Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

Alam, Rakibul; Hou, Chuang; Aeschliman, Spencer; Zhou, Yan; Guo, Zhaomiao

doi:10.1080/19427867.2022.2070092

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…Previous agent-based modelling exercises have provided insights into ridesourcing as a first / last mile solution for public transport 28,29 , driver earnings 25,28,30 , platform pricing policies 25,28,30,31 , the effect of labour market characteristics 25,28,30 , ride-pooling system performance [32][33][34] , and the design of charging infrastructure for an electric fleet 35,36 . An agent-based representation of the ridesourcing market was also used for studying income inequalities resulting from ridesourcing operations 19 .…”

Section: Introductionmentioning

confidence: 99%

Do Ridesourcing Platforms Need Socio-Economic Inequality to Thrive?

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2023

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Limited available market share data seems to suggest that ridesourcing platforms benefit from, even thrive on, socio-economic inequality. We suspect that this is associated with high levels of socio-economic inequality allowing for cheap labour as well as increasing the share of travellers with a considerably above-average willingness to pay for travel time savings and comfort. We test the relation between inequality and system performance by means of an agent-based simulation model representing within-day and day-to-day supply-demand interaction in the ridesourcing market. The model captures travellers' mode choice with a heterogeneous perception of relevant time components, as well as job seekers' participation choice with heterogeneous reservation wage. Our experiments cover scenarios for the entire spectrum ranging from perfect equality to extreme inequality. For several of such scenarios, we explore alternative platform pricing strategies. Our analysis shows a strong, positive relationship between socio-economic inequality and ridesourcing market share. This is the outcome of the combination of cheap labour and time-sensitive ridesourcing users, reinforced by network effects inherent to ridesourcing markets. We find that driver earnings are minimal in urban areas with large socio-economic inequality. In such contexts, drivers are likely to face a high platform commission, and yet, fierce competition for passengers.

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