A data-driven operations analytics project that applies mathematical modeling, optimization, and Python (Google OR-Tools) to improve bus scheduling and routing efficiency in urban transit systems.

This project was developed as part of Operations Analytics (DAMO 610-1) at the University of Niagara Falls, Canada.

Public transport plays a critical role in urban life. However, challenges such as demand fluctuations, traffic congestion, operational costs, and environmental impact make bus scheduling a complex optimization problem.

This project addresses these challenges by:

• Designing a mathematical optimization model
• Implementing it in Python using Google OR-Tools
• Extending it with real-world constraints (traffic, emissions, demand variation)
• Providing visualizations and insights for decision-making

• Minimize passenger waiting time
• Optimize bus allocation & fleet utilization
• Reduce operational costs (fuel, labor, maintenance)
• Integrate real-time traffic & congestion factors
• Ensure sustainable operations by controlling emissions
• Improve service reliability and social equity

• Decision Variables: Bus assignment (X_ij), arrival times (Y_ij)
• Objective Function: Minimize costs, delays, transfers, and emissions
• Constraints:
  • Demand satisfaction
  • Fleet size limitations
  • Travel time & congestion factors
  • Emission caps

• Library: Google OR-Tools
• Approach: Linear & Mixed Integer Programming (MIP)
• Extensions:
  • Dynamic traffic adjustments
  • Emission & fuel efficiency tracking
  • Passenger demand fluctuations

from ortools.linear_solver import pywraplp

def optimize_bus_scheduling():
    solver = pywraplp.Solver.CreateSolver('SCIP')
    num_stops = 5
    cost_matrix = [[0, 10, 15, 30, 25],
                   [10, 0, 20, 35, 40],
                   [15, 20, 0, 25, 30],
                   [30, 35, 25, 0, 15],
                   [25, 40, 30, 15, 0]]

    num_buses = 3
    X = {}

    for i in range(num_stops):
        for j in range(num_stops):
            if i != j:
                X[i, j] = solver.BoolVar(f'X[{i},{j}]')

    solver.Minimize(solver.Sum(cost_matrix[i][j] * X[i, j]
                               for i in range(num_stops) for j in range(num_stops) if i != j))

    for i in range(num_stops):
        solver.Add(sum(X[i, j] for j in range(num_stops) if i != j) == 1)

    status = solver.Solve()
    if status == pywraplp.Solver.OPTIMAL:
        print("Optimal solution found:")
        for i in range(num_stops):
            for j in range(num_stops):
                if i != j and X[i, j].solution_value() > 0.5:
                    print(f'Bus route: {i} -> {j}')
    else:
        print("No solution found.")

• Route Visualization → NetworkX + Matplotlib
• Heatmap of Travel Costs → Seaborn
• Passenger Demand by Time → Bar charts

• 🚍 Optimized allocation reduced idle time and overuse
• ⏱ Passenger waiting time decreased significantly
• 💰 Lower operational costs through efficient scheduling
• 🌱 Environmental impact reduced by emission control constraints
• 🚦 Real-time traffic-aware routing improved punctuality
• 📈 Service reliability increased → higher potential ridership

• Current model tested on synthetic data → needs integration with real GPS & IoT feeds
• Passenger demand forecasting can be improved with AI/ML models
• Future scope: Real-time dashboard for city planners to optimize dynamically

• Setu Chaudhari (NF1009018) – Code & Report
• Heta Chavda (NF1014555) – Presentation
• Enejo David Colonel – Report & Presentation

📚 Course: Operations Analytics (DAMO 610-1) 👨‍🏫 Professor: Cosimo Girolamo

• Python (Google OR-Tools, NumPy, NetworkX, Seaborn, Matplotlib)
• Optimization Algorithms (MIP, Linear Programming)
• Visualization Tools for insights

This project highlights how operations analytics and optimization can transform urban public transportation by:

• Reducing costs
• Enhancing passenger satisfaction
• Supporting sustainable urban development

By combining mathematical modeling, Python optimization, and visualization, the project provides a framework for smarter and greener public transit systems.