A review on research in transportation of hazardous materials

Accepted Manuscript A review on research in transportation of hazardous materials A. Ditta, O. Figueroa, G. Galindo, R. Yie-Pinedo PII:

S0038-0121(18)30068-5

DOI:

https://doi.org/10.1016/j.seps.2018.11.002

Reference:

SEPS 665

To appear in:

Socio-Economic Planning Sciences

Received Date: 7 March 2018 Revised Date:

15 September 2018

Accepted Date: 1 November 2018

Please cite this article as: Ditta A, Figueroa O, Galindo G, Yie-Pinedo R, A review on research in transportation of hazardous materials, Socio-Economic Planning Sciences (2018), doi: https:// doi.org/10.1016/j.seps.2018.11.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

A. Dittaa , O. Figueroaa , G. Galindoa* and R. Yie-Pinedoa a

RI PT

A Review on Research in Transportation of Hazardous Materials

Department of Industrial Engineering Universidad del Norte, Km 5 Antigua Via a Puerto Colombia, Barranquilla, Colombia

M AN U

SC

ABSTRACT Incidents that involve transportation have the potential of causing significant damages to the affected communities due to the release of hazardous substances. Therefore, it is important to reduce their impact as well as the risk of their occurrence. In this paper we review recent research in the field of transportation of hazardous materials. Our work extends a previous review from 2007. Based on our analysis, we identify current trends and research gaps as well as relevant future research directions.

AC C

EP

TE D

KEYWORDS Hazardous materials, transportation, risk, incidents, safety

*Corresponding author. Email: [email protected]. Address: Km 5 Antigua Via a Puerto Colombia, Barranquilla, Colombia. Tel.: +575 3509269

ACCEPTED MANUSCRIPT 1. Introduction

AC C

EP

TE D

M AN U

SC

RI PT

In recent decades, the dynamics of global industrialization has been associated with a higher rate of industrial production, which has also increased the shipments of hazardous materials (HAZMAT). In this regard, according to the US Census Bureau 2012 Commodity Flow Survey, 2.5 billion tons of HAZMAT was shipped in USA in 2012, an increment of 15.6% from 2002 estimates (US Census Bureau, 2015). As a consequence, the risk of having HAZMAT transportation incidents has also increased. For instance, according to the US Department of Transportation, in 2009 there were 14,816 HAZMAT incidents by different transportation modes, whereas the estimate for 2012 was 17,459, which resulted in 7 fatalities, 152 injuries and $68,045,434 in damages (USDT, nd). Notice that this implies that in 2012 there were in average almost 48 HAZMAT incidents per day. The increased risk associated to HAZMAT transportation incidents has raised the awareness of industries, government and academia. In this latter sector, some of the problems that are commonly addressed include, among others: (i) designing networks for HAZMAT transportation, which is known as the HAZMAT transport network design problem (HTNDP) (Bianco et al., 2009; Erkut and Gzara, 2008; Gzara, 2013; Xu et al., 2013); and (ii) defining the routes for HAZMAT vehicles (Han and Weng, 2011; Ma et al., 2013b; Qu et al., 2011; Si et al., 2012; Xie and Waller, 2012). To the best of our knowledge, Erkut et al. (2007) offer the most comprehensive and latest review in HAZMAT transportation to date. Erkut et al. (2007) cover the period from 1980s until 2007. Given that since the review by Erkut et al. (2007) the attention to HAZMAT transportation has significantly increased, it is time to update their review by considering recent related literature. In line with this, in this paper we extend the review from Erkut et al. (2007) by providing a literature review that covers the time-frame 2008 - 2016. In Erkut et al. (2007), the authors classify the literature into four main categories based on the type of problem: (i) Risk Assesment, (ii) Routing, (iii) Facility Location and Routing, and (iv) Network Design. They also classify papers according to the corresponding mode of transportation, e.g. railway, and urban roads. For comparison purposes, we use the same classifications scheme presented in Erkut et al. (2007) to classify papers in our review. This allows us to obtain insights about how the trends identified in Erkut et al. (2007) have evolved. Additionally, Erkut et al. (2007) identifies important research gaps and challenges. In this regard, we seek to answer the following questions: (i) what attempts have been made to overcome such challenges?, (ii) how successful were they?, and (iii) which still remain as challenges and why? Another reason for conducting an updated review is to achieve information about important aspects of HAZMAT transportation that were not considered in Erkut et al. (2007). Therefore, in addition to the classification scheme given by them, we offer an analysis about the most common assumptions in the field. This analysis is similar to that presented in Galindo and Batta (2013), where the authors classify assumptions as: (i) realistic, (ii) limited, and (iii) unrealistic. Also, in order to obtain insights regarding the type of research that have been recently published in HAZMAT transportation, we have applied the classification scheme offered by Denizel et al. (2003), which categorizes papers depending on several attributes such as: type of data (real or simulated), novelty of the problem, novelty of approach, and future implications, among others. Although the classification scheme by Denizel et al. (2003) was conceived for articles in the field of Operations Research and Management Sciences (OR/MS), we find it suitable to assess the quality of the papers in our survey, even though they may belong to other 2

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

disciplines. Additionally, we classify the papers in our survey based on their main contribution, as follows: (1) data analysis, (2) model, and (3) theory. The first category covers papers whose main focus is related to collecting, analyzing and transforming data; the second, refers to papers mainly focused on developing analytic models; whereas the third category comprises papers whose focus is to develop theoretical knowledge. As it can be seen, additionally to evaluating the evolution of the literature in relation to the findings from Erkut et al. (2007), we seek to gather a general updated picture of the field in order to identify new relevant trends. We are particularly interested in detecting what aspects of HAZMAT transportation have been understudied and that could benefit from more research. Furthermore, in order to contribute to the cohesion of the field, we detect important research gaps and challenges, which lead us to proposing future research directions. The remainder of this paper is organized as follows: Section 2 explains our search methodology and the scope of our research. Section 3 presents our findings when applying the same classification scheme given by Erkut et al. (2007). Section 4 provides our new taxonomy for HAZMAT transportation research. Then, Section 5 discusses our findings by taking into consideration those reported in Erkut et al. (2007) and provides a set of future research directions. Finally section 6 contains our final remarks and conclusions.

2. Search methodology and scope of the study

AC C

EP

TE D

This section describes the search methodology for collecting the papers for our survey. Recall that Erkut et al. (2007) represents the most recent literature review on HAZMAT transportation. Their review covers publications (which includes papers, books, and others) until 2007, whereas our review only considers papers published after 2007. Specifically, our review’s scope is limited to papers published in English in the time-frame 2008 - 2016. We used the following databases: Science Direct, Ebsco, ISI Web of Knowledge, Elsevier and Google Scholar. Initially, we began our search by looking for papers that jointly contained, anywhere in the document, the terms ”Hazardous materials” and ”transportation”. We only included papers published in journals, i.e. we excluded books, book chapters, thesis and conference papers. We consider any type of research in HAZMAT transportation, which means that we do not focus on a particular discipline of such a field. Then, we classified the articles based on the type of transportation mode (by road, rail, water, air and multimodal) and we further looked for articles in each type of such transportation modes. Additionally, we performed a forward reference process in order to complete the list of the papers to be considered in our survey. The amount of publications in HAZMAT transportation within the selected time frame is overwhelming (over a thousand). We filtered our results to limit the scope of our paper to articles published in top-ranked journals. In this regard, we applied the ranking provided by the Scientific Journal Rankings (SJR) and the Journal Citation Reports from ISI Web of Knowledge (ISI). These ranking institutions classify journals in quartiles, based on several parameters such as impact factor, journal self cites, etc. A lower quartile indicates a better ranking. We decided to use for our analysis only papers published in journals that had been ranked in quartiles Q1 and Q2 of these ranking schemes. In cases where the two institutions (SJR and ISI) ranked differently a certain journal, we opted to use the best ranking result. For instance, if there was a 3

RI PT

ACCEPTED MANUSCRIPT

Figure 1. Number of papers in HAZMAT transportation published between 2008 and 2016.

EP

TE D

M AN U

SC

journal that was ranked as Q4 by the ISI Web of Knowledge, but as Q2 by the SJR, we assumed that the journal belonged to Q2. Additionally, a single ranking institution, either SJR or ISI, can assign a certain journal different quartiles if such a journal can be categorized into several disciplines. For instance, a journal in SJR can be ranked Q2 in Economics and Q3 in Management. In those cases, we used the best ranking score i.e. Q2 for our example. After having screening our collection of papers to keep only those that belonged to Q1 and Q2 journals, we finally reached to a total of 162 papers. We would like to highlight that our selection of Q1 and Q2 journals only has effects regarding our survey and by no means we are suggesting any qualification or quality measure for the journals in our collection. Also, notice that our literature review is not the result from an exhaustive search. Nevertheless, we consider that it contains a representative sample of papers in HAZMAT transportation. Figure 1 shows the number of articles in our survey per year. As we can see, the number of articles in HAZMAT transportation had an increment between 2008 and 2009. However, later in 2010, it significantly declined. After that, the amount of research remained constant during two years and then it seems that it peaked until 2016. We do not identify any long-term trends in relation to the number of publications over the years. The next section provides a comparative analysis between the findings from this review and those reported in Erkut et al. (2007).

