Online shopping increased substantially during COVID-19 and associated lockdowns. This study examines how socio-demographic characteristics and latent psychological factors influence individuals' willingness-to-pay (WTP) an additional amount for online delivery services and how this behavior changes amid COVID-19. Using repeated cross-sectional survey data from New Delhi, India (2020-2022), a consistent Integrated Choice and Latent Variable (ICLV) model is applied across three waves to enable temporal comparison and behavioral transition analysis. Results indicate that younger individuals and those with higher technological enthusiasm exhibit higher WTP. Perceived usefulness positively influences WTP for online delivery services across all waves. Gender and income show no significant effect, while age remains consistently significant. In contrast, perceived risk negatively affected WTP during COVID-19 but became statistically insignificant in the post-pandemic period, highlighting the evolving role of psychological factors in online shopping behavior.

Transport disadvantage disproportionately affects persons with disabilities (PWDs), yet most studies treat them as a homogeneous group and focus narrowly on infrastructural design. This study examines the relative importance of mobility barriers and enablers across different disability categories - locomotor, visual, intellectual, and multiple disabilities - in two Indian cities (Varanasi and Kozhikode). Using survey data from 155 PWDs and applying the Bayesian Best–Worst Method, we generated disability-specific rankings of barriers and enablers. Further, a latent profile analysis is developed to assess how differently PWDs with different socio-demographic and disability characteristics perceive mobility barriers and enablers. The results show that while infrastructural barriers dominate for locomotor and visual groups, transportation and attitudinal barriers are more critical for intellectual disabilities, and policy and administrative barriers weigh most heavily for multiple disabilities. Correspondingly, enablers also vary: locomotor and visual groups prioritise ramps and obstacle-free paths; intellectual disabilities rely on clear bus branding and accessible navigation apps; and multiple disabilities emphasize technological aids and policy reforms. Further, latent profile analysis revealed six clusters for mobility barriers and two clusters for mobility enablers, indicating heterogeneity in perceptions of PWDs. These findings reveal pronounced barrier–enabler asymmetries, demonstrating that universal design alone is insufficient to ensure inclusive mobility. Instead, disability-specific strategies that integrate infrastructural retrofits, information clarity, administrative reforms, and attitudinal change are needed to reduce transport disadvantage and advance inclusive urban mobility.

The primary goal of this study is to develop and characterize the mechanical properties of ambient-cured alkali-activated concrete (AAC). The compressive and flexural strength values are assessed for standard laboratory specimens using numerical models developed in ABAQUS and validated through experimental data. Using Monte Carlo simulations and second-order reliability methods, these numerical models are used to generate data to determine the probabilities of failure of the AAC specimens under compression, splitting tension, and flexure. The limit state functions for AAC are developed for each of the three types of loading. Similar limit state functions are adopted for conventional Portland cement (PC) concrete specimens from the available literature. The probabilities of failure for these specimens are then determined under the aforementioned loading conditions. It is noticed that the results from Monte Carlo simulations are identical to those obtained from second-order reliability methods. The outcomes are further validated using analytical calculations. It is observed that AAC specimens exhibit similar failure trends to PC concrete failure. However, the probabilities of failure for PC concrete are at least 45% higher than those of AAC under each loading type. This shows the potential for practical usage of AAC as a viable alternative to PC concrete.

The travel behaviour of people with disabilities (PWDs) has been observed to differ from that of people without disabilities despite having similar needs and wants. PWDs encounter various obstacles restricting their mobility and ability to participate in activities. In this paper, we use a large nationwide dataset covering 102,977 PWDs to understand transit usage and barriers faced by PWDs while travelling at disaggregate and aggregate levels. The analysis comprises five different models: binary logit and multinomial logit at the disaggregate level, ordinary least square regression, spatial lag model, and spatial error model at the aggregate level. Findings reveal that transit use is significantly low among female PWDs, PWDs who lost work, PWDs who need a caregiver, and regions with a high percentage of workers. It is also observed that the level of marginalised populations in a region consistently shows a negative impact on public transport usage across most disabilities.

