Yupo Chan

LOCATION THEORY AND DECISION ANALYSIS

This book is aimed toward upperclass and graduate-level courses that include location decisionmaking. It includes the fundamental theories and analysis procedures of that process. With these fundamentals carefully and comprehensively compiled, it is well suited for courses such as management science, operations research, economics, civil and environmental engineering, industrial engineering, geography, urban and regional planning, and policy sciences. The book also serves as an overview of the relationship between location, transport, and land use decisions. As such, it introduces more advanced topics as documented in other works by the author and others. The book discusses how information can be stored in such a way that it can be directly translated to a format for real-time decisionmaking. This means simple and transparent models that are database compatible and require minimal data manipulation in the solution process. These models then become the tools for analysis and decisionmaking.

ESTIMATING AN ORIGIN-DESTINATION MATRIX WITH FUZZY WEIGHTS. PART II: CASE STUDIES

The Fuzzy‐weighted approach (FWA) to origin‐destination (O/D) estimation is tested on an example network and two case studies. This is to illustrate the versatility and robustness of FWA in solving a variety of link‐count inconsistency problems. In the numerical example, constructed from the experience at the Peoples Republic of China (PRC), vehicular‐traffic‐count fuzziness is traceable to bicycle obstruction. In Case Study one, drawn from an intercity highway corridor in Eastern Washington, USA, traffic‐count inconsistency is related to differences in road surface condition over time. In Case two, the most general of the three studies, urban traffic counts are subject to random human errors in sampling without apparent “causes.” The three studies represent different network geometries, covering both the regular grid network with multiple paths between O and D and the network where there is only one path between O and D. This is part of our experimental design to compare probabilistic (or multipath) aga...

Stochastic programming methods applied to network optimization

As communication technologies evolve, it becomes necessary to incorporate the stochastic effect of traffic flows into network models. This paper introduces the stochastic programming (SP) methodology for characterizing traffic. Two SP approaches, here-and-now (HN) and scenario tracking (ST), are described through case studies for a prototype network. A numerical optimization procedure is used to perform the simulation. It is clearly demonstrated that when the probability distributions can be estimated analytically, the HN approach can be attractive. Otherwise, the ST approach may be more appropriate.

A multi-criteria routing model for incident management

Incident management requires rerouting motorists around incidents and dispatching service vehicle to the site of the incident, often in a complex transportation network. This paper proposes a multicriteria optimization model and solution algorithm. Travel distance, expected value and variance of travel time, as well as risk constitute four important criteria in routing. Generalized dynamic programming is used to find nondominated alternate paths for motorists. At the same time, it allows describing the original network in terms of such paths. In so doing, we reduce the dimensionality of network representation. It is on this reduced network that a vehicle routing model with serving priorities is proposed. Such a model finds nondominant tours for service vehicles, dispatching them to the site of the incidents and route them back to the service depot. With a limited fleet of service vehicles, the inclusion of serving priority assures that multiple incidents happening at the same time can be served in accordance with their levels of severity. A preliminary study in Central Arkansas freeways verifies the serviceability of such a model.

Stage-wise solution to a bicriteria staff-scheduling model

This paper proposes an efficient, stage-wise optimisation model for scheduling part-time staff. During stage 1, the problem is formulated as a totally unimodular integer linear programme (ILP) that produces integer solution upon solving its LP relaxation. The model produces optimum work shifts of various lengths along with the optimal number of employees needed in each shift while minimising the total labour time. In the second stage, an assignment model is used to allocate employees to various work shifts while maximising their preferences. The stage-wise model has the necessary flexibility and computational efficiency to solve many real-world business scheduling problems, as illustrated by three separate case studies.

An analysis of internal transit systems requirements for central cities

The deficient performance of existing downtown transit distribution systems may significantly affect the overall performance of the urban transportation system in many metropolitan areas. This paper has two principal objectives. First, it describes an approach entitled Performance Requirements Analysis for developing a set of requirements or standards that a downtown transit system should satisfy and structuring these requirements so as to generate a small set of alternative generic systems for detailed evaluation. Second, it provides a preliminary assessment of performance requirements for distribution systems in 19 of the largest cities in the United States.

A graph-theoretic method to quantify the airline route authority

A U.S. certificated passenger airline must be authorized by the Civil Aeronautics Board before it can schedule a route to serve a set of cities. The route authority is described in a number of legal statements on a Route Certificate, which specifies the routing restrictions by which the airline must abide in offering the service. For cost considerations, an airline is often interested in the shortest flight-time routes between city pairs. This paper introduces a graph-theoretic method to quantify the legal statements in a Certificate. Through straightforward matrix operations, all the authorized nonstop and multistop routes, including the shortest time routes, can be generated. The method is a convenient tool to help an airline to generate alternative network routes as demonstrated by a case study of American Airlines.

Dynamic Shortest-Path Algorithm for Continuous Arc Travel Times: Implication for Traffic Incident Management

To achieve the desired computational speed, existing algorithms for time-dependent (or dynamic) shortest-path problems call for the unrealistic aggregation of travel time as multiples of a given time increment. While the time increment can be varied between, say, peak and off-peak periods, it still has its obvious limitation on granularity. An improved solution algorithm is proposed for the all-to-one or one-to-all time-dependent versions of the problem. The algorithms are solidly founded on Bellmans principle of optimality and apply to continuous or real-value arc travel times (rather than the current practice of discrete-time specifications). The new algorithms are practical in computational speed, and they capture a more realistic metric for route choice. In this case, a relatively coarse time increment can be used throughout the analysis, yet it is compatible with any continuous or real-value arc travel times. To support these claims, computational results that consider various network sizes, densities, and numbers of time increments are provided. For future extensions, a non-first-in-first-out fastest-path and minimum-cost-path algorithm is proposed. A generalized cost (or disutility arc function) is defined to combine the two, on the basis of which route choices are made. For route-choice decisions, it is stipulated that adoption of such a disutility specification mandates a continuous valuation of arc time and cost. Instead of an all-to-one implementation, a one-to-all implementation is suggested under these circumstances. The latter approach, when complemented by the speed of algorithmic execution, facilitates real-world applications. It allows the driver to value incident delay and risks in route choice on a real-time basis.

Telecommunications- and Information Technology–Inspired Analyses

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: (a) detecting and resolving an incident (possibly through crowdsourcing), (b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), (c) advising motorists about routing alternatives (possibly through real-time information), and (d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

Multicriteria Decision Making

While most of us have practiced multicriteria decision making (MCDM) in our business and personal life, it is relatively recent that the knowledge base for such a procedure has been organized and quantified into a formal set of methodologies. In some ways, it represents the amalgamation of descriptive and prescriptive models in the context of behavioral sciences. Descriptive models were defined in Chapter 3 to include such techniques as the conventional use of simulation and statistics that replicate the real world scenario. Prescriptive models, on the other hand, refer to procedures, such as optimization, which go one step further to arrive at a desirable course of action. We will show in this chapter that through the integration of both descriptive and prescriptive procedures, the role of quantitative analysis becomes clear in a pluralistic society with many interests and aspirations.