A multimodal material route planning problem considering key processes at work zones

In a growing and congested capital city, the challenges of maintaining traffic during construction of a multimillion dollar transportation program are immense. Managers are tasked with budgeting, planning, designing, procuring, constructing, and monitoring hundreds of projects in a dynamic and politically charged environment. Critically important infrastructure, utility, maintenance, and developer projects—as well as capital area special events—are frequently on a national (or international) stage with a high risk of traffic congestion, safety concerns, and negative public perception. Washington, D.C., is often listed as one of the top 10 tourist destinations in the United States. With a growing resident population, commuters nearly double the number of people in the city each weekday. In addition, Washington, D.C., is often listed as the most congested urban area in the nation. Aside from recurring congestion, project construction and special events frequently strain the existing roadway network. A proactive, citywide approach to managing all work zones is helping to reduce congestion and provide the District Department of Transportation with better control of how best to schedule and approve projects, as well as budget for cost-effective strategies to mitigate work zone congestion. A unique Citywide Transportation Management Plan, including a dynamic Work Zone Project Management System, has been deployed to coordinate and analyze project work zones and special events throughout the city for the next 5 years. Graphical, web-based software tools track and assess cumulative work zone effects. From these results, mitigation strategies are aimed at improving safety and mobility throughout Washington, D.C.

911 Emergency Medical Services and Re-Triage to Level I Trauma Centers

The Federal Highway Administration (FHWA) reported between 2016 and 2017, fatal crashes in work zones increased by 3%, while fatal crashes outside of work zones decreased by 1.5%. The FHWA also reported that work zones account for approximately 10% of the nation’s overall congestion and 24% of unexpected interstate delays. This paper reports on a study of 23 construction work zones that covered approximately 150 centerline miles of Indiana interstate roadway in the summer of 2019. Approximately 50% of all interstate crashes for the period of May to September 2019 occurred within or in an approach upstream or downstream of one of these work zones. Commercially available vehicle hard-braking event data is used for the study and geofenced to the work zone approaches and limits. This research examined 196,215 hard-braking events over a 2-month period in the summer of 2019 and 3132 crashes over the same 2-month period in 2018 and 2019 for the 23 interstate work zones. The study found there were approximately 1 crash/mile for every 147 hard-braking events in and around a construction site. The R2 was approximately 0.85. The paper concludes by recommending that hard-braking event data be used by agencies to quickly identify emerging work zone locations that show relatively large number of hard-braking events for further evaluation.

Optimizing schedule for improving the traffic impact of work zone on roads

State departments of transportation (DOTs) develop traffic control plans (TCPs) as part of construction projects. TCPs communicate required traffic control measures to the construction team and help ensure safe travel through construction work zones and a safe environment for construction workers. TCP quality can vary from project to project as a result of many factors, including the process by which the TCP is designed, reviewed, implemented, and inspected; the features and layout of the TCP and roadway; and the communication of the TCP to the constructor. This paper presents a research study conducted to understand how to improve work zone safety performance by improving the TCP design and implementation processes, identifying the TCP and roadway attributes that made TCP implementation challenging, and identifying factors that affected TCP quality and consistency. A sample of 39 construction projects of varying types and sizes was studied with a mixed methods research approach that included a survey of 12 key personnel on each project, a quantitative evaluation of each project's TCP, analysis of project crash data, and an on-site evaluation of the work zone. TCP quality and consistency and worker and motorist safety were found to be higher on projects for which constructability reviews and design peer reviews had been conducted and on projects on which the contractor super intendent and TCP designer had more years of experience and had undergone specialized TCP training. The TCP features found to increase TCP complexity to the greatest extent were multiple traffic control stages, multilane closures, and dense temporary signage.

This chapter presents solution procedures for solving unbalanced multi-objective multi-choice stochastic transportation problems in a hybrid fuzzy uncertain environment. In this chapter, various types of unbalanced multi-objective fuzzy stochastic transportation models are considered with the assumption that the parameters representing supplies of the products at the origins and demands of the products at the destinations, capacity of the conveyances, associated with the system constraints are either fuzzy numbers (FNs) or fuzzy random variables (FRVs) with some known continuous fuzzy probability distributions. The multi-choice cost parameters are considered as FNs. In this chapter, two objectives are considered: total transportation cost and total transportation time. As the transportation cost mainly depends on fuel prices and since fuel prices are highly fluctuating, the cost parameters are taken as multi-choice cost parameters with possibilistic uncertain nature. The time of transportation mainly depends on vehicle conditions, quality of roads, and road congestion. Due to these uncertain natures, the parameters representing time of transportation are also taken as fuzzy uncertain multi-choice parameters. In this transportation model, these objectives are minimized satisfying the constraints: product availability constraints, requirement of the product constraints, and capacity of the conveyance constraints. Numerical examples are provided for the sake of illustration of the methodology presented in this chapter, and also achieved solutions are compared with the solutions obtained by some existing methodologies to establish its effectiveness.