This book covers static and dynamic traffic assignment models used in transportation planning and network analysis. Traffic assignment is the final step in the traditional planning process, and recent decades have seen many advances in formulating and solving such models. The book discusses classical solution methods alongside recent ones used in contemporary planning software. The primary audience for the book is graduate students new to transportation network analysis, and to this end there are appendices providing general mathematical background, and more specific background in formulating optimization problems. We have also included appendices discussing more general optimization applications outside of traffic assignment. We believe the book is also of interest to practitioners seeking to understand recent advances in network analysis, and to researchers wanting a unified reference for traffic assignment content. A second volume is currently under preparation, and will cover transit, freight, and logistics models in transportation networks. A free PDF version of the text will always be available online at https://sboyles.github.io/blubook.html. We will periodically post updated versions of the text at this link, along with slides and other instructor resources.

The equitable deployment of Electric Vehicle Charging Infrastructure (EVCI) is essential to address range anxiety and ensure widespread adoption of electric vehicles. This paper aims to identify the unserved areas of Delhi in terms of public Electric Vehicle Charging Infrastructure (EVCI) using a novel accessibility analysis approach. This study addresses accessibility gaps to address the Delhi EV policy's ambitious target of providing 3000-m access to public EV charging stations. Enhanced Two-Step Floating Catchment Area (E2SFCA) method is employed to quantify the accessibility levels to EVCI's at 100 m grid level. Global Moran I and Local Moran I analysis is conducted to identify areas where intervention is required. The location-allocation models indicate that installing at least 105 additional EV charging stations in the urban core and 150 in the peri-urban fringes would allow 93 % of the population to achieve the accessibility targets and an additional service coverage of 176.6 km2. The proposed methodology aims to achieve equitable accessibility to ECVIs which would lead to a better match of the supply-demand gap hence leading to the successful implementation of these infrastructures. The optimized yet balanced growth methodology and case-study for EV charging network expansion presented in this study is expected to aid policymakers in ensuring equity and spatial distributive justice in transportation electrification efforts.

Metropolitan Planning Organizations (MPOs) are required to prepare a Transportation Improvement Plan (TIP) that outlines a fiscal strategy over a four-year period in order to qualify for federal funding. However, the growing population and limited financial resources available often pose significant challenges for transportation agencies in aligning their needs with available budgets. This article examines the project selection criteria used by 20 MPOs in the Southeastern United States to identify the best practices for prioritizing projects in TIPs. Using document analysis, this study categorizes the most commonly used criteria into nine broad groups: safety and security; environmental impacts; mobility, accessibility, and connectivity; preservation; environmental justice; equity; economic factors; alignment with other plans; and local support. Many of these categories are further divided into subcategories and metrics. Despite variations in criteria, weighting, scoring, and methodologies across these MPOs, the study identifies several shared factors that support effective decision-making in regional transportation planning. These findings can help transportation planners and policymakers refine their project prioritization strategies, promote consistency, and lead to improved decision-making frameworks for future TIP development.

Last-mile segments contribute significantly to the delivery costs and environmental impacts of e-commerce networks. Parcel lockers are a sustainable alternative to reduce the inefficiencies in last-mile delivery by offering secure collection points in apartment blocks, workplaces, and railway stations where customers can retrieve their packages at their convenience. However, research addressing optimal parcel-locker placement and its operational-capacity planning is limited in the literature. To this end, this paper presents a bi-level optimization model encompassing the planner’s strategic considerations and customer preferences. In the bi-level model, the upper level focuses on minimizing operational costs for the planner by strategically determining the optimal locations and capacities of parcel lockers. Simultaneously, the lower-level model maximizes customer benefits, factoring in their preferences and behaviors. A case study in Mumbai, India, is provided to demonstrate the effectiveness of the bi-level optimization model. The numerical results show that parcel lockers are located at, and a significant majority of customers are allocated to, those facilities that are in customers’ top two preferences.

This research focuses on understanding the impact of the type of shopping activity (general shopping goods, grocery, prepared meals) on shopping channel (in-person vs. online shopping) preferences. To better understand consumer decisions in the post-COVID era, this study used a large-scale consumer behaviour survey in New Delhi, India, with 1798 respondents to develop multivariate ordered probit models (MORP) involving in-person shopping travel frequencies (INFs) and online delivery frequencies (ONFs). Considering the online and physical shopping decision counterparts in a joint modelling framework enabled us to quantify the determinants of online shopping and in-person shopping frequencies and how they vary across consumption categories. The influences of demographic characteristics (e.g., car ownership, income, mode choice) and attitudes (e.g., tech-savviness, attitude towards perceived risks of online shopping) were delineated in the analysis by treating them as exogenous predictors. The model estimation results and discussions in this study are expected to help advance the understanding of how the emergence of online shopping and delivery-based services are influencing activity-travel patterns and choices in the aftermath of the pandemic.

