The term "climate change" has been used to describe changes in the global atmosphere that may be traced back to human activities in one way or another [(Pörtner et al., 2022); (Zhongming et al., 2022)]. Climate change's effects are increasingly being recognized based on solid scientific data [(Brozovsky et al., 2021); (Lindsey, 2019)]. As a result of climate change, sea-level rise is considered to be one of the most obvious and certain impacts. As one of the most evident and immediate consequences of climate change, sea level rise has emerged as a critical and urgent issue that threatens the existence of several coastal communities across the world (Milliman & Haq, 1996). Shortly, sea levels are predicted to rise gradually but slowly. Coastal communities will be inundated and flooded as a direct result of rising sea levels, causing significant social, economic, and ecological damage [(I.P.C.C., 2013); (James et al., 2021)].

The effects of increasing sea levels will have enormous implications across a variety of industries, including those relating to housing, employment, and infrastructure [(Cooper et al., 2013); (Ding et al., 2013); (MacDonald et al., 2012)]. Transportation infrastructure has been increasingly considered one of the most vulnerable sectors to sea level rise [(Asariotis, 2020); (Meguro & Ogi, 2018); (Tewari & Palmer, 2018)]. Because of the high population density near the coasts, there is a significant risk that the transportation infrastructure will be flooded. For example, along the Gulf of Mexico, an estimated 2,400 miles of major roadways and 246 miles of freight rail lines are at risk of permanent flooding (Burkett, 2002). This risk is posed by the fact that the sea level is expected to rise in the range of 5 feet over the next 50 to 100 years. As sea levels continue to rise, coastal roads and other types of transportation infrastructure will become more vulnerable to inundation by seawater, as well as low-lying areas that are already prone to flooding (Chinowsky et al., 2013). When the roads that are vulnerable to damage are closed, users will be forced to use alternative routes, which will increase the amount of time it takes to travel. Increased use of the surrounding open roads may produce even more severe traffic congestion, which will be felt by those already using the highways.

Numerous studies have been conducted in the last two decades that have addressed climate change and sea level rise impacts, vulnerability, economic evaluation, and adaptation [(Almeida & Mostafavi, 2016); (Dawson et al., 2016); (MacDonald et al., 2012); (Pörtner et al., 2022);(Titus, 2002); (Vantaggiato & Lubell, 2020)]. Much of the existing literature focuses particular attention on evaluating the social, political, economic, and infrastructure implications of climate change and sea level rise on coastal areas (e.g., impacts on housing, employment, transportation infrastructure, ecosystems, etc.) [(Bayard & Elphick, 2011); (Cooper et al., 2013); (Dawson et al., 2016); (Ding et al., 2013); (Gomaa & Peng, 2015, October 22-25);(Milliman & Haq, 1996); (Papakonstantinou et al., 2020); (Titus, 2002)]. Few studies, to the best of our knowledge, have attempted to provide a more comprehensive study of the implications on population relocation dynamics and transportation networks, to the best of our knowledge.

Research about climate change and sea level rise impacts on transportation systems has emerged very recently [(Asadabadi & Miller-Hooks, 2017); (Asariotis, 2020); (Chen & Region, 2019); (Fishman, 2018); (Jones, 2017); (Kwiatkowski, 2017); (Lykou et al., 2017); (Meguro & Ogi, 2018); (Papakonstantinou et al., 2020); (Shen & Kim, 2020); (Tewari & Palmer, 2018); (Tonn et al., 2021); (Wang et al., 2020); (Zhou et al., 2019)]. Several attempts have been made to quantify climate risks, adaptation, and transportation planning [(Bloetscher et al., 2012); (Burkett, 2002); (Freckleton et al., 2012); (Lambert et al., 2013); (Steyn, 2014); (Tang et al., 2013); (Webster et al., 2013)]. Nevertheless, it is necessary to address the restrictions of their work. As an illustration, the majority of these studies only concentrated on addressing the direct impacts of sea level rise on transportation systems [(Asariotis, 2020); (Burkett, 2002); (Lykou et al., 2017); (Titus, 2002)]. Some of them attempted to identify the physical transportation infrastructure at risk and to assess the vulnerability of transportation systems due to future scenarios of sea level rise [(Bloetscher et al., 2012); (Chen & Region, 2019); (Fishman, 2018); (Shen & Kim, 2020); (Tang et al., 2013); (Webster et al., 2013)]. Moreover, previous studies have reported the assessment and evaluation of different adaptation strategies and design measures to increase the resiliency and adaptability of transportation infrastructure [(Asariotis, 2020); (Kwiatkowski, 2017); (Meguro & Ogi, 2018); (Wang et al., 2020)].

