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Abstract Within the framework of numerical simulation and optimal control of partial differential equations, in this work we deal with the mathematical modelling and optimal management of urban road networks. In particular, we are interested in finding the optimal management of the network intersections in order to reduce traffic congestion and atmospheric pollution. So, we consider two different multi-objective control problems (the former from a cooperative viewpoint, the latter within a hierarchical paradigm), propose a complete numerical algorithm to solve them, and, finally, present several numerical tests for a realistic case posed in the Guadalajara Metropolitan Area (Mexico), where the possibilities of our methodology are shown. [ABSTRACT FROM AUTHOR]

Reinforcement learning method has a self-learning ability in complex multidimensional space because it does not need accurate mathematical model and due to the low requirement for prior knowledge of the environment. The single intersection, arterial lines, and regional road network of a group of multiple intersections are taken as the research object on the paper. Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve the current signal timing scheme. However, the traffic signal optimization strategy based on reinforcement learning is suitable for complex traffic environments (high flows and multiple intersections), and the effects of which are better than the current optimization methods in the conditions of high flows in single intersections, arteries, and regional multi-intersection. In a word, the problem of insufficient traffic signal control capability is studied, and the hierarchical control algorithm based on reinforcement learning is applied to traffic signal control, so as to provide new ideas and methods for traffic signal control theory. [ABSTRACT FROM AUTHOR]

Abstract In this paper we propose a new aggregated Variable-Length cell transmission Model (VLM) for traffic systems consisting of only three lumped state variables. The first two state variables are the lumped (or averaged) densities associated with the two cells of variable length into which the considered road section is subdivided. These cells are, respectively, a downstream congested cell, of length l , and an upstream free cell, of length L - l , L being the total length of the section. The third state, namely l , describes the position of the congestion wave front. The paper, apart from providing the new traffic model formulation, also investigates several mathematical properties of this model, ensuring the consistency with respect to the inherent mass (or vehicle) conservation law. Two case studies, a ring road and a urban road with traffic lights, are considered to assess the proposed model validity also relying on simulation evidence. [ABSTRACT FROM AUTHOR]

Highlights • Propose a unifiable multi-commodity kinematic wave model. • Construct unifiable commodity speed-density relations. • Analytically solve the Riemann problem for two commodities. • Propose a unifiable multi-commodity Cell Transmission Model. Abstract In the literature, many kinematic wave models have been proposed for multi-class vehicles on multi-lane roads; however, there lacks an explicit model of unifiable multi-commodity traffic, in which different commodity flows can have different speeds and violate the first-in-first-out (FIFO) principle, but there exists a speed-density relation for the total traffic. In this study, we attempt to fill the gap by constructing and solving a unifiable multi-commodity kinematic wave model. We first construct commodity speed-density relations based on generic generating functions. Then for two commodities we discuss the properties of the unifiable kinematic wave model and analytically solve the Riemann problem with a combination of total and commodity kinematic waves. We propose a unifiable multi-commodity Cell Transmission Model (CTM) with a general junction model for numerical simulations of network traffic flows, which are unifiable but may violate the FIFO principle. We prove that the CTM is well-defined under an extended CFL (Courant et al., 1928) condition. With examples we verify the consistency between the analytical and numerical solutions and demonstrate the convergence of the CTM. We conclude with several follow-up research directions for unifiable multi-commodity kinematic wave models. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a common problem that affects many cities around the world. In this paper, an actual urban traffic simulation model (AUTM) for traffic congestion predicting and avoiding is proposed, which includes three key components: the map and transfer (MT) conversion method, the optimized spatial evolution rules, and a congestion-avoidance routing algorithm. Three key techniques are combined in our proposed model: (1) The MT conversion method is proposed to get actual urban cellular spaces, which apply the optimized spatial evolution rules to simulate the vehicular dynamics better. (2) AUTM is proposed for simulating traffic congestion and predicting the effect of adding overpasses and roadblocks. (3) The congestion-avoidance routing algorithm is proposed for vehicles to dynamically update their routes toward their destinations, which can achieve traffic optimization in urban simulations. This paper presents the results of applying this novel model to a large-scale real-world case in different urban traffic congestion situations. Extensive experimental simulations in various actual cities have been carried out. Our results in the extreme case are encouraging: The prediction accuracy of traffic congestions is almost 89%, and the variance of prediction road density is less than 0.15. [ABSTRACT FROM AUTHOR]

Abstract To develop a short-term traffic load prediction model for satellite networks, a prediction algorithm based on spatiotemporal correlation and least square support vector machine (STLS-SVM) is presented. The prediction model fully exploits the regularity and periodicity of satellite constellations and uses the lag correlation coefficients to determine which satellite pairs have the highest spatiotemporal correlation. Then, the traffic time sequences of the most highly correlated satellites are taken as input feature vectors for training the LS-SVM for short-term traffic prediction. A simulation test shows that the algorithm has higher network flow prediction accuracy and that using the spatiotemporal correlation improves the predictive performance. [ABSTRACT FROM AUTHOR]

