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In contrast to the DUO models with fixed demand, the DUO models with variable demand consider both elastic traffic demand and route choice within a mathematical program framework. The essence of this framework is the existence of feedback on network conditions, where the amount of O-D demand is determined by the corresponding minimal route travel times, which are in turn affected by the O-D demand through the traffic assignment procedure. Since the interactions between the variable demand and route choice decisions are treated within a unified framework, the internal inconsistencies that are apt to characterize conventional transportation planning procedures can be avoided. In consideration of the departure time choices, two types of models can be further identified, i.e., the DUO variable demand/route choice model, and the DUO variable demand/departure time/route choice model.

In the standard traffic assignment problem, link capacities are determined exogenously and remain unchanged throughout the solution process. Even though such an assumption may have been necessary in the past to keep a traffic assignment problem manageable, it fails to reflect the real situation. In fact, the magnitude of link capacities are dependent on the prevailing conditions consisting of physical features of the roadway and traffic conditions. Traffic conditions are strongly influenced by the roadway characteristics. During short periods of time, however, the traffic conditions are mostly influenced by the traffic signal control system of the roadway. Thus, link capacities can be simplified as functions of traffic signals for dynamic analysis. To better represent the real situation, the effect of signal controls on the traffic flow pattern must be explicitly taken into account in the standard traffic assignment.

Dynamic user-optimal singly constrained O-D choice models assume that the total number of trips either leaving each origin, or arriving at each destination, is predetermined. From the computational point of view, both types of situations can be covered within a unified framework by simply reversing the direction of the traffic assignment procedure. As an illustration, we only consider DUO singly constrained models with trips fixed at origins throughout this chapter.

The purpose of the dynamic user-optimal (DUO) departure time/route choice problem is to determine the best departure time along with the best route choice decision. The DUO departure time/route choice problem is a generalization of the DUO route choice problem, since the departure times for travelers are not prespecified and are allowed to change in response to different levels of network congestioa The relaxation of fixed trip departure times is appropriate; in many situations, travelers tend to choose the departure time that minimizes their en route travel times, especially when there is no penalty for either early or late arrival. If we consider a work-to-home trip, the choice of departure time is influenced by the wish of the traveler to avoid unnecessary delay en route. In feet, by minimizing an individual’s en route travel time, more efficient utilization of the total network capacity can be reasonably expected. The day-to-day adjustment for commuters will gradually stabilize the departure time choice, thereby influencing the DUO route choice problem.

In this book, we have presented a fresh look on modeling discrete-time dynamic transportation networks mainly using the variational inequality approach. However, in addition to the most difficult accounting problems (non-convergence or oscillation behavior due to discretization of the temporal dimension), and the nonconvex property of the feasible solution, many other modeling issues still need more clarification including: dynamic link travel time functions, link capacity constraints, first-in-first-out requirements, computational efficiency, multiple user classes, nonconvex feasible regions, integration of analytical- and simulation-based dynamic models and others. Nevertheless, in their present form, the proposed dynamic travel choice models are still applicable to various engineering and economic problems, such as transportation planning, route guidance, economic networks and logistics. In this chapter, we address the remaining research issues in Section 14.1, and then discuss the possible applications in Section 14.2 before adding some final notes in Section 14.3.

Dynamic user-optimal mode choice models assume that the total number of trips are fixed, but travelers are allowed to choose their transportation mode according to personal preference. This situation may be exemplified by work trips, where the total number of woric trips is usually prespecified; however, the mode of transportation is decided by the behavior and characteristics of the travelers; in particular, certain travelers will be captive transit riders. The choice of mode of transportation may be determined in such a way that the necessary en route travel time (cost) is also minimal. In consideration of departure time choices, two types of models can be identified, i.e., the DUO mode choice/route choice model and the DUO mode choice/departure time/route choice model.

Intelligent transportation systems (ITS) have attracted much interest among researchers and practitioners in the past decade. The main objectives of ITS are to improve the efficiency of transportation networks, enhance traffic safety, and reduce delays and negative environmental effects by utilizing real-time or predicted traffic information. To this end, six system functions have been identified: advanced traveler information systems (ATIS), advanced traffic management systems (ATMS), commercial vehicle operations (CVO), advanced vehicle control systems (AVCS), advanced public transportation systems (APTS) and advanced rural transportation systems (ARTS). (In 1996, US DOT reclassified 29 ITS user services into six function groups, i.e., travel and traffic management, commercial vehicle operations, public transportation management, electronic payment, emergency management, and advanced vehicle control and safety systems. However, use of the previous acronyms persist.) The application of ITS technologies is characterized by the exchange of vast amounts of data among transportation system users, vehicles, transportation operators, and transportation infrastructure, which makes possible the warning and avoidance of congestion or hazardous conditions, automatic collection of tolls, efficient dispatching of trucks and buses, dramatic improvements in traffic safety and many other benefits. In order to advance the development of such technologies, both hardware and software components must become more sophisticated; otherwise, the full benefit of ITS cannot be attained.

In this chapter, algorithms are investigated for the dynamic user-optimal route choice model. All possible link interactions are first discussed in Section 5.1. A nested diagonalization method, embedding the Frank-Wolfe (FW) method, is then presented in Section 5.2. A nested projection method and a modified nested projection method are described in Sections 5.3 and 5.4, respectively. A numerical example is given to validate the correctness of the nested diagonalization method in Section 5.5. Finally, a concluding note is given in Section 5.6.

In this paper, an extended linehaul-feeder vehicle routing problem with time windows (ELFVRPTW) is addressed. The objective is to minimise the total fixed cost, travel cost, manpower cost and waiting cost for all vehicle routes. In the ELFVRPTW, vehicles are divided into two types (large and small vehicles) according to size and capacity. Small vehicles deliver goods within time windows to the customers that are located on narrow roads. When the load of a small vehicle en route is empty or near-empty, it may reload goods from a large vehicle residing at one virtual depot or directly from the physical depot. As compared with its earlier version, called the linehaul-feeder vehicle routing problem (LFVRPTW), the ELFVRPTW is more realistic, because several large vehicles and capacity limitation of large vehicles are taken into consideration. To solve this difficult combinatorial problem, a two-stage solution heuristics is developed. The test results show that the ELFVRPTW is advantageous over the LFVRPTW with respect to the average objective value and the average number of used vehicles.

As one of the newly industrialized countries, Taiwan is moving rapidly toward modernization. Accompanying this high economic growth rate are serious traffic problems. Unfortunately, traditional TSM strategies do not help a lot, and a more advanced traffic management system, which is referred to as IVHS, is thus necessitated. A general description about recent progress for IVHS development in Taiwan is presented. Seven relevant research areas and/or significant on-going transportation projects are included. Based on the understanding of our research capability, four intercorrelated IVHS projects are suggested in the future research directions.

Honoring David Boyce for his legendary contributions to the fields of transportation modeling and regional science, the chapters in this festschrift highlight and analyze state-of-the-art and state-of-the-practice methodologies and theories in transportation modeling, regional and urban planning.