Bridging the user equilibrium and the system optimum in static traffic assignment: a review

Solving the road congestion problem is one of the most pressing issues in modern cities since it causes time wasting, pollution, higher industrial costs and huge road maintenance costs. Advances in ITS technologies and the advent of autonomous vehicles are changing mobility dramatically. They enable the implementation of a coordination mechanism, called coordinated traffic assignment, among the sat-nav devices aiming at assigning paths to drivers to eliminate congestion and to reduce the total travel time in traffic networks. Among possible congestion avoidance methods, coordinated traffic assignment is a valuable choice since it does not involve huge investments to expand the road network. Traffic assignments are traditionally devoted to two main perspectives on which the well-known Wardropian principles are inspired: the user equilibrium and the system optimum. User equilibrium is a user-driven traffic assignment in which each user chooses the most convenient path selfishly. It guarantees that fairness among users is respected since, when the equilibrium is reached, all users sharing the same origin and destination will experience the same travel time. The main drawback in a user equilibrium is that the system total travel time is not minimized and, hence, the so-called Price of Anarchy is paid. On the other hand, the system optimum is an efficient system-wide traffic assignment in which drivers are routed on the network in such a way the total travel time is minimized, but users might experience travel times that are higher than the other users travelling from the same origin to the same destination, affecting the compliance. Thus, drawbacks in implementing one of the two assignments can be overcome by hybridizing the two approaches, aiming at bridging users’ fairness to system-wide efficiency. In the last decades, a significant number of attempts have been done to bridge fairness among users and system efficiency in traffic assignments. The survey reviews the state-of-the-art of these trade-off approaches

Distributed and dynamic traffic congestion controls without requiring demand forecasting: Tradable network permits and its implementation mechanisms

Tohoku University赤松隆課

Managing Rush Hour Congestion with Lane Reversal and Tradable Credits

Within the morning and evening rush hour, the two-way road flows are always unbalanced in opposite directions. In order to make full advantage of the existing lanes, the two-way road lane has to be reallocated to play the best role in managing congestion. On the other hand, an effective tradable credit scheme can help to reduce the traffic demand and improve fairness for all travelers. So as to alleviate the commute congestion in urban transportation network, a discrete bilevel programming model is established in this paper. In the bilevel model, the government at the upper level reallocates lanes on the two-way road to minimize the total system cost. The traveler at the lower level chooses the optimal route on the basis of both travel time and credit charging for the lanes involved. A numerical experiment is conducted to examine the efficiency of the proposed method

Simultaneous Optimization of Road Tolls and Tradable Credits in Public-private Mixed Networks

This paper investigates a hybrid management policy of road tolls and tradable credits in mixed road networks with both public and private roads. In the public sub-network, a tradable credit scheme is applied to mitigate traffic congestion. In the private sub-network, tolls are collected by the private company, but the toll levels and toll locations are determined by the government. The purpose of toll charge is two-fold: on the one hand, the government uses it as a tool for mitigating congestion; on the other hand, a threshold of revenue should be guaranteed for the profitability of the private company. A bi-level programming model is formulated to minimize the total travel time in the network by taking into account the user equilibrium travel behaviour and the revenue requirement of private firms. To obtain a  global optimum solution, the bi-level model is transformed into an equivalent single-level mixed integer linear program that can be easily solved with commercial software. Numerical examples are provided to demonstrate the effectiveness of the developed model and the efficiency of the proposed algorithm. It is shown that the mixed management schemes can achieve favourable targets, namely, joint implementation of road tolls and tradable credits can effectively mitigate traffic congestion and meanwhile maintain reasonable revenue for the private company.</p

Optimizing Transportation Systems with Information Provision, Personalized Incentives and Driver Cooperation