We consider switched queueing networks with a mix of heavy-tailed (i.e., arrival processes with infinite variance) and exponential-type traffic and study the delay performance of the max-weight policy, known for its throughput optimality and asymptotic delay optimality properties. Our focus is on the impact of heavy-tailed traffic on exponential-type queues/flows, which may manifest itself in the form of subtle rate-dependent phenomena. We introduce a novel class of Lyapunov functions (piecewise linear and nonincreasing in the length of heavy-tailed queues), whose drift analysis provides exponentially decaying upper bounds to queue-length tail asymptotics despite the presence of heavy tails. To facilitate a drift analysis, we employ fluid approximations, proving that if a continuous and piecewise linear function is also a "Lyapunov function" for the fluid model, then the same function is a "Lyapunov function" for the original stochastic system. Furthermore, we use fluid approximations and renewal theory in order to prove delay instability results, i.e., infinite expected delays in steady state. We illustrate the benefits of the proposed approach in two ways: (i) analytically, by studying the delay stability regions of single-hop switched queueing networks with disjoint schedules, providing a precise characterization of these regions for certain queues and inner and outer bounds for the rest. As a side result, we prove monotonicity properties for the service rates of different schedules that, in turn, allow us to identify "critical configurations" toward which the state of the system is driven, and that determine to a large extent delay stability; (ii) computationally, through a bottleneck identification algorithm, which identifies (some) delay unstable queues/flows in complex switched queueing networks by solving the fluid model from certain initial conditions. [ABSTRACT FROM AUTHOR]

Abstract: Taking account of the effect of the driver's memory, an extended car‐following model is proposed in this paper. A control signal including the velocity contrast of considered car and the following car is taken into account in this extended model. Numerical simulations are implemented to prove that the application of the model can suppress the traffic congestion successfully. [ABSTRACT FROM AUTHOR]

Large cities have been facing serious problems in the management of traffic, owing to the increasing number of vehicles and pedestrians. Traffic engineering is essential in managing traffic and improving urban mobility. This paper deals with the problem of fixed-time signal programming on traffic networks. A new bi-objective optimization model is proposed to maximize the average and minimize the variance of the vehicle speeds in the network. Although the first function is commonly discussed in the literature, the second one is novel, and its aim is to provide flow balance along the network. This combination of functions is optimized by the Memory-Based Variable-Length Nondominated Sorting Genetic Algorithm 2 (MBVL-NSGA2), which avoids the revaluation of candidate solutions. This approach was validated through experiments using the microscopic simulator GISSIM, in a multiintersection real network, using measured data from Belo Horizonte traffic engineering company (BHTRANS). The practical results of MBVL-NSGA2 were compared with four approaches: (1) current BHTRANS solutions; (2) a genetic algorithm optimizing the first function; (3) a genetic algorithm optimizing the second function, and; (4) the traditional NSGA2. Analysis showed that this proposal is able to generate better traffic signal plans, at the same time that it generates a diversified set of efficient candidate solutions. [ABSTRACT FROM AUTHOR]

The car-following model is an important micro-traffic model for simulating car-following behaviour in traffic engineering and research studies. Conventional car-following models are always presented using mathematical equations reflecting ideal traffic conditions. In the big data era, data-driven models become a popular trend. In this study, a data-driven car-following model based on the rough set theory is proposed to consider information hidden in a field data set. On the basis of field data obtained from measurement devices such as the next generation simulation (NGSIM) trajectory data set, and using the methods of the rough set theory, an optimal decision rule set is established. Redundant attributes and redundant attribute values are removed for simplifying the car-following behaviour decision problem. Attribute significance and weights are computed for selecting matching rules. A car-following behaviour decision algorithm is designed to choose appropriate rules to determine the follower's velocity according to current observations. Simulations illustrate that the proposed data-driven car-following model can simulate the micro-traffic behaviour of followers well. [ABSTRACT FROM AUTHOR]

In this paper, we provide a detailed discussion for the paper of Roca-Riu, Estrada and Trapote that was published in Transportation Research Part A: Policy and Practice 46(8): 1153-1165, (2012). Several questionable results will be pointed out that will help researcher realize this important paper of Roca-Riu, Estrada and Trapote. [ABSTRACT FROM PUBLISHER]