Key Factors Analysis of Work Zone Safety and Mobility based on Micro-simulation

This paper presents an approach to estimating the operational impacts of freeway work zones. The focus is on significant work zones on freeway corridors as defined by FHWA. The methodology is based on deterministic freeway capacity concepts described in the Highway Capacity Manual and allows the analyst to test impacts of a range of work zone configurations in an extended time–space domain. The focus on extended facilities refers to the analysis of multiple segments of various types, including basic freeway, ramp, and weaving segments. The cost-effective and time-efficient analysis approach can model effects of work zones such as lane closures, lower speed limits, capacity reduction, and the implicit effects of traffic diversion, peak reduction, and other intelligent transportation system strategies. Performance measures include total delay, queuing impacts, average running speed, and the potential to estimate user cost. The methodology focuses on a corridor-based analysis of work zones on freeways. It is not appropriate for network analysis, for long corridors, for signalized arterials, or for cases in which congestion on the arterial network significantly affects freeway operations. In these applications, a simulation-based analysis is more appropriate. However, the methodology presented in this paper is more cost-effective and less data intensive than simulation analysis and can readily be calibrated with local or regional data. This is a key advantage in challenging economic times, when agencies struggle to balance a constrained budget with the need to analyze multiple construction projects within their jurisdiction.

ABSTRACTThis paper mainly evaluates the previously published fleet planning researches which primarily utilize operations research and computational intelligence based techniques. An intermodal fleet planning framework with a classification scheme is also proposed by considering different problem/modeling characteristics, decision-making levels and multiple players or decision makers. Majority of the papers in this field generally focused on single mode fleet planning problems from the viewpoint of a single decision maker. However, there is a limited number of studies on intermodal fleet planning with multiple transport modes and resources. On the other hand, there is a strong interaction among the fleet planning components such as fleet sizing/composition, fleet deployment, fleet inventory control, empty vehicle repositioning problems etc. Nevertheless, these problems are mostly modeled and solved in a separated way in the available literature. Evaluation of the previous fleet planning researches enabled us to identify gaps in the literature and comment on the directions for future works which can lead to develop expert systems/decision support systems in the context of Logistics 4.0 for the integrated fleet planning problems with sustainability objectives under uncertain environments.

Environmental sustainability is receiving growing global attention, making the development of low-carbon and green transportation increasingly important. Low-carbon policies offer significant advantages in incentivizing energy conservation and reducing emissions in the transportation sector; however, it is vital to consider the impacts of regional differences on the implementation effect of low-carbon policies. This paper explores multimodal transportation route optimization under a carbon tax policy. First, a bi-objective route optimization model is constructed, with the goal of minimizing total transportation cost and time, while accounting for uncertain demand, fixed departure schedules, and regional differences. Trapezoidal fuzzy numbers are used to represent uncertain demand, and a fuzzy adaptive non-dominated sorting genetic algorithm is designed to solve the bi-objective optimization model. The algorithm is then tested on differently sized networks and on real-world transportation networks in eastern and western China to validate its effectiveness and to assess the impacts of regional differences. The experimental results show the following. (1) When considering transportation tasks at different network scales, the proposed fuzzy adaptive non-dominated sorting genetic algorithm outperforms the NSGA-II algorithm, achieving minimum differences in percentages of cost and time of 9.25% and 7.72%, respectively. (2) For transportation tasks assessed using real-world networks in eastern and western China, an increase in the carbon tax rate significantly affects carbon emissions, costs, and time. The degree of carbon emission reduction varies depending on the development of the regional transportation network. In the more developed eastern region, carbon emissions are reduced by up to 44.17% as the carbon tax rate increases. In the less developed western region, the maximum reduction in carbon emissions is 14.37%. The carbon tax policy has a more limited impact in the western region compared to the eastern one. Therefore, formulating differentiated carbon tax policies based on local conditions is an effective way to maximize the economic and environmental benefits of multimodal transportation.

Research performed to assess the effectiveness of a flashing warning-light system for use at work-zone lane closures is summarized. The system is composed of a series of interconnected, synchronized flashing warning lights that produce the illusion of motion. Researchers investigated motorist understanding and perceived usefulness of various designs of the warning-light system, and the potential of this system to yield significant operational or safety benefits in actual work-zone applications. Results from proving ground and field studies show that the flashing warning-light system used in the work-zone lane closure is perceived positively and is not confusing to the motoring public. The field-study results also revealed that the prototype warning-light system may encourage motorists to vacate a closed travel lane farther upstream from the work zone (which is believed to offer a potential safety benefit). When the warning-light system was activated at the urban freeway test site, a relatively new closure, there was a one-fourth reduction in the number of passenger vehicles and a two-thirds reduction in the number of trucks in the closed lane 305 m (1,000 ft) upstream of the lane closure. However, the system did not significantly affect lane choice at the rural road test site where the lane closure had been installed for 6 months. Thus, the greatest potential safety benefit of the warning-light system may be when it is used in conjunction with short-duration or intermediate-term maintenance or construction projects.

Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation.

