Real Traffic Network Research Articles

A Hybrid Genetic and Ant Colony Algorithm for Finding the Shortest Path in Dynamic Traffic Networks

Solving the dynamic shortest path problem has become important in the development of intelligent transportation systems due to the increasing use of this technology in supplying accurate traffic information. This paper focuses on the problem of finding the dynamic shortest path from a single source to a destination in a given traffic network. The goal of our studies is to develop an algorithm to optimize the journey time for the traveler when traffic conditions are in a state of dynamic change. In this paper, the models of the dynamic traffic network and the dynamic shortest path were investigated. A novel dynamic shortest path algorithm based on hybridizing genetic and ant colony algorithms was developed, and some improvements in the algorithm were made according to the nature of the dynamic traffic network. The performance of the hybrid algorithm was demonstrated through an experiment on a real traffic network. The experimental results proved that the algorithm proposed in this paper could effectively find the optimum path in a dynamic traffic network. This algorithm may be useful for vehicle navigation in intelligent transportation systems.

Dynamic traffic routing in a network with adaptive signal control

In real traffic networks, travellers’ route choice is affected by traffic control strategies. In this research, we capture the interaction between travellers’ route choice and traffic signal control in a coherent framework. For travellers’ route choice, a VANET (Vehicular Ad hoc NETwork) is considered, where travellers have access to the real-time traffic information through V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) infrastructures and make route choice decisions at each intersection using hyper-path trees. We test our algorithm and control strategy by simulation in OmNet++ (A network communication simulator) and SUMO (Simulation of Urban MObility) under several scenarios. The simulation results show that with the proposed dynamic routing, the overall travel cost significantly decreases. It is also shown that the proposed adaptive signal control reduces the average delay effectively, as well as reduces the fluctuation of the average speed within the whole network.

A numerical-analysis-based optimization method for location selection for planning residential areas in grid transportation networks

ABSTRACTChina's economy has been rapidly growing over the past few decades; however, increasingly more modern cities are facing problems caused by poor city planning, such as traffic networks, resource distribution and routine management. Optimization methods are urgently needed during city development. From location planning perspective, the residential area issue is discussed in this paper. Based on this issue, a numerical-analysis-based method is proposed for selecting proper residential areas in grid transportation networks. First, a grid layout is introduced to formalize a real traffic network. Furthermore, to guarantee its approximation, a considerable amount of data are obtained from real-world scenarios based on the shortest routes to demonstrate the problem of traffic jams. Then, a quantitative evaluation system is proposed, which quantitatively evaluates the specified location with the traffic flow distribution index, traffic congestion index and infrastructure convenience index. Each index reflects the attitude of citizens from a unique perspective, which affects the final location planning and selection. Finally, an analytic hierarchy process is designed to analyse all these indices together for a comprehensive analysis, and case studies and experiments are conducted to demonstrate the effectiveness of the proposed method. The generated residential location can be considered as a technical reference for further city planning and development decisions.

A modified Physarum-inspired model for the user equilibrium traffic assignment problem

• A modified Physarum -inspired model for the user equilibrium problem is proposed. • Two-way traffic characteristics and multiple OD pairs are considered. • The proposed Physarum -inspired model is very efficient in terms of both the accuracy and the execution time. The user equilibrium traffic assignment principle is very important in the traffic assignment problem. Mathematical programming models are designed to solve the user equilibrium problem in traditional algorithms. Recently, the Physarum shows the ability to address the user equilibrium and system optimization traffic assignment problems. However, the Physarum model are not efficient in real traffic networks with two-way traffic characteristics and multiple origin–destination pairs. In this article, a modified Physarum -inspired model for the user equilibrium problem is proposed. By decomposing traffic flux based on origin nodes, the traffic flux from different origin–destination pairs can be distinguished in the proposed model. The Physarum can obtain the equilibrium traffic flux when no shorter path can be discovered between each origin–destination pair. Finally, numerical examples demonstrate the rationality and convergence properties of the proposed model.

