic flow in the congested traffic network makes it difficult to be modeled by the deterministic method. It is necessary to make queueing analysis in the congested network. This paper presents a game theory based traffic assignment using queueing networks, which can balance between UE and SO and give more feasible traffic assignment. A comparative experiment is conducted among SO, UE and noncooperative models to validate the model.

Suppose that a road network model is given, together with some given demand for travel by (say) car and that the demand for travel varies with time of day but not from day to day. Suppose that this demand is given in the form of specified total outflow rates from each origin headed towards each destination, for each origin-destination pair and for each time of day, and that some initial time-dependent routeinflow rates, meeting the given demand, are given. Finally, suppose that within-day time is represented by a continuous variable. This paper specifies a natural smooth day-to-day route-swapping procedure wherein drivers swap toward less expensive routes as day succeeds day, and shows that under reasonable conditions there is an equilibrium state of this dynamical system. If such a collection of route-inflows has arisen today, say, then there is no incentive for any route-inflow to change tomorrow, in the sense that at each moment of today each of today's route-inflows isalready on a route which today yielded the smallest travel cost. Such a set of “no-incentive-to-change” route-inflows is called adynamic equilibrium, or adynamic user-equilibrium, and may be regarded as a solution of the dynamic equilibrium traffic assignment problem. Thus, the paper introduces a smooth day-to-day dynamic assignment model and, using this model, shows that there is a dynamic user-equilibrium in a continuous time setting. The paper briefly considers the day-to-day stability of the route-swapping process, also in a continuous setting. Finally, the paper gives a simple dynamical example illustrating the stability of the route-swapping process in a simple two-route network when there is deterministic queueing at bottlenecks.

We propose two types of extended time-of-day user equilibrium assignment models for an urban road network including expressways in this paper. In time-of-day traffic assignment it is significant to modify traffic demand remained on route at the end of a study period. These models are able to modify the traffic demand in considering the toll load for expressway. In the models the user equilibrium conditions are also able to be held based on travel time forarterial road users but based on general travel time considering the toll load for expressway users. These new models are applied to the arterial road network and the results show good accuracy and practicability.

This paper is based on data obtained from most recent transportation studies done in the Metropolitan Area of Valle de Aburrá, city of Medellín and other 9 municipalities. The studies were based on an Origin/Destination Survey (2005), Analysis of bus routes (2006), and Mobility Master Plan (2006). This paper explains the process of writing a software application for a given network (Network of Medellin) that solves the deterministic user equilibrium problem. The software code was implemented in Visual Basic .NET®,supported by some operations using Microsoft Excel®, and hardcoded for a segment of the Medellin network. The user equilibrium distribution of flow was found by using the Frank-Wolfe algorithm. The applied algorithm was analyzed in some aspects such as number of iterations, convergence patterns, response time, as well as changes in network demand. The traffic assignment models were analyzed by using the algorithm during the P.M. peak hour (hour of highest traffic congestion). The analysis was compared with the resultsfrom the traffic assignment procedure using TransCAD® (well-known and used transportation demand software) for the 2005 database and it was found that the software is somewhat faster than the algorithm, but the latter could be a good tool for practitioners and students for modeling small networks.

A combined model for time-dependent trip distribution and traffic assignment is proposed. The model assumes known time-dependent departure rates from origins and overall arrival rates at destinations, and it then seeks to estimate the origin-destination matrix according to the observed entropy value and to minimize the total system travel time. A solution algorithm is proposed based on the Dantzig-Wolfe decomposition principle and a Lagrangian relaxation approach. Computational results on a test network are also presented and discussed.

Path sets size, model specification, or model estimation: Which one matters most in predicting stochastic user equilibrium traffic flow?

