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Author: Aya Aboudina Publisher: ISBN: Category : Languages : en Pages :
Book Description
Congestion pricing is one of the most widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a system for large-scale optimal time-varying congestion pricing policy determination and evaluation. The proposed system integrates a theoretical model of dynamic congestion pricing, a distributed optimization algorithm, a departure time choice model, and a dynamic traffic assignment (DTA) simulation platform, creating a unified optimal (location- and time-specific) congestion pricing system. The system determines and evaluates the impact of optimal tolling on road traffic congestion (supply side) and travellers' behavioural choices, including departure time and route choices (demand side). For the system's large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel cluster. The system is applied to simulation-based case studies of tolling major highways in the Greater Toronto Area (GTA) while capturing the regional effects of tolling. The models are developed and calibrated using regional household travel survey data that reflect travellers' heterogeneity. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The main results indicate that: (1) more benefits are attained from variable tolling due to departure time rescheduling as opposed to mostly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic are observed across the regional network in response to tolling significant - yet limited - highways in the region, (3) optimal variable pricing mirrors congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays at all scales, (4) tolled routes have different sensitivities to identical toll changes, (5) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (6) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (7) toll payers benefit from tolling even before toll revenues are spent, and (8) the optimal tolling policies determined offer a win-win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Author: Aya Aboudina Publisher: ISBN: Category : Languages : en Pages :
Book Description
Congestion pricing is one of the most widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a system for large-scale optimal time-varying congestion pricing policy determination and evaluation. The proposed system integrates a theoretical model of dynamic congestion pricing, a distributed optimization algorithm, a departure time choice model, and a dynamic traffic assignment (DTA) simulation platform, creating a unified optimal (location- and time-specific) congestion pricing system. The system determines and evaluates the impact of optimal tolling on road traffic congestion (supply side) and travellers' behavioural choices, including departure time and route choices (demand side). For the system's large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel cluster. The system is applied to simulation-based case studies of tolling major highways in the Greater Toronto Area (GTA) while capturing the regional effects of tolling. The models are developed and calibrated using regional household travel survey data that reflect travellers' heterogeneity. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The main results indicate that: (1) more benefits are attained from variable tolling due to departure time rescheduling as opposed to mostly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic are observed across the regional network in response to tolling significant - yet limited - highways in the region, (3) optimal variable pricing mirrors congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays at all scales, (4) tolled routes have different sensitivities to identical toll changes, (5) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (6) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (7) toll payers benefit from tolling even before toll revenues are spent, and (8) the optimal tolling policies determined offer a win-win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Author: Dung-Ying Lin Publisher: ISBN: Category : Languages : en Pages : 340
Book Description
Dynamic Traffic Assignment (DTA) is gaining wider acceptance among agencies and practitioners because it serves as a more realistic representation of real-world traffic phenomena than static traffic assignment. Many metropolitan planning organizations and transportation departments are beginning to utilize DTA to predict traffic flows within their networks when conducting traffic analysis or evaluating management measures. To analyze DTA-based optimization applications, it is critical to obtain the dual (or gradient) information as dual information can typically be employed as a search direction in algorithmic design. However, very limited number of approaches can be used to estimate network-wide dual information while maintaining the potential to scale. This dissertation investigates the theoretical/practical aspects of DTA-based dual approximation techniques and explores DTA applications in the context of various transportation models, such as transportation network design, off-line DTA capacity calibration and dynamic congestion pricing. Each of the later entities is formulated as bi-level programs. Transportation Network Design Problem (NDP) aims to determine the optimal network expansion policy under a given budget constraint. NDP is bi-level by nature and can be considered a static case of a Stackelberg game, in which transportation planners (leaders) attempt to optimize the overall transportation system while road users (followers) attempt to achieve their own maximal benefit. The first part of this dissertation attempts to study NDP by combining a decomposition-based algorithmic structure with dual variable approximation techniques derived from linear programming theory. 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. It is therefore imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies. Satisfactory calibration of the DTA model is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. In this dissertation, the off-line DTA capacity calibration problem is studied in an attempt to devise a systematic approach for effective model calibration. Congestion pricing has increasingly been seen as a powerful tool for both managing congestion and generating revenue for infrastructure maintenance and sustainable development. By carefully levying tolls on roadways, a more efficient and optimal network flow pattern can be generated. Furthermore, congestion pricing acts as an effective travel demand management strategy that reduces peak period vehicle trips by encouraging people to shift to more efficient modes such as transit. Recently, with the increase in the number of highway Build-Operate-Transfer (B-O-T) projects, tolling has been interpreted as an effective way to generate revenue to offset the construction and maintenance costs of infrastructure. To maximize the benefits of congestion pricing, a careful analysis based on dynamic traffic conditions has to be conducted before determining tolls, since sub-optimal tolls can significantly worsen the system performance. Combining a network-wide time-varying toll analysis together with an efficient solution-building approach will be one of the main contributions of this dissertation. The problems mentioned above are typically framed as bi-level programs, which pose considerable challenges in theory and as well as in application. Due to the non-convex solution space and inherent NP-complete complexity, a majority of recent research efforts have focused on tackling bi-level programs using meta-heuristics. These approaches allow for the efficient exploration of complex solution spaces and the identification of potential global optima. Accordingly, this dissertation also attempts to present and compare several meta-heuristics through extensive numerical.