Routing Multiple Vehicles Cooperatively: Minimizing Road Network Breakdown Probability

Traffic congestion has always been an impending challenge for drivers as well as traffic authorities. It causes frustrations to millions of passengers. The estimated financial cost is $2,200 billion per year in developed countries worldwide. In this paper, we propose an intelligent routing algorithm to minimize the traffic jam occurrence through directing the paths of multiple vehicles cooperatively. According to Kerner's breakdown minimization principle, we can claim that the traffic network optimum has been achieved if the probability for spontaneous traffic jam occurrence over the entire road network during a given observation time period is minimized. The proposed multivehicle routing approach is fully scalable and distributed, which essentially makes it directly applicable to real traffic networks such as that in Singapore. Through numerical studies, the proposed algorithm is much faster in terms of convergence speed than that of state-of-the-art distributed computation approaches. Moreover, our approach always maintains a feasible route guidance solution during the computation process, which is applicable to scenarios with real time decision making requirements, i.e., the reaction time must be within seconds. Simulation results in arbitrarily large road networks with realistic settings show the effectiveness of the proposed algorithm.

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

Urban traffic jams severely affect many cities around the world. Therefore, there is an urgent need for accurate prediction of traffic jams in real time. With many existing approaches, it is difficult to obtain favorable results for dynamic and uncertain traffic flows in urban networks. This paper proposes a real-time traffic jam prediction method based on a two-dimension cellular automaton model inspired by the well-known Biham, Middleton and Levine (BML) model. The proposed model combines the simplicity and high efficiency of the BML model with real urban traffic networks. Our work can be divided into two parts: (1) the proposal of a practical approach for mapping the urban traffic topological structure into a modified BML (M-BML) model, and (2) the development of a novel strategy to deal with different cells of the M-BML model, especially conflict points and fuzzy points, which facilitates the recognition of real traffic jams at intersections. To the best of our knowledge, this is the first time that real urban traffic road networks are directly mapped into a descriptive model for jam prediction. Extensive experiments confirm the accuracy and efficiency of the proposed M-BML model in predicting traffic jams in dynamic and uncertain real urban traffic networks.

A stochastic process traffic assignment model considering stochastic traffic demand

ABSTRACTIn real traffic network, both link capacity and traffic demand are subject to stochastic fluctuations. These random fluctuations are major sources of travel time uncertainty. All existing stochastic process traffic assignment model models considering the uncertainty of travel time are presented with fixed traffic demand. In this study, a stochastic process traffic assignment model is presented to consider stochastic traffic demand. The traffic demand is assumed to be comprised of two groups of travelers: commuters with fixed traffic demand and irregular travelers with discrete random demand. With mild conditions, it is proved that our stochastic process traffic assignment model is ergodic and has a unique stable distribution. An algorithm is given to describe the stochastic process model. By conducting numerical test, we analyze the effect of commuters' memory length, irregular travelers' demand and commuters' perception error on the stable distribution of our model.

A dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity

SummaryThis paper investigates a strategic signal control, which anticipates travelers' route choice response and determines signal timings to optimize network‐wide objectives. In general traffic assignment models are used for anticipating this route choice response. However, model‐reality mismatch usually brings suboptimal solutions to the real system. A repeated anticipatory control resolves the suboptimality and addresses the modeling error by learning from information on model bias. This paper extends the repeated control approach and focuses on the estimation of flow sensitivity as well as its influence on control, which is a crucial issue in implementation of model bias correction. The main objective of this paper is first to analyze the estimation error in the real flow derivative that is estimated from noisy measurements. A dual control method is then presented, improving both optimization objective function and derivative estimation during the control process. The proposed dual algorithm is tested on a simple network as well as on a midsize network. Numerical examples confirm the reliable performance of the new reality‐tracking control strategy and its ability to identify (local) optimal solutions on real traffic networks. Copyright © 2016 John Wiley & Sons, Ltd.

Hybrid testbed for simulating in-vehicle automotive networks

This paper presents a novel method of automotive network simulation which is being used in an ongoing research effort to design cost effective, flexible in-vehicle networks suitable for use in multi-camera, high bandwidth environments. The simulation platform uses a hybrid simulation approach, and allows the coexistence of real network traffic streams and simulated network traffic. This approach provides a number of advantages, such as allowing for the injection and extraction of real network streams from a simulated network topology. We present details of the testbed architecture and compare its performance to that of native systems. We also demonstrate that results obtained are in line with those in the literature. Finally, we describe novel experiments and applications made possible using the developed simulation platform and how it can be used in the evaluation and development of automotive video applications.

