Population-based risk equilibration for the multimode hazmat transport network design problem

There is a body of literature which shows a change in the amount of S's risk taking from an individual to a group situation. A currently popular explanation for the risky shift is Brown's (1) view that there is a norm in our society to be risky. Upon entering a group, one or more of the individuals in the group find that they are not as risky as the other members and change their judgments accordingly. If Brown's cultural value of risk exists, then to live up to the norm an individual should view himself as being at least as risky as some hypothetical other individual. This discrepancy has been found by Wallach and Wing (8) using the choice-dilemma items. Similarly, Levinger and Schneider (4) found Ss tended to admire a position more risky than their own. Rettig's ( 5 ) Behavior Prediction Scale has also been used as a measure of risk taking. The scale is somewhat unique in that (a) it involves unethical behavior and ( b ) it breaks down the risk taking situation into several components. Rettig has found from an individual to a group condition an increase in both total risk taking and in emphasis of the reinforcement value of gain (RVgn). Horne ( 3 ) and Schulman ( 7 ) have found respectively a self-other discrepancy and an admire-shift for total scale's risk taking in a direction opposite to what one would predict from Brown's value position. Both Horne and Schulman ascribe their findings to the unethical content of Rettig's scale. Neither author, however, reports any data for the RVgn component of the scale. Twenty-three undergraduate introductory psychology students from Bowdoin College volunteered to take part in the study. Ss were first requested to respond as individuals to Rettig's scale under the usual instructions. After completing all 16 items, they were then requested to go back and for each item mark an M where they thought an average introductory psychology student at Bowdoin would respond. Consistent wlth Hotne's ( 3 ) finding, significantly (z = 3.06, p < .01, 2-tail) more Bowdoin Ss judged their fellow students to be more risky than themselves on the total score. Of 23 Ss, 3 had positive discrepancies and 18 had negative discrepancies in Total score; 2 showed no discrepancy. These results seem to indicate that Brown's value hypothesis does not explain the increase in Total risk taking on the scale. However, Ss themselves were significantly ( z = 2.62, p < .01, 2-tail) more sensitive to the RVgn component than was the hypothetical average other student. On RVgn, 17 students had positive discrepancies and 4 had negative discrepancies. These data indicate that the value interpretation may explain the increase in S's sensitivity to RVgn. There are three other component scores to the scale. None of these have shown a group shifc in past literature and none showed a significant sel€-other discrepancy in the present study, suggesting values may be a mediator of the shift on the RVgn component. On the total score, the unethical nature of the act may be in conflict with the value of risk or some other force acting to produce the risky shift. Presence of conflict is supported by the observation that shifts in total score are not as consistent between studies as is the shift in reinforcement value of gain (2, 6 ) .

Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.

This paper presents a variant of vehicle routing problem, incorporating factors of transportation costs in conjunction with adverse situations. The effects of hazardous materials (HazMat) transportation are examined on air-pollution exposure levels, as well as the risk and damage in the case of accidents. In an urban network, the various weights that can be are assigned to each road of a route are associated to the following factors: time, cost, pollution exposure levels, and the risk of damage in HazMat transportation. These weights express the travel cost and the estimated provoked adverse consequences at each road of a vehicle transit route between two disparate points of the network concerning the pollution and risk of unforeseen accident during hazardous materials transportation. The sum of the weights assigned to every road of a route represents the route value according to the corresponding factors. We introduce an optimization criterion in order to select the optimal route among all vehicle transition routes joining any given pair of the network points that does not surpass a predefined threshold route value for each factor. The predefined weights for each factor are a percentage increment of its corresponding shortest route value. The optimal location of a central depot is determined where each vehicle serves a customer using one specific route with respect to the posed restrictions. Subsequently, a suitable partition into sub-networks is carried out in the case of large urban areas and a depot is assigned to each sub-network by applying a specific optimization criterion. A branch and bound tree search algorithm is developed for the determination of the optimal routes while the locations of the depots are investigated within the framework of graph theory. Finally, small numerical examples are given.