AC C

3. Comparative Analysis

The classification scheme proposed by Erkut et al. (2007) is bi-criterion, where the two criteria being considered are: (1) the topic addressed in the paper, and (2) the transportation mode. Regarding the first criterion, the categories in the classification scheme given by Erkut et al. (2007) are: (i) Risk Assessment, (ii) routing, (iii) facility location and routing, and (iv) network design. The second criterion (transportation mode) comprises (i) road, (ii) rail, (iii) air and (iv) multimodal. To these categories we have also added the following two: (v) pipeline and (vi) NTMS (no transportation mode specified). Table 1 provides a summary of our results, whereas Table 2 provides the list of papers that fall in each category of the classification scheme. In Table 1 we also compare our results to those presented in Erkut et al. (2007). Notice that for Network Design neither Erkut et al. (2007) nor us have a detailed report based on the

4

ACCEPTED MANUSCRIPT Erkut et al. (2007)

Risk Assessment NTMS Road Rail Marine Air Pipeline Multimodal

50,31% 4,40% 16,35% 10,06% 2,53% 0,00% 10,06% 6,92%

44,330% N/A 28,87% 6,18% 1,55% 0,51% N/A 7,22%

Routing NTMS Road Rail Marine Air Multimodal

33,96% 3,15% 22,64% 3,14% 1,26% 0,00% 3,77%

RI PT

Our research

M AN U

SC

44,330% N/A 39,17% 2,06% 2,06% 0,00% 1,03%

Facility Location and Routing

10,69%

8,247%

Networking Design

5,03%

3,093%

Table 1. Summary statistics of articles in HAZMAT transportation

AC C

EP

TE D

type of transportation mode. The reason is that the amount of papers in this category is considerably small and most of them do not refer to any particular transportation mode. We can see that in Erkut et al. (2007), Risk Assessment and Routing equally dominated the other two categories. In our case, we can see that the percentage of papers related to Risk Assessment has increased (more than 50% of the papers in our review refer to this category) in detriment of the participation of Routing. The other two categories (Facility Location and Routing, and Network Design) are still well understudied in comparison to the other two. In fact, notice that Risk assessment and Routing represent more than 80% in the two time-frame covered. Also, notice from Table 1 that, considering the number of articles, Road is the most popular transportation mode in all of the topics. However, in Risk Assessment we can see an increased interest in studying other transportation modes such as Rail, Marine and Pipeline. Also, in Risk Assessment the percentage of articles that considers multimodal problems has decreased. The results of our review show some differences from those found by Erkut et al. (2007). Evidently, there have been slight changes in research trends from previous years. However, Risk Assessment still appears as one of the crucial priorities in the study of the transportation of HAZMAT. In this regard, researchers have recently focused on developing accurate mechanisms for Risk Assessment in different modes of transport. In fact, Verma (2010) states that the quantification of risk remains the most difficult challenge and a continual problem in the treatment of transport of dangerous substances. 5

ACCEPTED MANUSCRIPT Risk Assessment

Marine Pipeline

Multimodal

Routing General

Nagae (2008) Zhang et al. (2012) Zamparini and Reniers (2013) Jiang and Ying (2014) Zhou et al. (2014b) Bonvicini and Spadoni (2008) Chen et al. (2008) Mohaymany and Khodadadiyan (2008) Ma et al. (2008) Dadkar et al. (2008) Garrido (2008) Ghatee et al. (2009) Chin et al. (2009) Caramia et al. (2009) Dadkar et al. (2010) Androutsopoulos and Zografos (2010) Kim et al. (2011) Lozano et al. (2011) Li and Leung (2011) Pradhananga et al. (2011) Kazantzi et al. (2011) Chakrabarti and Parikh (2011b) Monprapussorn et al. (2011) Wang et al. (2012) Reilly et al. (2012) Xie and Waller (2012) Das et al. (2012b) Zhou et al. (2013) Wang et al. (2013) Toumazis and Kwon (2013) Desai and Lim (2013) Pradhananga et al. (2014) Mahmoudabadi and Seyedhosseini (2014a) Kang et al. (2014) Zhao and Zhu (2016) Fan et al. (2015) Bronfman et al. (2015) Chiou (2016) Kheirkhah et al. (2016) Esfandeh et al. (2016) Cappanera et al. (2016) Toumazis and Kwon (2015) Kawprasert and Barkan (2008b) Verma (2009) Kawprasert and Barkan (2009) Verma et al. (2011) Saat and Barkan (2011) Hennig et al. (2012), Siddiqui and Verma (2015). Verma and Verter (2010) Verma et al. (2012) Li et al. (2013) Talarico et al. (2015) Assadipour et al. (2015) Assadipour et al. (2016)

EP

TE D

Road

RI PT

Rail

SC

Road

Ghazinoory and Kheirkhah (2008) Clark and Besterfield-Sacre (2009) Gumus (2009) Milazzo et al. (2010) Si et al. (2012) Rebelo et al. (2014) Knoope et al. (2014) Kawprasert and Barkan (2009) Tr´ epanier et al. (2009) Kheirkhah et al. (2009) Qiao et al. (2009) Yang et al. (2010) Rashid et al. (2010) Chakrabarti and Parikh (2011a) Chakrabarti and Parikh (2011c) Changxi et al. (2011) Das et al. (2012a) Zhao et al. (2012) Chakrabarti and Parikh (2012) Roncoli et al. (2013) Chakrabarti and Parikh (2013b) Tena-Chollet et al. (2013) Xu et al. (2013) Liu et al. (2013a) Chakrabarti and Parikh (2013a) Szeto (2013) Kang et al. (2011) Shen et al. (2013) Faghih-Roohi et al. (2015) Inanloo and Tansel (2015) Qiu et al. (2015) Xin et al. (2013) Cordeiro et al. (2016) Milazzo et al. (2009) Xin et al. (2015) Kawprasert and Barkan (2008a) Van der Vlies and Suddle (2008) Hassan et al. (2009) Bagheri (2009) Kawprasert and Barkan (2010) Bagheri et al. (2010) Hassan et al. (2010) Lai et al. (2011) Verma (2011) Cheng and Wen (2011) Bagheri et al. (2012) Liu et al. (2013b) Saat et al. (2014) Liu et al. (2014) Mahmoudabadi and Seyedhosseini (2014b) Cheng et al. (2016) van Dorp and Merrick (2011) Qu et al. (2011) Carr and Stoddard (2015) Wang et al. (2016) Siddiqui and Verma (2013) Brito and de Almeida (2009) Nathanail et al. (2010) Sosa and AlvarezRamirez (2009) Han and Weng (2010) Han and Weng (2011) Ma et al. (2013b) Jamshidi et al. (2013) Ma et al. (2013a) Bonvicini et al. (2015) Vianello and Maschio (2014) Zhou et al. (2014a) Lu et al. (2015) Iesmantas and Alzbutas (2015) Bonvicini et al. (2015) Ambituuni et al. (2015) Vianello et al. (2016) Samuel et al. (2009) Bubbico et al. (2009) Reniers et al. (2010) Junior and M´ arcio de Almeida (2011) Sengul et al. (2012) Reniers and Dullaert (2013) Van Raemdonck et al. (2013a) Bagheri et al. (2014) van der Vlies (2015) Liu and Hong (2015) Strogen et al. (2016)

M AN U

General

AC C

Rail

Marine Multimodal

Facility Location and Routing Zografos and Androutsopoulos (2008) Ghatee and Hashemi (2009) Xie et al. (2012) Caro-Vela et al. (2013) Samanlioglu (2013) Ardjmand et al. (2014) Berglund and Kwon (2014) Zhao and Verter (2014) Wei et al. (2015) Mahmoudabadi and Seyedhosseini (2014a) Meiyi et al. (2015) Yu and Solvang (2016) Ardjmand et al. (2014) Tavakkoli-Moghaddam et al. (2016) Romero et al. (2016) Lau (2009)

Networking Design Verter and Kara (2008) Erkut and Gzara (2008) Bianco et al. (2009) Marcotte et al. (2009) Gzara (2013) Xin et al. (2013) Chong et al. (2015) Belaid et al. (2016) Table 2. List of papers in each category from Erkut et al. (2007)

6

ACCEPTED MANUSCRIPT 4. New Taxonomy

4.1. Denizel et al. (2003) classification

RI PT

In this section we classify the papers in our survey by using a different taxonomy from the one proposed in Erkut et al. (2007). We start by using a classification scheme based on Denizel et al. (2003); then we characterize articles based on their type of contribution; and finally, we classify the most common assumptions found in our survey according to their suitability to realistic settings.

AC C

EP

TE D

M AN U

SC

The classification scheme given by Denizel et al. (2003) is an extension of the one offered by Corbett and Van Wassenhove (1993), who proposed classifying research in OR/MS into three main categories, depending on the type of problem being addressed. The first category received the name of management science (MS), in which “the goal of solving problems is to develop new results to contribute to the body of knowledge in the discipline”. The second category was labelled management consulting (MC), where “the goal is to solve someones practical problems with existing and standards methods”. Finally, in the third category, called management engineering (ME), ”the goal is to solve practical problems for which it is necessary to adjust existing tools in novel ways” (Corbett and Van Wassenhove, 1993). For further details regarding the categories described above we refer the reader to Denizel et al. (2003) and Corbett and Van Wassenhove (1993).

Figure 2. Classification scheme from Denizel et al. (2003)

Denizel et al. (2003) further divided each category from Corbett and Van Wassenhove (1993) into two subtypes, which produced a total of six classification classes: 7

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

MS1, MS2, MC1, MC2, ME1, and ME2 (See Figure 2). In Figure 2, Setting denotes the context in which the study has been conducted. Data is considered as real only if the data involved in the study belongs to organizations or specific real case studies. Situation and approach examine if the research had been widely studied. In this regard, according to Denizel et al. (2003), a standard situation/approach has been well studied or is frequently found in the literature, whereas “novel” refers to a new problem/methodology. The first step for classifying a problem/approach as standard or novel is by following the authors’ statement in this regard. Generally, if the authors have addressed a novel problem or have used a novel approach, they present it as a distinguishing feature of their paper. Such information usually appears in the abstract or in the introduction. Results are specific if the outcomes of the research applies only to the organization being studied or if they are general enough to be put to use in other settings. Finally, reported studies can differ as to whether they suggest future research implications or not. The possible combinations of the six attributes, according to Denizel et al. (2003), are shown in Figure 2. As stated before, the classification scheme given by Denizel et al. (2003) was conceived for articles in OR/MS. However, we find their approach generic enough to be applied to other academic fields. Specifically, we consider it suitable to assess the quality of the papers in our survey and to uncover interesting insights, although not all of the articles in our collection are related to OR or MS. Table 3 shows the percentage of articles from our survey that fall into each category defined in Denizel et al. (2003).

Results

MS1 MS2 MC1 MC2 ME1 ME2

32,91% 3,16% 25,32% 14,56% 14,56% 9,49%

TE D

Category

EP

Table 3. Results for Denizel et al. (2003) classification scheme applied to recent research in HAZMAT Transportation

AC C

In Table 3, it can be noticed that most of the papers in our survey belong to MC (39,88%), whereas the category with less articles is ME (24,05%). An important remark is that more than 30% of the papers use random data. In this regard, we would like to highlight the importance of gathering real data since that is the only way in which we can perceive the actual limitations related to collecting the required input data for the problems in HAZMAT transportation. Additionally, it can be noticed that approximately half of the papers fit into categories MS1 or MC1, which indicates that such papers use real or random data, but did not present a novel situation or approach. Of course, finding novel problems or methodologies is not an easy task and it was expected not to find a great amount of papers with such characteristics. However, it was expected to find less papers in categories MC1 and MC2, since they do not provide strong contributions to the field of HAZMAT. In fact, this suggests that a considerable percentage of papers were devoted to solving widely studied problems with approaches that had already been used in the literature. In the next section we discuss the nature of the research contribution for each paper 8

ACCEPTED MANUSCRIPT considered in our article. 4.2. Type of Research Contribution

TE D

M AN U

SC

RI PT

In general, academic research relies on three main basic pillars: (i) problem understanding, (ii) data, and (iii) modeling. If one of such pillars is not well founded, the whole body of knowledge is in danger of falling apart. In order to assess the amount of work in HAZMAT transportation in relation to each of these pillars, we have classified articles in our survey into three groups based on the type of the contribution to the body of knowledge: data analysis, model and theory. Articles classified in the first category correspond to those that are mainly focused on collecting and analyzing data. The second category covers papers whose main purpose is to describe an analytical model. Lastly, articles that fit in the last category are those that highlight the theoretical foundations of the problem, which seek a fully understanding of the corresponding situation. This latter category comprises those papers whose focus is to develop a theoretical framework, to provide qualitative analysis, or to test hypothesis. Results are shown in Figure 3.

