The activity-based model system is being coined as the next-generation demand-forecasting model. The agent-based transport simulation toolkit MATSIM is a fully integrated system that models decisions from the long term to the short term, and these decisions in MATSIM are activity-based models. This paper describes the application of MATSIM in a large-scale multiagent-based transport simulation for Shanghai, China. First, algorithms for integrating new data in Shanghai with MATSIM inputs such as synthetic population, facilities, and network are separately designed according to data characteristics. Then activity-based modeling is introduced to generate population plans, and activity replanning is employed to learn the better travel plans; a utility-based approach is used to model scoring for a plan. Finally, a full MATSIM-based simulation platform for the Shanghai scenario is built in detail. The scenario contains 200,000 synthetic persons simulated on a network with 50,000 links. The relaxed state of the simulation system is reached after 100 iterations of replanning procedures, and the mode choice, route choice, and activity time allocation modules are used to optimize agents’ activity plans. The feasibility of transport simulation in Shanghai by MATSIM is validated against the mode split and the observed counts. Extensive simulation results for the designed Shanghai simulation scenarios indicate that most of the observed counts match quite well with the traffic simulation volumes and demonstrate the potential of MATSIM for large-scale dynamic transport simulation.
This paper presents the design and evaluation of a two-stage fuzzy logic traffic signal controller with online optimization for an isolated intersection. The controller is designed to be adaptive to real-time traffic demands and has the following two critical features: (1) designing a two-stage fuzzy logic controller. The fuzzy controller uses strategic and tactical vehicle loop detectors, placed respectively upstream and at the stop-line of the intersection on each approach, to measure approach arrival flows and estimate queues. This data is used to decide whether to extend or terminate the current signal phase. These decisions are made using the proposed two-stage fuzzy logic procedure. (2) Developing an adaptive optimization framework of fuzzy controller to adjust fuzzy membership functions and controller rules with on-line learning. This study models the performance index of average delays based on traffic status identification, and at regular time intervals, employs a hybrid genetic algorithm to efficiently yield the reliable solution through reappearance of statistical traffic flow. The performance of this controller is compared to those of fixed-time, actuated, classic fuzzy and two-stage fuzzy controller for different traffic conditions.
This paper proposes the design and Paramics-based evaluation of a two-stage fuzzy logic traffic signal controller (TSTFC) for an isolated intersection. The fuzzy controller employs traffic intensity-based two stage fuzzy logic procedure, and the design principles of two-stage fuzzy controller are first presented. Then, the COM-based hybrid programming is employed to make the micro traffic simulator Paramics interact with fuzzy controllers developed by Matlab, and a Paramics-based simulation platform of TSTFC is developed via Paramics API. Finally, experiments are conducted on a typical urban isolated intersection, and the performance of the developed two-stage fuzzy control simulation model is validated by comparison to those of fixed-time, actuated, and classic fuzzy controllers for different traffic conditions. Extensive simulation results have demonstrated the potential of developed hybrid programming in time efficiency and simulating real traffic conditions and indicated that the signal strategy derived from TSTFC is more effective when traffic status at intersections is above low saturation.
Because of the intensively use of urban expressway for a long time, any small disturbance like traffic incidents may cause a large-scale congestion on expressway network in Chinese metropolises. How to know well about the formation mechanism and propagation characteristics is being increasingly perceived as the most challenging problems for safety expressway management. Traffic incident duration is one of most important parameters to reflect traffic congestion intensity, and numerous measures have been developed to describe the characteristics of traffic incidents and the vast majority of these studies use data mining. Though these measures describe what objective elements of the environment may influence traffic incident, the how question-via what way these physical features affect traffic incident-is largely unexplored. Based on the historical traffic data of viaduct expressways in Shanghai, this paper introduces survival analysis into the analysis of traffic incident mechanism, and a survival Analysis-Based Modeling of urban traffic incident duration is presented. This model first analyzes the time attributes of many traffic incident samples, and employs nonparametric regression based Kaplan-Meyer model to estimate hazard-based traffic incident duration time. Then, the key influence factors of traffic incident are divided into five types and the spatial-temporal distribution characteristics of traffic incident duration time are analyzed. Finally, COX regression is used to model the co-evolution between multidimensional influencing factors of traffic incident duration. The key characteristic parameters of expressway incident management in Shanghai are optimized to analyze the evolution mechanism of incident duration. The result shows that, for different type of influencing factors, the spatial-time distribution of traffic incident duration in Shanghai expressway exists significant difference, and factors like day & night, incident type, related vehicle number, related lane number, location, bottleneck and trailer will affect the incident duration significantly.
In major domestic cities, the development of urban expressway is network oriented. The traffic flow forecasting system is the important prerequisite and foundation for realizing real-time traffic management and control. However, the traffic flow forecasting research is mainly based on highways. Research and application of short-term traffic forecasting for urban expressway is severely insufficient. Therefore, the study of urban expressway flow forecasting is discussed and a short-term traffic flow forecasting system for urban expressway based on k-NN nonparametric regression model is proposed in this study. First, the study analyzes the characteristics and needs of the urban expressway traffic flow, introduces the k-NN nonparametric regression model, and designs the short-term traffic flow forecasting system based on k-NN overall. Then, the short-term urban expressway flow forecasting system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the forecasting plan. Finally, a short-term traffic forecasting for urban expressway based on k-NN nonparametric regression model is developed in the VS2010 VC++ platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the forecasting result is analyzed. Results show that the accuracy of the proposed method, under the five-minute interval, is over 90%, which best proves the reasonableness of the proposed forecasting model based on the k-NN nonparametric model.
In the light that traditional traffic signal timing models consider vehicle's traffic efficiency and management benefit, thus ignoring traffic environmental benefit, a traffic emission-saving traffic signal timing model for urban isolated intersections is presented. Firstly, with different statuses of vehicles on the road, for example moving with a constant speed, slowing down speed, idling speed or an increasing speed, there are different kinds of degree of contamination. Based on which the urban road pollutant emissions model, and the criteria pollutant emissions model are established. Secondly, in order to analyze the dependence of the traffic signal evaluation indexes, the qualitative analysis and the quantitative analysis based on the numerical statistics are adapted. Also, based on the selecting principle of evaluation index, selected performance indicators for the emission factors, and taken them into consideration while establishing the traffic signal timing model based on relative evaluation index system. Then, an improved real-coded genetic algorithm to solve the traffic signal timing model is presented. Lastly, the three algorithms are proved by a great deal of numerical calculation. The result shows that the presented algorithm has a high precision while solving the models, and has a very good effect on reducing emissions and the efficient of controlling the traffic roads.
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