ESTIMATION OF OPTIMAL PATH ON URBAN ROAD NETWORKS USING AHP ALGORITHM

This paper describes to develop a multi criteria decision based methodology to find optimal path in real urban road network. Over the year several studies were conducted but most of which rely on single variable like travel distance or travel time as cost function. In this study, seven different attributes influencing the traffic network i.e. distance, time, traffic volume, road width, no. of intersection, parking and encroachment on road are used to define cost function using multi criterion decision making approach. These variables are combined using a Multi-Dimensional Cost Model (MDCM) using the Analytical Hierarchical Process (AHP). The models developed were implemented and closely evaluated in Nagpur city of India. Model is considered for determining optimal path between various Origins and Destinations in real urban traffic network. Composite weighted AHP scored were used to generate AHP decision surface. Finally, the best decision was proposed by generating the least cost path which is considered as optimal path. The resulting routes showed to be more accurate than those obtained utilizing one-dimensional cost functions and AHP is found to be effective tool to deal with optimal route selection problem.

Method for the reliable shortest path search in time-dependent stochastic networks and its application to GIS-based traffic control

A reliable shortest path problem in time-dependent stochastic networks is considered in this paper. We develop and research a method for reliable routing that uses actual and forecast information of traffic flow parameters. We compare the performance of the proposed algorithm with that of a well-known algorithm on a real traffic network in the city of Samara, Russia. On the basis of computing experiments it is shown that while being a bit more computationally challenging, the proposed method increases the possibility of successfully solving the shortest path problem in a time-dependent stochastic network.

Air traffic network optimization via Laplacian energy maximization

Designing robust air traffic network is an ongoing research effort that seeks to improve the network robustness when one or more air routes are added to the existing network. We demonstrate that Laplacian energy is a fair and promising measure of network robustness based on a case study of a real air traffic network and extensive numerical experiments. Therefore, this paper aims at maximizing the Laplacian energy to enhance network robustness. The corresponding weighted Laplacian energy maximization problem is formulated as flight route addition problem to facilitate practical operations. Three methods are proposed to solve the flight route addition problem, including tabu search, greedy algorithm and integer program. Their trade-off between optimality performance and computational efficiency is demonstrated through the numerical results on a scale-free network. A case study on a real air traffic network is also included for further investigation.

A swarm intelligent method for traffic light scheduling: application to real urban traffic networks

Traffic lights play an important role nowadays for solving complex and serious urban traffic problems. How to optimize the schedule of hundreds of traffic lights has become a challenging and exciting problem. This paper proposes an inner and outer cellular automaton mechanism combined with particle swarm optimization (IOCA-PSO) method to achieve a dynamic and real-time optimization scheduling of urban traffic lights. The IOCA-PSO method includes the inner cellular model (ICM), the outer cellular model (OCM), and the fitness function. Our work can be divided into following parts: (1) Concise basic transition rules and affiliated transition rules are proposed in ICM, which can help the proposed phase cycle planning (PCP) algorithm achieve a globally sophisticated scheduling and offer effective solutions for different traffic problems; (2) Benefited from the combination of cellular automaton (CA) and particle swarm optimization (PSO), the proposed inner and outer cellular PSO (IOPSO) algorithm in OCM offers a strong search ability to find out the optimal timing control; (3) The proposed fitness function can evaluate and conduct the optimization of traffic lights' scheduling dynamically for different aims by adjusting parameters. Extensive experiments show that, compared with the PSO method, the genetic algorithm method and the RANDOM method in real cases, IOCA-PSO presents distinct improvements under different traffic conditions, which shows a high adaptability of the proposed method in urban traffic network scales under different traffic flow states, intersection numbers, and vehicle numbers.

Analysis of Road Traffic Network Cascade Failures with Coupled Map Lattice Method

In recent years, there is growing literature concerning the cascading failure of network characteristics. The object of this paper is to investigate the cascade failures on road traffic network, considering the aeolotropism of road traffic network topology and road congestion dissipation in traffic flow. An improved coupled map lattice (CML) model is proposed. Furthermore, in order to match the congestion dissipation, a recovery mechanism is put forward in this paper. With a real urban road traffic network in Beijing, the cascading failures are tested using different attack strategies, coupling strengths, external perturbations, and attacked road segment numbers. The impacts of different aspects on road traffic network are evaluated based on the simulation results. The findings confirmed the important roles that these characteristics played in the cascading failure propagation and dissipation on road traffic network. We hope these findings are helpful to find out the optimal road network topology and avoid cascading failure on road network.