Figure 3. Types of research contributions

AC C

EP

From Figure 3 it can be concluded that almost half of recent papers in HAZMAT transportation focus on developing models. For instance, in the case of Routing and Network Design the greatest amount of research is devoted to generating models, while there is little research related to data analysis or problem understanding. Of course, the development of analytic models is highly valuable, but it is important not to neglect research in the other two categories. In this regard, it is important first to fully understand and characterize HAZMAT related problems, and then propose models that are based on information and data that is as realistic as possible. On the other hand, it is interesting to note that in Risk Assessment, the different types of research contribution are more evenly distributed with more than 60% of the papers contributing for a better understanding of problems through theoretical bases. 4.3. Research assumptions This section analyzes the most common assumptions that are found in the papers of our survey. We classify such assumptions into three main categories: (i) reasonable, (ii) limited, and (iii) unrealistic. As in Galindo and Batta (2013), we define an 9

ACCEPTED MANUSCRIPT

RI PT

assumption as reasonable if it does not compromise the applicability of the study; limited assumptions are those “that apply to some problems, but that are impractical to others” (Galindo and Batta, 2013); finally, unrealistic assumptions refer to those that are not actually suitable for general HAZMAT transportation settings, and therefore they could compromise the applicability of the research to real-life contexts. For instance, when assessing risk of HAZMAT transportation, it would not be realistic to assume a constant measurement of the consequence of releasing HAZMAT despite of variations in problem settings. We would not like to miss the opportunity of remarking the importance of carefully evaluating the appropriateness of the assumptions when developing research, since otherwise, the applicability, utility and prevalence of the work would be compromised.

AC C

EP

TE D

M AN U

SC

(1) Constant parameter values per edge/route: Limited Various authors consider parameters such as population density and impact magnitude as constant along a given edge or route, e.g. Zhou et al. (2013), Liu et al. (2013a), Kim et al. (2011). For instance, Zografos and Androutsopoulos (2008) and Kawprasert and Barkan (2008a) assume that exposure of the population, accident probability, and other factors that might affect risk, are homogeneous throughout a certain route. Similar assumptions can be found regarding travel time and transportation risk. These assumptions must be examined with great care. An interesting alternative approach is given by Chakrabarti and Parikh (2011b) who divide routes into segments where it is reasonable to find similar characteristics within a single segment. (2) Time or distance correlations for HAZMAT incidents: Limited. Some authors suggest that the probability of HAZMAT incidents are dependent on the time or distance from previous events. For instance, Sosa and AlvarezRamirez (2009) state that it is possible to observe time periods with agglomerations of pipelines incidents. Then, this information can be used to predict the occurrence of future events. However, there are some limitations in this regard. For instance, incidents with a large severity index are highly unpredictable. Additionally, it might be hard to predict incidents for small time horizons. Another perspective of this assumption is given in Erkut and Gzara (2008), where the authors assume that accident frequency is a linear function of distance. Hence they compute the accident frequency in an edge by multiplying the corresponding accident rate by the length of the edge. In this regard, such simplifications must be undertaken with care and provide evidence that they are coherent with what would be expected in reality. (3) Deterministic parameters: Limited In HAZMAT transportation, uncertainty is an inherent characteristic of most parameters such as demand, time or risk. However, numerous studies assume that decision makers have accurate and certain information available regarding such parameters. For instance, Szeto (2013) assumes that travel time on each link in the base network is known and fixed; Lau (2009) assumes linear travel times and constant speed, which allows knowing vehicles position through interpolation using coordinates and time as parameters; the work given by Androutsopoulos and Zografos (2010) is also based on the assumption that arc travel times and risk values are deterministic; another example is given by Xie et al. (2012) who consider deterministic travel risk and costs. A more suitable approach for cases in which it is questionable to assume deterministic parameters is to model them in 10

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

a stochastic way by defining probability distributions or using robust approaches as in Toumazis and Kwon (2015) and Xin et al. (2015). The use of stochastic parameters belongs to the discussion of our next group of assumptions, which is presented below. (4) Parameters are estimated based on probability functions: Reasonable. According to the specific type of problem, it is reasonable to use probability functions to model certain behaviors. Parameters such as probabilities of HAZMAT releases, accident frequency, and population distributions could be estimated using this methodology. For instance, Kazantzi et al. (2011) use probability functions to model release likelihood. Other examples are the work given by Dadkar et al. (2008) who assume that the probability of accidents varies following a gamma distribution; Milazzo et al. (2010), who use lognormal and normal distribution for the frequency of accidents on rail tracks; Bonvicini and Spadoni (2008), who use a uniform distribution for the population distribution in centers of aggregated population; and Kawprasert and Barkan (2008b), who assume a Poisson distribution for modeling the occurrence of tank-car derailment. The difficulty of this approach is to being able to calibrate an appropriate probability function, which requires having available enough reliable information. In this regard, there are articles such as Milazzo et al. (2010), where the authors build a probability function for release events through a procedure based on historical data; and van Dorp and Merrick (2011) and Han and Weng (2011), who estimate the failure rate of pipelines for different accident scenarios based on historical accidents database. The difficulty of estimating probability functions based on historical data is that HAZMAT accidents do not occur on a daily basis. On the contrary, they are low frequency events that do not offer a great amount of high-quality historic data. In fact, in Section 5, scarcity of data is identified as one of the challenges in HAZMAT transportation. (5) Potential affected zones are considered as symmetrical shapes: Limited. For simplicity, most authors consider symmetrical contour shapes in their approaches. For instance, in Chakrabarti and Parikh (2012), circular contours are used to model impact zones. On the other hand, in the paper given by Zhou et al. (2013), the authors create instances based on real network topology, where nodes are randomly and uniformly distributed in rectangles. It would be of value to examine how asymmetrical or irregular shapes would affect the study of HAZMAT problems. One paper that offers an interesting alternative is that given by Zografos and Androutsopoulos (2008). In their methodology, the authors use a worst-case scenario to compute a maximum possible impact radius. Then, they select several locations per edge and generates one circular impact area per location, whose radius is the maximum impact radius and its center is the corresponding location. This generates several circular areas, one per selected location. Then they measure what they have called a ‘cautious’ consequence (it is called ‘cautious’ since their analysis is based on worst-case scenarios) by considering the population within all of the generated circles. In relation to the problem of computing the size of the impact area is commonly approached by assuming it to be constant or by not explicitly mentioning any specific consideration in this regard (Belaid et al., 2016; Chin et al., 2009; Verma, 2009). An attempt to overcome this issue is provided by Garrido (2008), where the impact radius depends on the type of HAZMAT and on the topology and climate conditions under the incident. (6) Homogeneus likelihood of release scenarios throughout the day: Unrealistic 11

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Several authors simplify routing models by disregarding the effects of time variations throughout the day. For instance, Kawprasert and Barkan (2008a) assume that shipments during the day or night are equally likely and that chances of releases are 50-50. Additionally, they do not consider the effect of the day or night in the exposed population. Similarly, in the approach given byVan Raemdonck et al. (2013b), parameter values do not change based on peak hours or on the time of the day. In reality, it is expected that most of the parameters e.g. the amount of vehicles, the visibility, probability of accidents, and the effects on population, take different values throughout the day. For instance, Chen et al. (2008) highlights that nuclear wastes are often transported at midnight in order avoid impacts on prevailing traffic and on the transportation risk. Dadkar et al. (2008) also remarks that accident probability varies depending on whether the link is urban or rural and whether it is traversed in the day or night. In our review, one of the papers that attempts to incorporate day/night related risk is given by Zografos and Androutsopoulos (2008). Specifically, the authors propose a model that may be iteratively applied for covering the demand and the HAZMAT routes during different times of the day. However they do not provide a clear discrimination about the probability of HAZMAT releases for day and night time. Other papers that tackle the distinctions between day and night for HAZMAT transportation are those given by Lozano et al. (2011), Meiyi et al. (2015), and Desai and Lim (2013). The first paper analyzes the exposure of travelers in case of a chlorine spill under two extreme situations: daytime rush-hour and nighttime without traffic. The approach in the second paper captures morning road congestion. Finally, the third paper offers a time-dependent routing policy, focused on avoiding routes with high traffic congestion, population concentrations, and/or travel delays in particular road segments at different times of day. Other interesting approaches can be found in the papers given by Qiao et al. (2009) and Dadkar et al. (2010). In this case, even though they do not refer to day or night schedules, they do consider climate characteristics (raining, snowing, fog, blowing dust, smoke), surface conditions (dry, wet, muddy, snowy) and road conditions (obstructions that are not lighted at night or not marked during the day) to define the probabilities of an incident. (7) The probability of accident for HAZMAT shipment does not depend on the type of material being transported: limited. To compute the probability of an accident, it is usual to consider aspects such as characteristics of the road (Kawprasert and Barkan, 2008a), transportation route (Ghazinoory and Kheirkhah, 2008) and vehicle speed (Hassan et al., 2009). However, the type of substance being transported could be an additional factor that is not often considered. For instance, some types of substances, such as explosives, may be more attractive to terrorist attacks. As an example, a truck that is carrying C4 explosives may attract the interest of terrorists, either to steal the explosives for their own usage or to simply destroy them so as their enemies cannot use them in future encounters. Either way, carrying this type of substance may increment the likelihood of an incident. In this regard, we have found some articles that consider the type of HAZMAT when measuring the consequence and exposure of the population (Bonvicini and Spadoni, 2008; Cordeiro et al., 2016; Das et al., 2012a; Nathanail et al., 2010; Vianello et al., 2016). However, none of them use the type of HAZMAT to define the corresponding probability of an incident, which remains a gap in this field. 12