Perimeter control based on the Macroscopic Fundamental Diagram (MFD) is widely developed for alleviating or postponing congestion in a protected region. Recent studies reveal that traffic conditions might not be improved if the perimeter control strategies are applied to unstable systems where high demand generates heavy and heterogeneously distributed traffic congestion. Therefore, considering stability of the targeted traffic system is essential, for the sake of developing a feasible and then optimal control strategy. This paper sheds light on this direction. It integrates a stability characterization algorithm of MFD system equations into the Model Predictive Control (MPC) scheme, and features respectively an upper and a lower bound of the feasible control inputs, to guarantee system stability. Firstly, the dynamics of traffic heterogeneity and its effect on the MFD are analyzed, using real data from Guangzhou in China. Piecewise affine functions of average flow are proposed to capture traffic heterogeneity in both regional and subregional MFDs. Secondly, stability of a three-state two-region system is investigated via stable equilibrium and surface boundaries analysis. Finally, a three-layer hierarchical control strategy is introduced for the studied two-region heterogeneous urban networks. The first layer of the controller calculates the stable surface boundaries for the given traffic demands and then determines the bounds of control input (split rate). An MPC approach in the second layer is used to solve an optimization problem with two objectives of minimizing total network delay and maximizing network throughput. Heterogeneity among the subregions is minimized in the last layer by implementing simultaneously a subregional perimeter flow control and an internal flow control. The effectiveness and stability of the proposed control approach are verified by comparison with four existing perimeter control strategies. [ABSTRACT FROM AUTHOR]

This paper presents an empirical study of the fundamental relationship between speed, v, and flow, q, (denoted vqFR) under low flow in the uncongested regime. Using new analytical techniques to extract more information from loop detector data, the vqFR from a time of day HOV lane exhibits high v that slowly drops as q increases. This curve arises after binning several million vehicles by q and only considering those bins with q < 1200 vph. A surprising thing happens when further binning the data by the adjacent lane speed (v2): the vqFR expands in to a fan of curves that decrease in magnitude and slope with decreasing v2. Yet each curve in the fan continues to exhibit uncongested trends, ranging from a flat curve consistent with recent editions of the Highway Capacity Manual to downward sloping curves. It is shown that this behavior was not due to the HOV operations per se, the same behavior also arises in the non-HOV period when the lane serves all vehicles and it is also observed at another facility without any HOV restrictions. This dependency on the adjacent lane is absent from most traffic flow theories. Taking a broader view, four different factors appear to limit the speed a driver takes: (i) the roadway geometry, (ii) the posted speed limit, (iii) the vehicle ahead (car following), and (iv) traffic conditions in the adjacent lane. Whichever constraint is most binding determines the driver's speed. While the first three constraints are found in the literature, this work contributes the fourth, as per above. When the speed limit is the most binding constraint the uncongested regime of the vqFR is roughly flat with a near constant speed over a wide range of q. When the roadway geometry is the binding constraint, e.g., due to the lack of speed limits, drivers are able to travel fast enough to be sensitive to the vehicle ahead and exhibit lower v as q increases. Car following is by definition in the congested regime and thus, beyond the scope of this paper. Finally, the present work shows that as the adjacent lane moves slower, the uncongested drivers choose speeds below the speed limit and once more exhibit lower v as q increases. Although the chosen v is below the speed limit, the drivers continue to exhibit behavior consistent with the uncongested regime. [ABSTRACT FROM AUTHOR]

The macroscopic fundamental diagram (MFD) is introduced in recent studies to present the relationship between the flow and the density of the network in large urban regions (neighborhoods). The MFD can be also rescaled to approximate network outflow as a function of the vehicular accumulation of the system in the morning commute problem. In this research, we develop a bathtub model (macro-scale traffic congestion model) by combining Vickrey's (1969) model of dynamic congestion with the MFD to formulate the user equilibrium over the peak as an ordinary differential equation (ODE). This problem can be solved numerically to estimate the exact solution of the morning commute problem. Alternatively, the morning commute problem can be solved analytically by approximating the solution of the ODE using a well-behaved function. Here, we present a quadratic and also a linear approximation of the equilibrium solution for a semi-quadratic MFD, considering that the declining part of the MFD is shown to be well estimated by a quadratic function. To optimize the system, we present pricing strategies for network users (dynamic tolling) and employers inside the region (dynamic taxing) that can minimize the generalized cost of the system by keeping the outflow maximized over the peak. Finally, we compare the exact and the approximate solutions of the problem, and also the proposed pricing strategies of the region in a numerical example. [ABSTRACT FROM AUTHOR]

In part I of this study (Lee et al., 2017), the formulation of a theoretical framework for a group-based adaptive traffic-control method for isolated signalized junctions is presented, which includes tactical and local levels of signal timing optimization. The global level control aims to determine the time-varying cycle structure, with a resolution of cycles, and the real-time adjustment of the green phase, with a resolution of seconds, based on longer-term traffic information observed by traffic detectors. Overall, the purpose of the study is to actualize a multi-resolution strategy for a group-based adaptive signal-control method and establish a microscopic simulation platform to implement the proposed methodology and test its effectiveness. To actualize the global proactive-optimization scheme, in this paper, a rolling-horizon approach to the temporal and spatial variables, signal structures for four-arm intersections, and discrete directional search methods is applied using the developed mathematical framework. The formulation of the group-based max-pressure policy is realized using the logical form of the local reactive-control policy at a typical directional three-lane, four-arm approach to an isolated intersection. The integrated group-based adaptive traffic-signal control is actualized using VISSIM, Fortran, and VBA based on the developed tactical and local levels of signal timing optimization. The results of the computer simulations and the case study presented in this paper show that the integrated group-based adaptive traffic-signal-control logic outperforms the other methods over a wide range of traffic conditions, from free-flowing traffic to extreme congestion. Moreover, the proposed models perform much better than the existing fixed-signal plan and the actuated signal-control in asymmetric traffic conditions. [ABSTRACT FROM AUTHOR]