A comprehensive modeling framework for hazmat network design, hazmat response team location, and equity of risk

Hazardous Materials Transportation Network (HMTN) has very tight coupling with Traffic Flow Network of Hazmat Transportation (TFNHT). We build the interdependent system for the two networks. One of them is the HMTN of Zhangjiagang City, another is generated with power-law parameters which is considered as TFNHT. One node from the HMTN is random failed as initial emergency failure. It has an impact on one dependent node from the TFNHT. The dependent node would give traffic pressure on connected nodes from the HMTN. In order to know the stability and robustness of each single network, we propose the Node Failure Rate (NFR) to show the percentage of non-function with iterations going on. The two variables in simulation are different average degrees of the TFNHT and initial failure of one random selecting node which belongs to the set of different degrees. In terms of the basic data and the simulation results, we found that (1) the high average degree of the TFNHT has more impact on the interdependent network than the low average degree of it. It means that high values of average degree can lead interdependent weakness and instability; (2) We suggest that the node of low degree should be considered as the part of hazmat transportation routes, and it reduces as far as possible selecting the nodes of high degree when we design the HMTN for improving the robustness; (3) In the interdependent transportation network, the HMTN is more stability than the connected TFNHT and the two networks have the same NFR rising trend.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road, or rail, are intrinsically coupled through passenger transfers and are interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. This paper provides a case report of the Asiana crash in San Francisco International Airport (SFO) on July 6, 2013, and its repercussions on the multimodal transportation network. It studies the resulting propagation of disturbances on the transportation infrastructure in the USA, particularly on the U.S. air transport network and the ground transportation in the Bay Area. The perturbation takes different forms and varies in scale and time frame: cancelations and delays snowball in the airspace, with up to 86% of cancelations in the U.S. due to the SFO crash; highway traffic near the airport is impacted by congestion in previously not congested locations, with low speed and high delays on US 101; and transit passenger demand exhibits unusual traffic peaks in between airports in the Bay Area, with up to 180 passengers more per hour between SFO and Oakland International Airport Bay Area Rapid Transit stations. This paper also investigated the effect of the crash on the social media Twitter. This paper, through a case study, aims at stressing the importance of further data-driven research on interdependent infrastructure networks. The end goal is to form the basis for optimization models behind providing more reliable passenger door-to-door journeys and improved transport network resilience.

We estimate how an acquiring firm’s risk changes depending on whether the market initially judges the acquisition to be neutral, strongly negative, or strongly positive for the shareholders of the acquiring firm. We found that for an average neutral acquisition, the annualized standard deviation of an acquiring firm’s total return declines by 5%. In contrast, acquisitions judged negatively by the market result in a 5% increase in total risk, while acquisitions judged positively by the market feature a 30-basis-point increase in total risk. We found the median acquisition to be value creating, not value destructive. Value destruction tends to be concentrated among large firms and to be associated with extreme negative outliers. Acquiring firms with longholder CEOs are more prone to undertake acquisitions and more prone to take on risk, but are less prone to engage in value-destructive acquisitions than acquiring firms with non-longholder CEOs. In this respect, acquiring firms with non-longholder CEOs are more apt to undertake risky bad acquisitions, especially when their prior returns lie above the industry average. In addition, acquiring firms with non-longholder CEOs are less prone to take on good acquisitions that are high in risk. As a general matter, firms with longholder CEOs are less risk sensitive to changes in prior returns than firms with non-longholder CEOs.

Many credible threats against hazardous materials (hazmat) transportation exist in the United States. This paper outlines the overall regulatory approach to hazmat transportation security by commercial motor carriers in the United States and focuses on the well-received training and outreach efforts that have been put in place to improve the overall level of security in the United States. In the post-September 11, 2001 (9/11), environment, safety-oriented agencies are being asked to accommodate a new role in the security arena. Many regulations affect the highway transportation of hazmat. The Federal Motor Carrier Safety Administration is primarily concerned with the enforcement of safety regulations, and the Research and Special Programs Administration (RSPA) regulates hazmat transportation across all modes. Recently, RSPA implemented new requirements for security assessments and security plans. Because many motor carriers previously did not have much experience with security issues, the guidance provide...