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

(8) Congestion is neglected or not considered: Unrealistic. In some cases, the computation of travel times, costs and risks does not consider congestion. For instance, in Ardjmand et al. (2014), transportation costs depend only on distances. In reality, such an assumption does not necessarily hold true since a given path may be shorter than another, but still more expensive due to traffic congestion. Disregarding this issue may lead to erroneous conclusions with serious implications. As stated by Lozano et al. (2011) the role of congestion in the population exposure could be more important than other factors such as the night-time atmosphere. The importance of traffic is also highlighted by Ma et al. (2008), who points out that road traffic conditions of the routes affect the HAZMAT material safety, as well as by Cordeiro et al. (2016), according to whom accident rate is directly proportional to the road and traffic characteristics. In our review, papers that have incorporated traffic congestion include Chen et al. (2008), who use traffic for risk calculation, under the assumption that links with high traffic volume leads to the higher accident risks; Kawprasert and Barkan (2009), who include traffic volume as a factor that affects risk; Desai and Lim (2013), who use a time-dependent routing policy to further enhance safety procedures by avoiding routes with high traffic congestion, population concentrations, and/or travel delays in particular road segments at different times of day; Pradhananga et al. (2014), who utilizes the static real traffic information based on historical records; and Esfandeh et al. (2016), who use a duration-populationfrequency risk measure to model the dependence of HAZMAT risk on congestion induced by regular traffic. (9) HAZMAT network users have perfect information about the status of the network: Realistic. Nowadays, with new technologies and Geographic Information Systems (GIS), it is possible to attain perfect information in real time about some attributes of road networks e.g. distances, times and traffic, among others. Hence, some authors assume that network users have perfect information, as in Wang et al. (2012). The use of GIS has significantly extended in HAZMAT research. For instance, Zografos and Androutsopoulos (2008) use a GIS functionality that estimates the total population within a specific designated area; Kawprasert and Barkan (2008a) use GIS to compute the distance and the type of traffic control system on a HAZMAT transportatin network; Rashid et al. (2010) develop a GIS application to build a spatial model of a liquified petroleum gas release, where information can be collected and transmmited to any location in order to predict potential damage of new incidents; Kim et al. (2011) offer a GIS tool that provide online routing instructions for HAZMAT vehicles given the vehicles current location and updated information concerning traffic and weather conditions; in Samanlioglu (2013) data is obtained by means of a GIS software combined with the region geographical database. However, the assumption of immediate perfect information should be regarded with care when considering case studies in developing countries, where access to information technologies might still be limited.

An important contribution of the analysis presented in this section, is that it allows to identify relevant future research directions that will aid to strengthen research in the field of HAZMAT transportation. Some of them are included in the next section.

13

ACCEPTED MANUSCRIPT 5. Final Discussion and Future Research Directions This section discusses our findings in relation to the trends and gaps reported in Erkut et al. (2007). Additionally, we provide insights about new gaps that give rise to potential future research directions. 5.1. Our findings versus Erkut et al. (2007): what has changed?

M AN U

SC

RI PT

In Table 1, it can be noticed that the general trends identified in Erkut et al. (2007) still persist in recent literature. Specifically, topics corresponding to Routing and Risk Assessment continue to dominate Facility Location and Network Design. Furthermore, in Erkut et al. (2007), the authors identify some important gaps that relate mainly to the following issues: (1) risk calculation and assessment of accident consequences; (2) availability of realistic data; (3) criteria for Risk Assessment; (4) perceived risk and equity; (5) multiple actors, disciplines, objectives and modes of transportation; and (6) security. In this section we briefly describe each of such gaps and compare them to our findings. The main conclusion is that not much has changed, since most of the gaps from Erkut et al. (2007) still remain relatively unexplored. 5.1.1. Risk calculation and assessment of accident consequences

AC C

EP

TE D

According to Erkut et al. (2007), some researchers adopt feeble approaches when assessing risks as well as their consequences, such as assuming uniform population density along transport links. In this regard, even though some researchers still use simplistic approaches, we have found some research with more realistic perspectives. For example, numerous authors are using GIS in their methodologies (Cordeiro et al., 2016; Inanloo and Tansel, 2015; Kawprasert and Barkan, 2008a; Kim et al., 2011; Lau, 2009; Rashid et al., 2010; Samanlioglu, 2013) and census-based data (Cheng et al., 2016; Desai and Lim, 2013; Garrido, 2008; Kang et al., 2011; Kawprasert and Barkan, 2009; Verter and Kara, 2008; Vianello et al., 2016; Zografos and Androutsopoulos, 2008). The main use of GIS is to generate population distributions that are more accurate. Other researchers consider differences in population between day and night time (Desai and Lim, 2013; Lozano et al., 2011; Meiyi et al., 2015); moreover, some approaches take into consideration the existence of high-density population locations such as schools and hospitals (Lau, 2009; Van der Vlies and Suddle, 2008; Vianello et al., 2016). However, there are still some gaps to be filled. For instance, according to Cheng et al. (2016), there is a need to develop more sophisticated approaches based on analytitcal methodologies that incorporate cost. In this regard, Vianello et al. (2016) address the problem of network design while considering the trade-off between the cost of incorporating safety measures into the network desing versus the expected reduction in the social risk. However Vianello et al. (2016) do not provide a formal model to assess the economical costs of the incident or its consequences. Also, Assadipour et al. (2016) point out the need of modeling population distributions that change over time. This latter issue would likely require to develop a stochastic framework. 5.1.2. Availability of realistic data Similarly to other fields of research, in HAZMAT transportation it is most relevant to count with realistic and reliable data. However, as mentioned before, in some cases it is highly difficult to obtain accurate values of input parameters. This issue had 14

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

already been pointed out by Erkut et al. (2007). An important problem that arises with the poor quality of input data is that it can undermine the improvements in other aspects of HAZMAT. For instance, in recent literature, there is a trend for designing more powerful methods, such as those based on heuristics to provide better and faster answers to problems related to the routing of HAZMAT. However, issues regarding the quality of the input data compromise the applicability of the solutions found by these methods. In this sense, Dadkar et al. (2008) highlight the need to develop better data to be used in such algorithms as an important field of research. Yang et al. (2010) also point out the need to create mechanisms for data collection. In fact, additionally to the scarcity of data, another problem is that there is a lack of tools and initiatives for taking advantage of the available information. For instance, some countries with a significant amount of HAZMAT transportation do not count with centers to collect valuable information for future research. In this regard, we would like to remark the necessity of improving methods for acquiring real data. Erkut et al. (2007) reported that, as a way to handle the absence of realistic data, some authors opt to use national data uniformly on all links while ignoring fundamental differences among them. In this regard, (Erkut et al., 2007) pointed out that it is common to assign a unique incident probability along a given edge, based solely on the road type such as in Garrido (2008). In our review, one paper that attempts to overcome this issue, is given by Qiao et al. (2009), which develops a methodology based on route-dependent parameters (namely lane number, weather and poopulation density) as well as on route-independent parameters (namely truck configuration, container capacity, and driver experience) to estimate accident frequencies. Even though Qiao et al. (2009) considers other variables different from road type, there is still the challenge of including relevant features such as hot spots, tunnels, and bridges which may lead to different incident probabilities along the same edge. Notice that these shortcomings on the availability of high detailed data directly affects risk calculation, as discussed in the preceding section. As mentioned in Section 4, Chakrabarti and Parikh (2011b) proposes an alternative by dividing edges into segments which can be assumed to have homogeneous characteristics. Another problem that arises with lack of data is that some researchers use static or constant data values in cases where it would be more realistic to model data as stochastic (Faghih-Roohi et al., 2015; Inanloo and Tansel, 2015; Kang et al., 2011; Liu et al., 2013b). Based on the analysis of assumptions in section 6, this type of methodology persists in recent approaches. On the other hand, according to (Erkut et al., 2007), in an effort to incorporate real and reliable data into HAZMAT analyses related to Risk Assessment, several authors use empirical incident probabilities. As mentioned before, the weakness of this approach is that the quality and amount of historic data in the case of HAZMAT problems is questionable. An appropriate alternative is to develop robust models and to test their performance under different scenarios by means of technical methodologies such as sensitivity analysis. Some papers in our review that considers several scenarios are those given by Cheng et al. (2016) and Cordeiro et al. (2016). An interesting paper that incorporates robust optimization in recent literature is that given by Toumazis and Kwon (2015). In the aforementioned paper the authors develop a new robust optimization approach for routing HAZMAT trucks while considering worst-case scenario under data uncertainty. Another paper that includes a robust optimization approach is given by Xin et al. (2015). In that case, the authors consider a multicommodity version of the HAZMAT transportation network design problem with uncertain edge risk. Xin et al. (2015) presents a robust heuristic that builds subnetwork composed of the robust shortest path for each commodity based on 15

ACCEPTED MANUSCRIPT the minimax regret criterion. 5.1.3. Criteria for Risk Assessment

TE D

5.1.4. Perceived risk and equity

M AN U

SC

RI PT

Erkut et al. (2007) pointed out that research in risk measurement often pursues objectives that are focused on: (i) human fatalities, (ii) injuries, and (iii) property damage, which suggests that environmental impacts have been understudied. Given the vulnerability of ecosystems nowadays and the potential effect that the release of HAZMAT may have to the environment, it is important to incorporate environmental consequences among the considerations for Risk Assessment. Some examples of research that have contibuted to fill this gap are given by Ma et al. (2008), Brito and de Almeida (2009), Cordeiro et al. (2016), and Mohaymany and Khodadadiyan (2008). The first paper determines the safety level of HAZMAT by considering ecological related conditions; the second, defines environmental consequences as the extension (in square meters) of vegetation destroyed; the third, establishes environmental vulnerability by considering several sub-criteria related vegetation, topography, hydrography, population density, and rural-economic activity, among others. However, there are still some limitations regarding the inclusion of environmental consequences in HAZMAT related analyses. For instance, in Mohaymany and Khodadadiyan (2008) it is not clear how to accurately define what can be considered as environmental exposure. Moreover, there is not a unique set of standard indicators to measure the environmental impact of HAZMAT incidents. In this regard, it would be of value to develop a formal methodology for: (i) characterizing those components that should be considered when assessing environmental risks, e.g. vegetation, soil, bodies of water; and (ii) defining and estimating the parameters required to assess each of such components.

