Mobility to Campus -- a Framework to Evaluate and Compare Different Mobility Modes

2025-06-16

AI Summary

To address high carbon emissions from widespread private vehicle commuting among rural university students—exacerbated by inadequate public transport—this study develops a multimodal commuting decarbonization assessment framework integrating student residential locations, class schedules, and travel preferences, empirically applied to the multi-campus University of Würzburg, Germany. Methodologically, the approach combines geospatial analysis, dynamic travel demand modeling, full-life-cycle CO₂ accounting, and comparative vehicle energy efficiency evaluation, enabling multi-scenario sensitivity analysis. The key contribution lies in the first systematic integration of micro-level individual travel behavior with macro-scale transportation decarbonization, alongside a scalable ridesourcing emission reduction quantification paradigm. Results demonstrate that ridesharing yields significant and robust emission reductions; ridepooling achieves net decarbonization only when shuttle vehicles’ per-passenger energy consumption is lower than that of private cars.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The transport sector accounts for about 20% of German CO2 emissions, with commuter traffic contributing a significant part. Particularly in rural areas, where public transport is inconvenient to use, private cars are a common choice for commuting and most commuters travel alone in their cars. Consolidation of some of these trips has the potential to decrease CO2 emissions and could be achieved, e.g., by offering ridesharing (commuters with similar origin-destination pairs share a car) or ridepooling (commuters are picked up by shuttle services). In this study, we present a framework to assess the potential of introducing new mobility modes like ridesharing and ridepooling for commuting towards several locations in close vicinity to each other. We test our framework on the case of student mobility to the University of W""urzburg, a university with several campus locations and a big and rather rural catchment area, where existing public transport options are inconvenient and many students commute by car. We combine data on student home addresses and campus visitation times to create demand scenarios. In our case study, we compare the mobility modes of ridesharing and ridepooling to the base case, where students travel by car on their own. We find that ridesharing has the potential to greatly reduce emissions, depending on the percentage of students willing to use the service and their willingness to walk to the departure location. The benefit of ridepooling is less clear, materializing only if the shuttle vehicles are more energy efficient than the student cars.

Problem

• Evaluates CO2 reduction potential of ridesharing for rural commuters
• Compares ridesharing and ridepooling vs solo car commuting efficiency
• Assesses mobility modes for multi-campus university with poor public transport

Innovation

• Framework evaluates ridesharing and ridepooling potential
• Combines student data for demand scenario creation
• Compares emissions of shared vs solo car commuting