Urban Traffic Network Model Research Articles

Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine

This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed.

Large-scale traffic flow simulation based on intelligent PSO

With the rapid development of urban traffic, a large number of vehicles in cities not only bring convenience to people, but also bring a series of traffic problems, including traffic congestion and high traffic accident rates. Driving speed and waiting time of vehicles are two important factors of traffic problems. To simulate the real urban road traffic flow, a one-dimensional traffic flow grid model was proposed, which considered the nearest and next neighbour car at the same time, and connected the front and rear neighbour cars to optimize the traffic flow. The experiment results showed that our traffic flow grid model can simulate the real urban road traffic flow. In addition, we tried to optimize the urban traffic network model and improved the traffic speed of vehicles and reduced the waiting time.

Public transport trajectory planning with probabilistic guarantees

The paper proposes an eco-cruise control strategy for urban public transport buses. The aim of the velocity control is ensuring timetable adherence, while considering upstream queue lengths at traffic lights in a probabilistic way. The contribution of the paper is twofold. First, the shockwave profile model (SPM) is extended to capture the stochastic nature of traffic queue lengths. The model is adequate to describe frequent traffic state interruptions at signalized intersections. Based on the distribution function of stochastic traffic volume demand, the randomness in queue length, wave fronts, and vehicle numbers are derived. Then, an outlook is provided on its applicability as a full-scale urban traffic network model. Second, a shrinking horizon model predictive controller (MPC) is proposed for ensuring timetable reliability. The intention is to calculate optimal velocity commands based on the current position and desired arrival time of the bus while considering upcoming delays due to red signals and eventual queues. The above proposed stochastic traffic model is incorporated in a rolling horizon optimization via chance-constraining. In the optimization, probabilistic guarantees are formulated to minimize delay due to standstill in queues at signalized intersections. Optimization results are analyzed from two particular aspects, (i) feasibility and (ii) closed-loop performance point of views. The novel stochastic profile model is tested in a high fidelity traffic simulator context. Comparative simulation results show the viability and importance of stochastic bounds in urban trajectory design. The proposed algorithm yields smoother bus trajectories at an urban corridor, suggesting energy savings compared to benchmark control strategies.

Signal Preemption Control of Emergency Vehicles Based on Timed Colored Petri Nets

This paper focuses on the use of timed colored Petri nets (TCPN) to study emergency vehicle (EV) preemption control problem. TCPN is adopted to establish an urban traffic network model composed of three submodels, namely, traffic flow model, traffic signal display and phase switch model, and traffic signal switch control model. An EV preemption optimization control system, consisting of monitoring subsystem, phase time determination subsystem, and phase switching control subsystem, is designed. The calculation method of the travelling speed of EV on road sections is presented, and the methods of determining the actual green time of current phase and the other phase are given. Some computational comparisons are performed to verify the signal preemption control strategies, and simulation results indicate that the proposed approach can provide efficient and safe running environments for emergency vehicles and minimize EV’s interference to social vehicles simultaneously.

Impact of Driving Behavior on Traffic Delay at a Congested Signalized Intersection

This paper proposes a methodology to categorize drivers’ behaviors at a congested signalized intersection. As a discrete event system model, timed Petri nets (TPNs) are used in this paper to formally define two kinds of behaviors: non-jam-induced driving behavior and jam-induced one. In order to systematically assess the performances of both behaviors, a new urban traffic network model is built: a cell transmission model is used to depict the road link traffic that is consistent with the kinematic property of traffic flow, and TPNs are used to model the behaviors and the conflicting traffic flow at the intersection. Some simulation results are given to evaluate the impact of driving behavior on the traffic delay.

An optimal general type-2 fuzzy controller for Urban Traffic Network

Urban traffic network model is illustrated by state-charts and object-diagram. However, they have limitations to show the behavioral perspective of the Traffic Information flow. Consequently, a state space model is used to calculate the half-value waiting time of vehicles. In this study, a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers.