AC C

EP

One of the gaps identified in Erkut et al. (2007) relates to the need of incorporating an assessment of perceived risk. According to Erkut et al. (2007), it could be expected that the perception of risk changes as a function of the type of hazardous substance, its quantity, and the distance to the hazardous activity. As stated by Kang et al. (2011), “The Perceived Risk model” adds a weight parameter on consequences to reflect the public preference on the risk, where different models reflect the different risk preferences of decision makers toward the risk evaluation. In our review, the only article that included a perceived risk perspective is that given by Pradhananga et al. (2014), where the authors apply a decision support system that considers the perceived importance of risk and travel time of several stakeholders. An approach that could be used in order to obtain insights regarding perceived risks, is to design and apply stated preference surveys that include latent variables. Notice that perceived risk can become a critical issue when designing HAZMAT transportation routes and public policies. On the other hand, Erkut et al. (2007) brought attention to the importance of reaching equity in the spatial distribution of risk. The concept of equity is associated to a fair distribution of impacts (Litman, 2002). It has been identified as an important component for humanitarian objectives (Huang et al., 2015). The only paper in our review that considers equity is that given by Romero et al. (2016), which uses Gini coefficient to consider social equity to make facility location and routing decisions regarding HAZMAT transportation. Their model classifies individuals into several groups according to the corresponding income in an attempt to ensure that 16

ACCEPTED MANUSCRIPT

RI PT

lower income groups are not more exposed than higher income groups. As stated by Litman (2002), in transportation there are two types of equity: (i) horizontal, where it is assumed that every person has the same capability to handle the impact of a given event and therefore risk should be equal for all the individuals; and (ii) vertical, which relates to distributing risk by considering that different individuals may have different affordability levels, abilities, and needs. More research is required to consider both types of equity while taking into account various perspectives and impacts. Furthermore, it might be interesting to develop a combined analysis of equity in the perceived risk. This type of analysis may contribute to designing public policies in HAZMAT transportation that are well received by the communities. 5.1.5. Multiple actors, disciplines, objectives and modes of transportation

AC C

EP

TE D

M AN U

SC

Erkut et al. (2007) highlighted the lack of work in relation to network design considering multiple players and objectives. Another gap identified by Erkut et al. (2007) was the lack of multidisciplinary work in HAZMAT transportation. These remain as gaps in recent literature. In this regard, it would be important to bring together carriers, government entities, and companies in order to design integrated structures for the transportation of HAZMAT in aims to obtain the safest possible schemes. Furthermore, it would be useful to gather additional insights from systematically reviewing the policies and responsibilities of federal, state, and local agencies in HAZMAT transportation, in order to grasp a better understanding of their commitments and limitations. In our review, articles that consider the interactions of several actors are given by Dadkar et al. (2010), which develops a framework based on a gametheoretic model for the interactions among government agencies, shippers/carriers and terrorists; Kim et al. (2011), where the authors consider the interaction between dispatcher and carriers; Reilly et al. (2012), which analyses a three-player game of the interactions among a government agency, a carrier and a terrorist; Esfandeh et al. (2016), in which the authors explore the users equilibrium decision of the local government, vehicle drivers and HAZMAT carriers.; and Zografos and Androutsopoulos (2008), which proposes a DSS that includes the participation of emergency service providers, hazardous, materials customers, carriers, public authorities and policy makers. It would be also important to consider a multidisciplinary perspective that brings together multiple fields, each of them with valuable contributions in HAZMAT transportation. Some of such fields include civil engineering, operations research, economics and psychology, among others. Additionally, it would be of value to address the unique implications of multimodal transportation of HAZMAT, an issue that has been well understudied. Finally, we would like to highlight the importance of bringing closer academic and non-academic actors in order to build an integral panorama of the problems in HAZMAT transportation. 5.1.6. Security

In Erkut et al. (2007), the authors brought attention to a concern that was considered as “new” at that time: security. This is in fact even a greater concern in the present given that attacks to HAZMAT transportation have significantly increased around the world. As evidence, consider reports from Yie Pinedo (2013) who has pointed out that, for instance, in Pakistan, the number of attacks to HAZMAT-supply transportation increased from 8 attacks in 2008 to 108 in 2011. Also the number of deaths due to such attacks has increased dramatically. Moreover, Abkowitz (2002) has correctly ex17

ACCEPTED MANUSCRIPT

M AN U

SC

RI PT

posed the fact that HAZMAT vehicles could be desirable targets for terrorists, and that certain HAZMAT vehicles can become “weapons of mass destruction”. Four articles in our review address the issue of terrorism in HAZMAT transportation: (i) Dadkar et al. (2010), (ii) Reilly et al. (2012), (iii) Talarico et al. (2015) and (iv) Milazzo et al. (2009). The first two papers use a Game Theory approach with terrorists being one of the players. The third article considers security-related risks in order to develop strategies against potential terrorist attacks that could target critical transportation systems. Specifically, it focuses on enabling detection and warning systems for impeding terrorist attacks on transportation infrastructures, as well as on adopting subsequent security-counter measures. It highlights the importance of preventing terrorist attacks for avoiding human and economic losses. Finally, the fourth paper uses an approach based on dynamic geo-events to address the problem of emergency management associated with terrorist attacks on HAZMAT transportation under urban settings. Even though there is evidence of an increased concern of the role of terrorism in HAZMAT transportation, there is still room for novel considerations. One of them relates to countries with ongoing armed conflicts, which can be an important issue to be addressed in future research. Moreover, it is not uncommon that countries with on-going conflicts are developing countries with precarious transportation networks. Additionally, such countries typically present particular sociological characteristics that negatively affect diverse issues such as coordination among different actors, and compliance of official security standards, among others. The complex combination of these factors require a different approach for these type of settings, in relation to what we could expect in first-world countries. 5.2. What are other Additional Challenges?

TE D

Additional to the gaps found in Erkut et al. (2007), we have found some additional challenges, as follows:

AC C

EP

• Different types of HAZMAT. As stated by several authors, the type of substance is a fundamental factor for determining the risk in HAZMAT transportation (Cordeiro et al., 2016). In contrast, in our survey it is common to find models that do not distinguish among different types of HAZMAT. For instance, in Kawprasert and Barkan (2008a), it is assumed that the probability of a HAZMAT tank car derailment does not depend on the type of material. This is not necessary true since the kind of HAZMAT being transported may limit vehicles speed and other characteristics that can affect the probability of an incident. Additionally, the possible effects on humans and the environment also varies with the type of HAZMAT, as pointed out by Sengul et al. (2012). Moreover, depending on the type of HAZMAT, different specifications for network design and facility location might be required. For instance, if the type of HAZMAT is fuel, it could be expected a less rigorous transportation scheme than for explosives. • Network dynamic characteristics. It is important to acknowledge that some characteristics of transportation networks are dynamic, e.g. traffic (Verma et al., 2011) and weather (Kim et al., 2011). Others change throughout a certain route, e.g. elevation (Strogen et al., 2016) and population. In this regard, some researchers design models that neglect such variations and that simply assume that expected values apply in the whole network during the whole time, e.g. Verma (2009) does not consider congestion, and Bonvicini and Spadoni (2008) 18

ACCEPTED MANUSCRIPT

RI PT

and Kawprasert and Barkan (2008a) assume homogenous population across the routes. This approach is not always suitable, hence more flexible approaches are needed that can incorporate such variations. • Holistic perspective. Even though there has been an important progress in HAZMAT research, there is still the need of bringing all the pieces together. In this regard, research in HAZMAT has grown in extension, but it has profound grooves that must be filled. Some of such grooves are on the basis of the field (e.g. lack of realistic input data), and could jeopardize research and findings that are being adding to the body of knowledge. In this regard, recall that Figure 3 shows that a great percentage of the articles in our survey focuses on models. Perhaps, it would be appropriate to concentrate more efforts in other topics such as data analysis and study of the problem in order to strengthen all of the aspects involved in HAZMAT research.

SC

6. Conclusion

AC C

EP

TE D

M AN U

In this paper we have presented a review of the recent literature on HAZMAT transportation. Our work extends the study given by Erkut et al. (2007). A limitation of our survey is that we only consider papers in top-ranked journals. However, Such journals are likely to comprise articles that are in the vanguard of HAZMAT research. Therefore, we would expect that in general, they represent the most important trends and progress in the field. However, a future extension could be to perform an analysis that covers a shorter period of time but that includes all types of journals. Based on the papers in our collection, we have found that there are some topics that remain understudied, which are (1) Facility Location and Routing, and (2) Network Design. Also, we have conducted an analysis of the most common assumptions in the papers in our survey. From our analysis, we have recommended some alternative approaches that might contribute to generate more realistic research. For instance, we have identified the need of incorporating congestion information into the models; the fact that not always users will have perfect information about the transportation network; and the drawbacks of using deterministic data to represent some features that are better modeled as stochastic parameters. Additionally, we have provided a set of future research directions based on our findings and those reported by Erkut et al. (2007). Among them, we would like to bring attention to the lack of reliable data. As pointed out before, having available realistic data is fundamental for validating the methodologies and analyses performed by researchers in the field of HAZMAT. In this regard, our findings show that researchers are inclined to the development of mathematical modeling. This is an important task, but it should be accompanied of a proper statistical analysis of the data, as well as of a profound conceptual and theoretical analysis of the problem. Our main finding is that the gaps identified in Erkut et al. (2007) still persist in recent literature. This brings doubts about whether researchers have been actually working on filling such gaps. It does not seem like it, which rises several questions: is the persistence of gaps due to their difficulty? What are we missing to deal with such gaps? Why are they not so attractive for conducting research? Are researchers working as a whole team to solve HAZMAT problems as a unit composed of different parts? Or, on the contrary, are researchers working as separate and unconnected entities? Perhaps there are not straight answers for these questions, but we should keep them in mind for future HAZMAT research. 19

ACCEPTED MANUSCRIPT

RI PT

In general, it is gratifying to find so many researchers attempting to make a difference from their disciplines in such an important social problem which is HAZMAT transportation. In fact, one of our main findings is that the amount of research in HAZMAT is extraordinarily large. This means that HAZMAT is currently an attractive topic for researchers of different disciplines. We hope that this review will serve to establish new directions for research and as a starting point for a more cohesive body of knowledge in HAZMAT transportation. We expect to provide a solid foundation that contributes to the professional benefit of the research community in the field.

References

AC C

EP

TE D

M AN U

SC

Abkowitz, M. D. (2002). Transportation risk management: A new paradigm. Security Papers (Knoxville: Southeastern Transportation Center, University of Tennessee), pages 93–103. Ambituuni, A., Amezaga, J. M., and Werner, D. (2015). Risk assessment of petroleum product transportation by road: A framework for regulatory improvement. Safety science, 79:324– 335. Androutsopoulos, K. N. and Zografos, K. G. (2010). Solving the bicriterion routing and scheduling problem for hazardous materials distribution. Transportation Research Part C: Emerging Technologies, 18(5):713–726. Ardjmand, E., Weckman, G., Park, N., Taherkhani, P., and Singh, M. (2014). Applying genetic algorithm to a new location and routing model of hazardous materials. International Journal of Production Research, 53(3):916–928. Assadipour, G., Ke, G. Y., and Verma, M. (2015). Planning and managing intermodal transportation of hazardous materials with capacity selection and congestion. Transportation Research Part E: Logistics and Transportation Review, 76:45–57. Assadipour, G., Ke, G. Y., and Verma, M. (2016). A toll-based bi-level programming approach to managing hazardous materials shipments over an intermodal transportation network. Transportation Research Part D: Transport and Environment, 47:208–221. Bagheri, M. (2009). Risk analysis of stationary dangerous goods railway cars: a case study. journal of Transportation Security, 2(3):77–89. Bagheri, M., Saccomanno, F., and Fu, L. (2012). Modeling hazardous materials risks for different train make-up plans. Transportation research part E: logistics and transportation review, 48(5):907–918. Bagheri, M., Saccomanno, F. F., and Fu, L. (2010). Effective placement of dangerous goods cars in rail yard marshaling operation. Canadian Journal of Civil Engineering, 37(5):753– 762. Bagheri, M., Verma, M., and Verter, V. (2014). Transport mode selection for toxic gases: Rail or road? Risk Analysis, 34(1):168–186. Belaid, E., Rigo, P., Cools, M., Limbourg, S., and Mostert, M. (2016). Bi-objective road and pipe network design for crude oil transport in the sfax region in tunisia. Procedia Engineering, 142:108–114. Berglund, P. G. and Kwon, C. (2014). Robust facility location problem for hazardous waste transportation. Networks and spatial Economics, 14(1):91–116. Bianco, L., Caramia, M., and Giordani, S. (2009). A bilevel flow model for hazmat transportation network design. Transportation Research Part C: Emerging Technologies, 17(2):175– 196. Bonvicini, S., Antonioni, G., Morra, P., and Cozzani, V. (2015). Quantitative assessment of environmental risk due to accidental spills from onshore pipelines. Process Safety and Environmental Protection, 93:31–49. Bonvicini, S. and Spadoni, G. (2008). A hazmat multi-commodity routing model satisfying risk criteria: A case study. Journal of Loss Prevention in the Process Industries, 21(4):345–358. Brito, A. J. and de Almeida, A. T. (2009). Multi-attribute risk assessment for risk ranking of