Drones and electric vehicles (EVs) represent promising technologies for enhancing the efficiency and sustainability of last-mile delivery services. This paper focuses on the optimization of customer deliveries through the integration of a plug-in hybrid electric vehicle (PHEV) and a drone. Our model, named the plug-in hybrid electric vehicle traveling-salesman problem with drone (PHEVTSPD), assumes the PHEV can be recharged, either fully or partially, at charging stations, while the drone can be launched or retrieved from the EV. Both the EV and drone are capable of independently serving the customer. In comparison with traditional truck-only or drone-only delivery models, the hybrid EV-drone model overcomes the limitations of drone payload capacity and EV service area, thereby significantly improving delivery efficiency and reducing greenhouse-gas emissions. This research presents a three-index mixed-integer linear program (MILP) formulation of PHEVTSPD. Additionally, a linear or piecewise linear approximation of the concave time-state-of-charge (SoC) function is adopted in the model. To solve the proposed problem, we introduce an adaptive large-neighborhood search (ALNS) metaheuristic. Numerical analysis results reveal that the proposed ALNS method outperforms variable neighborhood search (VNS) with an average optimality gap of approximately 3% when solving instances with 10 nodes. Furthermore, a piecewise linear function with a six-line-segment approximation demonstrates an average of 10.8% lower cost compared with a linear approximation.

This article discusses a robust network interdiction problem considering uncertainties in arc capacities and resource consumption. The problem involves two players: an adversary seeking to maximize the flow of a commodity through the network and an interdictor whose objective is to minimize this flow. The interdictor plays first and selects network arcs to interdict, subject to a resource constraint. The problem is formulated as a bilevel problem, and an upper bound single level mix-integer linear formulation is derived. The upper bound formulation is solved using three heuristics tailored for this problem and the network structure, based on Lagrangian relaxation and Benders’ decomposition. On average, each heuristic provides a reduction in run time of at least 85% compared to a state-of-the-art solver. Enhanced Benders’ decomposition achieves a solution with an optimality gap of less than 5% for all tested instances. Sensitivity analyses are conducted for the level of uncertainty in network parameters and the uncertainty budget. Robust decisions are also compared to decisions not accounting for uncertainty to evaluate the value of robustness, showing a reduction in simulation maximum flows by as much as 89.5%.

This study proposes a multi-period facility location formulation to maximize coverage while meeting a coverage reliability constraint. The coverage reliability constraint is a chance constraint limiting the probability of failure to maintain the desired service standard, commonly followed by emergency medical services and fire departments. Further, uncertainties in the failure probabilities are incorporated by utilizing robust optimization using polyhedral uncertainty sets, which results in a compact mixed-integer linear program. A case study in the Portland, OR metropolitan area is analyzed for employing unmanned aerial vehicles (UAVs) or drones to deliver defibrillators in the region to combat out-of-hospital cardiac arrests. In the context of this study, multiple periods represent periods with different wind speed and direction distributions. The results show that extending to a multi-period formulation, rather than using average information in a single period, is particularly beneficial when either response time is short or uncertainty in failure probabilities is not accounted for. Accounting for uncertainty in decision-making improves coverage significantly while also reducing variability in simulated coverage, especially when response times are longer. Going from a single-period deterministic formulation to a multi-period robust formulation boosts the simulated coverage values by 57%, on average. The effect of considering a distance-based equity metric in decision-making is also explored.