The original extreme learning machine (ELM) was designed for the balanced data, and it balanced misclassification cost of every sample to get the solution. Weighted extreme learning machine assumed that the balance can be achieved through the equality of misclassification costs. This paper improves previous weighted ELM with decay-weight matrix setting for balance and optimization learning. The decay-weight matrix is based on the sample number of each class, but the weight sum values of each class are not necessarily equal. When the number of samples is reduced, the weight sum is also reduced. By adjusting the decaying velocity, classifier could achieve more appropriate boundary position. From the experimental results, the decay-weighted ELM obtains the better effects in solving the imbalance classification tasks, particularly in multiclass tasks. This method was successfully applied to build the prediction model in the urban traffic congestion prediction system. [ABSTRACT FROM AUTHOR]

This study attempts to make use of traffic behaviour on the aggregate level to estimate congestion on urban arterial and sub-arterial roads of a city exhibiting heterogeneous traffic conditions by breaking the route into independent segments and approximating the origin-destination based traffic flow behaviour of the segments. The expected travel time in making a trip is modelled against sectional traffic characteristics (flow and speed) at origin and destination points of road segments, and roadway and segment traffic characteristics such as diversion routes are also tried in accounting for travel time. Predicted travel time is then used along with free flow time to determine the state of congestion on the segments using a congestion index (CI). A development of this kind may help in understanding traffic and congestion behaviour practically using easily accessible inputs, limited only to the nodes, and help in improving road network planning and management. [ABSTRACT FROM AUTHOR]

Traffic control in a targeted area is one of the most discussed topics in today's scenario. The increase in number of vehicles in the past few years, has caused a spike in traffic jams at intersections, disrupting entire roadway transportation. These situations demand more efficient methodology to control traffic. This paper proposes a framework to counter the above stated issues using a combination of efficient optimization and decision making algorithms. The framework relies on gathering real time data from commuters, transmitting and analyzing the same to determine vehicle densities on specific routes. The vehicle density on a specific route becomes an important factor in analyzing the traffic conditions in that specific area. The framework also proposes a model to optimize the traffic signal timers at a targeted traffic junction or cross- section, by performing predictive analysis using decision trees. [ABSTRACT FROM AUTHOR]

Road traffic congestion, which occurs when the traffic demand exceeds the operational capacity of the roadway, has become a serious problem all over the world. Traffic signals are effective in managing the traffic demand if properly designed. Traditionally, signal design has been carried out based on the objective of minimizing the overall queue and associated delays at upstream sections and has not considered the effect on downstream sections and midblock regions. Efficiency of signals may be improved if their design takes this factor into account, which is attempted in the present study. In this paper, a control scheme that regulates the traffic density in the downstream section was developed using a macroscopic-model-based approach with traffic density and speed as the state variables of interest. These state variables were estimated based on a Kalman filter approach. The performance of the estimation scheme was corroborated using field data. A closed-loop system with the developed control scheme was implemented in a simulated road stretch developed by integrating microscopic traffic-simulation software and mathematical simulation software. The results from the implementation of the closed-loop system demonstrated the effectiveness of the developed traffic-control scheme. It was observed that the objective of maintaining the density inside the section within the required limit was satisfied. [ABSTRACT FROM AUTHOR]

This work contributes to the rapid approximation of solutions to optimization problems that are constrained by iteratively solved transport simulations. Given an objective function, a set of candidate decision variables and a black-box transport simulation that is solved by iteratively attaining a (deterministic or stochastic) equilibrium, the proposed method approximates the best decision variable out of the candidate set without having to run the transport simulation to convergence for every single candidate decision variable. This method can be inserted into a broad class of optimization algorithms or search heuristics that implement the following logic: (i) Create variations of a given, currently best decision variable, (ii) identify one out of these variations as the new currently best decision variable, and (iii) iterate steps (i) and (ii) until no further improvement can be attained. A probabilistic and an asymptotic performance bound are established and exploited in the formulation of an efficient heuristic that is tailored towards tight computational budgets. The efficiency of the method is substantiated through a comprehensive simulation study with a non-trivial road pricing problem. The method is compatible with a broad range of simulators and requires minimal parametrization. [ABSTRACT FROM AUTHOR]