20

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

natural gas pipelines. Reliability Engineering & System Safety, 94(2):187–198. Bronfman, A., Marianov, V., Paredes-Belmar, G., and Ler-Villagra, A. (2015). The maximin {HAZMAT} routing problem. European Journal of Operational Research, 241(1):15–27. Bubbico, R., Di Cave, S., Mazzarotta, B., and Silvetti, B. (2009). Preliminary study on the transport of hazardous materials through tunnels. Accident Analysis & Prevention, 41(6):1199–1205. Cappanera, P., Nonato, M., and Visintin, F. (2016). Routing hazardous materials by compulsory check points in case of variable demand. Electronic Notes in Discrete Mathematics, 52:53–60. Caramia, M., Giordani, S., and Iovanella, A. (2009). On the selection of k routes in multiobjective hazmat route planning. IMA journal of management mathematics, 21:239–251. Caro-Vela, M., Paralera, C., and Contreras, I. (2013). A dea-inspired approach to selecting parking areas for dangerous-goods trucks. EJTIR, 13(3):184–200. Carr, J. R. and Stoddard, S. W. (2015). Probabilistic hazardous materials contamination model for a municipal water source. JOURNAL AWWA, 107:2. Chakrabarti, U. and Parikh, J. (2013a). Risk-based route evaluation against country-specific criteria of risk tolerability for hazmat transportation through indian state highways. Journal of Loss Prevention in the Process Industries, 26(4):723–736. Chakrabarti, U. K. and Parikh, J. K. (2011a). Class-2 hazmat transportation consequence assessment on surrounding population. Journal of Loss Prevention in the Process Industries, 24(6):758–766. Chakrabarti, U. K. and Parikh, J. K. (2011b). Route evaluation for hazmat transportation based on total risk–a case of indian state highways. Journal of Loss Prevention in the Process Industries, 24(5):524–530. Chakrabarti, U. K. and Parikh, J. K. (2011c). Route risk evaluation on class-2 hazmat transportation. Process Safety and Environmental Protection, 89(4):248–260. Chakrabarti, U. K. and Parikh, J. K. (2012). Applying hazan methodology to hazmat transportation risk assessment. Process Safety and Environmental Protection, 90(5):368–375. Chakrabarti, U. K. and Parikh, J. K. (2013b). A societal risk study for transportation of class3 hazmats–a case of indian state highways. Process Safety and Environmental Protection, 91(4):275–284. Changxi, M., Yixin, G., and Bo, Q. (2011). Study on the transportation route decision-making of hazardous material based on n-shortest path algorithm and entropy model. pages 282– 289. Chen, Y.-W., Wang, C.-H., and Lin, S.-J. (2008). A multi-objective geographic information system for route selection of nuclear waste transport. Omega, 36(3):363–372. Cheng, J., Verma, M., and Verter, V. (2016). Impact of train-makeup on hazmat risk in a transport corridor. Journal of Transportation Safety & Security, (just-accepted):00–00. Cheng, J. and Wen, C. (2011). Risk assessment model approach for dangerous goods transported by railway. Journal of Transportation Security, 4(4):351–359. Chin, S.-M., Hwang, H.-L., Peterson, B. E., Han, L. D., and Chin, C. (2009). Routing hazardous materials around the district of columbia area. Journal of Transportation Safety & Security, 1(4):296–313. Chiou, S.-W. (2016). A bi-objective bi-level signal control policy for transport of hazardous materials in urban road networks. Transportation Research Part D: Transport and Environment, 42:16–44. Chong, P., Shuai, B., Deng, S., Yang, J., and Yin, H. (2015). Analysis on topological properties of dalian hazardous materials road transportation network. Mathematical Problems in Engineering, 2015. Clark, R. M. and Besterfield-Sacre, M. E. (2009). A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables. Risk analysis, 29(3):344–354. Corbett, C. J. and Van Wassenhove, L. N. (1993). The natural drift: What happened to operations research? Operations Research, 41(4):625–640.

21

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Cordeiro, F. G., Bezerra, B. S., Peixoto, A. S. P., and Ramos, R. A. R. (2016). Methodological aspects for modeling the environmental risk of transporting hazardous materials by road. Transportation Research Part D: Transport and Environment, 44:105–121. Dadkar, Y., Jones, D., and Nozick, L. (2008). Identifying geographically diverse routes for the transportation of hazardous materials. Transportation Research Part E: Logistics and Transportation Review, 44(3):333–349. Dadkar, Y., Nozick, L., and Jones, D. (2010). Optimizing facility use restrictions for the movement of hazardous materials. Transportation research part B: methodological, 44(2):267–281. Das, A., Gupta, A., and Mazumder, T. (2012a). A comprehensive risk assessment framework for offsite transportation of inflammable hazardous waste. Journal of hazardous materials, 227:88–96. Das, A., Mazumder, T., and Gupta, A. (2012b). Pareto frontier analyses based decision making tool for transportation of hazardous waste. Journal of hazardous materials, 227:341–352. Denizel, M., Usdiken, B., and Tuncalp, D. (2003). Drift or shift? continuity, change, and international variation in knowledge production in or/ms. Operations Research, 51(5):711– 720. Desai, S. and Lim, G. J. (2013). Solution time reduction techniques of a stochastic dynamic programming approach for hazardous material route selection problem. Computers & Industrial Engineering, 65(4):634–645. Erkut, E. and Gzara, F. (2008). Solving the hazmat transport network design problem. Computers & Operations Research, 35(7):2234–2247. Erkut, E., Tjandra, S. A., and Verter, V. (2007). Hazardous materials transportation. Handbooks in operations research and management science, 14:539–621. Esfandeh, T., Kwon, C., and Batta, R. (2016). Regulating hazardous materials transportation by dual toll pricing. Transportation Research Part B: Methodological, 83:20–35. Faghih-Roohi, S., Ong, Y.-S., Asian, S., and Zhang, A. N. (2015). Dynamic conditional valueat-risk model for routing and scheduling of hazardous material transportation networks. Annals of Operations Research, pages 1–20. Fan, T., Chiang, W.-C., and Russell, R. (2015). Modeling urban hazmat transportation with road closure consideration. Transportation Research Part D: Transport and Environment, 35:104–115. Galindo, G. and Batta, R. (2013). Review of recent developments in or/ms research in disaster operations management. European Journal of Operational Research, 230(2):201–211. Garrido, R. A. (2008). Road pricing for hazardous materials transportation in urban networks. Networks and Spatial Economics, 8(2-3):273–285. Ghatee, M. and Hashemi, S. M. (2009). Optimal network design and storage management in petroleum distribution network under uncertainty. Engineering Applications of Artificial Intelligence, 22(4):796–807. Ghatee, M., Mehdi Hashemi, S., Zarepisheh, M., and Khorram, E. (2009). Preemptive prioritybased algorithms for fuzzy minimal cost flow problem: An application in hazardous materials transportation. Computers & Industrial Engineering, 57(1):341–354. Ghazinoory, S. and Kheirkhah, A. S. (2008). Transportation of hazardous materials in iran: A strategic approach for decreasing accidents. Transport, 23(2):104–111. Gumus, A. T. (2009). Evaluation of hazardous waste transportation firms by using a two step fuzzy-ahp and topsis methodology. Expert Systems with Applications, 36(2):4067–4074. Gzara, F. (2013). A cutting plane approach for bilevel hazardous material transport network design. Operations Research Letters, 41(1):40–46. Han, Z. and Weng, W. (2010). An integrated quantitative risk analysis method for natural gas pipeline network. Journal of Loss Prevention in the Process Industries, 23(3):428–436. Han, Z. and Weng, W. (2011). Comparison study on qualitative and quantitative risk assessment methods for urban natural gas pipeline network. Journal of hazardous materials, 189(1):509–518. Hassan, C., Puvaneswaran, B., Aziz, A., Zalina, M. N., Hung, F., and Sulaiman, N. (2010). Quantitative risk assessment for the transport of ammonia by rail. Process Safety Progress,

