Novel Operational Algorithms for Ride-Pooling as On-Demand Feeder Services: A Simulation-Based Comparative Study

Core Concepts

Ride-pooling (RP) services, utilizing novel operational algorithms, demonstrate superior performance compared to traditional on-demand feeder services, particularly in low-density demand scenarios, as evidenced by a microscopic simulation platform.

Abstract

• Bibliographic Information: Fan, W., Yan, X., Sun, Z., & Yang, X. (2024). Novel operational algorithms for ride-pooling as on-demand feeder services. Elsevier.
• Research Objective: This paper investigates the potential of ride-pooling (RP) as an efficient on-demand feeder service, comparing its performance to existing alternatives like Ride-Sharing as Feeder (RSaF) and Flexible-Route Feeder-Bus Transit (Flex-FBT).
• Methodology: The authors develop a microscopic simulation platform to evaluate the performance of RP as a feeder service (RPaF) with tailored operational algorithms, including batch-based matching, adaptive dispatching, and urgency-based repositioning. The simulation platform incorporates modules for operator decisions, patron behavior, and vehicle movement within a realistic street network.
• Key Findings: RPaF, employing the proposed algorithms, consistently outperforms RSaF in terms of service rates and Flex-FBT in terms of average trip times for patrons, given the same fleet size. The study highlights the impact of operational differences between these on-demand feeder services on their performance.
• Main Conclusions: The research concludes that RPaF, with its distributed operation and efficient algorithms, presents a promising solution for on-demand feeder services, particularly in low-density demand scenarios. The simulation platform developed serves as a valuable tool for evaluating and optimizing various on-demand feeder services.
• Significance: This study contributes to the growing body of research on shared mobility and on-demand transportation systems, offering insights into the design and operation of efficient feeder services for urban transportation.
• Limitations and Future Research: The study focuses on a single-hub system and assumes inelastic demand. Future research could explore the application of RPaF in multi-hub networks and investigate the impact of demand elasticity on system performance.

Stats

Cruising speed of RPaF vehicles is S′ [km/h] on suburban streets and S [km/h] on freeways.
Vehicles incur a fixed delay td [seconds] for each stop to pick up or drop off.
Travel demand between the suburb and the hub is bidirectional, denoted λOB [patrons/sq. km/hour] for outbound trips and λIB [patrons/sq. km/hour] inbound trips.
Matching buffer distance of ∆[km] for each available vehicle.
Target number of requests U.
Average time to pool U requests within an area of A(∆) is u/(λOBA(∆)).
Average travel time for visiting u points within an area of A(∆) can be approximated by (k√A(∆)u)/(S′(u)(u/(U+1))).

Quotes

"This makes RP a promising on-demand feeder service for patrons with a common trip end in urban transportation."
"Comparisons reveal that given the same fleet size, RPaF generally outperforms RSaF in higher service rates (i.e., the percentage of requests served over all requests) and Flex-FBT in shorter average trip times for patrons."
"The proposed simulation platform offers a valuable testbed for evaluating and optimizing various on-demand feeder services."

Key Insights Distilled From

Novel operational algorithms for ride-pooling as on-demand feeder services

by Wenbo Fan, X... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.00787.pdf

Deeper Inquiries

How would the integration of real-time traffic conditions and demand prediction models impact the performance of RPaF services?

Integrating real-time traffic conditions and demand prediction models could significantly enhance the performance of RPaF services in several ways:

Improved Matching and Routing: Real-time traffic data would allow the matching algorithm to consider actual travel times instead of relying solely on distance-based metrics. This would lead to more efficient vehicle-request assignments, minimizing both passenger waiting times and overall travel times. Similarly, the routing algorithm could leverage real-time traffic information to dynamically adjust routes, avoiding congestion and minimizing delays.
Proactive Vehicle Repositioning: Demand prediction models could anticipate future demand hotspots and proactively reposition idle vehicles to those areas. This would reduce waiting times for passengers in high-demand locations and improve the overall responsiveness of the RPaF system.
Enhanced Dispatching Decisions: By combining real-time traffic conditions and predicted demand, the dispatching algorithm could make more informed decisions about when to dispatch vehicles. For instance, if a surge in demand is predicted, the algorithm could delay dispatching a partially full vehicle to accommodate more requests, optimizing vehicle utilization and reducing passenger wait times.
Personalized Service Offerings: With accurate demand predictions, the RPaF system could offer personalized service options to passengers. For example, passengers traveling during peak hours could be presented with options for shared rides with slightly longer travel times but lower fares, while those traveling during off-peak hours could be offered faster, direct rides.
Overall, integrating real-time traffic and demand prediction would enable the RPaF system to be more dynamic, adaptive, and efficient, ultimately leading to a better passenger experience and improved operational efficiency.