The kinematic wave model of traffic flow on a road network is a system of hyperbolic conservation laws, for which the Riemann solver is of physical, analytical, and numerical importance. In this paper, we present a new Riemann solver at a general network junction in the demand-supply space. In the Riemann solutions, traffic states on a link include the initial, stationary, and interior states, and a discrete Cell Transmission Model flux function in interior states is used as an entropy condition, which is consistent with fair merging and first-in-first-out diverging rules. After deriving the feasibility conditions for both stationary and interior states, we obtain a set of algebraic equations, and prove that the Riemann solver is well-defined, in the sense that the stationary states, the out-fluxes of upstream links, the in-fluxes of downstream links, and kinematic waves on all links can be uniquely solved. In addition, we show that the resulting global flux function in initial states is the same as the local one in interior states. Hence we presents a new definition of invariant junction models, in which the global and local flux functions are the same. We also present a simplified framework for solving the Riemann problem with invariant junction flux functions. [ABSTRACT FROM AUTHOR]

In this paper, we present a parameter learning method to reflect the rapidly changing behaviors in the traffic flow simulation process, in which we insert virtual vehicles into the real trajectory data. We come up with a real-virtual interaction model and then we use genetic algorithm to learn some parameters in the model with the purpose to get some specific driving characteristics. Then we propose a real-virtual interaction system to vividly simulate the various interaction behaviors between the real vehicles and the virtual ones. Our results are compared to the existing methods to prove the effectiveness of our presented method. [ABSTRACT FROM AUTHOR]

Population growth and the massive production of automotive vehicles have lead to the increase of traffic congestion problems. Traffic congestion today is not limited to large metropolitan areas, but is observed even in medium-sized cities and highways. Traffic engineering can contribute to lessen these problems. One possibility, explored in this paper, is to assign tolls to streets and roads, with the objective of inducing drivers to take alternative routes, and thus better distribute traffic across the road network. This assignment problem is often referred to as the tollbooth problem and it is NP-hard. In this paper, we propose mathematical formulations for two versions of the tollbooth problem that use piecewise-linear functions to approximate congestion cost. We also apply a biased random-key genetic algorithm on a set of real-world instances, analyzing solutions when computing shortest paths according to two different weight functions. Experimental results show that the proposed piecewise-linear functions approximate the original convex function quite well and that the biased random-key genetic algorithm produces high-quality solutions. [ABSTRACT FROM AUTHOR]

A multidepot VRP is solved in the context of total urban traffic equilibrium. Under the total traffic equilibrium, the multidepot VRP is changed to GDAP (the problem of Grouping Customers + Estimating OD Traffic + Assigning traffic) and bilevel programming is used to model the problem, where the upper model determines the customers that each truck visits and adds the trucks' trips to the initial OD (Origin/Destination) trips, and the lower model assigns the OD trips to road network. Feedback between upper model and lower model is iterated through OD trips; thus total traffic equilibrium can be simulated. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Network (VANET) makes real-time traffic information accessible to vehicles en routes, thus possesses a great potential to improve traffic safety and mobility in the near future. Existing literature shows that we are still lack of approaches to track information spreading dynamics via VANET, which will prevent the potential applications from success. Motivated by this view, this research develops an information-traffic coupled cell transmission model (IT-CTM) to capture information spreading dynamics via VANET. More exactly, this study considers information spreading over a road segment forms a wave with a front and tail, each of which goes through the road segment following an intermittent transmission pattern due to traffic flow dynamics. The approach of IT-CTM discretizes a road segment into a number of cells. Each cell covers several intermittent transmissions. Mathematical methods are developed to capture the inner-cell and inter-cell movements of information front and tail, which enable us to track the information spreading dynamics along cells. Numerical experiments based on simulation and field data indicate that the IT-CTM can closely track the dynamic movements of information front and tail as well as the dynamic information coverage as a single or multiple piece(s) of information propagating via VANET on a one-way or two-way road segment. The mean absolute error (MAE) for tracking dynamic information coverage is <5% across all experiments in this study. [ABSTRACT FROM AUTHOR]

Congestion, accidents, greenhouse gas emission and others seem to become unsolvable problems for all levels of management in modern large cities worldwide. The increasing motorization dynamics requires development of innovative methodological tools and technical devices to cope with problems emerging on the road networks. First of all, control system for urban traffic area has to be created to support decision makers by processing a big volume of transportation data. The input for such a system is a volume of travel demand between origins and destinations -- OD-matrix. The present work is devoted to the problem of OD-matrix estimation. Original OD-matrix estimation technique is offered by virtue of plate scanning sensors location. Mathematically developed technique is based on a dual formulation of the traffic assignment problem (equal journey time by alternative routes between any OD-pair). Traffic demand between certain OD-pair is estimated due to journey time obtained from plate scanning sensors. Moreover, the explicit relationship between traffic demand and journey time is obtained for network of parallel routes with one OD-pair. Eventually, the developed method was experimentally implemented to the Saint-Petersburg road network. [ABSTRACT FROM AUTHOR]

