Towards an Artificial Intelligence based Decision Support System to Evaluate Various Transit Projects
type: CreativeWork
description: This dissertation addressed a subset of the Transit Network Design Problem (Transit-NDP), where the main evaluation parameter is the impact of various transit projects – Bus Lines, Tram, Light Rail, Rail – on the Network Travel Time (NTT) of the vehicles. The NTT is estimated using the static four-step travel demand model (trip generation, trip distribution, modal split, and traffic assignment). A set of five metaheuristic models were developed and implemented for this specific Transit-NDP, namely, Simulated Annealing (SA)-VISUM, Simulated Annealing-Random Forest Regression Model (SA-RF), Tabu Search (TS)-Random Forest (TS-RF), SA-RF-TS-VISUM, SA-RF-VISUM. These models were evaluated using two test networks (Halle Salle with 6521 OD pairs and Karlsruhe with 527,076 OD pairs). An enumeration and sampling study provided insights into the impact of various combinations of transit projects on the NTT. At the same time, it also served as the dataset that the RF model was based on. A set of demand-budget combinations were tested for each network – Demand (100%, 120%, 140%, 160%, 180%) and Budget (0%, 20%, 40%, 60%). The best solutions found in the Halle Salle network ranged between 100% demand, 48.23% NTT reduction to a 180% demand, 53.47% NTT reduction. Correspondingly, the best solutions found in the Karlsruhe Network ranged between 100% demand and 39.52% NTT reduction, whereas 180% demand resulted in a 53.99% NTT reduction. The principal conclusion for implementing these metaheuristics is that the utilization of an ensemble metaheuristic model encompassing a set of metaheuristics will be most beneficial as no dominating metaheuristics were found to outperform the others. The budget-demand sensitivity analysis produced the following significant outcomes: 1) the best solution found at higher budget levels does not necessarily include transit projects that were included at lower budget levels, 2) the best solution found at higher demand levels does not necessarily include…
abstract: This dissertation addressed a subset of the Transit Network Design Problem (Transit-NDP), where the main evaluation parameter is the impact of various transit projects – Bus Lines, Tram, Light Rail, Rail – on the Network Travel Time (NTT) of the vehicles. The NTT is estimated using the static four-step travel demand model (trip generation, trip distribution, modal split, and traffic assignment). A set of five metaheuristic models were developed and implemented for this specific Transit-NDP, namely, Simulated Annealing (SA)-VISUM, Simulated Annealing-Random Forest Regression Model (SA-RF), Tabu Search (TS)-Random Forest (TS-RF), SA-RF-TS-VISUM, SA-RF-VISUM. These models were evaluated using two test networks (Halle Salle with 6521 OD pairs and Karlsruhe with 527,076 OD pairs). An enumeration and sampling study provided insights into the impact of various combinations of transit projects on the NTT. At the same time, it also served as the dataset that the RF model was based on. A set of demand-budget combinations were tested for each network – Demand (100%, 120%, 140%, 160%, 180%) and Budget (0%, 20%, 40%, 60%). The best solutions found in the Halle Salle network ranged between 100% demand, 48.23% NTT reduction to a 180% demand, 53.47% NTT reduction. Correspondingly, the best solutions found in the Karlsruhe Network ranged between 100% demand and 39.52% NTT reduction, whereas 180% demand resulted in a 53.99% NTT reduction. The principal conclusion for implementing these metaheuristics is that the utilization of an ensemble metaheuristic model encompassing a set of metaheuristics will be most beneficial as no dominating metaheuristics were found to outperform the others. The budget-demand sensitivity analysis produced the following significant outcomes: 1) the best solution found at higher budget levels does not necessarily include transit projects that were included at lower budget levels, 2) the best solution found at higher demand levels does not necessarily include…
author: Vicuna, Patricio F
keywords: TNDP, Transportation Engineering
date published: 2022-01-01T08:00:00Z
date modified: 2026-07-24T01:57:59Z
provider: City University of New York - City College
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: City University of New York - City College