Traffic assignment is the last step in the conventional four-step transportation planning model, following trip generation, trip distribution, and mode choice. It concerns selection of routes between origins and destinations on the traffic network. Traditional traffic assignment methods do not consider trip chaining behavior. Since commuters always make daily trips in the form of trip chains, meaning a traveler’s trips are sequentially made with spatial correlation, it makes sense to develop models to feature this trip chaining behavior. Network performance in congested areas depends not only on the total daily traffic volume but also on the trip distribution over the course of a day. Therefore, this research makes an effort to propose a network traffic assignment framework featuring commuters’ trip chaining behavior. Travelers make decisions on their departure time and route choices under a capacity-constrained network. The modeling framework sequentially consists of an activity origin-destination (OD) choice model and a dynamic user equilibrium (DUE) traffic assignment model. A heuristic algorithm in an iterative process is proposed. A solution tells commuters’ daily travel patterns and departure distributions. Finally, a numerical test on a simple transportation network with simulation data is provided. In the numerical test, sensitivity analysis is additionally conducted on modeling parameters.

A general framework is presented for replacing static traffic assignment with dynamic traffic assignment within the standard four step transportation planning model. Issues including model consistency and the implementation of a proper feedback loop are explored. The new model is compared with the standard four step model in order to highlight the benefits of using dynamic traffic assignment rather than static. The model is then extended to include a term for the difference between experienced and free-flow travel times, which can be used as a proxy for travel time reliability and highlights the benefits of time-dependent DTA. Additionally, a study on improving the quality of convergence for dynamic traffic assignment is conducted in order to help facilitate the usefulness of this modeling approach in practice. A variety of equilibration techniques are tested, and analysis is performed to contrast these techniques with the method of successive averages. / text

Transportation planning and operation requires determining the state of the transportation system under different network supply and demand conditions. The most fundamental determinant of the state of a transportation system is time-varying traffic flow pattern on its roadway segments. It forms a basis for numerous engineering analyses which are used in operational- and planning-level decision-making process. Dynamic traffic assignment (DTA) models are the leading modeling tools employed to determine time-varying traffic flow pattern under changing network conditions. DTA models have matured over the past three decades, and are now being adopted by transportation planning agencies and traffic management centers. However, DTA models for large-scale regional networks require excessive computational resources. The problem becomes further compounded for other applications such as congestion pricing, capacity calibration, and network design for which DTA needs to be solved repeatedly as a sub-problem. This dissertation aims to improve the efficiency of the DTA models, and increase their viability for various planning and operational applications. To this end, a suite of computational methods based on the combinatorial approach for dynamic traffic assignment was developed in this dissertation. At first, a new polynomial run time combinatorial algorithm for DTA was developed. The combinatorial DTA (CDTA) model complements and aids simulation-based DTA models rather than replace them. This is because various policy measures and active traffic control strategies are best modeled using the simulation-based DTA models. Solution obtained from the CDTA model was provided as an initial feasible solution to a simulation-based DTA model to improve its efficiency – this process is called “warm starting” the simulation-based DTA model. To further improve the efficiency of the simulation-based DTA model, the warm start process is made more efficient through parallel computing. Parallel computing was applied to the CDTA model and the traffic simulator used for warm starting. Finally, another warm start method based on the static traffic assignment model was tested on the simulation-based DTA model. The computational methods developed in this dissertation were tested on the Anaheim, CA and Winnipeg, Canada networks. Models warm-started using the CDTA solution performed better than the purely simulation-based DTA models in terms of equilibrium convergence metrics and run time. Warm start methods using solutions from the static traffic assignment models showed similar improvements. Parallel computing was applied to the CDTA model, and it resulted in faster execution time by employing multiple computer processors. Parallel version of the traffic simulator can also be embedded into the simulation-assignment framework of the simulation-based DTA models and improve their efficiency. / text

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 92-95). Also available in electronic version. Access restricted to campus users.

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Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

The Multi-resolution Assignment and Loading of Transport Activities (MALTA) system is a simulation-based Dynamic Traffic Assignment model that exploits the advantages of multi-processor computing via the use of the Message Passing Interface (MPI) protocol. Spatially partitioned transportation networks are utilized to estimate travel time via alternate routes on mega-scale network models, while the concurrently run shortest path and assignment procedures evaluate traffic conditions and re-assign traffic in order to achieve traffic assignment goals such as User Optimal and/or System Optimal conditions.Performance gain is obtained via the spatial partitioning architecture that allows the simulation domains to distribute the work load based on a specially designed Recursive On-line Load Balance model (ROLB). The ROLB development describes how the transportation network is transformed into an ordered node network which serves as the basis for a minimum cost heuristic, solved using the shortest path, which solves a multi-objective NP Hard binary optimization problem. The approach to this problem contains a least-squares formulation that attempts to balance the computational load of each of the mSim domains as well as to minimize the inter-domain communication requirements. The model is developed from its formal formulation to the heuristic utilized to quickly solve the problem. As a component of the balancing model, a load forecasting technique is used, Fast Sim, to determine what the link loading of the future network in order to estimate average future link speeds enabling a good solution for the ROLB method.The runtime performance of the MALTA model is described in detail. It is shown how a 94% reduction in runtime was achieved with the Maricopa Association of Governments (MAG) network with the use of 33 CPUs. The runtime was reduced from over 60 minutes of runtime on one machine to less than 5 minutes on the 33 CPUs. The results also showed how the individual runtimes on each of the simulation domains could vary drastically with naïve partitioning methods as opposed to the balanced run-time using the ROLB method; confirming the need to have a load balancing technique for MALTA.

We present versions of the Frank-Wolfe method for linearly constrained convex programs, in which consecutive search directions are made conjugate. Preliminary computational studies in a MATLAB environment applying pure Frank-Wolfe, Conjugate direction Frank-Wolfe (CFW), Bi-conjugate Frank-Wolfe (BFW) and ”PARTANized” Frank-Wolfe methods to some classical Traffic Assignment Problems show that CFW and BFW compare favorably to the other methods. This spurred a more detailed study, comparing our methods to Bar-Gera’s origin-based algorithm. This study indicates that our methods are competitive for accuracy requirements suggested by Boyce et al. We further show that CFW is globally convergent. We moreover point at independent studies by other researchers that show that our methods compare favourably with recent bush-based and gradient projection algorithms on computers with several cores. / <p>Updated from "E-publ" to published. QC 20130625</p>

The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.

The agent-based micro-simulation modelling technique for transportation planning is rapidly developing and is being applied to practice in recent years. In contrast to conventional four-step modelling with static assignment theory, this emerging technique employs a dynamic assignment principle. Based on summary of various types of traffic assignment models and algorithms, the thesis elucidates in detail the theories of two models, MATSim and EMME/2, which represent two genres of traffic assignment, i.e., dynamic stochastic stationary state assignment and static deterministic user equilibrium assignment. In the study, the two models are compared and validated to reflect both spatial and temporal variation of the traffic flow pattern. The comparison results indicate that numerical outputs produced by MATSim are not only compatible to those by EMME/2 but more realistic from a temporal point of view. Therefore, agent-based micro-simulation models reflect a promising direction of next generation of transportation planning models.