In this paper, we apply a maximum-principle-satisfying finite volume compact weighted scheme to numerical modeling traffic flow problems on networks. Road networks can be numerically model as a graph, whose edges are a finite number of roads that join at junctions. The evolution on each road is described by a scalar hyperbolic conservation law, and traffic distribution matrices are used to formulate coupling conditions at the network junctions. In order to achieve maximum-principle of the traffic density on each road, the maximum-principle-satisfying polynomial rescaling limiter is adopted. Numerical results for road networks with rich solution structures are presented in this work and indicate that the finite volume compact weighted scheme produces essentially non-oscillatory, maximum principle preserving and high resolution solutions. [ABSTRACT FROM AUTHOR]

In this paper we propose a LWR-like model for traffic flow on networks which allows one to track several groups of drivers, each of them being characterized only by their destination in the network. The path actually followed to reach the destination is not assigned a priori, and can be chosen by the drivers during the journey, taking decisions at junctions. The model is then used to describe three possible behaviors of drivers, associated to three different ways to solve the route choice problem: 1. Drivers ignore the presence of the other vehicles; 2. Drivers react to the current distribution of traffic, but they do not forecast what will happen at later times; 3. Drivers take into account the current and future distribution of vehicles. Notice that, in the latter case, we enter the field of differential games, and, if a solution exists, it likely represents a global equilibrium among drivers. Numerical simulations highlight the differences between the three behaviors and suggest the existence of multiple Wardrop equilibria. [ABSTRACT FROM AUTHOR]

We consider a given region Ω where the traffic flows according to two regimes: in a region C we have a low congestion, where in the remaining part Ω \ C the congestion is higher. The two congestion functions H1 and H2 are given, but the region C has to be determined in an optimal way in order to minimize the total transportation cost. Various penalization terms on C are considered and some numerical computations are shown. [ABSTRACT FROM AUTHOR]

We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian process dynamic congestion model that can effectively characterize both the dynamics and the uncertainty of congestion conditions. Our model is efficient and thus facilitates real-time adaptive routing in the face of uncertainty. Using this congestion model, we develop efficient algorithms for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. A key property of our approach is the ability to efficiently reason about the long-term value of exploration, which enables collectively balancing the exploration/exploitation trade-off for entire fleets of vehicles. Our approach is validated by traffic data from two large Asian cities. Our congestion model is shown to be effective in modeling dynamic congestion conditions. Our routing algorithms also generate significantly faster routes compared to standard baselines, and achieve near-optimal performance compared to an omniscient routing algorithm. We also present the results from a preliminary field study, which showcases the efficacy of our approach. [ABSTRACT FROM AUTHOR]

For the past few years, phenomena such as traffic congestion and traffic accidents in big cities have occurred one after another, greatly and adversely affecting the people's lives. Intelligent transportation system is perceived as an optimum solution to solve this question. This paper delves into several common route guidance algorithms, analyzes respective characteristics, improves and parallelizes ant colony algorithm on the basis of these characteristics, and designs and implements based on Map Reduce under Hadoop. Faced with specific application of transportation system and based on Map Reduce model and distributed data management technology, this paper delves into transportation network data management methods, analyzes transportation data by using VISSIM and designs the transportation network data structure. By abstractly establishing the transportation network data model catering to ant colony algorithm, the parallel ant colony system on the basis of cloud computing is implemented. This experiment has demonstrated good actual value the intelligent transportation system possesses. [ABSTRACT FROM AUTHOR]

In operations research and networking problems, random-order-of-service (ROS) provides a well-known alternative to the classic first-come-first-served (FCFS) policy. Models with ROS policy are typically harder to analyze than their FCFS counterparts, with some performance measures notoriously hard to obtain. While significant progress has been realized in the analysis of random-order-of-service models with homogeneous customer service demands, the impact of heterogeneous customer demand is largely unknown. The current contribution studies this impact, with a discrete-time random-order-of-service queue serving customers with heterogeneous demands. Customer service times are independent random variables with type-dependent distribution. The numbers of new arrivals in each slot are independent and identically distributed over time, but can be type-correlated within a single slot. This corresponds to bursty input traffic generated by a finite number of sources, with one source for each type. The burstiness consists in correlation among sources (or types): the rate at which a source generates customers depends on the instantaneous rate of all other sources (and vice versa). Using a transform-based approach yields closed-form formulas for the first few moments of the customer waiting time. Facilitator is a multi-stage description of the customer sojourn, which allows establishing a relation between the so-called conditional waiting time and the actual steady-state customer waiting time. A somewhat unexpected result shows that, under certain conditions on the arrival and service processes, the random-order-of-service policy outperforms the first-come-first-served policy in terms of mean waiting time. A number of numerical examples illustrate this finding. [ABSTRACT FROM AUTHOR]