Test for Influence of Socket Connection Structure on Dynamic Response of Prefabricated Pier under Vehicle Collision

As the last process of traditional four processes management method, traffic assignment plays an important role both in theory and practice. User Equilibrium (UE) traffic assignment is a basic problem in the field of urban traffic equilibrium assignment and many studies focus on it. Ant colony algorithm is a new simulating evolution algorithm which has solved many typical combinatorial optimization problems. This paper tries to solve UE traffic assignment problem with ant colony algorithm and improve the goal function value and the formula of transition probability according to the character of UE traffic assignment. The algorithm may get brush-fire optimum result and stop earlier in large scale problems. To solve this problem, this paper optimizes the formula of trail pheromone increment and use max-min ant system to limit the concentration of path pheromone into a scope. Finally, a numerical example is given to prove the feasibility and validity of this algorithm; the result is also compared with the one gotten by traditional algorithm.

Having recognized the mutual interaction that exists between traffic signal control and route choice behavior, this paper considers the traffic assignment problem for a traffic signal controlled network. The interaction is characterized by (1) the effects of delay, due to traffic signals, on route choice, and (2) the influence of the resulting traffic assignment, due to the changes in route selection, on the timing of traffic signals. In this paper Webster’s method for calculating green splits and the HCM delay model are used to develop a link performance function that implicitly produces optimal signal settings for the arriving traffic volumes. This link-performance function is then used in an assignment procedure to develop a combined traffic signal control/traffic assignment model. The model provides simultaneous solution of the traffic signal control problem and the traffic assignment problem. The result is a set of signal settings that optimally matches the assigned traffic flows. The combined model results in a non-convex problem which produces multiple equilibria. A modification of Wardrop’s two principles is proposed to produce an assignment which converges to a single solution.KeywordsTraffic SignalRoute ChoiceDelay FunctionTraffic AssignmentArrival Flow RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Introduction: Reducing the health impacts of air pollution in highly polluted countries such as China requires understanding the relative impacts of specific source sectors. . Targeted air quality management can lead to substantial health benefits and reductions in climate-forcing emissions. Methods: We combined chemical transport model simulations with estimates of exposure to air pollution and the burden of disease to estimate deaths in China from major air pollution sources in 2013 and under four future scenarios for 2030. Scenarios included future energy mixes and pollution control measures. National and province-level estimates were developed. Results: In China ambient PM2.5 contributed to 916,102 premature deaths in 2013. Coal combustion was the most important contributor to ambient particle (PM2.5) pollution in China in 2013, responsible for 40% of population exposure. 366,000 deaths were attributed to coal combustion in 2013 in China. Industrial (coal and non-coal) emissions contributed 255,000 attributable deaths in 2013 with domestic combustion (biomass and coal) leading to 177,000 attributable deaths, more than the contribution from transportation (137,000), or coal combustion in power plants (86,500). Compared to 2013, deaths attributable to ambient PM2.5 in 2030 were increased by 8 – 38% under scenarios of pollution reduction where population-weighted exposure was decreased by 8 – 50%. These projected mortality increases were due to population aging and increases in ischemic heart disease, stroke, COPD and lung cancer in the Chinese population. Even under the most stringent energy use and pollution control scenario, coal remained the single largest contributor to ambient PM2.5 and attributable burden in 2030. Conclusions: These projections underscore the importance of population dynamics in determining future mortality due to ambient PM2.5. PM levels must be substantially lowered to stabilize or reduce burden given demographic trends.

Stackelberg games and multiple equilibrium behaviors on networks

The integration of solar photovoltaic (PV) power generation technology into electric vehicle (EV) charging systems is of great significance, and it is very important to analyze the influencing factors of vehicle operating parameters on the generation power of vehicle PV system. In this paper, a model is developed to predict the power generated by vehicle PV system through a combination of a Fluent simulation study and Simulink modeling. Then the generation power of vehicle PV system under 7 different vehicle speeds in different months in Northeast China is analyzed. Through the Spearman correlation analysis and multilevel function fitting of the data, the mathematical model of a vehicle PV system power is proposed. Its variation with vehicle speed is obtained, as affected by solar radiation intensity and ambient temperature. According to the mathematical model analysis, the relevant vehicle characteristics and rules for vehicle PV systems were obtained. The simulation results show that the generation power of vehicle PV system in Northeast China increases significantly with the increase of vehicle speed in summer, but does not change significantly with the increase of vehicle speed in winter.