The other limitation of existing literature is that these efforts have mostly focused on the direct implications of transportation infrastructure and system performance rather than the economic evaluation of climate change's effects on road traffic [(Asariotis, 2020); (Burkett, 2002); (Dawson et al., 2016); (Lykou et al., 2017); (Titus, 2002)]. Recently, investigators have examined the effects of insurance, incentives, and public assistance on achieving transportation infrastructure resilience (Tonn et al., 2021). Surveys that have been conducted by (Chinowsky et al., 2013) showed that the assessment of climate change adaptation costs for the US road network has been underestimated.

Numerous studies have been conducted to estimate the effects that rising sea levels will have on the economy [(Chinowsky et al., 2013); (Gomaa & Peng, 2015, October 22-25);(Yevdokimov, 2017)]. The findings of this research indicated the effects of rising sea levels on coastal elevation, ocean coasts, the sustainability of coastal wetland ecosystems, vulnerable species, population, land use, and infrastructure. Inundation, erosion of the coast, and regular flooding are just examples of the direct impacts that these factors have on human communities. Some have indirect consequences that could potentially cause problems in the long run, such as the disruption of ecosystems or the extinction of certain species. Loss of economic activity and assets will be the result of the effects of continued sea level rise, which will cause these consequences.

A large and growing body of literature has investigated the impacts of sea-level rise on transportation and population, vulnerability analysis, and adaptation strategies [(Bloetscher et al., 2012); (Chen & Region, 2019); (Dawson et al., 2016); (Meguro & Ogi, 2018); (Papakonstantinou et al., 2020); (Tang et al., 2013); (Tewari & Palmer, 2018); (Titus, 2002); (Vantaggiato & Lubell, 2020)]. Most of the currently available research focuses only on the direct effects that rising sea levels will have on transportation infrastructure. The flooding of transportation systems, properties, and infrastructure is the primary source of direct damage (Burkett, 2002). However, few research endeavors have been conducted to study the impacts of sea level rise within a future transportation network model based on different scenarios for population relocation (Steyn, 2014). There are a considerable number of indirect effects, such as the loss of employment opportunities and the relocation of affected populations; effects on the housing market in vulnerable and surrounding areas; potential labor loss because some vulnerable property owners may choose to relocate; and the loss of service infrastructures such as roads, utilities, and medical services [(MacDonald et al., 2012); (Pörtner et al., 2022)]. These indirect impacts caused by sea level rise can result in significantly higher economic costs than the direct losses. If these indirect influences are ignored, the potential economic benefits of any adaptation strategy may be greatly underestimated, which could result in the selection of a less effective adaptation strategy [(Lindsey, 2019); (Shen & Kim, 2020); (Wang et al., 2020)].

This study, therefore, aims to identify and assess the impacts of sea level rise on population relocation dynamics and future transportation systems, utilizing data from the transportation model for the year 2035 in the Tampa Bay region, Florida. This research, in contrast to prior efforts for assessing the vulnerability analysis of the effects of sea level rise, takes into consideration not only the direct implications of sea level rise but also the indirect implications on the transportation system, which were considered within three different relocation scenarios for the affected population in the inundated areas.

The analysis of this research utilizes the NOAA-provided digital elevation data, as well as the two-foot, Mean Higher High-Level sea surface data to represent a worst-case scenario for the year 2035. The study estimated the total population and employment will be affected assuming a homogenous distribution within each transportation analysis zone (TAZ), as well as the transportation networks that will be affected based on segment direction capacity reduction rates. Then, the impact of each scenario has been studied on the transportation system through a series of indicators for vehicular congestion and travel time.