Poor performance of the transportation systems has many detrimental effects such as higher travel times, increased travel costs, higher energy consumption, and greenhouse gas emissions, etc. This thesis optimizes the transportation systems by addressing the traffic congestion problem and climate change impact resulting from the inefficient operation of these systems. I first focus on the key player of the transportation systems e.g., human being/traveler, and model travelers\u27 route choice behavior with real-time information. In this study, I define looking-ahead behavior in route choice as a traveler\u27s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Subjects participated in route-choice experiments in a driving simulator as well a PC-based environment. Three types of maps in increasing levels of complexity and information availability are used. Aggregate data analysis shows that network complexity negatively affects subjects\u27 ratio of choosing the risky route given an experiment environment. Higher cognitive load in the driving simulator results in a higher level of risk aversion than in the PC-based environment for the simplest map. I specify and estimate a mixed logit model with two latent classes, looking-ahead and myopic, taking into account the panel effect. The estimated latent class membership function suggests that some subjects can look ahead while others are myopic in making their route choices, and drivers learn to look ahead over time. The experiment environment plays a role in the risk attitude of myopic subjects. A bias against information is found for subjects who look ahead, however, is not significant among myopic subjects. I then shift my focus to influencing the travel patterns of individual travelers to reduce the energy and environmental impacts of the transportation sector. I present the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation systems. From the user\u27s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives each with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated to the available alternatives, in order to minimize the system-wide energy consumption, under a limited token budget. I present a method to solve this complex optimization problem and describe the system architecture, the multimodal simulation-based optimization model and the heuristic method for the on-line computation of the optimized token allocation. I then present the framework with the simulation results. Finally, I optimize the systems travel time by addressing the equity issue of congestion pricing. I propose an alternative approach to an equitable and Pareto-improving transportation systems based on cooperation among travelers assisted by defector penalty. Theoretical analysis shows the existence condition of the cooperative scheme for heterogeneous value of time (VOT) of travelers. I formulate a mathematical programming problem for the optimal cooperative scheme problem in a general network with Pareto-improving constraints and practical considerations on the length the cooperation cycle. I then conduct computational tests on a simple network and evaluate the solutions in terms of efficiency improvement (total system travel time) and equitability (Gini index)

