Integrated Special Event Traffic Management Strategies in Urban Transportation Network

How to effectively optimize and control spreading traffic in urban network during the special event has emerged as one of the critical issues faced by many transportation professionals in the past several decades due to the surging demand and the often limited network capacity. The contribution of this dissertation is to develop a set of integrated mathematical programming models for unconventional traffic management of special events in urban transportation network. Traffic management strategies such as lane reorganization and reversal, turning restriction, lane-based signal timing, ramp closure, and uninterrupted flow intersection will be coordinated and concurrently optimized for best overall system performance. Considering the complexity of the proposed formulations and the concerns of computing efficiency, this study has also developed efficient solution heuristics that can yield sufficiently reliable solutions for real-world application. Case studies and extensive numerical analyses results validate the effectiveness and applicability of the proposed models

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.</p

Evaluation Of Lane Use Management Strategies

The limited funding available for roadway capacity expansion and the growing funding gap, in conjunction with the increasing congestion, creates a critical need for innovative lane use management options. Various cost-effective lane use management strategies have been implemented in the United States and worldwide to address these challenges. However, these strategies have their own costs, operational characteristics, and additional requirements for field deployment. Hence, there is a need for systematic methodologies to evaluate lane use management strategies. In this thesis, a systematic simulation-based methodology is proposed to evaluate lane use management strategies. It involves identifying traffic corridors that are suitable for lane use management strategies, and analyzing the strategies in terms of performance and financial feasibility. The state of Indiana is used as a case study for this purpose, and a set of traffic corridors is identified. From among them, a 10-mile stretch of the I-65 corridor south of downtown Indianapolis is selected as the study corridor using traffic analysis. The demand volumes for the study area are determined using subarea analysis. The performance of the traffic corridor is evaluated using a microsimulation-based analysis for alleviating congestion using three strategies: reversible lanes, high occupancy vehicle (HOV) lanes and ramp metering. Furthermore, an economic evaluation of these strategies is performed to determine the financial feasibility of their implementation. Results from the simulation based analysis indicate that the reversible lanes and ramp metering strategies improve traffic conditions on the freeway in the major flow direction. Implementation of the HOV lane strategy results in improved traffic flow conditions on the HOV lanes but aggravated congestion on the general purpose lanes. The HOV lane strategy is found to be economically infeasible due to low HOV volume on these lanes. The reversible lane and ramp metering strategies are found to be economically feasible with positive net present values (NPV), with the NPV for the reversible lane strategy being the highest. While reversible lanes, HOV lanes and ramp metering strategies are effective in mitigating congestion by optimizing lane usage, they do not generate additional revenue required to reduce the funding deficit. Inadequate funds and worsening congestion have prompted federal, state and local planning agencies to explore and implement various congestion pricing strategies. In this context, the high occupancy toll (HOT) lanes strategy is explored here. Equity concerns associated with pricing schemes in transportation systems have garnered increased attention in the recent past. Income inequity potentially exists under the HOT strategy whereby higher-income travelers may reap the benefits of HOT lane facilities. An income-based multi-toll pricing approach is proposed for a single HOT lane facility in a network to simultaneously maximize the toll revenue and address the income equity concern, while ensuring a minimum level-of-service on the HOT lanes and that the toll prices do not exceed thresholds specified by a regulatory entity. The problem is modeled as a bi-level optimization formulation. The upper level model seeks to maximize revenue for the tolling authority subject to pre-specified upper bounds on toll prices. The lower level model solves for the stochastic user equilibrium solution based on commuters\u27 objective of minimizing their generalized travel costs. Due to the computational intractability of the bi-level formulation, an approximate agent-based solution approach is used to determine the toll prices by considering the tolling authority and commuters as agents. Results from numerical experiments indicate that a multi-toll pricing scheme is more equitable and can yield higher revenues compared to a single toll price scheme across all travelers

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

Optimizing capacity of signalized road network with reversible lanes

This paper studies the network capacity problem on signalized road network with reversible lanes. A Mixed Network Design Problem (MDNP) is formulated to describe the problem where the upper-level problem is a mixed integer non-linear program designed to maximize the network capacity by optimizing the input parameters (e.g. the signal splits, circles, reassigned number of lanes and O–D demands), while the lower-level problem is the common Deterministic User Equilibrium (DUE) assignment problem formulated to model the drivers’ route choices. According to whether one way strategy is permitted in practice, two strategies for implementing reversible roadway are considered. In the first strategy, not all lanes are reversible and the reversible roadways always hold its ability to accommodate the two-way traffic flow. In the second strategy, one-way road is allowed, which means that all the lanes are reversible and could be assigned to one flow direction if the traffic flow in both directions is severally unsymmetrical. Genetic Algorithm (GA) is detailedly presented to solve the bi-level network capacity problem. The application of the proposed method on a numerical example denotes that Strategy 2 can make more use of the physical capacity of key links (signal controlled links), thus, the corresponding network capacity outperforms it is of Strategy 1 considerably. First published online 14 January 201

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

Optimal Scheduling of Evacuation Operations with Contraflow

Congestion due to evacuations can be catastrophic and life threatening. The sudden increase in demand will result in excessive loads on roads not typically designed to handle them, leading to network breakdown at the worst possible time. Moreover, since building new roads is infeasible, efficient utilization of the available network resources during disasters becomes one of the few options available to facilitate the movement of residents to safety. One option is to address the demand side of the problem, through demand scheduling. By scheduling the evacuation demand over a longer period, the congestion is staved off and network degradation is delayed. Advising traffic on when to evacuate, where to evacuate, and which route to take has the potential to improve evacuation times, especially in no-notice emergency conditions. Another option is to address the supply side of the problem, through network re-design. By reversing the direction of wisely selected lanes in a process known as contraflow, a temporary increase in the operational capacity is achieved without any major infrastructure changes. Both options, if planned correctly, have the potential to greatly ease network degradation and allow evacuees to reach safety sooner. Therefore, the ability to determine the joint optimal demand scheduling and network contraflow policies is of critical nature to the success of any evacuation plan. The objective of this study is to develop a simulation-based dynamic traffic assignment model that minimizes network clearance time at a minimum cost to the travelers by jointly considering demand scheduling and contraflow strategies