A manipulator game model of urban public traffic network

Urban public traffic networks are typical complex systems. Understanding their evolution mechanism attracts much attention in recent years. Here, we propose that the evolution of urban public traffic network can be considered as a game process between two network manipulators, i.e., passengers and company, and the equilibrium solution to the game determines the steady-state behavior of the network. Both analytical solution and numerical simulations to such game model can well describe the empirical data collected from the urban public traffic systems in four Chinese cities (Beijing, Shanghai, Nanjing, and Hangzhou) and the Boston subway. Our results suggest that the manipulator game model grasps the fundamental characteristics of the evolution mechanism of the urban public traffic systems. Similar idea may be extended to other complex systems which have small number of manipulators.

Design optimization traffic light timing using the fuzzy logic at a Diphasic's Isolated intersection

In this paper, to overcome the problem of increasing traffic, using state space equations, a comprehensive model of urban traffic network are obtained in a isolated intersection. Then, two fuzzy lo...

Design optimization traffic light timing using the fuzzy logic at a Diphasic's Isolated intersection

In this paper, to overcome the problem of increasing traffic, using state space equations, a comprehensive model of urban traffic network are obtained in a isolated intersection. Then, two fuzzy logic controllers, one for optimized green time of traffic lights and one green phase is used for an extended time. In this system, types of sensors is video detection, they are placed strategically at incoming and outgoing legs (lines) and the controller utilize the information received from these sensors to make optimal decisions to minimize the goal function. The simulation results show that TAOSF (Traffic Actuated Optimization Signal Fuzzy control) had a great improvement in 6 different traffic conditions such as certain, uncertain, using random data, and in 4 different traffic conditions using the real case study of OSTANDARI intersection in Mashhad, Iran, in comparison with the fixed time controller, real operation of Australian SCATS (Sydney Coordinated Adaptive Traffic System) system, TASFC (Traffic Actuated Signal Fuzzy Control) system in previous work for estimation of the goal function which is minimizing the queue length and the delay time of the vehicles.

Coordinated signal control for arterial intersections using fuzzy logic

Abstract Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people’s time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.

Controlling multi variable traffic light timing in an isolated intersection using a novel fuzzy algorithm

Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. To improve this problem, we first designed a comprehensive model of urban traffic network in this article, using state space equations and SIMULINK Program MATLAB and then by using a novel algorithm, two fuzzy controllers for controlling traffic light in a two phase isolated intersection in the multi Variable Control System. In this system, types of sensors is video detection, they are placed strategically at incoming and outgoing legs lanes and the controller utilize the information received from these sensors to make optimal decisions to minimize the goal function. The simulation results show that TASFC Traffic Actuated Signal Fuzzy Control had a great improvement in 6 different traffic conditions such as certain, uncertain, using random data, and in 3 different traffic conditions using the real case study of EBADI-KASHANI intersection in Mashhad, Iran, in comparison with the fixed time controller, real operation of Australian SCATS system and FSCS Fuzzy Signal Control System system in previous work for estimation of the goal function which is minimizing the queue length and the delay time of the vehicles.

Classification of Urban Traffic Network Model Based on Multi-Class Support Vector Machine

For poor accuracy of detection of the urban traffic network classification, the Support Vector Machine (SVM)is applied to classification of traffic incidents.This paper presents a traffic pattern classification method based on multi-class support vector machine, and design the network structure of the detection system. Simulation results show that: compared to other algorithms, the network, which is valid for urban transportation classification, has the advantages of high detection rate and low false alarm rate for small samples.

A simplified macroscopic urban traffic network model for model-based predictive control

Abstract A model predictive control (MPC) approach offers several advantages for controlling and coordinating urban traffic networks. To apply MPC in large urban traffic networks, a fast model that has a low on-line computational complexity is needed. In this paper, a simplified macroscopic urban traffic network model is proposed and tested. Compared with a previous model, the model reduces the computing time by enlarging its updating time intervals, and preserves the computational accuracy as much as possible. Simulation results show that the simplified model reduces the computing time significantly, compared with the previous model that provided a good trade-off between accuracy and computational complexity. We also illustrate that the simplifications introduced in the simplified model have a limited impact on the accuracy of the simulation results. As a consequence, the simplified model can be used as prediction model for MPC for larger urban traffic network.