Design and Analysis of Mobility Permit-based Traffic Management Schemes

High demand for mobility has undeniably been causing numerous negative impacts on the economy, the society and the environment. As a potential solution to address this challenge, a rapid transition is taking place in the transportation sector with emerging concepts of mobility marketplace. The basic premise is to treat the transportation system and its use as a collection of commodities or services that can be bought from the transportation market. This concept is increasingly becoming a reality with the technological developments in automotive industry such as connected and autonomous vehicles (CAVs). However, there are many policy, design and operation related issues that must be addressed before these traffic management schemes become reality. This thesis research aims at addressing some of these challenges and issues with a specific focus on the two most promising market-driven instruments, namely, mobility permits (MP)- and mobility credits (MC)-based traffic management schemes, which have been proposed to manage travel demand and mitigate traffic congestion by controlling roadway-use right. This research has made several distinctive contributions into the literature. We first conduct a critical review of the state-of-the-art methodological advances on MP- and MC-based travel demand management schemes. We synthesize the relevant body of literature with an in-depth discussion on related studies to provide an improved understanding of the fundamental constructs of these problems, including problem variants, methodologies, and modeling attributes. We also discuss the research gaps and challenges and suggest some possible perspectives and directions for future research. Based on the gaps identified in the literature review, an integrated framework is proposed for implementing various roadway-use right-based traffic management programs such as MP and MC-based schemes. This framework entails a unique construct for integrating the needs of multiple stakeholders (e.g., road users and authorities), diverse network conditions, and traffic control methods. It allows easy incorporation of different components required for implementing a coordinative mobility scheme, taking into account the influence of the participating players and the underlying issues. The framework can be served as a road-map to future studies on different roadway-use right-based solutions for traffic congestion management. With our proposed framework, we then focus on addressing various specific challenges arising in designing and implementing MP-based and MC-based schemes, such as, representation of realistic user characteristics (e.g., utility function, user priorities and cooperation), availability of information on users and traffic conditions, uncertainty in system conditions and user behaviors, and circulation of mobility rights in market place. For the MP-based scheme, we focus specifically on designing a mobility scheme for single-bottleneck roadways. Roads with bridges, tunnels and business districts with limited parking spaces are the most obvious examples of a simple roadway with a single-bottleneck in a transportation network. We deal with observing operational objectives, specifically, balancing efficiency, equity (users priorities), and revenue outcome of distributing mobility permits under the “fairness” constraint. We explore the theoretical properties of the proposed scheme and show that the proposed scheme can achieve an optimal traffic pattern. Particularly, we show that the proposed scheme is a Pareto-improving and strategy-proof scheme capable of achieving efficient and effective market prices suitable for travelers. Our computational results indicate the effectiveness of the proposed scheme as an alternative solution for MP-based traffic management on single-bottleneck roadways. We then investigate the case of traffic congestion management in a general road network through a MC-based scheme. Specifically, we propose a MC-based traffic management scheme in a road network consisting of a mixed-fleet traffic with connected and autonomous vehicles (CAVs) and conventional vehicles (non-CAVs). The basic premise of the proposed scheme is to regulate or influence travel demand and congestion with regards to the supply (capacity) of road networks, implementing a market-driven traffic management paradigm. A set of revenue-neutral, Pareto-improving MC-based charge and reward policies applicable to stochastic traffic environments are developed, considering different characteristics of users such as cooperative versus selfish routing behaviors, human-associated factors (e.g., level of uncertainty) and interactions due to a shared infrastructure setting. Path-free mathematical programming models are formulated, obviating computationally intractable path enumeration process pertinent to the existing studies. This makes the proposed scheme suitable for examining the theoretical characteristics of large-scale realistic transport networks. We examine several theoretical properties related to the proposed MC-based scheme, including the existence and uniqueness of the equilibrium price, and existence of Pareto-improving credit charges and rewards rates that can promote travel decision behaviors of individual travelers towards a network-wide optimal state. Our comprehensive computational results indicate that the proposed MC-based scheme can be an effective tool for managing travel demand and routing decisions in mixed-vehicle traffic settings

AN INTEGRATED SCORE-BASED TRAFFIC LAW ENFORCEMENT AND NETWORK MANAGEMENT IN CONNECTED VEHICLE ENVIRONMENT

The increasing number of traffic accidents and the associated traffic congestion have prompted the development of innovative technologies to curb such problems. This dissertation introduces a novel Score-Based Traffic Law Enforcement and Network Management System (SLEM), which leverages connected vehicle (CV) and telematics technologies. SLEM assigns a score to each driver which reflects her/his driving performance and compliance with traffic laws over a predefined period of time. The proposed system adopts a rewarding mechanism that rewards high-performance drivers and penalizes low-performance drivers who fail to obey traffic laws. The reward mechanism is in the form of a route guidance strategy that restricts low-score drivers from accessing certain roadway sections and time periods that are strategically selected in order to shift the network traffic distribution pattern from the undesirable user equilibrium (UE) pattern to the system optimal (SO) pattern. Hence, it not only incentivizes drivers to improve their driving performance, but it also provides a mechanism to manage network congestion in which high-score drivers experience less congestion and a higher level of safety at the expense of low-performing drivers. This dissertation is divided into twofold. iv First, a nationwide survey study was conducted to measure public acceptance of the SLEM system. Another survey targeted a focused group of traffic operation and safety professionals. Based on the results of these surveys, a set of logistic regression models was developed to examine the sensitivity of public acceptance to policy and behavioral variables. The results showed that about 65 percent of the public and about 60.0 percent of professionals who participated in this study support the real-world implementation of SLEM. Second, we present a modeling framework for the optimal design of SLEM’s routing strategy, which is described in the form of a score threshold for each route. Under SLEM’s routing strategy, drivers are allowed to use a particular route only if their driving scores satisfy the score threshold assigned to that route. The problem is formulated as a bi-level mathematical program in which the upper-level problem minimizes total network travel time, while the lower-level problem captures drivers’ route choice behavior under SLEM. An efficient solution methodology developed for the problem is presented. The solution methodology adopts a heuristic-based approach that determines the score thresholds that minimize the difference between the traffic distribution pattern under SLEM’s routing strategy and the SO pattern. The framework was applied to the network of the US-75 Corridor in Dallas, Texas, and a set of simulation-based experiments was conducted to evaluate the network performance given different driver populations, score class aggregation levels, recurrent and non-recurrent congestion scenarios, and driver compliance rates

