Nationwide lockdown for COVID-19 created an urgent demand for public transportation among migrant workers stranded at different parts of India to return to their native places. Arranging transportation could spike the number of COVID-19 infected cases. Hence, this paper investigates the potential surge in confirmed and active cases of COVID-19 infection and assesses the train and bus fleet size required for the repatriating migrant workers. The expected to repatriate migrant worker population was obtained by forecasting the 2011 census data and comparing it with the information reported in the news media. A modified susceptible-exposed-infected-removed (SEIR) model was proposed to estimate the surge in confirmed and active cases of COVID-19 patients in India's selected states with high outflux of migrants. The developed model considered combinations of different levels of the daily arrival rate of migrant workers, total migrant workers in need of transportation, and the origin of the trip dependent symptomatic cases on arrival. Reducing the daily arrival rate of migrant workers for states with very high outflux of migrants (i.e., Uttar Pradesh and Bihar) can help to lower the surge in confirmed and active cases. Nevertheless, it could create a disparity in the number of days needed to transport all repatriating migrant workers to the home states. Hence, travel arrangements for about 100,000 migrant workers per day to Uttar Pradesh and Bihar, about 50,000 per day to Rajasthan and Madhya Pradesh, 20,000 per day to Maharashtra and less than 20,000 per day to other states of India was recommended.

A horizontal alignment can be represented by three key factors: number of horizontal points of intersection (HPIs), their locations, and corresponding horizontal curve radii. Deciding all the three factors simultaneously requires extensive effort, which is not practically feasible in the manual alignment development process. Most available computer‐aided methods prioritize some or all the three factors in the automated alignment development processes. However, approximation in HPI location or pre‐selection of HPI number and curve radius are the few limitations of these methods. This study presents a modified motion‐planning based algorithm for developing new horizontal alignments with optimized costs and impacts. It simultaneously uses a low‐discrepancy sampling technique to develop increasingly dense potential HPIs, rapidly exploring random trees to find a suitable number of intermediate HPIs at appropriate locations and sequential quadratic algorithm to select optimally fitted curve radii. The proposed algorithm is integrated with the GIS database for realistic location‐dependent cost and environmental impact assessment. Two real‐world study areas were selected to compare the results with the one reported in the literature and to evaluate backtracking capability. Results indicated the proficiency of the proposed algorithm in developing new alignments. The sensitivity analyses revealed the effect of design speed and right‐of‐way width on the alignment generation. The proposed algorithm can automate the new horizontal highway alignment development process and support highway engineers in planning and development.

The vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non‐overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non‐convex, noisy, constrained optimization problem with many local minima. Further, the gradient related constraints and vertical curvature are non‐linear. This paper presents an innovative exploring and exploiting ant colony optimization (E&E‐ACO) algorithm with an appropriate point sampling, vertical curve fitting strategies, and an intuitive feasible region identification approach for solving the vertical alignment optimization problem. The E&E‐ACO algorithm extensively explores the feasible search space to generate a set of potential solutions and effectively exploit the space around the potential solutions for developing the optimized vertical alignment. The efficacy of the proposed method is demonstrated using two case studies. In one case study, the optimized solution by the proposed method had a marginally better objective function value and about three times lesser computational time than the solution by the mesh adaptive direct search method. The optimized alignment satisfied the elevation constraints of fixed control points and imitated the manually designed real‐world vertical alignment. The linearly varying exploration and exploitation parameters had better convergence rate than the other tested variations. Further, the proposed method at the end of 1000 iterations yielded about six times better result than the traditional ACO algorithm.

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