Author: Islam Refaat Kamel Taha Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Modern travel demand management (TDM) strategies, such as time and distance-based congestion pricing, require evidence-based quantitative assessment to measure the potential effects on the transportation network performance and people's responses to the dynamic consequences of such applications. This thesis focuses on building an integrated framework of departure time choice, mode choice, and dynamic multi-modal route assignment for optimal design of TDM strategies applied to large-scale transportation networks, with the focus on time-based transit pricing. The proposed platform integrates a simulation-based dynamic multi-modal multi-user-class route assignment model with an econometric model that jointly estimates departure time and mode choices and a genetic algorithm engine. The proposed platform has been used in optimizing time-dependent fares as a potential strategy to manage peak-hour transit crowding. Considering the traffic and transit networks as one system, the objective is to minimize travel times during peak periods by influencing travellers to alter their choice of transport mode, departure time, and/or route. The anticipated effect is to pace and spread out demand across space and time to yield the optimal spatio-temporal distribution of demand that minimizes end-to-end travel time. The control variables are the time-dependent transit fares. As a large and realistic use case, a model of the Greater Toronto Area has been developed to demonstrate and validate the results of this research. The main contributions of this research include: (1) developing a simulation-based large-scale dynamic route assignment model that captures the interactions between the traffic and transit sides; (2) integrating the route assignment model with a joint econometric model of departure time and mode choice to build a comprehensive model (the METRO platform) that can be used to assess dynamic TDM strategies; (3) integrating the METRO model with a cloud-based genetic algorithm engine to enable optimizing the design of TDM policies with emphasis on transit fares; and lastly (4) optimizing time-dependent transit fares in Toronto to minimize average weighted door-to-door travel time of all individuals in the system using all driving and transit modes, and quantitatively assessing the impacts of the resulting time-dependent fares as a policy and its strengths and weaknesses in addressing transit crowding.
Author: Wei Shen Publisher: ISBN: Category : Languages : en Pages :
Book Description
The system optimal dynamic traffic assignment (SO-DTA) problem determines the time-dependent traffic flow pattern that minimizes the total system cost in a road network. This problem is of great importance for benchmarking and designing diverse congestion alleviation strategies. Despite its great importance, SO-DTA for large-scale general networks remains one of the most challenging problems in transportation research. Solutions with conventional optimization methods are often problematic, due to the high dimensionality and non-convexity of the formulations. This dissertation focuses on the SO-DTA problem with a single destination (S-SO-DTA), which is the building block for solving the general SO-DTA problem. Using a graph-theoretic approach, we reveal the intrinsic connection between the S-SO-DTA problem and the minimum cost flow (MCF) problem in graph theory. It is demonstrated that non-convexity and high-dimensionality, the two major obstacles for solving the S-SO-DTA problem, can be decoupled and tackled separately by relaxation and transformation techniques. The entire decoupling process uncovers a series of interesting and insightful properties, which give birth to a two-stage innovative solution procedure for the S-SO-DTA problem. The first stage solves a special minimum cost flow problem by an augmented network simplex method, while the second stage transforms an optimal traffic flow pattern of the minimum cost flow problem to an optimal traffic flow pattern of the S-SO-DTA problem by applying a pseudo dynamic network loading procedure. By exploiting specialties of network structure, this two-stage procedure is capable of efficiently obtaining global optimal solutions for large-scale S-SO-DTA problems. An extended version of the S-SO-DTA problem, the S-SO-DTA problem with departure time choice, is also discussed. It is shown that most of the graph-theoretic properties as well as the solution procedure can be easily extended with minor modifications. Finally, this thesis work also investigates the potential applications of the S-SO-DTA results in a variety of operational contexts, including emergency evacuation, access control in monocentric networks, and dynamic congestion pricing. Guidelines for designing efficient dynamic traffic management measures are provided.
Author: Ravindra K. Ahuja Publisher: Springer Science & Business Media ISBN: 3642054641 Category : Computers Languages : en Pages : 439
Book Description
Scheduled transportation networks give rise to very complex and large-scale networkoptimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. Examples of scheduled transportation include bus, ferry, airline, and railway networks, with the latter being a prime application domain that provides a fair amount of the most complex and largest instances of such optimization problems. Scheduled transport optimization deals with planning and scheduling problems over several time horizons, and substantial progress has been made for strategic planning and scheduling problems in all transportation domains. This state-of-the-art survey presents the outcome of an open call for contributions asking for either research papers or state-of-the-art survey articles. We received 24 submissions that underwent two rounds of the standard peer-review process, out of which 18 were finally accepted for publication. The volume is organized in four parts: Robustness and Recoverability, Robust Timetabling and Route Planning, Robust Planning Under Scarce Resources, and Online Planning: Delay and Disruption Management.
