A Discrete-Time Stochastic Traffic Assignment Model

A discrete time stochastic traffic assignment model is proposed. The model provides a discrete time description of the variations of flows on a road network during a day or a peak period. The congestion effect at links and link junctions are taken into account. The first in first out principle is enforced on all links at all periods of the day. A stochastic user equilibrium assignment is achieved when the tripmaker is unable to find better travel alternatives. A computational procedure is also presented.

Lazy loading algorithm for traffic assignment of road networks under fixed charge condition

The measures of path charge are important considerations in traffic assignment of road networks. Factors, such as travel time, fixed charge and traffic congestion which affect road users＇ choices of trip paths, are analyzed. Travelers usually decide their trip paths based on their personal habits, preferences and the information at hand. By considering both deterministic and stochastic factors which affect the value of time （VOT） during the process of path choosing, a variational inequality model is proposed to describe the problem of traffic assignment. A lazy loading algorithm for traffic assignment is designed to solve the proposed model, and the calculation steps are given. Numerical experiment results show that compared with the all-or-nothing assignment, the proposed model and the algorithm can provide more optimal traffic assignments for road networks. The results of this study can be used to optimize traffic planning and management.

Direct computing methods for turn flows in traffic assignment

Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks. The first one is designed on the path-turn incidencerelationship, and it is similar to the computational procedure of link flows. It applies to thetraffic assignment algorithms that can provide detailed path structures. The second utilizes thelink-turn incidence relationship and the conservation of flow on links, a law deriving from thisrelationship. It is actually an improved version of Dial's logit assignment algorithm. The proposedapproaches can avoid the shortcomings both of the estimation methods, e. g. Furness's model andFrator's model, and of the network-expanding method in precision, stability and computation scale.Finally, they are validated by numerical examples.

Behaviours in a dynamical model of traffic assignment with elastic demand

This paper investigates the dynamical behaviour of network traffic flow. Assume that trip rates may be influenced by the level of service on the network and travellers are willing to take a faster route. A discrete dynamical model for the day-to-day adjustment process of route choice is presented. The model is then applied to a simple network for analysing the day-to-day behaviours of network flow. It finds that equilibrium is arrived if network flow consists of travellers not very sensitive to the differences of travel cost. Oscillations and chaos of network traffic flow are also found when travellers are sensitive to the travel cost and travel demand in a simple network.

Research on traffic congestion mechanism and countermeasures based on dynamic traffic assignment

Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.

Air Route Network Generation Based on Traffic Assignment

Air route network is the carrier of air traffic flow,and traffic assignment is a method to verify the rationality of air route network structure.Therefore,air route network generation based on traffic assignment has been becoming the research focus of airspace programming technology.Based on link prediction technology and optimization theory,a bi-level programming model is established in the paper.The model includes an upper level of air route network generation model and a lower level of traffic assignment model.The air route network structure generation incorporates network topology generation algorithm based on link prediction technology and optimal path search algorithm based on preference,and the traffic assignment adopts NSGA-Ⅲalgorithm.Based on the Python platform NetworkX complex network analysis library,a network of 57 airports,383 nodes,and 635 segments within China Airspace Beijing and Shanghai Flight Information Regions and 187975 sorties of traffic are used to simulate the bilevel model.Compared with the existing air route network,the proposed air route network can decrease the cost by 50.624%,lower the flight conflict coefficient by 33.564%,and reduce dynamic non-linear coefficient by 7.830%.

Modified origin-based algorithm for traffic equilibrium assignment problems

Key tactics of origin-based user equilibrium (OUE) algorithm was studied,which involved the algorithm procedure and several implementation issues.To speed up the convergence,update policies of flows,costs and bushes were proposed.The methods of step-size searching and bush construction are proved to be practical.The modified OUE algorithm procedure was also optimized to take the advantage of multi-thread process.Convergence performances were compared with those of other algorithms by different sizes of urban transportation networks.The result shows this modified OUE algorithm is more efficient and consumes less time to achieve the reasonable relative gap in practical applications.

A DYNAMIC MODEL OF MULTIPLE PATH TRAFFIC ASSIGNMENT

A “Random Shortest Path”traffic assignment model and its algorithm arepresented by simulating the trip-makers’route-choice characters,and the dynamic meth-od is introduced in the assignment model.It is a ideal multiple path assignment modelwhich can be carried out by the dynamic method and static method,can better reflect boththe shortest path factor and the random factor in the route-choice,and is of reasonableassignment volumes.Besides,both dynamic and static softwares particularly suited to thetraffic assignment of large and medium-sized transportation networks arc developed.

Traffic Assignment Forecast Model Research in ITS

As an important role in the development of ITS, traffic assignment forecast is always the research focus. Based on the analysis of classic traffic assignment forecast models, an improved traffic assignment forecast model, multi-ways probability and capacity constraint (MPCC) is presented. Using the new traffic as- signment forecast model to forecast the traffic volume will improve the rationality and veracity of traffic as- signment forecast.