22

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

29(1):60–63. Hassan, C. R. C., Balasubramaniam, P. A., Raman, A. A. A., Mahmood, N. Z., Hung, F. C., Sulaiman, N. M. N., et al. (2009). Inclusion of human errors assessment in failure frequency analysisa case study for the transportation of ammonia by rail in malaysia. Process Safety Progress, 28(1):60–67. Hennig, F., Nygreen, B., Christiansen, M., Fagerholt, K., Furman, K. C., Song, J., Kocis, G. R., and Warrick, P. H. (2012). Maritime crude oil transportation–a split pickup and split delivery problem. European Journal of Operational Research, 218(3):764–774. Huang, K., Jiang, Y., Yuan, Y., and Zhao, L. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 75:1–17. Iesmantas, T. and Alzbutas, R. (2015). Bayesian reliability of gas network under varying incident registration criteria. Quality and Reliability Engineering International, 32(5):1903– 1912. Inanloo, B. and Tansel, B. (2015). Explosion impacts during transport of hazardous cargo: Gis-based characterization of overpressure impacts and delineation of flammable zones for ammonia. Journal of environmental management, 156:1–9. Jamshidi, A., Yazdani-Chamzini, A., Yakhchali, S. H., and Khaleghi, S. (2013). Developing a new fuzzy inference system for pipeline risk assessment. Journal of Loss Prevention in the Process Industries, 26(1):197–208. Jiang, M.-w. and Ying, M. (2014). Study on route selection for hazardous chemicals transportation. Procedia engineering, 71:130–138. Junior, I. C. L. and M´ arcio de Almeida, D. (2011). Modal choice for transportation of hazardous materials: the case of land modes of transport of bio-ethanol in brazil. Journal of Cleaner Production, 19(2-3):229–240. Kang, Y., Batta, R., and Kwon, C. (2011). Value-at-risk model for hazardous material transportation. Annals of Operations Research, pages 1–27. Kang, Y., Batta, R., and Kwon, C. (2014). Generalized route planning model for hazardous material transportation with var and equity considerations. Computers & Operations Research, 43:237–247. Kawprasert, A. and Barkan, C. P. (2008a). Effects of route rationalization on hazardous materials transportation risk. Transportation Research Record: Journal of the Transportation Research Board, 2043(1):65–72. Kawprasert, A. and Barkan, C. P. (2008b). Reducing the risk of rail transport of hazardous materials by route rationalization. Transp. Res. Rec, 2043:65–72. Kawprasert, A. and Barkan, C. P. (2009). Communication and interpretation of results of route risk analyses of hazardous materials transportation by railroad. Transportation Research Record: Journal of the Transportation Research Board, 2097(1):125–135. Kawprasert, A. and Barkan, C. P. (2010). Effect of train speed on risk analysis of transporting hazardous materials by rail. Transportation Research Record: Journal of the Transportation Research Board, 2159(1):59–68. Kazantzi, V., Kazantzis, N., and Gerogiannis, V. C. (2011). Risk informed optimization of a hazardous material multi-periodic transportation model. Journal of Loss Prevention in the Process Industries, 24(6):767–773. Kheirkhah, A., Navidi, H., and Messi Bidgoli, M. (2016). A bi-level network interdiction model for solving the hazmat routing problem. International Journal of Production Research, 54(2):459–471. Kheirkhah, A. S., Esmailzadeh, A., and Ghazinoory, S. (2009). Developing strategies to reduce the risk of hazardous materials transportation in iran using the method of fuzzy swot analysis. Transport, 24(4):325–332. Kim, M., Miller-Hooks, E., and Nair, R. (2011). A geographic information system-based realtime decision support framework for routing vehicles carrying hazardous materials. Journal of Intelligent Transportation Systems, 15(1):28–41. Knoope, M., Raben, I., Ramrez, A., Spruijt, M., and Faaij, A. (2014). The influence of

23

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

risk mitigation measures on the risks, costs and routing of {CO2} pipelines. International Journal of Greenhouse Gas Control, 29(0):104–124. Lai, Y.-C., Kawprasert, A., Lin, C.-Y., Saat, M. R., Liang, C.-H., and Barkan, C. P. (2011). Integrated optimization model to manage risk of transporting hazardous materials on railroad networks. Transportation Research Record: Journal of the Transportation Research Board, 2261(1):115–123. Lau, K. H. (2009). A gis-based stochastic approach to generating daytime population distributions for vehicle route planning. Transactions in GIS, 13(5-6):481–502. Li, R. and Leung, Y. (2011). Multi-objective route planning for dangerous goods using compromise programming. Journal of geographical systems, 13(3):249–271. Li, R., Leung, Y., Huang, B., and Lin, H. (2013). A genetic algorithm for multiobjective dangerous goods route planning. International Journal of Geographical Information Science, 27(6):1073–1089. Litman, T. (2002). Evaluating transportation equity. World Transport Policy & Practice, 8(2):50–65. Liu, X. and Hong, Y. (2015). Analysis of railroad tank car releases using a generalized binomial model. Accident Analysis & Prevention, 84:20–26. Liu, X., Saat, M. R., and Barkan, C. P. (2013a). Integrated risk reduction framework to improve railway hazardous materials transportation safety. Journal of hazardous materials, 260:131–140. Liu, X., Saat, M. R., and Barkan, C. P. (2013b). Safety effectiveness of integrated risk reduction strategies for rail transport of hazardous materials. Transportation Research Record: Journal of the Transportation Research Board, 2374(1):102–110. Liu, X., Saat, M. R., and Barkan, C. P. (2014). Probability analysis of multiple-tank-car release incidents in railway hazardous materials transportation. Journal of Hazardous Materials, 276:442–451. ´ Mac´ıas, L., and Ant´ Lozano, A., Mu˜ noz, A., un, J. P. (2011). Hazardous materials transportation in mexico city: Chlorine and gasoline cases. Transportation research part C: emerging technologies, 19(5):779–789. Lu, L., Liang, W., Zhang, L., Zhang, H., Lu, Z., and Shan, J. (2015). A comprehensive risk evaluation method for natural gas pipelines by combining a risk matrix with a bow-tie model. Journal of Natural Gas Science and Engineering, 25:124–133. Ma, C., Wu, F., and Lu, R. (2008). Decision-making method of hazardous material transportation route based on particle swarm optimization algorithm and neural network. In Computational Intelligence and Industrial Application, 2008. PACIIA’08. Pacific-Asia Workshop on, volume 2, pages 1023–1027. IEEE. Ma, L., Cheng, L., and Li, M. (2013a). Quantitative risk analysis of urban natural gas pipeline networks using geographical information systems. Journal of Loss Prevention in the Process Industries, 26(6):1183–1192. Ma, L., Li, Y., Liang, L., Li, M., and Cheng, L. (2013b). A novel method of quantitative risk assessment based on grid difference of pipeline sections. Safety Science, 59:219–226. Mahmoudabadi, A. and Seyedhosseini, S. M. (2014a). Developing a chaotic pattern of dynamic hazmat routing problem. IATSS Research, 37(2):110–118. Mahmoudabadi, A. and Seyedhosseini, S. M. (2014b). Solving hazmat routing problem in chaotic damage severity network under emergency environment. Transport Policy, 36:34– 45. Marcotte, P., Mercier, A., Savard, G., and Verter, V. (2009). Toll policies for mitigating hazardous materials transport risk. Transportation science, 43(2):228–243. Meiyi, W., Xiang, L., and Lean, Y. (2015). Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation. Transportation Research Part C: Emerging Technologies, 57:146–165. Milazzo, M. F., Ancione, G., Lisi, R., Vianello, C., and Maschio, G. (2009). Risk management of terrorist attacks in the transport of hazardous materials using dynamic geoevents. Journal of loss prevention in the process industries, 22(5):625–633.

24

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Milazzo, M. F., Lisi, R., Maschio, G., Antonioni, G., and Spadoni, G. (2010). A study of land transport of dangerous substances in eastern sicily. Journal of Loss Prevention in the Process Industries, 23(3):393–403. Mohaymany, A. S. and Khodadadiyan, M. (2008). A routing methodology for hazardous materials transportation to reduce the risk of road network. IUST International Journal of Engineering Science, 19(3):57–65. Monprapussorn, S., Thaitakoo, D., and Banomyong, R. (2011). Sustainability framework for hazardous materials transport route planning. International Journal of Sustainable Society, 3(1):33–51. Nagae, T. (2008). A risk-cost minimization model for catastrophe averse shipment of hazardous materials. In SICE Annual Conference, 2008, pages 1165–1170. IEEE. Nathanail, E., Zaharis, S., Vagiokas, N., and Prevedouros, P. (2010). Risk assessment for transportation of hazardous materials through tunnels. Transportation Research Record: Journal of the Transportation Research Board, (2162):98–106. Pradhananga, R., Hanaoka, S., and Sattayaprasert, W. (2011). Optimisation model for hazardous material transport routing in thailand. International Journal of Logistics Systems and Management, 9(1):22–42. Pradhananga, R., Taniguchi, E., Yamada, T., and Qureshi, A. G. (2014). Bi-objective decision support system for routing and scheduling of hazardous materials. Socio-Economic Planning Sciences, 48(2):135–148. Qiao, Y., Keren, N., and Mannan, M. S. (2009). Utilization of accident databases and fuzzy sets to estimate frequency of hazmat transport accidents. Journal of hazardous materials, 167(1):374–382. Qiu, S., Sacile, R., Sallak, M., and Sch¨on, W. (2015). On the application of valuation-based systems in the assessment of the probability bounds of hazardous material transportation accidents occurrence. Safety science, 72:83–96. Qu, X., Meng, Q., and Suyi, L. (2011). Ship collision risk assessment for the singapore strait. Accident Analysis & Prevention, 43(6):2030–2036. Rashid, Z., El-Harbawi, M., and Shariff, A. (2010). Assessment on the consequences of liquefied petroleum gas release accident in the road transportation via gis approaches. Journal of Applied Sciences, 10:1157–1165. Rebelo, A., Ferra, I., Gon¸calves, I., and Marques, A. M. (2014). A risk assessment model for water resources: Releases of dangerous and hazardous substances. Journal of environmental management, 140:51–59. Reilly, A., Nozick, L., Xu, N., and Jones, D. (2012). Game theory-based identification of facility use restrictions for the movement of hazardous materials under terrorist threat. Transportation research part E: logistics and transportation review, 48(1):115–131. Reniers, G. and Dullaert, W. (2013). A method to assess multi-modal hazmat transport security vulnerabilities: Hazmat transport sva. Transport Policy, 28:103–113. Reniers, G. L., Jongh, K. D., Gorrens, B., Lauwers, D., Leest, M. V., and Witlox, F. (2010). Transportation risk analysis tool for hazardous substances (trans)–a user-friendly, semiquantitative multi-mode hazmat transport route safety risk estimation methodology for flanders. Transportation Research Part D: Transport and Environment, 15(8):489–496. Romero, N., Nozick, L. K., and Xu, N. (2016). Hazmat facility location and routing analysis with explicit consideration of equity using the gini coefficient. Transportation Research Part E: Logistics and Transportation Review, 89:165–181. Roncoli, C., Bersani, C., and Sacile, R. (2013). A risk-based system of systems approach to control the transport flows of dangerous goods by road. Systems Journal, IEEE, 7(4):561– 570. Saat, M. R. and Barkan, C. P. (2011). Generalized railway tank car safety design optimization for hazardous materials transport: Addressing the trade-off between transportation efficiency and safety. Journal of hazardous materials, 189(1-2):62–68. Saat, M. R., Werth, C. J., Schaeffer, D., Yoon, H., and Barkan, C. P. (2014). Environmental risk analysis of hazardous material rail transportation. Journal of hazardous materials,