Members of the genus Vibrio are ecologically significant bacteria native to aquatic ecosystems globally, and a few can cause diseases in humans. Vibrio-related illnesses have increased in recent years, primarily attributed to changing environmental conditions. Therefore, understanding the role of environmental factors in the occurrence and growth of pathogenic strains is crucial for public health. Water, oyster, and sediment samples were collected between 2009 and 2012 from Chester River and Tangier Sound sites in Chesapeake Bay, Maryland, USA, to investigate the relationship between water temperature, salinity, and chlorophyll with the incidence and distribution of Vibrio parahaemolyticus (VP) and Vibrio vulnificus (VV). Odds ratio analysis was used to determine association between the likelihood of VP and VV presence and these environmental variables. Results suggested that water temperature threshold of 20°C or higher was associated with an increased risk, favoring the incidence of Vibrio spp . A significant difference in salinity was observed between the two sampling sites, with distinct ranges showing high odds ratio for Vibrio incidence, especially in water and sediment, emphasizing the impact of salinity on VP and VV incidence and distribution. Notably, salinity between 9-20 PPT consistently favored the Vibrio incidence across all samples. Relationship between chlorophyll concentrations and VP and VV incidence varied depending on sample type. However, chlorophyll range of 0-10 µg/L was identified as critical in oyster samples for both vibrios. Analysis of odds ratios for water samples demonstrated consistent outcomes across all environmental parameters, indicating water samples offer a more reliable indicator of Vibrio spp. incidence.Importance Understanding the role of environmental parameters in the occurrence of Vibrio species posing significant public health risks and economic burdens such as Vibrio parahaemolyticus and Vibrio vulnificus are of paramount importance. These aquatic bacteria are responsible for various human diseases, including gastroenteritis and wound infections, which can be severe and sometimes fatal. Recent observations suggest that certain environmental conditions may favor the growth of Vibrio , leading to more severe disease outcomes. By investigating the environmental factors that influence the occurrence of Vibrio parahaemolyticus and Vibrio vulnificus , the need to gain insights into the favorable ranges of environmental variables is apparent. The significance of this research is in identifying the favorable ranges of environmental and ecological factors, which holds the potential to provide an aid in the intervention and mitigation strategies through the development of predictive models, ultimately enhancing our ability to manage and control diseases caused by these pathogens.

The severity of hurricanes, and thus the associated impacts, is changing over time. One of the understudied threats from damage caused by hurricanes is the potential for cross-contamination of water bodies with pathogens in coastal agricultural regions. Using microbiological data collected after hurricanes Florence and Michael, this study shows a dichotomy in the presence of pathogens in coastal North Carolina and Florida. Salmonella typhimurium was abundant in water samples collected in the regions dominated by swine farms. A drastic decrease in Enterococcus spp. in Carolinas is indicative of pathogen removal with flooding waters. Except for the abundance presence of Salmonella typhimurium, no significant changes in pathogens were observed after Hurricane Michael in the Florida panhandle. We argue that a comprehensive assessment of pathogens must be included in decision-making activities in the immediate aftermath of hurricanes to build resilience against risks of pathogenic exposure in rural agricultural and human populations in vulnerable locations.

The idea of deploying electric vehicles and unmanned aerial vehicles (UAVs), also known as drones, to deliver packages in logistics operations has attracted increasing attention in the past few years. In this paper, we propose an innovative problem where a battery electric vehicle (BEV) paired with drone is utilized to deliver first-aid items in a rural area. This problem is termed battery electric vehicle traveling salesman problem with drone (BEVTSPD). In BEVTSPD, the BEV and the drone perform delivery tasks coordinately while the BEV can serve as a drone hub. The BEV can also refresh its battery energy to full capacity in battery-swap stations available in the network. An arc-based mixed-integer programming model defined in a multigraph is presented for BEVTSPD. An exact branch-and-price (BP) algorithm and a Variable Neighborhood Search (VNS) heuristic are developed to solve instances with up to 25 customers in one minute. Numerical experiments show that the heuristic is much more efficient than solving the arc-based model using the ILOG CPLEX solver and BP algorithm. A real-world case study and the sensitivity analysis of different parameters are also conducted and presented. The results indicate that drone speed has a more significant effect on delivery time than the BEV’s driving range.