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS’s capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions.

The transportation of hazardous materials is a growing problem world6 wide due to the increasing volumes being transported. In fact, as a consequence of industrial development, huge quantities of hazardous materials are yearly produced, and obviously the production of them goes together with their transportation. Historical evidence has shown that accidents due to hazardous releases during transportation can lead to consequences as serious as those created by fixed plants, and therefore quantified risk analysis also has to be carried out for transportation networks. In this paper a flexible decision support system is proposed to quantify risk due to different transportation of dangerous substances – by road, rail, pipe14 line and inland waterways. First, a brief discussion is given on methodology adopted for evaluating risk. Then, the steps of the algorithm that has been implemented are sketched out in some detail. Finally a description of the software is provided. Supported by a GIS-data bank, where parameters of the different transport networks are geo-referenced (i.e incident rate, number of vehicles per year), the software provides a method of quantifying Individual and Population Risk. Some applications in relation to possible risk mitigation are discussed, such as the influence of a new road or reduction of quantity transported, thus confirming a possible use of this instrument by the decision maker in territorial planning and emergency management.

In this paper, we consider the problem of network design for hazardous materials transportation in a road network in an uncertain environment. Assume that the parameters such as transport risks of road sections are uncertain and can be represented by fuzzy numbers. A minimum risk Steiner tree network model is developed for this network design problem and there exists one and only one route between any given origin and destination in the tree network. The objective is to minimize the total risk of the hazardous materials transportation. To solve the tree network model with fuzzy parameters, we provide a fuzzy chance-constrained programming model for it, then convert it to the crisp equivalent, and lastly solve the crisp equivalent model using an off-the-shelf solver. The numerical example shows that different confidence levels lead to different Steiner tree networks and in order to design a robust hazardous materials transportation network, we should take into account the fuzziness of the parameters.

A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods

Nowadays, transport is the basis for the functioning of national, continental, and global economies. Thus, many governments recognize it as a critical element in ensuring the daily existence of societies in their countries. Those responsible for the proper operation of the transport sector must have the right tools to model, analyze, and optimize its elements. One of the most critical problems is the need to prevent bottlenecks in transport networks. Thus, the main aim of the article was to define the parameters characterizing the transportation network vulnerability and select algorithms to support their search. The parameters proposed are based on characteristics related to domination in graph theory. The domination, edge-domination concepts, and related topics, such as bondage-connected and weighted bondage-connected numbers, were applied as the tools for searching and identifying the bottlenecks in transportation networks. Furthermore, the algorithms for finding the minimal dominating set and minimal (maximal) weighted dominating sets are proposed. This way, the exemplary academic transportation network was analyzed in two cases: stationary and dynamic. Some conclusions are presented. The main one is the fact that the methods given in this article are universal and applicable to both small and large-scale networks. Moreover, the approach can support the dynamic analysis of bottlenecks in transport networks.

Hazardous materials truck transportation problems: A classification and state of the art literature review

To reduce the risks associated with hazardous materials transportation by optimizing routes and delivery schedules, a multiobjective routing and scheduling program with multiple time windows and time varying attributes in hazardous materials transportation network is studied. First, a mathematical model for this program is proposed and two punishing functions are introduced to the model for converting multiple time windows into the penalty variables of model. Second, a two-step solving algorithm for model is developed, and the first step of algorithm is to find the set of non-dominated paths with specific departure time from the origin based on the improved dynamic programming method, the second step is to identify the optimal alternatives from the non-dominated set by using the grey correlation analysis method. Finally, a case study is given to show that the model and algorithm can effectively solve the routing and scheduling program in hazmat transportation network, and can identify the optimal transportation paths for the decision maker.