25

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

264:560–569. Samanlioglu, F. (2013). A multi-objective mathematical model for the industrial hazardous waste location-routing problem. European Journal of Operational Research, 226(2):332–340. Samuel, C., Keren, N., Shelley, M., and Freeman, S. A. (2009). Frequency analysis of hazardous material transportation incidents as a function of distance from origin to incident location. Journal of Loss prevention in the Process Industries, 22(6):783–790. Sengul, H., Santella, N., Steinberg, L. J., and Cruz, A. M. (2012). Analysis of hazardous material releases due to natural hazards in the united states. Disasters, 36(4):723–743. Shen, X., Yan, Y., Li, X., Xie, C., and Wang, L. (2013). Analysis on tank truck accidents involved in road hazardous materials transportation in china. Traffic injury prevention, (just-accepted). Si, H., Ji, H., and Zeng, X. (2012). Quantitative risk assessment model of hazardous chemicals leakage and application. Safety science, 50(7):1452–1461. Siddiqui, A. and Verma, M. (2013). An expected consequence approach to route choice in the maritime transportation of crude oil. Risk Analysis, 33(11):2041–2055. Siddiqui, A. W. and Verma, M. (2015). A bi-objective approach to routing and scheduling maritime transportation of crude oil. Transportation Research Part D: Transport and Environment, 37:65–78. Sosa, E. and Alvarez-Ramirez, J. (2009). Time-correlations in the dynamics of hazardous material pipelines incidents. Journal of hazardous materials, 165(1):1204–1209. Strogen, B., Bell, K., Breunig, H., and Zilberman, D. (2016). Environmental, public health, and safety assessment of fuel pipelines and other freight transportation modes. Applied Energy, 171:266–276. Szeto, W. (2013). Routing and scheduling hazardous material shipments: Nash game approach. Transportmetrica B: Transport Dynamics, 1(3):237–260. Talarico, L., Reniers, G., S¨ orensen, K., and Springael, J. (2015). Mistral: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries. Reliability Engineering & System Safety, 138:105–114. Tavakkoli-Moghaddam, R., Abolghasem, A., and Mahmoudabadi, A. (2016). Comparison between combined and separate approaches for solving a location-routing problem in hazardous materials transportation. International Journal of Transportation Engineereing, 3(1):67–77. Tena-Chollet, F., Tixier, J., Dusserre, G., and Mangin, J.-F. (2013). Development of a spatial risk assessment tool for the transportation of hydrocarbons: Methodology and implementation in a geographical information system. Environmental Modelling & Software, 46:61–74. Toumazis, I. and Kwon, C. (2013). Routing hazardous materials on time-dependent networks using conditional value-at-risk. Transportation Research Part C: Emerging Technologies, 37:73–92. Toumazis, I. and Kwon, C. (2015). Worst-case conditional value-at-risk minimization for hazardous materials transportation. Transportation Science, 50(4):1174–1187. Tr´epanier, M., Leroux, M.-H., and de Marcellis-Warin, N. (2009). Cross-analysis of hazmat road accidents using multiple databases. Accident Analysis & Prevention, 41(6):1192–1198. US Census Bureau (2015). United states: 2012. hazardous materials. 2012 economic census: Transportation. 2012 commodity flow survey. Retrieved from https://www.census.gov/ content/dam/Census/library/publications/2015/econ/ec12tcf-us-hm.pdf. USDT (n.d.). Pipeline and hazardous materials safety administration: 10 year incident report. Retrieved from https://www.phmsa.dot.gov/about-phmsa/offices/ office-hazardous-materials-safety on 09-04-2018. Van der Vlies, A. and Suddle, S. (2008). Structural measures for a safer transport of hazardous materials by rail: The case of the basic network in the netherlands. Safety science, 46(1):119– 131. van der Vlies, V. (2015). A qualitative approach to risk management of hazardous materials in the netherlands: lessons learned from 7 sluice cases. Journal of Risk Research, 18(7):947–964. van Dorp, J. R. and Merrick, J. R. (2011). On a risk management analysis of oil spill risk using maritime transportation system simulation. Annals of Operations Research, 187(1):249–277.

26

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Van Raemdonck, K., Macharis, C., and Mairesse, O. (2013a). Risk analysis system for the transport of hazardous materials. Journal of safety research, 45:55–63. Van Raemdonck, K., Macharis, C., and Mairesse, O. (2013b). Risk analysis system for the transport of hazardous materials. Journal of safety research, 45:55–63. Verma, M. (2009). A cost and expected consequence approach to planning and managing railroad transportation of hazardous materials. Transportation research part D: transport and environment, 14(5):300–308. Verma, M. (2011). Railroad transportation of dangerous goods: A conditional exposure approach to minimize transport risk. Transportation research part C: emerging technologies, 19(5):790–802. Verma, M. and Verter, V. (2010). A lead-time based approach for planning rail–truck intermodal transportation of dangerous goods. European Journal of Operational Research, 202(3):696–706. Verma, M., Verter, V., and Gendreau, M. (2011). A tactical planning model for railroad transportation of dangerous goods. Transportation science, 45(2):163–174. Verma, M., Verter, V., and Zufferey, N. (2012). A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials. Transportation research part E: logistics and transportation review, 48(1):132–149. Verter, V. and Kara, B. Y. (2008). A path-based approach for hazmat transport network design. Management Science, 54(1):29–40. Vianello, C. and Maschio, G. (2014). Quantitative risk assessment of the italian gas distribution network. Journal of Loss Prevention in the Process Industries, 32:5–17. Vianello, C., Mocellin, P., Macchietto, S., and Maschio, G. (2016). Risk assessment in a hypothetical network pipeline in uk transporting carbon dioxide. Journal of Loss Prevention in the Process Industries, 44:515–527. Wang, H., Xiao, G., and Wei, Z. (2013). Optimizing route for hazardous materials logistics based on hybrid ant colony algorithm. Discrete dynamics in nature and society, 2013. Wang, J., Kang, Y., Kwon, C., and Batta, R. (2012). Dual toll pricing for hazardous materials transport with linear delay. Networks and Spatial Economics, 12(1):147–165. Wang, X., Zhu, J., Ma, F., Li, C., Cai, Y., and Yang, Z. (2016). Bayesian network-based risk assessment for hazmat transportation on the middle route of the south-to-north water transfer project in china. Stochastic Environmental Research and Risk Assessment, 30(3):841–857. Wei, M., Yu, L., and Li, X. (2015). Credibilistic location-routing model for hazardous materials transportation. International Journal of Intelligent Systems, 30(1):23–39. Xie, C. and Waller, S. T. (2012). Optimal routing with multiple objectives: efficient algorithm and application to the hazardous materials transportation problem. Computer-Aided Civil and Infrastructure Engineering, 27(2):77–94. Xie, Y., Lu, W., Wang, W., and Quadrifoglio, L. (2012). A multimodal location and routing model for hazardous materials transportation. Journal of hazardous materials, 227:135–141. Xin, C., Letu, Q., and Bai, Y. (2013). Robust optimization for the hazardous materials transportation network design problem. pages 373–386. Xin, C., Qingge, L., Wang, J., and Zhu, B. (2015). Robust optimization for the hazardous materials transportation network design problem. Journal of Combinatorial Optimization, 30(2):320–334. Xu, J., Gang, J., and Lei, X. (2013). Hazmats transportation network design model with emergency response under complex fuzzy environment. Mathematical Problems in Engineering, 2013. Yang, J., Li, F., Zhou, J., Zhang, L., Huang, L., and Bi, J. (2010). A survey on hazardous materials accidents during road transport in china from 2000 to 2008. Journal of Hazardous materials, 184(1):647–653. Yie Pinedo, R. D. (2013). Route optimization while improving safety using escort vehicles. PhD thesis, The State University of New York at Buffalo. Yu, H. and Solvang, W. D. (2016). An improved multi-objective programming with augmented ε-constraint method for hazardous waste location-routing problems. International journal

27

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

of environmental research and public health, 13(6):548. Zamparini, L. and Reniers, G. (2013). A comparative study of hazardous material transportation security issues in flanders and in apulia. Security Journal, 26(2):142–156. Zhang, J., Li, J., Liang, X., and Zhu, D. (2012). Optimal scheduling of hazardous materials transportation considering risk and cost. Advanced Science Letters, 5(2):949–952. Zhao, J. and Verter, V. (2014). A bi-objective model for the used oil location-routing problem. Computers & Operations Research, 62:157–168. Zhao, J. and Zhu, F. (2016). A multi-depot vehicle-routing model for the explosive waste recycling. International Journal of Production Research, 54(2):550–563. Zhao, L., Wang, X., and Qian, Y. (2012). Analysis of factors that influence hazardous material transportation accidents based on bayesian networks: A case study in china. Safety science, 50(4):1049–1055. Zhou, Y., Hu, G., Li, J., and Diao, C. (2014a). Risk assessment along the gas pipelines and its application in urban planning. Land Use Policy, 38:233–238. Zhou, Y.-f., Li, Z., and Zou, K. (2014b). Vehicles scheduling of hazardous materials transportation considering safety and customer satisfaction. Journal of Chemical and Pharmaceufical Research, 6(6):1565–1571. Zhou, Z., Chu, F., Che, A., and Zhou, M. (2013). e-constraint and fuzzy logic-based optimization of hazardous material transportation via lane reservation. Intelligent Transportation Systems, IEEE Transactions on, 14(2):847–857. Zografos, K. G. and Androutsopoulos, K. N. (2008). A decision support system for integrated hazardous materials routing and emergency response decisions. Transportation Research Part C: Emerging Technologies, 16(6):684–703.

28

ACCEPTED MANUSCRIPT

Highlights

AC C

EP

TE D

M AN U

SC

• •

We provide an analysis of trends and gaps on recent review on transportation of hazardous materials We have developed a taxonomy for classifying the papers in our survey. We analyze the appropriateness of main research assumptions in the field of transportation of hazardous materials.

RI PT

•

ACCEPTED MANUSCRIPT

Miss Andrea Ditta is an M.Sc candidate at the Department of Industrial Engineering, Universidad

del Norte, Colombia. His major interest is transportation of hazardous materials. Mr. Oswaldo Figueroa is an M.Sc candidate at the Department of Industrial Engineering, Universidad del Norte, Colombia. His major interest is transportation of military convoys.

SC

RI PT

Dr. Gina Galindo is an assistant professor at the Department of Industrial Engineering, Universidad del Norte, Colombia. Dr. Galindo holds a doctoral degree in Industrial Engineering from The State University of New York at Buffalo, in 2013. Her research is mainly related to humanitarian logistics. In this area, Dr. Galindo has undertaken several research projects for prepositioning supplies in preparation for a foreseen hurricane. From this research, Dr. Galindo has achieved several publications in international journals. Additionally, she has experience in Quality Control and Production Planning and Control. She has worked in projects related to production programming and humanitarian logistics.

AC C

EP

TE D

M AN U

Dr. Ruben Yie-Pinedo is an assistant professor at the Department of Industrial Engineering, Universidad del Norte, Colombia. Dr. Yie-Pinedo holds a doctoral degree in Industrial Engineering from The State University of New York at Buffalo, in 2013. Dr. Yie-Pinedo has research experience in Military Transportation, General Transportation Networks, Systems Dynamics, Optimization and Heuristics, Production Planning and Control and Simulation.