AbstractMembers of the genusVibrioare ecologically significant bacteria native to aquatic ecosystems globally, and a few can cause diseases in humans. Vibrio-related illnesses have increased in recent years, primarily attributed to changing environmental conditions. Therefore, understanding the role of environmental factors in the occurrence and growth of pathogenic strains is crucial for public health. Water, oyster, and sediment samples were collected between 2009 and 2012 from Chester River and Tangier Sound sites in Chesapeake Bay, Maryland, USA, to investigate the relationship between water temperature, salinity, and chlorophyll with the incidence and distribution ofVibrio parahaemolyticus(VP) andVibrio vulnificus(VV). Odds ratio analysis was used to determine association between the likelihood of VP and VV presence and these environmental variables. Results suggested that water temperature threshold of 20°C or higher was associated with an increased risk, favoring the incidence ofVibrio spp. A significant difference in salinity was observed between the two sampling sites, with distinct ranges showing high odds ratio forVibrioincidence, especially in water and sediment, emphasizing the impact of salinity on VP and VV incidence and distribution. Notably, salinity between 9-20 PPT consistently favored theVibrioincidence across all samples. Relationship between chlorophyll concentrations and VP and VV incidence varied depending on sample type. However, chlorophyll range of 0-10 µg/L was identified as critical in oyster samples for both vibrios. Analysis of odds ratios for water samples demonstrated consistent outcomes across all environmental parameters, indicating water samples offer a more reliable indicator ofVibrio spp.incidence.ImportanceUnderstanding the role of environmental parameters in the occurrence ofVibriospecies posing significant public health risks and economic burdens such asVibrio parahaemolyticusandVibrio vulnificusare of paramount importance. These aquatic bacteria are responsible for various human diseases, including gastroenteritis and wound infections, which can be severe and sometimes fatal. Recent observations suggest that certain environmental conditions may favor the growth ofVibrio, leading to more severe disease outcomes. By investigating the environmental factors that influence the occurrence ofVibrio parahaemolyticusandVibrio vulnificus, the need to gain insights into the favorable ranges of environmental variables is apparent. The significance of this research is in identifying the favorable ranges of environmental and ecological factors, which holds the potential to provide an aid in the intervention and mitigation strategies through the development of predictive models, ultimately enhancing our ability to manage and control diseases caused by these pathogens.

We study the mean‐standard deviation minimum cost flow (MSDMCF) problem, where the objective is minimizing a linear combination of the mean and standard deviation of flow costs. Due to the nonlinearity and nonseparability of the objective, the problem is not amenable to the standard algorithms developed for network flow problems. We prove that the solution for the MSDMCF problem coincides with the solution for a particular mean‐variance minimum cost flow (MVMCF) problem. Leveraging this result, we propose bisection (BSC), Newton–Raphson (NR), and a hybrid (NR‐BSC)—method seeking to find the specific MVMCF problem whose optimal solution coincides with the optimal solution for the given MSDMCF problem. We further show that this approach can be extended to solve more generalized nonseparable parametric minimum cost flow problems under certain conditions. Computational experiments show that the NR algorithm is about twice as fast as the CPLEX solver on benchmark networks generated with NETGEN.

Travel time reliability is a key metric of interest to practitioners and researchers because it affects travel choice and the economic competitiveness of urban areas. This research focuses on three travel time reliability metrics – buffer index, modified buffer index, and the relative width of travel time distributions. The key novel contributions of this research include using the multivariate delta method to prove that the sampling distributions of the three travel time reliability metrics are asymptotically normal. The asymptotic standard error for the three reliability metrics is derived. The asymptotic normality and the standard error result are used to arrive at a formula for the confidence interval. The derivations are non-parametric since they do not impose any shape requirement on travel time distributions. A case study based on a highway corridor in Portland, OR, is utilized to estimate confidence intervals for the three travel time reliability metrics for several travel time distributions with different shapes and levels of skewness. The performance of the proposed method is compared against several bootstrapping-based confidence intervals with favorable performance. Finally, upper-tailed, lower-tailed, and two-tailed one-sample hypothesis testing procedures are developed, and numerical tests show a positive performance and high statistical power for sample sizes that can be readily obtained.

In this paper, the two-echelon multi-period multi-product location–inventory problem with partial facility closing and reopening is studied. For each product and period, plants serve warehouses, which serve consolidation hubs, which service customers with independent, normally distributed demands. The schedule of construction, temporary partial closing, and reopening of modular capacities of facilities, the continuous-review inventory control policies at warehouses, the allocation of customer demands to hubs, and the allocation of hubs to warehouses are determined. The service levels for stockout at warehouses during lead time and the violation of warehouse and hub capacities are explicitly considered. The proposed mixed-integer non-linear program minimizes the weighted summation of the number of different facilities and logistical costs, so that the number of different facilities can be controlled. Since the proposed model is np-hard, the multi-start construction and tabu search improvement heuristic (MS-CTSIH) with two improvement strategies and the modified MS-CTSIH incorporating both strategies are proposed. The experiment shows that the two improvement strategies appear non-dominated, and the modified MS-CTSIH yields the best results. The comparison of the modified MS-CTSIH and a commercial solver on a small instance shows the efficiency and effectiveness of the modified MS-CTSIH. The sensitivity analyses of problem parameters are performed on a large instance.