This research aims to determine how different population relocation scenarios induced by sea level rise will cause transportation capacity changes. This research identifies these results by developing different relocation scenarios for the affected populations and studying the impacts of each scenario on the transportation model. The key objectives of this research are; 1) to estimate the affected population and transportation infrastructure under the twofoot SLR scenario in 2035; 2) to develop different scenarios for the relocation of the affected population, and 3) to study the impact of each scenario on the transportation system.

To evaluate the extent to which SLR will impact the capacity of the highway network. Using Navteq Streets data, this analysis identified the actual highway network that would be inundated. To ensure that the model appropriately represents the inundated roadway capacity, it was calculated based on lane miles. The model's network consists of the majority of minor arterials and all major highways. However, the network's capacity in a certain direction through a TAZ also includes minor and parallel facilities.

Roads have been classified into three categories: 1) two-lane facilities with one lane in each direction, 2) four or sixlane facilities with two or three lanes in each direction; and 3) eight or more-lane facilities with four or more lanes in each direction. Category two roads were given five lane miles for every highway mile because the study region has many important four-and six-lane facilities. Five lanes were seen as a fair compromise because better data does not exist for all roads, and within the built-up area of the study region, both cross-sections are common. Category three roads were given eight lane miles for each highway mile because there are very few facilities in the region with more than four lanes in each direction.

Many of the inundated roads travel parallel to shorelines, and in some places, roads running perpendicular to the shoreline do not become inundated. This could result in TAZs where highway capacity is constrained in one direction but not necessarily in the other. As a result, we calculated the percentage of lane miles inundated in each TAZ for roads traveling generally north-south (45 degrees from the north) and east-west (45 degrees from the east). The total number of lane miles within each TAZ was compared to those touching the two-foot inundation to create a separate capacity reduction index for north-south and east-west roads within each TAZ. The 2035 highway network was then given the capacity reduction factors to represent the overall changes in highway capacity within each TAZ for each direction.

The results demonstrate that the direct 2 ft inundation scenario will have an impact on total congestion. Specifically, there will be a total of 6,318.93 minutes of total congestion before the inundation (Table 5), while the model predicts that there will be a difference in overall congestion due to direct inundation. Table 5 indicates that the overall congestion time will increase by 15.24, 19.30, and 78.13 minutes in the three proposed scenarios. However, these times are rather modest in comparison to the model's total congestion time.

The rise in sea level will have both direct and indirect effects on population and employment, in addition to influencing transportation networks. As a result of increasing sea levels, low-lying areas are inundated in water, beach, and wetland ecosystems are destroyed, and the severity of storm surges and flooding caused by rainfall is exacerbated. All of these consequences could potentially have an impact on the modes of transportation. According to the findings of this study, the total population and employment that will be impacted (under the assumption of a homogenous distribution within each TAZ), as well as the transportation networks that will be impacted (based on segment direction capacity reduction rates), have been estimated. After that, we investigated the effects of each scenario on the transportation system by analyzing several indicators for the amount of time spent in traffic and the distance traveled.

The findings of this study provide insights into the potential methods in which future estimations of transportation network analyses could be affected by future scenarios of sea level rise. This study proposed three possible relocation scenarios for the affected population and employment and then evaluated how those varied scenarios impacted the transportation model. The scenarios involved the relocation of the affected population and employment.

One of the most important findings from this research is that the immediate inundation that would be caused by a rise of two feet in sea level would have a significant impact on both employment and population. This was one of the most significant findings that emerged from this investigation. According to the model estimate for the year 2035 for the Tampa Bay area, assuming an even distribution of housing and employment within each TAZ, a total of 96,688 people will be directly impacted by the inundation, and approximately 78,493 jobs will either be eliminated or relocated because of the inundation of the business zones. In addition, the scenario in which there is direct flooding of two feet will influence a variety of components of the transportation system. According to the findings of this study, a total of more than 357.84 kilometers will be lost due to direct inundation in the study region.

