Dung Ying Lin

Integration of Activity-Based Modeling and Dynamic Traffic Assignment

The traditional trip-based approach to transportation modeling has been used for the past 30 years. Because of limitations of traditional planning for short-term policy analysis, researchers have explored alternative paradigms for incorporating more behavioral realism in planning methodologies. On the demand side, activity-based approaches have evolved as an alternative to traditional trip-based transportation demand forecasting. On the supply side, dynamic traffic assignment models have been developed as an alternative to static assignment procedures. Much of the research effort in activity-based approaches (the demand side) and dynamic traffic assignment techniques (the supply side) has been undertaken relatively independently. To maximize benefits from these advanced methodologies, it is essential to combine them through a unified framework. The objective of this paper is to develop a conceptual framework and explore practical integration issues for combining the two streams of research. Technical, computational, and practical issues involved in this demand–supply integration problem are discussed. The framework is general, but specific technical details related to the integration are explored by using CEMDAP for activity-based modeling and VISTA for dynamic traffic assignment modeling. Solution convergence properties of the integrated system, specifically examining different criteria for convergence, different methods of accommodating time of day, and the influence of step size on convergence are studied. The integrated system developed is empirically applied to two sample networks selected from the Dallas–Fort Worth system in Texas.

Using Genetic Algorithms to Optimize Stopping Patterns for Passenger Rail Transportation

The stopping pattern optimization problem of a passenger railroad company determines the stopping strategy of a train. This pattern takes multiple train classes, station types, and customer origin-destination (OD) demand into consideration in order to maximize the profit made by a rail company. This article proposes an integer program for this problem and provides a systematic approach to determining the optimal train stopping pattern for a rail company. The article discusses how commonly used commercial optimization packages cannot solve this complex problem efficiently. The stopping pattern is traditionally decided by rule of thumb, an approach that leaves much room for improvement. Therefore, the authors of this article develop two genetic algorithms. The first is a binary-coded genetic algorithm (BGA) and the second is an integer-coded genetic algorithm (IGA). The chromosome was coded using the binary alphabet as BGA in many of the past programming studies and the encoding and genetic operators of BGA are straightforward and relatively simple to implement. However, the article shows how it is difficult for the BGA to converge to feasible solutions for the stopping pattern optimization problem. The numerical results presented in the article show that the proposed IGA can solve real-world problems that are beyond the reach of commonly used optimization packages. For this reason, new encoding mechanisms and genetic operators are proposed.

The economic effects of center-to-center directs on hub-and-spoke networks for air express common carriers

Integrated Global air express common carriers offer time-guaranteed freight delivery for international shippers. The hub-and-spoke network and its variations consolidate partial loads creating an efficient network structure that is widely used. We compare the economic effects of hub-and-spoke networks with center-to-center directs on the carriers operations. Carriers were allowed to simultaneously load and off-load freight at each intermediate center. This permits multiple usage of aircraft carrying capacity. The model of hub-and-spoke networks is formulated as an integer program with center directs in the paths, evaluating the results against the Federal Express AsiaOne express network. Using the same aircraft configurations that FedEx uses, our solutions required a smaller fleet than hub-and-spoke with stopovers.

Evacuation Planning Using the Integrated System of Activity-Based Modeling and Dynamic Traffic Assignment

The occurrence of natural disasters in the coastal regions and numerous potential events in urban regions have drawn considerable attention among transportation stakeholders. Federal, state, and local officials need to be effectively prepared to address the challenges raised by an evacuation. The focus of this research effort is to develop a tool to study the repercussions of evacuation of an entire regional transportation network recognizing the human behavior element. Neglecting these seemingly chaotic traffic flow patterns would lead to inaccurate system assessment and predictions. The influences of evacuees’ locations in the urban region at the moment of emergency alert are studied. In addition, the locations of all members of the household are identified, and household member interactions are explicitly considered. Further, the accurate times the individuals enter the network to evacuate the study region are studied; times can vary according to where the other household members are located at that time and the travel time on the network to reach those locations. To accomplish the goals, the integration framework of activity-based modeling and dynamic traffic assignment is used to study the evacuation traffic flow patterns at the time of evacuation. Specifically, the paper describes the evacuation problem, discusses the utility of deploying the integrated module of activity-based modeling and dynamic traffic assignment for evacuation planning, and outlines the challenges in integrating these two tools.

Time-Varying Lane-Based Capacity Reversibility for Traffic Management

Many metropolitan areas have adopted various traffic management techniques to maintain an efficient traffic flow. This article proposes a new bi-level formulation for the time-varying lane-based capacity reversibility problem for traffic management. The problem is formulated as a bi-level program where the lower level is the cell-transmission-based user-optimal dynamic traffic assignment (UODTA). Due to its Non-deterministic Polynomial-time hard (NP-hard) complexity, the genetic algorithm (GA) with the simulation-based UODTA is adopted to solve multi-origin multi-destination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4, with a jam-density factor parameter (JDF), employ time-dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears to be the best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs the best. The GA with the appropriate inclusion of problem-specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.

