Multi-criterion system optimization model for urban multimodal traffic network

State estimation in urban traffic networks: A two-layer approach

An Efficient Model for Urban Traffic Network Control

The main objective of this project is to model and simulate a discrete based urban traffic network system. Nowadays, the congestion in traffic network is affecting the citizen’s standard of living especially for those who lives in urban area. This phenomenon cannot be avoided due to the increment of population in urban area. The effective and fast way to reduce this problem is by applying traffic lights optimiser at urban traffic network. Hence, high accuracy of traffic model is required to optimise the traffic lights signalisation. Cellular Automata is proposed to model the multiple intersections of urban traffic network system. Microscopic traffic network model is used to model the traffic flow of the traffic network. The vehicle parameters are determined for the computation of traffic condition. The purpose of applying these parameters is to estimate the traffic flow at the intersection more accurately. There are differences in the traffic flow when the vehicles are transferring from one intersection to another. The vehicles tend to decelerate for turning to left or right and accelerate for going straight. Besides, the probability of vehicles transferring to other lanes is required to model the real traffic network condition. Therefore, the modelling of the lane changing behaviour at the intersections has enhanced the precision of the developed model. Traffic light signalisation for the developed model is controlled by Petri Net. This traffic network model with consideration of more vehicle parameters such as time delay, maximum velocity, acceleration and deceleration has achieved a higher accuracy of traffic condition estimation.

In this research, we formulate the emergency evacuation and rescue network reconstruction problem as a bi-level programming network design model. The upper level problem is a decision of minimized system cost under limited resources. The lower level problem is one of the constraints for the upper level problem that considers users equilibrium route choice behaviors. We also consider the multi-class users' route choice behaviors in the lower level problem. In solving the problem, the variational inequality sensitivity analysis method, generalized inverse matrix method, diagonalization method, and gradient projection method are adopted to develop the solution algorithms for the bi-level model. The numerical test results indicated that the Stackelberg equilibrium solution of the emergency evacuation and rescue network reconstruction for natural disasters bi-level programming model do exist. More significantly, the optimal network reconstruction planning, disaster victim emergency evacuation and rescue route planning under multi-class user route choice conditions can be achieved.

Network traffic flow is an aggregated result of a huge number of travelers' route choices, which is influenced by the travelers' choice behaviors. So day-to-day traffic flow is not static, but presents a complex and tortuous day-to-day dynamic evolution process. Studying day-to-day dynamic evolution of network traffic flow, we can not only know whether the traffic network equilibrium can be reached and how the process is achieved, but also can know what phenomenon will occur in the evolution of network traffic flow if the equilibrium is not reached. In a real traffic system, taking day as scale unit, the day-to-day network traffic demand is variable and changes with everyday's traffic network state. The travelers' route choices are also influenced by the previous day's behaviors and network state. Then, will the day-to-day network traffic flow evolution be stable? If it is unstable, when will bifurcation and chaos occur? In this paper we discuss the day-to-day dynamic evolution of network traffic flow with elastic demand in a simple two-route network. The dynamic evolution model of network traffic flow with elastic demand is formulated. Based on a nonlinear dynamic theory, the existence and uniqueness of the fixed point of dynamic evolution model are proved, and an equilibrium stability condition for the dynamic evolution of network traffic flow with elastic demand is derived. Then, the evolution of network traffic flow is investigated through numerical experiments by changing the three parameters associated with travelers, which are the sensitivity of travelers' travel demand to travel cost, the randomness of travelers' route choices, and travelers' reliance on the previous day's actual cost. Our findings are as follows. Firstly, there are three kinds of final states in the evolution of network traffic flow: stability and convergence to equilibrium, periodic motion and chaos. The final state of the network traffic flow evolution is related to the above three parameters. It is found that under certain conditions the bifurcation diagram of the network traffic flow evolution reveals a complicated phenomenon of period doubling bifurcation to chaos, and then period-halving bifurcation. Meanwhile, the chaotic region is interspersed with odd periodic windows. Moreover, the more sensitive to cost the travelers' travel demand the more likely the system evolution is to be stable. The smaller the randomness of travelers' route choices, the less likely the system evolution is to be stable. The lower the degree of travelers' reliance on the previous day's actual cost, the more likely the system evolution is to be stable.

