Optimal Automated Demand Responsive Feeder Transit Operation and Its Impact

Lee, Young-Jae; Nickkar, Amirreza · 2018 · ROSA P / Urban Mobility & Equity Center

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Summary

This research addresses the operational challenges and economic viability of automated, demand-responsive feeder transit services. While human-driven demand-responsive buses exist, they are rarely deployed due to high labor costs and operational complexity. The authors argue that the advent of autonomous vehicles will make small-scale, flexible, door-to-door feeder services economically feasible. The study aims to develop an optimization algorithm for these future services and evaluate how reductions in unit operating costs—driven by automation—affect network characteristics, total costs, and passenger travel times. The methodology employs a Simulated Annealing (SA) algorithm to solve a vehicle routing problem that minimizes total costs, defined as the sum of vehicle operating costs and passenger in-vehicle travel time costs. The model incorporates specific constraints for multi-station and multi-train environments, including the relocation of buses between stations when local demand exceeds available supply. A key innovation is the inclusion of a "Degree of Circuity" (DOC) constraint, which limits the ratio of actual travel time to shortest possible travel time for each passenger, ensuring service quality. The algorithm was tested on a hypothetical network with varying maximum DOC thresholds (2.5, 3, 3.5, and 4) to assess the trade-off between passenger convenience and operational efficiency. Additionally, the study estimated future operating costs by analyzing the impact of electrification and automation on conventional bus costs. The results demonstrate that the SA algorithm successfully generates optimal routes and handles bus relocations effectively. Regarding the DOC constraints, higher maximum circuity values allowed for fewer buses, shorter vehicle travel distances, and lower total service costs, though this came at the expense of increased individual passenger travel times. In the cost analysis, the study found that as unit operating costs decline (simulating automation), total operating costs and total system costs decrease significantly. Furthermore, lower unit costs lead to reduced average passenger travel distances and lower total passenger travel costs. The optimization process shifts its focus toward minimizing passenger costs as the proportion of operating costs in the total cost structure decreases. The significance of this work lies in providing a predictive framework for transit agencies preparing for autonomous vehicle integration. The findings suggest that automation will not only reduce overall system expenses but also improve passenger experience by lowering travel times and distances. By establishing a baseline for efficient automated feeder operations, the study supports future comparisons between automated feeder transit and other emerging modes like ridesharing, aiding in the strategic planning of equitable and efficient urban mobility systems.

Key finding

Decreasing unit operating costs leads to reductions in total operating costs, total costs, and average passenger travel distance, while increasing the ratio of total operating costs to unit operating costs.

Methodology

modeling

Provenance

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