This study presents two models for proactive variable speed limit (VSL) control on a recurrently congested freeway segment. The proposed model uses embedded traffic flow relations to predict the evolution of congestion patterns over the projected time horizon, and then computes the time-varying optimal speed limit to smooth traffic flows. To contend with the uncertainties associated with drivers' responses to VSL control, this study has also proposed an advanced model that further adopts Kalman Filter to enhance the traffic state estimation. Both models have been investigated with two control objectives-travel time minimization and speed variance minimization. Our extensive simulation analysis with a VISSIM simulator, calibrated with field data from our previous VSL field demonstration, has revealed the benefits of the proposed VSL control models. Also, the experimental results indicated that the proposed advanced models with both control objectives can significantly reduce the travel time over the recurrent bottleneck locations. With respect to several selected measure of effectiveness (MOEs), such as average number of stops and average travel time, the research results confirm that the control models with the objective of minimizing speed variance can offer the promising properties for field implementation. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the Seoul metropolitan area in Korea, as the city has sprawled drastically over the past several decades. Although the area has a considerable length of expressways, commuters travelling on them ironically suffer from recurrent traffic congestion. The present study focused on the factors that lead commuters to depend on expressways regardless of the disadvantages in terms of travel costs and even travelling times. The impact of commuters' behavioural or attitudinal latent propensities on determination of their usual route for their commute to work was investigated based on a binary logit choice model. Route- and individual-specific variables, which have been widely adopted in conventional route choice studies, were also included in the model. A total of 522 commuters participated in the survey and provided actual information of the routes they used to travel to work. The results showed that behavioural or attitudinal latent determinants other than travel times and costs also contributed to commuters' dependence on expressways. [ABSTRACT FROM AUTHOR]

Diverging junctions are important network bottlenecks, and a better understanding of diverging traffic dynamics has both theoretical and practical implications. In this paper, we present a new framework for constructing kinematic wave solutions to the Riemann problem of kinematic wave models of diverging traffic with jump initial conditions. Within this framework, the function space of weak solutions is enlarged to include interior states that occur with stationary discontinuities, and various discrete flux functions in cell transmission models of diverging traffic are used as entropy conditions to pick out unique physical solutions. Then in demand-supply space, we prove that the Riemann problem can be uniquely solved for existing discrete flux functions, in the sense that stationary states and, therefore, shock or rarefaction waves on all links, can be uniquely determined under given initial conditions. We show that the two diverge models by Lebacque and Daganzo are asymptotically equivalent. We also prove that the supply proportional and priority-based diverge models are locally optimal evacuation strategies. With numerical examples, we demonstrate the validity of the analytical solutions of interior states, stationary states, and corresponding kinematic waves. This study presents a unified framework for analyzing traffic dynamics arising at diverging junctions and could be helpful for developing emergency evacuation strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the cell-transmission model (CTM)-based single destination system optimum dynamic traffic assignment (SO-DTA) problem, focusing attention on a case where the cell properties are time-invariant. We show the backward propagation of congestion in CTM does not affect the optimal arrival flow pattern of SO-DTA, if the fundamental diagram is of triangular/trapezoidal shape as in the CTM. We mathematically prove that the set of earliest arrival flows (EAFs) not constrained by the traffic wave propagation equations obtained on the node-arc network without cell division is a subset of the SO-DTA. This finding leads to a new approach to the SO-DTA that solves the EAF. Such an EAF can be obtained by merely applying static flow techniques and turning the static flows into dynamic flows over time. Therefore, SO-DTA can theoretically be solved with a run time at the link level depending polynomially on log T . We use numerical examples to verify the results and report the computational benefits of the proposed method by solving SO-DTA on a real-world network. [ABSTRACT FROM AUTHOR]

Road network capacity expansions are frequently proposed as solutions to urban traffic congestion but are controversial because it is thought that they can directly “induce” growth in traffic volumes. This article quantifies causal effects of road network capacity expansions on aggregate urban traffic volume and density in U.S. cities using a mixed model propensity score (PS) estimator. The motivation for this approach is that we seek to estimate a dose-response relationship between capacity and volume but suspect confounding from both observed and unobserved characteristics. Analytical results and simulations show that a longitudinal mixed model PS approach can be used to adjust effectively for time-invariant unobserved confounding via random effects (RE). Our empirical results indicate that network capacity expansions can cause substantial increases in aggregate urban traffic volumes such that even major capacity increases can actually lead to little or no reduction in network traffic densities. This result has important implications for optimal urban transportation strategies. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In case of considerable traffic congestion in an airport, transportation of passengers to city centre may become a challenging task, since airports are generally located far from the centre. In this study, transportation of passengers, who arrive at an airport, to the city centre has been handled. A mathematical model has been developed, which minimises total costs arising from both, the time passed, while passengers are waiting for a vehicle, and the dispatching of a significant amount of vehicles for transporting the passengers. By utilising the proposed model, in addition to matter of when to dispatch, it is also possible to specify which type of vehicles, and how many of them should be dispatched at each time interval. Ultimately, it is reported that such a plan has a great potential to enhance the productivity. [ABSTRACT FROM AUTHOR]