Author: Don Graham Publisher: CRC Press ISBN: 1003811809 Category : Architecture Languages : en Pages : 152
Book Description
Road congestion imposes major financial, social, and environmental costs. One solution is the operation of high-occupancy toll (HOT) lanes. This book outlines a method for dynamic pricing for HOT lanes based on non-linear programming (NLP) techniques, finite difference stochastic approximation, genetic algorithms, and simulated annealing stochastic algorithms, working within a cell transmission framework. The result is a solution for optimal flow and optimal toll to minimize total travel time and reduce congestion. ANOVA results are presented which show differences in the performance of the NLP algorithms in solving this problem and reducing travel time, and econometric forecasting methods utilizing vector autoregressive techniques are shown to successfully forecast demand. The book compares different optimization approaches It presents case studies from around the world, such as the I-95 Express HOT Lane in Miami, USA Applications of Heuristic Algorithms to Optimal Road Congestion Pricing is ideal for transportation practitioners and researchers.
Author: Kjell Hausken Publisher: Springer ISBN: 3319116746 Category : Technology & Engineering Languages : en Pages : 226
Book Description
Maximizing reader insights into the interactions between game theory, excessive crowding and safety and security elements, this book establishes a new research angle by illustrating linkages between different research approaches and through laying the foundations for subsequent analysis. Congestion (excessive crowding) is defined in this work as all kinds of flows; e.g., road/sea/air traffic, people, data, information, water, electricity, and organisms. Analysing systems where congestion occurs – which may be in parallel, series, interlinked, or interdependent, with flows one way or both ways – this book puts forward new congestion models, breaking new ground by introducing game theory and safety/security into proceedings. Addressing the multiple actors who may hold different concerns regarding system reliability; e.g. one or several terrorists, a government, various local or regional government agencies, or others with stakes for or against system reliability, this book describes how governments and authorities may have the tools to handle congestion, but that these tools need to be improved whilst additionally ensuring safety and security against various threats. This game-theoretic analysis sets this book apart from the current congestion literature and ensures that the book will be of use to postgraduates, researchers, 3rd/4th-year undergraduates, policy makers, and practitioners.
Author: Gerd Sammer Publisher: Routledge ISBN: 1317006550 Category : Political Science Languages : en Pages : 268
Book Description
Throughout the world, traffic levels are increasing and, in urban areas, these increasing levels have led to pressures on the road networks which are causing serious economic, environmental and social problems. This book examines the full range of 'push and pull' Travel Demand Management measures. This covers areas of regulatory, pricing, planning and persuasive policies to encourage individuals to make their trips in off-peak periods, by a different mode or to find another way of carrying out the trip purpose. Applying such measures can result in a more efficient transport system, improved environmental conditions and improvements in safety as well as revenue generation for use on alternative transport systems. The editors conclude with a summary of findings within the book and suggestions for best future practice.
Author: Kenneth A. Small Publisher: Taylor & Francis ISBN: 135165344X Category : Business & Economics Languages : en Pages : 433
Book Description
This new edition of the seminal textbook The Economics of Urban Transportation incorporates the latest research affecting the design, implementation, pricing, and control of transport systems in towns and cities. The book offers an economic framework for understanding the societal impacts and policy implications of many factors including congestion, traffic safety, climate change, air quality, COVID-19, and newly important developments such as ride-hailing services, electric vehicles, and autonomous vehicles. Rigorous in approach and making use of real-world data and econometric techniques, the third edition features a new chapter on the special challenges of managing the energy that powers transportation systems. It provides fully updated coverage of well-known topics and a rigorous treatment of new ones. All of the basic topics needed to apply economics to urban transportation are included: Forecasting demand for transportation services under various conditions Measuring costs, including those incurred by users and incorporating two new tools to describe congestion in dense urban areas Setting prices under practical constraints Evaluating infrastructure investments Understanding how private and public sectors interact to provide services Written by three of the field’s leading researchers, The Economics of Urban Transportation is essential reading for students, researchers, and practicing professionals in transportation economics, planning, engineering, or related disciplines. With a focus on workable models that can be adapted to future needs, it provides tools for a rapidly changing world.
Author: Aya Aboudina
Author: Aya Aboudina
Author: Dung-Ying Lin
Author: Islam Refaat Kamel Taha
Author: Wei Shen
Author: Martin J. Beckmann
Author: Ravindra K. Ahuja
Author: Don Graham
Author: Kjell Hausken
Author: Gerd Sammer
Author: Kenneth A. Small