With new aviation market entrants such as Urban Air Mobility (UAM) vehicles, the role aviation will take in seamless, end-to-end multimodal transportation is evolving. Travelers will likely be able to request on-demand, point-to-point transportation through air taxis in the not-to-distant future. Previous multimodal research efforts have focused on surface urban mobility, where multiple modes of ground transportation compete with and complement each other, ignoring the growing role that aviation will play going forward. Existing public transportation data exchange models, tailored for their respective systems, likewise, have not yet accounted for this looming transportation transformation nor for widespread multimodal end-to-end, seamless transporting. To date, the FAA has succeeded in establishing a robust data exchange architecture to support traditional air transportation, but to facilitate this evolution of the public airspace system with respect to UAM and other emerging air vehicle systems, new and revised data exchange models are necessary. Existing data exchange models in need of revision include the International Civil Aviation Organization's (ICAO) Flight Information Exchange Model (FIXM) and the General Transit Feed Specification (GTFS) [2], [3]. New data exchange models in need of creation include the Passenger Information Exchange Model (PIXM), the Operation Information Exchange Model (OIXM), and Vehicle Information Exchange Model (VIXM) [1]. The research here explored these data models, identifying and defining preliminary conceptual data elements necessary to support seamless, end-to-end mobility. The conceptual model herein proposed is holistic and considers all modes of transportation – walk, car, bus, rail, boat, air, etc. - and looks to incorporate the most promising transportation data exchange models from among these transportation systems.A realistic multimodal travel scenario subject to disruptive events (such as severe weather) was developed for the purpose of identifying the necessary exchange of data to reduce the impact of the disruptive events on the traveler. This work helped inform the next steps in the project, the definition of multimodal data exchange models and the development of a corresponding multimodal transportation conceptual data model. The scenario includes Urban Air Mobility (UAM) vehicles that are intended to serve as short haul providers operating in a congested urban metro environment to bypass surface traffic congestion. New aviation market entrants, such as UAM, will likely not operate entirely within a traditional gate to gate model and are eventually likely to use a model similar to on-demand surface transportation such as ride share. Uber Elevate has already been proposing this type of application for their future air taxis [4]. Accommodating these new aviation market entrants will require further extensions to the FIXM (a second package for Unmanned Aircraft System (UAS) is already planned by the FIXM committee), adoption and enhancement of existing industry data exchange models, and entirely new data models. GTFS is the industry-leading and international de facto public transportation data exchange standard and has an extension called "flex" for supporting on-demand mobility, though has been limited to surface applications thus far [3], [5]. Alongside FIXM and GTFS, new models are necessary to facilitate seamless, end-to-end mobility. Proposed models include the Vehicle Information Exchange Model (VIXM), the Passenger Information Exchange Model (PIXM), and the Operator Information Exchange Model (OIXM) [1].This paper serves as an initial exploration of a data exchange model for future of transportation. The authors envision a future where a traveler can easily plan, dynamically re-plan, and track the real-time progress of a multimodal trip from the convenience of a smartphone application. The authors include a description of a future smartphone application to help illustrate the concept (Section 5). Section 6 provides a brief history of multimodal data exchange initiatives. Section 7 includes a brief discussion of next steps and recommendations for continued research.

Warner (1965) introduced the randomized response model as an alternative survey technique for socially undesirable or incriminating behaviour questions in order to reduce response error, protect a respondent’s privacy, and increase response rates. In multivariate stratified surveys with multiple randomised response data the choice of optimum sample sizes from various strata may be viewed as a multi-objective nonlinear programming problem. The allocation thus obtained may be called a “compromise allocation” in sampling literature.In this paper, we have formulated two stage stratified Warner’s Randomised Response model (RRM) as a multi-objective integer non-liner optimization problem. In this problem of RRM we have minimized the square root of coefficient of variations instead of variations for different characteristics because the coefficient of variation is unit free, subject to the linear and quadratic cost constraint. The multi-objective optimization problem of RRM has been solved by lexicographic goal programming integrated with fixed priority - distance method. The solution obtained by lexicographic goal programming Integrated with fixed priority - distance have been compared with various existing approaches namely the value function approach, goal programming techniques, - constraint method and distance-based method and Khuri & Cornel distance based method. A numerical example is also been presented to illustrate the computational details.https://doi.org/10.28919/jmcs/3376

In this paper, we present a multi-objective fractional probabilistic programming problem where the parameters in the objective functions and right-hand side constraints are multi-choice in nature. Multi-choice multi-objective objective fractional probabilistic programming problem is formulated. The multi-choice parameters in the right-hand side of constraints are random variables following Cauchy distribution. Multi-choice multi-objective fractional probabilistic programming problem is transformed to deterministic equivalent multi-choice multi-objective fractional programming problem using chance-constrained programming problem. Lagrange interpolating polynomial approach is proposed to deal with multi-choice parameters. The multi-choice multi-objective fractional programming problem is transformed to non-linear mixed integer multi-objective programming problem. An \(\epsilon -\) constrained method is applied to find the compromise solutions of non-linear mixed integer multi-objective programming problem using different values of \(\epsilon \). LINGO software is applied to find the optimal solution of single objective non-linear mixed integer programming problem. Finally, a numerical example is given to illustrate the proposed programming problem.KeywordsMulti-choice programming problemMulti-objective programming problemProbabilistic programming problemFractional programming problem\(\epsilon \)-constraint method