Traffic congestion caused by either insufficient road capacity or unexpected events has been a major problem in urban transportation networks. To disseminate accurate traveler information and reduce congestion impact, it is desirable to develop an adaptive model to predict travel time. The proposed model is practically implementable to capture dynamic traffic patterns under various conditions, which integrates the features of exponential smoothing and the Kalman filter by utilizing both real-time and historic data. The model is simple in formulation while robust in performance in terms of accuracy and stability. With a constraint or nonconstraint smoothing factor, the proposed model is tested with both real world and simulated data and demonstrated itself a sound model that outperforms others (e.g., Kalman filter and simple exponential smoothing) specifically under recurring and nonrecurring congestion. [ABSTRACT FROM AUTHOR]

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as-weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases-(i) discovering homogeneous regions, and (ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation. [ABSTRACT FROM AUTHOR]

With our interest to reduce traffic congestion, simulations are necessary to investigate how to achieve this goal. Because of the difficulty of real-time experiments, traffic simulators are a good and widely used tool for analyzing specific situations of the traffic flow virtually. This paper introduces TraffSim, a new platform-independent framework, which is capable of simulating microscopic vehicular road traffic in a time-continuous manner. It is able to simulate scenarios close to reality by using OpenStreetMap but also artificial road networks. TraffSim is able to represent multilane motorways or dense city road networks including multiple types of intersections or one-way. Apart from comprehensive support for models for longitudinal movement, lane-changing and fuel-consumption, a supplementary component combines congestion detection, prediction and minimization of traffic jams through intelligent rerouting algorithms. We present the architectural and logical aspects of the simulator, as well as the user interface and statistical features. [ABSTRACT FROM AUTHOR]

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly. [ABSTRACT FROM AUTHOR]

This paper shows that a wide range of stochastic extensions of the kinematic wave model tend to the same parameter-free expression for the probability of congestion at a given time-space point. This is shown for white noise initial density with deterministic and stochastic fundamental diagram in the case of Riemann problems and the bottleneck problem. It is also found that the stochastic solution (i) preserves the structure of the deterministic solution and (ii) tends to the deterministic solution with time at a given location. [ABSTRACT FROM AUTHOR]

The study aims to develop a novel evolution model of speed perturbations for quantifying the probability of the traffic flow breakdown on congested freeway flow. The model is applicable to freeway operation and management. Time headway, driver behavior, and state transitions with stochastic nature are the most important factors for the breakdown and recovery of a disturbance. Two types of drivers, 'adventurers' and 'conservatives,' and two styles of reacting behavior, 'copy' and 'shift' are defined to characterize driving behavior. The time headway distributions for arriving and discharging traffic are distinguished for the existence of the 'hysteresis' phenomenon. The Monte Carlo simulation is carried out to obtain the estimation of the probability of breakdown (PB). The calculated PB is a monotonically increasing function of freeway flow with an 'S' shape. The simulation results indicate that the proposed model can describe the changes in spatial extent and magnitude of a disturbance, which have potential use for future works and field applications. [ABSTRACT FROM AUTHOR]

The automatic incident detection system (AID) has provided an effective way to achieve the freeway incident detection. To have a good command of the traffic running state on highways and provide reliable efficient decision support in disposing an emergency, authors extend two-class classification and SVM to multi-class classification and SVM. According to the process of traffic incident, the traffic state is divided into three classes: the traffic state of free flow, the traffic state of increased congestion, the traffic state of dissipated congestion. This method researches on the VISSIM to obtain the original data set, and selects input traffic features through the principle component analyses (PCA) method, then constructs the multi-class classification and support vector machine (SVM) model, and applies the genetic algorithm (GA) to optimize the model parameters. Finally, the multi-class classification and SVM model with GA has obtained the satisfactory effect. [ABSTRACT FROM AUTHOR]

Abstract: A link-based elastic demand equilibrium model with explicit link-exit capacities and explicit bottleneck or queuing delays is specified. The model, which is at the borderline between steady state and dynamic equilibrium transport models, may be used to compare different proposed short run ITS strategies (using small or even zero elasticity) and also different proposed long term strategies (using greater elasticity); increasing the consistency between short and long run model evaluations. It is shown that, under very weak conditions, there is a solution to the model. A solution algorithm is suggested and a proof of convergence, under natural conditions, is provided. The model is based on link rather than route flows; so it is suitable for large networks as well as small networks. [Copyright &y& Elsevier]