Route Optimization, Traffic Management, and Emission Reduction

Module: Sustainable Tech | Difficulty: Premium

Vehicle Emission Model

EV Energy Consumption

Comparison

Python Implementation

import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import shortest_path

class GreenTransportOptimizer:
    def __init__(self, distance_matrix):
        self.dist = distance_matrix

    def vehicle_emissions(self, distance, speed, vtype='gasoline'):
        factors = {'gasoline': 0.21, 'diesel': 0.17, 'electric': 0.05}
        ef = -0.001 + 0.01 * speed + 0.0005 * speed**2 + 2.0 / speed
        return ef * distance * factors.get(vtype, 0.21)

    def optimal_route(self, start, end):
        graph = csr_matrix(self.dist)
        _, predecessors = shortest_path(graph, directed=True, return_predecessors=True)
        route, current = [], end
        while current != start:
            route.append(current)
            current = predecessors[start, current]
        route.append(start)
        return route[::-1]

Research Insight: Multi-agent RL traffic management reduces urban emissions by 15-25%.