Two Applications of Statistics to Traffic Models

Two statistical applications for estimation and prediction of flows in traffic networks are presented. In the first, the number of route users are assumed to be independent α-shifted gamma Γ(θ, λ0) random variables denoted H(α, θ, λ0), with common λ0. As a consequence, the link, OD (origin-destination) and node flows are also H(α, θ, λ0) variables. We assume that the main source of information is plate scanning, which permits us to identify, totally or partially, the vehicle route, OD and link flows by scanning their corresponding plate numbers at an adequately selected subset of links. A Bayesian approach using conjugate families is proposed that allows us to estimate different traffic flows. In the second application, a stochastic demand dynamic traffic model to predict some traffic variables and their time evolution in real networks is presented. The Bayesian network model considers that the variables are generalized Beta variables such that when marginally transformed to standard normal become multivariate normal. The model is able to provide a point estimate, a confidence interval or the density of the variable being predicted. Finally, the models are illustrated by their application to the Nguyen Dupuis network and the Vermont-State example. The resulting traffic predictions seem to be promising for real traffic networks and can be done in real time.

Applying Projection‐Based Methods to the Asymmetric Traffic Assignment Problem

AbstractThis article examines the application of a path‐based algorithm to the static and fixed demand asymmetric traffic assignment problem. The algorithm is of the simplicial decomposition type and it solves the equilibration or master problem step by means of five existing projection methods for variational inequality problems to evaluate their performance on real traffic networks. The projection methods evaluated are: (1) a cost approximation‐based method for minimizing the Fukushima's gap function, (2) the modified descent method of Zhu and Marcotte (), (3) the double projection method of Khobotov () and three of its recently developed variants (Nadezhkina and Takahashi, ; Wang et al., ; and He et al., 2012); (4) the method of Solodov and Svaiter (); and (5) the method of Solodov and Tseng (). These projection methods do not require evaluation of the Jacobians of the path cost functions. The source for asymmetries are link costs with interactions, as in the case of priority ruled junctions. The path‐based algorithm has been computationally tested using the previous projection methods on three medium to large networks under different levels of congestion and the computational results are presented and discussed. Comparisons are also made with the basic projection algorithm for the fixed demand asymmetric traffic assignment problem. Despite the lack of monotonicity properties of the test problems, the only method that failed to converge under heavy congestion levels was the basic projection algorithm. The fastest convergence was obtained in all cases solving the master problem step using the method of He et al. (2012), which is a variant of Khobotov's method.

A fast decomposition approach for traffic control

Real-time road traffic control has been the subject of active research efforts for more than fifty years. In recent years, however, the convergence of ubiquitous sensing with seamless communication technologies has motivated the development of more computationally efficient control methods, able to operate in real-time in a live environment. In this work, we present a fast decomposition method for network optimization problems, with application to real-time traffic control. Our approach is based on a nonlinear programming formulation of the network control problem and consists of an alternating directions method using forward numerical simulation in place of one of the optimization subproblems. The method is scalable to realistic city-size road networks for real-time applications, and is shown to perform well on synthetic and real traffic networks.

Faster hyperpath generating algorithms for vehicle navigation

This article interprets hyperpath in the context of risk-averse vehicle navigation and proposes several accelerated algorithms for generating hyperpaths based on studies in shortest path problems. A hyperpath search can be accelerated in two ways: first, by using the optimistic travel time as heuristic node potentials and, second, by introducing a node-directed search that reduces the node/link set to be checked. These two acceleration techniques can be applied independently or jointly to the existing Spiess and Florian (SF) algorithm or hyperpath-Dijkstra algorithm without changing the hyperpath, which results in eight algorithms including the existing ones. Having investigated the performance of each algorithm numerically in both synthetic and real traffic networks, we found that the optimistic node potential-based SF algorithm implemented by a node-directed search was the best one for the traffic networks tested.

Development of a Shortest Path Searching Algorithm Using Minimum Expected Weights

This paper developed a new shortest path searching algorithm based on Dijkstra`s algorithm and algorithm, so it guarantees to find a shortest path in efficient manner. In this developed algorithm, minimum expected weights implies the value that straight line distance from a visiting node to the target node multiplied by minimum link unit, and this value can be the lowest weights between the two nodes. In behalf of the minimum expected weights, at each traversal step, developed algorithm in this paper is able to decide visiting a new node or retreating to the previously visited node, and results are guaranteed. Newly developed algorithm was tested in a real traffic network and found that the searching time of the algorithm was not as fast as other algorithms, however, it perfectly found a minimum path in any case. Therefore, this developed algorithm will be effective for the domain of searching in a large network such as RGV which operates in wide area.

Splitting of two-component package flow in network

The article consider a method that allows for a real traffic network without markov properties to perform the markov approximation, which allows usage of standard methods of queuing theory for solving the control problems