Optimization Models for Sustainable Design and Management of Biopower Supply Chains

This dissertation presents optimization models to aid with the sustainable design and management of biopower (biomass cofiring) supply chains. We address three main challenges associated with today’s biopower projects: i) high cost of biomass collection, storage and delivery, ii) inefficiency of the mechanisms used to incentivize biomass usage for generating electricity, and iii) lack of clear understanding about the trade-offs between economic and environmental impacts of biopower supply chains. In order to address the high cost of delivering biomass, we present a novel mixed integer nonlinear program that integrates production and transportation decisions at power plants. Proposed model captures the loss in process efficiencies from using biomass, in-vestment and operational costs associated with cofiring, and savings due to production tax credit (PTC), a major governmental incentive to support biopower. We develop a La-grangian relaxation approach to provide upper bounds, and two linear approximations to provide lower bounds for the problem. An important finding is that the one-size-fits-all approach of PTC is not effective in motivating plants to utilize biomass and there is a need for sophisticated incentive schemes. In order to address the second issue, we propose alter-natives for the existing PTC incentive. The proposed flexible alternatives are functions of plant capacity and biomass cofiring ratio. We use a resource allocation framework to model and analyze the profit-earning potentials and fairness of the proposed incentive schemes. Finally, in order to address the last challenge, we propose a stochastic biobjective optimiza-tion model to analyze the economic and environmental impacts of biopower supply chains. The economic objective function maximizes the potential profits in the supply chain and the environmental objective function minimizes the life cycle greenhouse gasses (GHG). We use a life cycle assessment (LCA) approach to derive the emission factors for this objective function. We capture uncertainties of biomass quality and supply via the use of chance constraints. The results of this dissertation work are useful for electric utility companies and policy makers. Utility companies can use the proposed models to identify ways to improve biopower production, have better environmental performance, and make use of the existing incentives. Policy makers would gain insights on designing incentive schemes for a more efficient utilization of biomass and a fairer distribution of tax-payers money

An MPCC Formulation and Its Smooth Solution Algorithm for Continuous Network Design Problem

Continuous network design problem (CNDP) is searching for a transportation network configuration to minimize the sum of the total system travel time and the investment cost of link capacity expansions by considering that the travellers follow a traditional Wardrop user equilibrium (UE) to choose their routes. In this paper, the CNDP model can be formulated as mathematical programs with complementarity constraints (MPCC) by describing UE as a non-linear complementarity problem (NCP). To address the difficulty resulting from complementarity constraints in MPCC, they are substituted by the Fischer-Burmeister (FB) function, which can be smoothed by the introduction of the smoothing parameter. Therefore, the MPCC can be transformed into a well-behaved non-linear program (NLP) by replacing the complementarity constraints with a smooth equation. Consequently, the solver such as LINDOGLOBAL in GAMS can be used to solve the smooth approximate NLP to obtain the solution to MPCC for modelling CNDP. The numerical experiments on the example from the literature demonstrate that the proposed algorithm is feasible.</p

Improving the Performance of the Bilevel Solution for the Continuous Network Design Problem

For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.</p