As the poor applicability of rail transit and elevated road limited by economy, population and many other factors, the waste of road resources caused by bus lane, maximizing application of available road resources and bus facilities, reducing the environmental pollution is the critical issue faced by current situation of urban transportation development in China. Guided by Bus Priority Oriented, utilized comprehensive techniques, such as ABC classification, bi-level programming model, sensitivity analysis, this paper constructed a theoretical frame of elastic equilibrium based on system optimization. Taking time and environment as optimized objectives, This paper established the Bi-Level multiobjective programming model based on travel time, and programmed with MATLAB for the calculation. Finally, the case study is illustrated. the software VISSIM was adopted to realize the simulation of the volume before and after the implementation of elastic plan in urban areas of PingDingshan. The simulation results showed the validity and feasibility of the modeling and simulation of Bus Priority Oriented urban road network elastic equilibrium.

Transit assignment, a problem of predicting passengers’ route choices and determining the usage for transit services, is an important research field because the results of transit assignment models are crucial for evaluating the performance of transit networks. Essentially, the passengers’ route choices in transit assignment models depend on the supply of transit networks and a principle describing how passengers select transit routes. This thesis contributes to the literature of transit assignment by incorporating the stochasticity of the supply side of transit networks and proposing the reliability-based user equilibrium conditions that define the passengers’ route choice principle in uncertain transit networks.
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\nThis thesis proposes three general frameworks based on the linear programming approach, the complementarity problem approach, and the variational inequality problem approach for modeling and analyzing reliability-based user equilibrium problems. Based on these frameworks, this thesis develops mean standard-deviation and mean-variance formulations wherein either hard capacity constraints or soft capacity constraints are incorporated. Within each of these frameworks, the existence of a solution is guaranteed given continuous cost functions and a convex compact solution set. The properties of these formulations are discussed and illustrated.
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\nTo solve the proposed formulations, this thesis develops different algorithms. For the mean standard-deviation formulations, the column generation technique is adopted, while for the mean-variance formulations, the dynamic programming method is used. In particular, this thesis proposes a revised extragradient method. This method only requires a mild assumption for convergence and its computational advantages are demonstrated in a realistic size network.
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\nThis thesis also initially investigates the supply side paradox issues in transit area. Two types of paradoxes are pointed out. One is a Braess-like paradox, which is a phenomenon that the system performance deteriorates, in terms of the total effective travel cost, after providing a new line or increasing the frequency of a transit line. The other is a capacity paradox phenomenon that the network throughout reduces after adding a new line or increasing the frequency of a transit line.
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\nFinally, one of the proposed reliability-based transit assignment models is applied into a bilevel framework for solving a transit network design problem. The upper level problem is to determine the frequency setting of transit lines, while the lower level problem is the mean-variance formulation of the transit assignment problem with hard capacity constraints. A decent direction method is developed to solve the bilevel problem. Numerical studies are also provided to investigate the properties of the problem. Future research directions are also pointed out.

Urban traffic network (UTN) models are usually illustrated by state-charts (CH) and object-diagrams (OD). So, a state space model is usually used to calculate the half-value waiting time of vehicles in UTN with a stabled time control. In this study, a combination of the general type-2 fuzzy logic sets (GT2FLS) and the modified black hole (MBH) algorithm techniques is used in order to control the traffic signal scheduling and phase succession so as to guarantee the smooth flow of traffic with the least waiting time and average queue length. A comparison is made between the results obtained regarding the performance of the proposed model and those of the optimal type-1 fuzzy logic (OT1FL) and conventional type-1 fuzzy logic (CT1FL) controllers, which are the most recent researches in the problem at hand. The simulation results prove the successfulness and effectiveness of the proposed controller.

Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model

A dynamic route optimization scheme based on short-term traffic flow prediction is designed. The overall idea of this paper is to use the real-time information of each section of the urban road communication network to predict the short-term traffic speed of the road in the next 5 min, in order to reflect the changes of the road traffic state of each section of the urban road traffic network. Then, the speed prediction information of each section in the urban road traffic network is converted into the estimated average travel time of each section by using the speed-time conversion formula, and then the road weight of each section in the urban road traffic network is set. Finally, the optimal route at the current time is calculated by using the improved algorithm based on the traditional Dijkstra algorithm. Before the traffic participant arrives at the destination, the scheme will cycle through the short-term prediction and route optimization of the traffic flow until the traffic participant arrives at the destination. And then achieve the balance of traffic conditions of various sections in the urban road traffic network, improve the situation of urban road traffic congestion, and avoid the emergence of bad road traffic conditions such as “navigation congestion”.

In the context of a rapid developing urban economy and the increasing number of motor vehicles, urban commuting transportation has witnessed a serious mismatch between the supply of and the demand for transportation network resources. In developing an urban multi-mode traffic network, using a urban traffic transfer hub to coordinate the transportation capabilities among different traffic networks is perceived to be highly effective for exploring the network transportation capacity of an entire transportation system, and improving travel efficiency and experiences for the public. Based on the super-network model, this paper develops a topological structure for a multi-mode traffic network, in which two typical combined travel modes are selected to establish the path impedance function for that network. Moreover, the multi-mode traffic allocation model and the solving algorithm are constructed in the research. The paper studies the impact of two types of factors related to the transfer capacity of passenger flows based on the regular traffic network of a bus-and-subway transfer hub using a sensitivity analysis of the transfer time and the associated penalty. The findings suggest that both changes in transfer walking time and the transfer penalty have no significant effect on the bus passenger flow.

Combined Model for Flow Assignment and Mode Split in Two-modes Traffic Network

Carpooling has been long deemed a promising approach to better utilizing existing transportation infrastructure, the carpooling system can alleviate the problems of traffic congestion and environmental pollution effectively in big cities. However, algorithmic and technical barriers inhibit the development of taxi carpooling, and it is still not the preferred mode of commute. In order to improve carpooling efficiency in urban, a taxi carpooling scheme based on multi-objective model and optimisation algorithm is presented. In this paper, urban traffic road network nodes were constructed from the perspective of passenger carpooling. A multi-objective taxi carpooling scheme selection model was built based on an analysis of the main influences of carpooling schemes on passengers. This model aimed to minimise get-on-and-get-off distance, carpooling waiting time and arriving at the destination. Furthermore, a two-phase algorithm was used to solve this model. A rapid searching algorithm for feasible routes was established, and the weight vector was assigned by introducing information entropy to obtain satisfying routes. The algorithm is applied to the urban road, the Simulation experimental result indicates that the optimisation method presented in this study is effective in taxi carpooling passengers.

Urban traffic network (UTN) models are usually illustrated by state-charts (CH) and object-diagrams (OD). So, a state space model is usually used to calculate the half-value waiting time of vehicles in UTN with a stabled time control. In this study, a combination of the general type-2 fuzzy logic sets (GT2FLS) and the modified black hole (MBH) algorithm techniques is used in order to control the traffic signal scheduling and phase succession so as to guarantee the smooth flow of traffic with the least waiting time and average queue length. A comparison is made between the results obtained regarding the performance of the proposed model and those of the optimal type-1 fuzzy logic (OT1FL) and conventional type-1 fuzzy logic (CT1FL) controllers, which are the most recent researches in the problem at hand. The simulation results prove the successfulness and effectiveness of the proposed controller.

A manipulator game model of urban public traffic network