• Issue 1 (2024)

Sensitivity Analysis on the Impact of Candidate Transit Projects on the Network Travel Time Evaluated by a Travel Demand Model

JUSPN, volume-20 , Issue 1 (2024), PP 09 - 22

Published: 23 Jan 2024

DOI: 10.5383/JUSPN.19.01.006

by Camille Kamga, Patricio Vicuna, Kyriacos Mouskos from University Transportation Research Center Region 2, New York City, New York, USA and Transportation System Analyst, Nicosia, Cyprus

Abstract: This research aimed to address the Transit-Network Design Problem (NDP) where the Upper Level optimizes the vehicles' Network Travel Time (NTT) subject to a Budget constraint and subjects to the Lower Level that estimates the vehicles' NTT based on a Travel Demand Model (TDM). The study objective is to explore the impact of a naïve model of the more complex Transit-NDP, where stakeholders propose a priori a group of potential transit projects, including bus lines, tramways, light rail systems, and rail networks. The experimental setup involved a sensitivity analysis of budget-demand combinations for two test networks – where the Halle and the Karlsruhe test networks have 37 and 48 candidate transit projects, respectively. A complete evaluation enumeration of each budget-demand combination was conducted when it was computationally feasible, and a sampling evaluation set of 100 for the remaining evaluation test combinations. The Halle network exhibits a 47.6% NTT reduction for the base demand and a 50.8% reduction for the 160% demand level with all transit projects activated. The Karlsruhe Network exhibits an NTT reduction ranging between a base demand, resulting in a 39.7% reduction, whereas a 160% demand results in a 52.4% reduction with all transit projects activated. In both networks, a set of candidate transit projects were members of the best solution in all test runs. The sensitivity analysis demonstrated that some candidate transit projects selected at lower budget-demand combinations were not necessarily included at the best solutions of higher budget-demand levels. Overall, 18,432 test runs were conducted for the Halle network and 26,704 test runs for the Karlsruhe network, which required a total of 222 days. This analysis demonstrated the computational feasibility of conducting such a large set of experiments. In addition, the resulting dataset of the enumeration and sampling method was utilized to develop a Random Forest Regression (RFR) model, which was then utilized within a set of metaheuristics to solve this specific Transit-NDP. read more... read less...

Keywords: Transit-NDP, Travel Demand Model, Random Forest Regression Model

Sharing Digital Solutions with the Public for a Climate-Friendly Smart City District via an Ecosystem Map: Concepts and Solutions

JUSPN, volume-20 , Issue 1 (2024), PP 01 - 08

Published: 23 Jan 2024

DOI: 10.5383/JUSPN.19.01.005

by Frank Elberzhager, Anna Schmitt, Sven Storck, Stefan Schweitzer from Fraunhofer IESE, Germany, Fraunhofer-Platz 1, 67663 Kaiserslautern

Abstract: Climate change and its consequences are currently a very pressing topic. This is also true for cities. Digital solutions can be helpful in this regard. They can be used, for example, to inform citizens and provide digital services aimed at fostering climate-friendly behavior. In the EnStadt:Pfaff research project, we are developing a climate-friendly smart city district. Our focus in the project is on digitalization, with the main goal being the implementation of a digital ecosystem providing apps to the people of the district, which is currently under construction. To date, we have developed several digital solutions with different technology readiness levels. One major issue we have faced, however, has been how to inform the public about these solutions and share our ideas with them. We decided to do this in various ways, e.g., via a classical project webpage, events, and videos. In order to have one main entry point for the public to explore our solutions, we have developed a so-called ecosystem map. Four digital solutions are presented in this map to show what kind of digital solutions might be part of the future digital ecosystem in the smart city district. In this paper, we report on how we created the digital map, provide the four digital solution examples shown on the map, and present feedback we received regarding our solutions. This has also led to ideas for the future development of the map. We share three basic ones in this paper. read more... read less...

Keywords: Smart City, Sustainability, Digitalization, Information Sharing, Digital Ecosystem Map