Development of a Methodology to Evaluate Projects Using Dynamic Traffic Assignment Models

The identification and selection of performance measures play an important role in any decision making process. Additionally, millions of dollars are spent on appropriate planning and identification of prospective projects for improvements. As a result, current practitioners spend a lot of time and money in prioritizing their limited resources. This research proposes two tasks: 1) estimation of performance measures using a simulation based on dynamic traffic assignment model, and 2) development of a methodology to evaluate multiple projects based on benefit-cost analysis. The model, DynusT, is used for the Las Vegas roadway network during the morning peak time period. A comparative analysis of the results from proposed methodology with existing California Benefit-Cost (Cal-B/C) models is presented. The results indicate that the new methodology provides an accurate benefit-cost ratio of the projects. In addition, it signifies that the existing Cal-B/C models underestimate the benefits associated with the prospective project improvements. The major contribution of this research is the simultaneous estimation of the performance measures and development of a methodology to evaluate multiple projects. This is helpful to decision makers to rank and prioritize future projects in a cost-effective manner. Planning and operational policies for the transportation systems can be developed based on the gained insights from this study.

A Trip-Chain Based User Equilibrium Traffic Assignment Model with Flexible Activities Scheduling Order

This study developed a user equilibrium traffic assignment model based on trip-chains with flexible activity scheduling order and derived the corresponding optimality conditions. We based on the gradient projection method to develop a solution algorithm, the accuracy of which was verified using the test network of UTown. This model could be used to estimate the transportation demands with and without activities scheduling restriction between OD （origin-destination） pairs based on trip-chains, as well as based on trips. Thus, the proposed model is more generalization than conventional trip based or trip-chain based traffic assignment models.

A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment

With emergent interest of Simulation-Based Dynamic Traffic Assignment(SBDTA)in the field of transportation network modeling,deployment of SBDTA models for traffic operations and transportation planning have increased significantly in recent years.In parallel,research and development of innovative approaches of the SBDTA model have enhanced the quality of both the assignment component,i.e,improvement of convergence quality of the Dynamic User Equilibrium(DUE)problem,and the traffic simulation element.However,computational requirement remains to be one of the great challenges for DTA implementations on large-scale networks with a long analysis period.This paper presents a temporal decomposition scheme for large spatial-and temporal-scale dynamic traffic assignment,in which the entire analysis period is divided into Epochs.Vehicle assignment is performed sequentially in each Epoch,thus improving the model scalability and confining the peak run-time memory requirement regardless of the total analysis period.A proposed self-turning scheme adaptively searches for the run-time-optimal Epoch setting during iterations regardless of the characteristics of the modeled network.Extensive numerical experiments confirm the promising performance of the proposed algorithmic schemes.

Bus frequency optimization in a large-scale multi-modal transportation system:integrating 3D-MFD and dynamic traffic assignment

A properly designed public transport system is expected to improve traffic efficiency.A high-frequency bus service would decrease the waiting time for passengers,but the interaction between buses and cars might result in more serious congestion.On the other hand,a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars.It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users.It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices.However,as far as the authors know,most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function,and do not properly consider the congestion dynamics and their impacts on mode choices.To fill this gap,this paper proposes a bi-level optimization model.A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics.A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm.A surrogate model-based algorithm is used to solve the bi-level programming problem.

IMPROVED MODEL OF MULTIPATH TRAFFIC ASSIGNENT AND NODE ASSIGNENT ALGORITHM

In the paper, multipeth traffic assignment model is improved, a quick aI8orithm,Nede Assignment Algorithm, of this medel is presented, and some problems such as dispolof network, arransement of assigned nodes and determination of parameters are discussed

Combinatorial Model Involving Mixed Deterministic Freight Volume Distribution and User's Equilibrium Assignment

Considering characteristics of Chinese urban mixed traffic,the author develops a combinatorial model involving the mixed deterministic traffic volume distribution and user's equilibrium (UE) assignment on the basis of symmetrical link travel time function (or deterrence).Its uniqueness and equivalance to the Wardropian principle of UE are also proved.Finally,we give the algorithm of model.

Routing with constraint and wavelength assignment based on GMPLS support

To solve the routing and wavelength assignment problem in the optical network, this paper put forward the metric model with sparse wavelength conversion and routing algorithm with pruning （RAP） in order to efficiently realize the resource management in the unified way. Related methods with dynamic and adaptation characters considered the link state conditions and certain heuristic information. They can be applied to current network environments. In particular, the simulation was made according to the selfsimilar traffic and the results showed that the corresponding methods not only provided service differentiation but also reduced the overall average blocking Drobabilitv.

Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

Dynamical evolution processes of traffic flow and travel cost in urban transportation networks

Considering such a fact that travellers dynamically adjust their routes and the resultant link traffic flows in a network evolve over time, this paper proposes a dynamical evolutionary model of the traffic assignment problem with endogenous origin-destination （OD） demands. The model＇s stability is analysed and the resultant user equilibrium （UE） state is shown to be stable under certain conditions. Numerical results in a grid network indicate that the model can generate convergent flow patterns and finally terminates at the UE state. Impacts by the parameters associated with OD demand function and link cost function are also investigated.