The second major finding shows that diverse types of trips in the transportation analysis will be affected by the direct two-foot sea-level rise inundation scenario. The evaluation of the total trips for the proposed relocation scenarios reveals the extent to which the inundated zones may impact the model estimations. When comparing different relocation scenarios, the change in total trips was significant. The potential effects on the model outputs have been greater. In one of the scenarios where the population will move out of the region, the total number of trips produced decreased significantly. There was a drop of 37,633 total SOV trips, 12,737 carpooling trips, and 50,370 highway trips. While in the other scenario where the inundated population relocate proportionately based on the growth rate, the increase in total trips produced was greater. The total increase in trips produced by SOV, carpooling, and highways were 6267, 2554, and 8821, respectively.

Furthermore, the results indicate that direct flooding will affect both the volume-to-capacity ratio and total congestion estimations. The findings of the volume-to-capacity ratio estimations reveal that the majority of the relocation scenarios exceed network capacity by between 16.3 and 16.5 percent. While in terms of total congestion, there will be a total of 6,318.93 minutes of total congestion before the inundation. Additionally, the overall congestion time will increase by 15.24, 19.30, and 78.13 minutes in the three proposed scenarios.

The interpretation of the results of this study is not without certain limitations. Firstly, the only roadways that have been determined to be impacted by increasing sea levels are those that are located within the flood zones. If storm surges and coastline erosion were also taken into consideration, the impact that rising sea levels have on the transportation network will be substantially more severe. Second, because the transportation model has limits on the networks that it uses, this analysis does not take into account all of the transportation networks. Inside the TAZ, local routes that could potentially be exploited as detours have been replaced with centroid links so that traffic can move more efficiently.

Furthermore, other limitations of this study are related to the proposed distribution scenarios of the projected population and employment. Other distribution scenarios should be considered to build more reliable and reasonable relocation models. In addition to the assumptions made in the population relocation methodology, this methodology could not recognize whether an entire portion of the network is cut off by SLR or if just parts of the capacity are reduced. A comprehensive survey of the inundated links along coastal routes would need to be performed to identify places where the directional network proportional capacity reduction methodology is inappropriately indicating that capacity is still present within a corridor.

Numerous concerns could be the focus of future research. First, increasing the number of different relocation scenarios that could occur could lead to a more varied set of results. Additionally, in scenario III, population and employment were distributed in a manner that corresponded to the growth rate of each TAZ. Utilizing several other distribution methods for this investigation would affect the results. In addition, within each scenario, critical links may be identified for preservation, and the model may be approximated to examine the effects of retaining individual links. For instance, it may be more vital to retain a road that connects two large areas that are not at risk of flooding than it would be to preserve a route that serves only low-lying communities because the former road travels through a low-lying area. This model and the GIS analysis that preceded it would be a valuable starting point for the development of a methodology that would allow for the consistent ranking and prioritization of corridors that should be preserved as part of a regional SLR adaptation strategy.

Sea-level rise will have considerable direct and indirect impacts on population and employment, as well as on transportation systems and infrastructure. The primary goal of this research is to incorporate the potential effects of population relocation due to sea level rise into transportation modeling. In general, different scenarios for relocating population and employment generate new traffic demands, which could result in traffic congestion. Thus, transportation planners should simulate future sea level rise scenarios and evaluate their impact on the current transportation system. Then the new transportation planning should take into consideration these possible adverse impacts produced by the relocation development.

The results of this investigation show that more than 357.84 miles will be directly inundated in the study region under a two-foot sea-level rise. More than 78,493 jobs will be lost or relocated due to the inundation of the business zones. On the other hand, the evaluation of the total trips for the proposed relocation scenarios reveals the extent to which the inundated zones may impact the model estimations. In one of the scenarios where the population would move out of the region, the total number of trips produced decreased significantly. While in the other scenario, where the inundated population relocates proportionately based on the growth rate, the increase in total trips produced was greater. Finally, between 16.3 and 16.5 percent of the relocation scenarios exceed network capacity. In terms of total congestion, there will be a total of 6,318.93 minutes before the inundation. Additionally, the overall congestion time will increase by 15.24, 19.30, and 78.13 minutes in the three proposed scenarios.

The results of this study have several significant implications for future practice. These findings can assist local transportation planners, engineers, and decision-makers in identifying the most vulnerable areas and transportation facilities to rising sea levels. As a result, these individuals will be able to make more informed decisions regarding adaptation planning and resiliency.