The stochastic user equilibrium (SUE) traffic assignment model integrates the concepts of random assignment and Wardrop equilibrium, and it makes the assignment results more reasonable. The conventional Logit-based SUE model is explicitly built upon path flow variables, which encumbers its large-scale applications and further theoretical analyses. In this paper, a link-based SUE traffic assignment model with link capacity constraints is developed by using the network equilibrium modeling approach. The solution equivalence and uniqueness of the proposed model are rigorously proved. An efficient solution algorithm is also proposed to obtain the equilibrium solution. Numerical results indicate that the proposed link-based SUE model can reflect the real route choice behavior of travelers more reasonable and also facilitate the algorithmic development and further theoretical analyses.

Electricity generation is the major emission source of air pollutants in the highly industrialized Pearl River Delta Region. In a compact region like the Pearl River Delta, pollutants can easily transfer from one city to another. The research question of this study is to construct an optimal and mutual agreeable scheme to reduce electricity consumption in the Pearl River Delta Region, which involves the collaboration of all cities in the Region. The main objective of the study is to conduct a cooperative scheme that internalizes the external social cost of electricity consumption through optimal electricity consumption reduction.
\nThis research first surveys papers on urban environmental problems, especially environmental problems caused in Pearl River Delta Region. Literature review indicates that public electricity generation is the major emission source of air pollutants in this region. Secondly, this research reviews literatures on the social costs of electricity consumption. Reviews show that external costs of electricity consumption in different countries differ widely, ranging from 13% to 700% of electricity price. This study adopts the lower quartile of this range, which is 13% of electricity price.
\nThirdly, urban operations research is reviewed, and a major policy instrument for environmental improvement, environmental tax, is investigated. This study develops a hierarchical structure of urban operations research to study the collaborative management of electricity consumption reduction in the Pearl River Delta Region. This urban operations research model includes seven essential steps: problem definition; objectives identification; performance measures; data analysis; analytical framework construction; model solution and courses of actions; and policy implementation.
\nMoreover, this novel urban operations research model is applied in collaborative management of electricity consumption reduction in the Pearl River Delta. This research uses statistical and mathematical methods to estimate the parameters relevant to GDP, electricity consumption, external costs of electricity consumption, and environmental tax, and then formulates the operational model. Then, this model is employed to evaluate non-cooperative equilibrium condition among the eleven Pearl River Delta cities under a non-cooperative market outcome; to derive individual city’s external cost of electricity; to derive environmental levy and optimal electricity consumption reduction; and to design a compensation plan. In the compensation plan, under cooperation, in both 2013 and 2014, four cities (Guangzhou, Shenzhen, Foshan, and Dongguan) have to pay for their net spillover external cost of electricity consumption. The other seven cities (Zhuhai, Jiangmen, Huizhou, Zhaoqing, Hong Kong, Macao, and Zhongshan) would receive compensation. 
\nThe urban operations research model for regional cooperation in electricity consumption reduction developed in this study provides an instrument to deal with the pollution problem in the Pearl River Delta Region. It facilitates the exploration of hitherto intractable problems in regional environmental cooperation and established solution plans. The urban operations model is expected to provide practical policy choices for a Pearl River Delta environmental collaboration scheme. This research represents the first attempt on an application of urban operations research model of collaborative management scheme of electricity consumption reduction in Pearl River Delta Region.

The approximation of the traversal cost is a critical component of dynamic traffic assignment model. In link based traffic assignment, it assumes that the constraints sets are linear or convex and it is not realistic in general traffic networks. Comparing with the link based model, the path cost in the objective function of the path based traffic assignment model is implicitly nonlinear or non-convex, which is difficult to solve. In this paper, a path based traffic assignment model combining the generalized expansion method in M/G/c/c model with the point queue model is proposed to extend the link traversal cost to the travel cost along the path. Comparing with the link based model without considering intersection effects, this proposed path based model can take into account queuing delays between intersections and it is easy to implement. In order to validate the proposed path based model, a comparative experiment is implemented by comparing with the traditional travel cost models in Sydney traffic networks. Taking into account travel flow changes and blocking time, the proposed path based model is more effective for travellers from the uncongested traffic to congested traffic networks. In addition, the results from traffic assignment model show that the proposed model can achieve feasible results.