Could the efficiency of RPaF be compromised in areas with highly fluctuating demand patterns or during peak hours with significantly increased demand?

Yes, the efficiency of RPaF services could be challenged in scenarios with highly fluctuating demand patterns or peak hour surges:

Matching Difficulties: During peak hours or demand surges, the influx of requests might overwhelm the system, making it difficult for the matching algorithm to efficiently pair riders with similar itineraries. This could lead to longer waiting times and potentially force the system to dispatch vehicles with lower occupancy, reducing efficiency.
Increased Routing Complexity: High demand often coincides with increased traffic congestion. In such situations, finding optimal routes that minimize travel times for all passengers in a shared ride becomes more complex and computationally demanding. This could lead to suboptimal routing decisions and longer travel times.
Strained Vehicle Availability: If the RPaF system operates with a fixed fleet size, a sudden surge in demand might lead to a shortage of available vehicles. This would result in longer waiting times for passengers and potentially missed ride requests, impacting service reliability.
Compromised Service Quality: To cope with high demand, the system might be forced to increase the maximum allowed detours or waiting times for shared rides. This could negatively impact the passenger experience, particularly for those with time-sensitive trips.
Mitigating these challenges requires strategies like:

Dynamic Pricing: Implementing surge pricing during peak hours can help to moderate demand and incentivize some riders to opt for alternative transportation modes or travel during off-peak times.
Fleet Sizing and Rebalancing: Employing a dynamic fleet sizing strategy that adjusts the number of active vehicles based on real-time and predicted demand can help to ensure sufficient vehicle availability during peak periods. Additionally, proactive vehicle repositioning based on demand predictions can help to preemptively allocate vehicles to areas expecting high demand.
Algorithm Optimization: Developing and implementing more sophisticated matching and routing algorithms that can effectively handle large-scale, dynamic demand patterns is crucial. This might involve exploring techniques like machine learning and optimization heuristics to improve the system's ability to adapt to fluctuating conditions.

What are the potential social equity implications of implementing RPaF services, particularly concerning accessibility for underserved communities and potential biases in algorithmic decision-making?

While RPaF services offer potential benefits, it's crucial to address potential social equity implications:

Accessibility for Underserved Communities:

Affordability: RPaF fares, even with sharing, might be inaccessible for low-income communities who rely heavily on affordable public transit. This could exacerbate transportation inequities.
Digital Divide: Reliance on smartphones and apps for RPaF access could disadvantage individuals without reliable internet access or technological literacy, often prevalent in underserved communities.
Service Area Coverage: RPaF operators, driven by profitability, might prioritize service areas with higher demand and affluence, potentially neglecting lower-income neighborhoods with less concentrated demand.

Algorithmic Bias:

Data Biases: Algorithms trained on historical data reflecting existing transportation inequities (e.g., under-served areas) might perpetuate those biases in RPaF service provision.
Proxy Discrimination: Algorithms using seemingly neutral factors (e.g., trip origin, time) could indirectly discriminate against certain demographics if those factors correlate with protected characteristics.
Lack of Transparency: Opaque algorithms make it difficult to identify and rectify biases, potentially leading to unfair or discriminatory outcomes for certain passenger groups.
Ensuring equitable RPaF implementation requires:

Affordability Programs: Subsidized fares or partnerships with social service agencies can make RPaF accessible to low-income riders.
Digital Inclusion Initiatives: Providing affordable internet access, digital literacy programs, and alternative booking methods (e.g., phone dispatch) can bridge the digital divide.
Equitable Service Area Coverage: Mandating minimum service levels for underserved areas or offering incentives for operators to serve those areas can ensure equitable access.
Algorithmic Auditing and Fairness: Regularly auditing algorithms for bias, using diverse and representative training data, and incorporating fairness metrics into algorithm design can mitigate discriminatory outcomes.
Community Engagement: Involving communities in the planning and implementation of RPaF services ensures their needs and concerns are addressed, fostering trust and equitable outcomes.