Modelling of Traffic Safety and Operation in Urban Networks Using DSTPN

Abstract The objective of this work is to develop a simulation model for the analysis of the performance of urban traffic networks using Deterministic Stochastic Timed Petri Nets (DSTPN). Factors considered in the model include the physical characteristics of an intersection, such as the number of lanes in each direction, and the turning classification of each lane. The arrival process of vehicles at the intersection, the departure process from the conflict area, the traffic signal control strategy used in controlling traffic flow and the dynamic behaviour of traffic at the phase transition instant are the main parts of the model. Operational analysis is used to compute one or more traffic performance Measures of Effectiveness (MOE) such as queue length, traffic flow at a specific lane in certain period of time and system throughput. These MOE are used to evaluate the operation of signalized intersections and to compare the simulated results with the field ones. A Petri net simulator was used to prove the functionality and the applicability of the developed model. The developed urban traffic network model was also used to assess safety measures of a signalized urban area. Red light violations and its interaction with the signal timing plan, and traffic conflict technique were used to quantify the safety of signalized intersections,. The model was tested on a real network consisted of seven signalized intersections in the middle of the city of “Braunschweig”’ and the results are promising specially with the short computational time.

An Efficient Model for Urban Traffic Network Control

In order to control the urban traffic network through optimization methods, it is necessary to establish a proper urban traffic network model. This model should be both accurate in describing the network traffic and simple to compute. In this paper, an existing link model is at first improved to describe the main traffic dynamic behaviors more accurately. Then, a general urban traffic network topology is proposed. By modeling the network elements, a macroscopic model for urban traffic networks can be established. The model is compared with the microscopic traffic model CORSIM. It shows that this model well balances the accuracy and simpleness, and is thus suitable to real-time control.

Dynamic route guidance algorithm based on artificial immune system

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems, this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the metaphor mechanism of vertebrate immune systems. This algorithm, applied to the urban traffic network model established by the node-expanding method, can expediently realize K-shortest paths search in the urban traffic guidance systems. Because of the immune memory and global parallel search ability from artificial immune systems, K shortest paths can be found without any repeat, which indicates evidently the superiority of the algorithm to the conventional ones. Not only does it perform a better parallelism, the algorithm also prevents premature phenomenon that often occurs in genetic algorithms. Thus, it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications. A case study verifies the efficiency and the practicability of the algorithm aforementioned.

An urban traffic network model via coloured timed Petri nets

This paper deals with modelling of traffic networks (TNs) for control purposes. A modular framework based on coloured timed Petri nets (CTPNs) is proposed to model the dynamics of signalized TN systems: places represent link cells and crossing sections, tokens are vehicles and token colours represent the routing of the corresponding vehicle. In addition, ordinary timed Petri nets model the signal timing plans of the traffic lights controlling the area. The proposed modelling framework is applied to a real intersection located in Bari, Italy. A discrete event simulation of the controlled intersection validates the model and tests the signal timing plan obtained by an optimization strategy presented in the related literature.

An urban traffic network model via coloured timed petri nets

This paper deals with modelling of traffic networks for real time control purposes. A modular framework based on coloured timed Petri nets is proposed to model the dynamics of signalized traffic network systems: places represent link cells and crossing sections, tokens are vehicles and token colours represent the routing of the corresponding vehicle. In addition, an ordinary timed Petri net models the signal timing plan controlling the area. The proposed modelling framework is applied to a real intersection located in Bari, Italy. A discrete event simulation of the controlled intersection validates the model and tests the signal timing plan obtained by an optimization strategy presented in the related literature.