Models for Minimizing Backhaul Costs Through Freight Collaboration

The competitive nature of the trucking industry has forced trucking firms to develop innovative solutions to improve their operational efficiency and decrease marginal costs. One way carriers and shippers are accomplishing these tasks is by collaborating on operations through various strategies. Two optimization models are developed to route the carrier of interests backhaul routes and select collaborative shipments to fulfill; one is formulated as an integer program, and the other is formulated as a mixed integer program. Two solution methodologies, a greedy heuristic and tabu search, are used to solve the two problems, and numerical analysis is performed with a real-world freight network. Numerical analysis reveals that the percentage of cost savings for backhaul routes can be as high as 27%.

A Dual Variable Approximation-Based Descent Method for a Bi-level Continuous Dynamic Network Design Problem

The transportation network design problem (NDP) considers modifying network topology or parameters, such as capacity, to optimize system performance by taking into account the selfish routing behavior of road users. The nature of the problem lends itself to a bi-level formulation of a problem that represents a static case of a Stackelberg game. The NDP is complex because users’ individual objectives do not necessarily align with system-wide objectives and it is difficult to determine the optimal allocation of limited resources. To solve the bi-level dynamic NDP, this study develops a dual variable approximation-based heuristic. This dual variable identifies the system-wide gradient as a descent direction and designs an iterative solution framework. Descent direction-based approaches designed to solve bi-level programming problems typically suffer from non-differentiability, which can hamper the solution process. The proposed method addresses this issue by approximating the descent direction with dual variables that correspond to cell transmission model constraints and using the constructed rational direction to iteratively decrease the upper-level objective while maintaining the feasibility of the lower-level program. The proposed method was empirically applied to three networks of various sizes. The results obtained from this empirical solution were compared with the results from an exact Kth-best algorithm and a genetic algorithm. The results demonstrate the efficacy and efficiency of the proposed descent method.

A Dantzig-Wolfe Decomposition-Based Heuristic for Off-line Capacity Calibration of Dynamic Traffic Assignment

One of the critical elements in considering any real-time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. Dynamic traffic assignment (DTA) is therefore gaining wider acceptance among agencies and practitioners as a more realistic representation of traffic phenomena than static traffic assignment. Though it is imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies, DTA calibration is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. To compliment other research on behavioral and trip table issues, this work focuses on DTA capacity calibration and presents an efficient Dantzig-Wolfe decomposition-based heuristic that decomposes the problem into a restricted master problem (RMP) and a series of pricing problems. The restricted master problem is a capacity manipulation problem, which can be solved by a linear programming solver. The pricing problem is the user optimal DTA which can be optimally solved by an existing combinatorial algorithm. In addition, the proposed set of dual variable approximation techniques is one of a very limited number of approaches that can be used to estimate network-wide dual information in facilitating algorithmic designs while maintaining scalability. Two networks of various sizes are empirically tested to demonstrate the efficiency and efficacy of the proposed heuristic. Based on the results, the proposed heuristic can calibrate the network capacity and match the counts within a 1% optimality gap.

The mixed-product assembly line sequencing problem of a door-lock company in Taiwan

This paper investigates the mixed-product assembly line sequencing problem in the door-lock manufacturing industry. Companies in the door-lock industries schedule their production processes to minimize their costs while meeting customer demand. The variances and diversities of each locks components complicate the mixed-product assembly line sequencing problem and directly influence the material requirement planning and human resource costs. In the current research, we study one of the largest ironware manufacturing companies in Asia, company F. For this company, an export-oriented strategy makes its main products (such as door locks and door closers) available around the globe. The primary customers of company F are the largest home improvement co-op stores (such as Home Depot, Lowes and True Value in the US) from the region of North America. The sales from this region account for over 80% of company Fs total sales. The remaining company sales are geographically distributed around the world in areas such as Europe, Asia and Australia. However, as labor cost is the major concern, this company seeks supply sources in southeast Asia, China and Taiwan. In this paper, we analyze company F and formulate an integer programming mathematical model with constraints regarding production lines, labor, warehouse capacity and order fulfillment rates to minimize the total cost. The customer demand is derived from real data from company F. We use the branch and bound algorithm (CPLEX) to solve this problem and analyze the results. Salient results and practical issues involved in this unique problem are discussed in detail in this paper.

Modeling routing behavior for vacant taxicabs in Urban traffic networks

Taxicabs account for a significant portion of traffic in many Asian cities, and route choice by taxi drivers is an active research area. The routing objectives of a taxi driver vary, depending on taxi occupancy. If a taxi is occupied by customers, then a least-cost path is usually sought. Several paradigms in the literature are related to such a routing objective. However, the taxi drivers route choice behavior when a taxi is vacant is not well understood. A routing model for vacant taxis is proposed in which taxi drivers are assumed to minimize the expected search time for customers when making routing decisions at intersections. A probabilistic dynamic programming formulation of the problem and the solution algorithm are presented. A numerical analysis was conducted on a hypothetical network resembling the traffic network structure in the city of Taipei, Taiwan. The proposed model exhibited realistic and reasonable properties. This research sheds light on the routing decisions of taxi drivers and therefore directly supports areawide traffic management.