OPTIMIZATION OF STATION LOCATIONS AND TRACK ALIGNMENTS FOR RAIL TRANSIT LINES

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, right-of-way type, and various other system characteristics. The existing studies overlook the complex interactions between railway alignments and station locations in a practical design process. This study proposes a comprehensive methodology that helps transit planners to concurrently optimize station locations and track alignments for an urban rail transit line. The modeling framework resolves the essential trade-off between an economically efficient system with low initial and operation cost and an effective system that provides convenient service for the public. The proposed method accounts for various geometric requirements and real-world design constraints for track alignment and stations plans. This method integrates a genetic algorithm (GA) for optimization with comprehensive evaluation of various important measures of effectiveness based on processing Geographical Information System (GIS) data. The base model designs the track alignment through a sequence of preset stations. Detailed assumptions and formulations are presented for geometric requirements, design constraints, and evaluation criteria. Three extensions of the base model are proposed. The first extension explicitly incorporates vehicle dynamics in the design of track alignments, with the objective of better balancing the initial construction cost with the operation and user costs recurring throughout the system's life cycle. In the second extension, an integrated optimization model of rail transit station locations and track alignment is formulated for situations in which the locations of major stations are not preset. The concurrent optimization model searches through additional decision variables for station locations and station types, estimate rail transit demand, and incorporates demand and station cost in the evaluation framework. The third extension considers the existing road network when selecting sections of the alignment. Special algorithms are developed to allow the optimized alignment to take advantage of links in an existing network for construction cost reduction, and to account for disturbances of roadway traffic at highway/rail crossings. Numerical results show that these extensions have significantly enhanced the applicability of the proposed optimization methodology in concurrently selecting rail transit station locations and generating track alignment

The Green Choice: Learning and Influencing Human Decisions on Shared Roads

Autonomous vehicles have the potential to increase the capacity of roads via platooning, even when human drivers and autonomous vehicles share roads. However, when users of a road network choose their routes selfishly, the resulting traffic configuration may be very inefficient. Because of this, we consider how to influence human decisions so as to decrease congestion on these roads. We consider a network of parallel roads with two modes of transportation: (i) human drivers who will choose the quickest route available to them, and (ii) ride hailing service which provides an array of autonomous vehicle ride options, each with different prices, to users. In this work, we seek to design these prices so that when autonomous service users choose from these options and human drivers selfishly choose their resulting routes, road usage is maximized and transit delay is minimized. To do so, we formalize a model of how autonomous service users make choices between routes with different price/delay values. Developing a preference-based algorithm to learn the preferences of the users, and using a vehicle flow model related to the Fundamental Diagram of Traffic, we formulate a planning optimization to maximize a social objective and demonstrate the benefit of the proposed routing and learning scheme.Comment: Submitted to CDC 201

An Alignment Optimization Model for a Simple Highway Network

A new highway addition to an existing road network is typically considered for improving traffic performance in that road network. However, finding the new highway that best improves the existing network is a very complex problem since many factors affect the road construction. Besides changes in traffic flow patterns due to the new highway, various costs associated with highway construction as well as design specifications, safety, environmental, and political issues affect such a project. Until recently, many studies have dealt separately with the problems of highway alignment optimization and network design. However, no models have been found that integrate these problems comprehensively and effectively. This dissertation seeks to find a realistic three-dimensional highway alignment that best improves an existing network, while considering its costs, geometric design, and environmental impacts on the study area. To fulfill this objective, an effective network model is developed that can simultaneously optimize (i) highway alignments and (ii) junction points with existing roads. In addition, the model's optimization process considers traffic impacts due to the highway addition as well as factors associated with its construction. This dissertation starts by investigating the major cost components and important constraints in the highway design processes. Next, existing models for optimizing highway alignments are reviewed by assessing their advantages and disadvantages. Effective solution search methods are then developed to help solve the complex optimization problem. Development of the search methods is essential since an equilibrium traffic assignment as well as alignment optimization is undertaken in the proposed network model. Precise formulations of various highway costs and constraints are also developed for evaluating the various candidate alternatives. Cost functions for system improvements that can be obtained from the new highway addition are proposed. These are calculated based on the equilibrium traffic flows found from the assignment process. Complex geographical constraints including user preferences and environmentally sensitive areas are realistically represented, along with design standards required for highways. To represent highway alignments, sets of tangents, circular curves and transition spirals are used; in addition, three-leg structure models are also developed for representing the highway endpoints. Finally, several case studies are conducted to test the performance of the proposed models

