arXiv subjects

Olha Shulika

Publications and source records attributed to Olha Shulika.

4 recordsLinked to original sources

arXiv2026

Demand-agnostic assessment of on-demand pooled transit services

This study proposes a method to assess the potential of pooled on-demand transit feeder services in urban areas where demand is not yet known. We introduce the fraction of demand, reflecting the probability that a resident will use the service. Demand is generated on the distribution of residents address points at varying demand fraction levels. Through simulations, we match travellers into pooled rides and evaluate the service potential using three performance indicators (KPIs). We observe how these KPIs change with varying demand fractions and identify the most promising hub for each area. By setting KPI thresholds, we select the optimal combination of area and hub that meets these thresholds at the lowest demand fraction. This approach provides municipalities with a structured tool for pre-deployment evaluation, helping them choose the most suitable areas for launching new services despite the absence of exact demand data. We illustrate its application through a case study in Krakow, ranking 12 pre-selected areas for feeder service deployment.

physics.soc-ph↗

arXiv2025

SimFLEX: a methodology for comparative analysis of urban areas for implementing new on-demand feeder bus services

On-demand feeder bus services present an innovative solution to urban mobility challenges, yet their success depends on thorough assessment and strategic planning. Despite their potential, a comprehensive framework for evaluating feasibility and identifying suitable service areas remains underdeveloped. Simulation Framework for Feeder Location Evaluation (SimFLEX) uses spatial, demographic, and transport-specific data to run microsimulations and compute key performance indicators (KPIs), including service attractiveness, waiting time reduction, and added value. SimFLEX employs multiple replications to estimate demand and mode choices and integrates OpenTripPlanner (OTP) for public transport routing and ExMAS for calculating shared trip attributes and KPIs. For each demand scenario, we model the traveler learning process using the method of successive averages (MSA), stabilizing the system. After stabilization, we calculate KPIs for comparative and sensitivity analyzes. We applied SimFLEX to compare two remote urban areas in Krakow, Poland - Bronowice and Skotniki - the candidates for service launch. Our analysis revealed notable differences between analyzed areas: Skotniki exhibited higher service attractiveness (up to 30%) and added value (up to 7%), while Bronowice showed greater potential for reducing waiting times (by nearly 77%). To assess the reliability of our model output, we conducted a sensitivity analysis across a range of alternative-specific constants (ASC). The results consistently confirmed Skotniki as the superior candidate for service implementation. SimFLEX can be instrumental for policymakers to estimate new service performance in the considered area, publicly available and applicable to various use cases. It can integrate alternative models and approaches, making it a versatile tool for policymakers and urban planners to enhance urban mobility.

physics.soc-ph↗

arXiv2023

Spatiotemporal variability of ride-pooling potential -- half a year New York City experiment

Ride-pooling systems, despite being an appealing urban mobility mode, still struggle to gain momentum. While we know the significance of critical mass in reaching system sustainability, less is known about the spatiotemporal patterns of system performance. Here, we use 1.5 million NYC taxi trips (sampled over a six-month period) and experiment to understand how well they could be served with pooled services. We use a utility-driven ride-pooling algorithm and observe the pooling potential with six performance indicators: mileage reductions, travellers' utility gains, share of pooled rides, occupancy, detours, and potential fleet reduction. We report distributions and temporal profiles of about 35 thousand experiments covering weekdays, weekends, evenings, mornings, and nights. We report complex spatial patterns, with gains concentrated in the core of the network and costs concentrated on the peripheries. The greatest potential shifts from the North in the morning to the Central and South in the afternoon. Offering pooled rides at the fare 32% lower than private ride-hailing seems to be sufficient to attract pooling yet dynamically adjusting it to the demand level and spatial pattern may be efficient. The patterns observed in NYC were replicated on smaller datasets in Chicago and Washington, DC, the occupancy grows with the demand with similar trends.

physics.soc-ph↗

arXiv2022

Can we start sharing our rides again? The postpandemic ride-pooling market

Before the pandemic ride-pooling was a promising emerging mode in urban mobility. It started reaching the critical mass with a growing number of service providers and the increasing number of travellers (needed to ensure ride-pooling efficiency and sustainability). However, the COVID pandemic was disruptive for ride-pooling. Many services were cancelled, several operators needed to change their business models and travellers started avoiding those services. In the postpandemic period, we need to understand what is the future of ride-pooling: whether the ride-pooling system can recover and remain a relevant part of future mobility. Here we provide an overview of the postpandemic ride-pooling market based on the analysis of three components: a) literature review, b) empirical pooling availability survey and c) travellers' behaviour studies. We conclude that the core elements of the ride-pooling business model were not affected by the pandemic. It remains a promising option for all the parties involved, with a great potential to become attractive for travellers, drivers, TNC platforms and policymakers. The travel behaviour changes due to the pandemic seem not to be long-lasting, our virus awareness is no anymore the key concern and our willingness to share and reduce fares seem to be high again. Yet, whether ride-pooling will get another chance to grow remains open. The number of launches of ride-pooling start-ups is unprecedented, yet the financial perspectives are unclear.

physics.soc-ph↗