Estimating the Welfare Effects of Digital Infrastructure

While much economic policy presumes that more information infrastructure yields higher economic returns, little empirical work measures the magnitudes of these returns. We examine investment by local exchange telephone companies in fiber optic cable, ISDN lines and signal seven software, infrastructure which plays an essential role in bringing digital technology to local telephone networks. We estimate the elasticity of the derived demand for infrastructure investment faced by local exchange companies, controlling for factors such as local economic activity and the political disposition of state regulators. Our model postulates a regulated profit maximizing local exchange firm and a regulatory agency with predetermined political leanings in favor of consumer prices or firm profits. The model accounts for variation in state regulation and local economic conditions. In all our estimates we find that consumer demand is sensitive to investment in modern infrastructure, particularly as represented by fiber optic cable. Our estimates imply that infrastructure investment is responsible for a substantial fraction of the recent growth in consumer surplus and business revenue in local telecommunication services.

Optimal train control on various track alignments considering speed and schedule adherence constraints

The methodology discussed in this dissertation contributes to the field of transit operational control to reduce energy consumption. Due to the recent increase in gasoline cost, a significant number of travelers are shifting from highway modes to public transit, which also induces higher transit energy consumption expenses. This study presents an approach to optimize train motion regimes for various track alignments, which minimizes total energy consumption subject to allowable travel time, maximum operating speed, and maximum acceleration/deceleration rates. The research problem is structured into four cases which consist of the combinations of track alignments (e.g., single vertical alignment and mixed vertical alignment) and the variation of maximum operating speeds (e.g., constant and variable). The Simulated Annealing (SA) approach is employed to search for the optimal train control, called golden run . To accurately estimate energy consumption and travel time, a Train Performance Simulation (TPS) is developed, which replicates train movements determined by a set of dynamic variables (e,g., duration of acceleration and cruising, coasting position, braking position, etc.) as well as operational constraints (e.g., track alignment, speed limit, minimum travel time, etc.) The applicability of the developed methodology is demonstrated with geographic data of two real world rail line segments of The New Haven Line of the Metro North Railroad: Harrison to Rye Stations and East Norwalk to Westport Stations. The results of optimal solutions and sensitivity analyses are presented. The sensitivity analyses enable a transit operator to quantify the impact of the coasting position, travel time constraint, vertical dip of the track alignment, maximum operating speed, and the load and weight of the train to energy consumption. The developed models can assist future rail system with Automatic Train Control (ATC), Automatic Train Operation (ATO) and Positive Train Control (PTC), or conventional railroad systems to improve the planning and operation of signal systems. The optimal train speed profile derived in this study can be considered by the existing signal system for determining train operating speeds over a route

Optimized scheduling of highway work zones

Highway maintenance activities usually require lane closures and disrupt traffic operations. Because of budget constraints, project deadlines, and the resulting traffic impact, the objective of this dissertation is to improve the efficiencies of traffic operation and maintenance work, and minimize the total project cost (i.e., agency cost and road user cost) by optimizing work zone schedules. This dissertation focuses on the maintenance projects on multiple-lane highways. The objective total cost function is formulated while considering a discrete maintenance time-cost function and time-dependent traffic diversions. However, the work zone scheduling problem is a combinatorial optimization problem and difficult to solve analytically. This dissertation transformed the complicated problem into two separate steps: determining the time-dependent traffic diversion by the User Equilibrium Assignment, and minimizing the total project cost by a Genetic Algorithm. An iterative algorithm that integrates the two steps was developed. The optimized work zone schedule and the associated optimal diverted traffic flow can be found simultaneously after multiple iterations. Case studies and extensive sensitivity analyses were conducted to analyze various scheduling scenarios with or without a time-cost function and traffic diversion. The relations among key decision variables were analyzed. Conclusions and recommendations are provided, and directions of future research efforts are discussed

OPTIMAL CONGESTION CHARGES IN GENERAL EQUILIBRIUM

This paper deals with pricing and investment decision problems of multi-route and multi-period highway systems in which the congestion is a significant factor in the assessment of system costs. This study approaches this congestion pricing scheme with two different social welfare maximization problems, both of which search for the optimal solutions through general equilibrium analysis. These two optimization problems have an identical structure except financial constraints that reflect different decision environments. One welfare maximization problem involves estimating the first-best social optimal solution. This problem yields the optimal solution for the implementation scheme to impose the differentiated congestion charge for each trip alternative in terms of travel route and trip period. The optimal congestion charge for this problem has the expression similar to that derived in previous studies dealing with congestion pricing. Another maximization problem involves characterizing the second-best optimal solution. In this problem, it is assumed to impose the congestion toll only on a single highway link. This problem yields the second-best congestion toll different from the first-best one. This second-best optimal congestion toll has the structure to reflect its impact on other highway links exempt from the congestion charge program. Document type: Articl

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster