Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets

Dandl, Florian; Hyland, Michael; Bogenberger, Klaus; Mahmassani, Hani S.

doi:10.1007/s11116-019-10007-9

Cited by 56 publications

(30 citation statements)

References 30 publications

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“…Babicheva et al (2018) evaluate six different methods to apply redistribution and their results show that the combination of simple nearest neighbours and index-based redistribution method provide very promising results. To know the effect of spatial and temporal aggregation of demand forecast which is used for vehicle redistribution, the readers are referred to Dandl et al (2019). They conclude that higher the spatial disaggregation of demand forecast, better is the fleet performance in terms of user wait time and empty fleet miles, though the demand forecast quality is decreased at higher disaggregation.…”

Section: Traffic Assignmentmentioning

confidence: 99%

Shared autonomous vehicle services: A comprehensive review

Narayanan

Chaniotakis

Antoniou

2020

Transportation Research Part C: Emerging Technologies

488

139

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The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Landuse, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.

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Section: Traffic Assignmentmentioning

confidence: 99%

Shared autonomous vehicle services: A comprehensive review

Narayanan

Chaniotakis

Antoniou

2020

Transportation Research Part C: Emerging Technologies

488

139

View full text Add to dashboard Cite

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“…This would lead to severe repercussions, such as high customer wait time and ineffective fleet utilization, which, in turn, adversely affects customer satisfaction and revenue. An accurate prediction of the demand for ODM services could facilitate better planning to overcome these challenges (Ma et al, 2014;Santi et al, 2014;Dandl et al, 2019). Moreover, demand estimation is critical to both fleet operators and transit authorities as their common goal is to reduce the number of empty air taxis crowding the airspace.…”

Section: Demand Prediction For Air Taxismentioning

confidence: 99%

Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities

Rajendran

Srinivas

2020

Transportation Research Part E: Logistics and Transportation Re

138

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Expected to operate in the imminent future, air taxi service (ATS) is an aerial on-demand transport for a single passenger or a small group of riders, which seeks to transform the method of everyday commute. This uncharted territory in the emerging transportation world is anticipated to enable consumers bypass traffic congestion in urban road networks. By adopting an electric vertical takeoff and landing concept (eVTOL), air taxis could be operational from skyports retrofitted on building rooftops, thus gaining advantage from an implementation standpoint. Motivated by the potential impact of ATS, this study provides a review of air taxi systems and associated operations. We first discuss the current developments in the ATS (demand prediction, air taxi network design, and vehicle configuration). Next, we anticipate potential future challenges of ATS from an operations management perspective, and review the existing literature that could be leveraged to tackle these problems (ride-matching, pricing strategies, vehicle maintenance scheduling, and pilot training and recruitment). Finally, we detail future research opportunities in the air taxi domain.

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“…Such means have been used at relatively lower prices compared with taxis since late 2010. Additionally, several researchers have investigated Mobility as a Service (MaaS) [6][7][8] and other mobility services that depend on autonomous driving technology [9][10][11], which is one of the essential components in the artificial intelligence industry. By following the trend of the mobility-related technology development up to date, there were two possible expectations: the diversification of the types of mobility and the unification of all the mobility services using one platform.…”

Section: Introductionmentioning

confidence: 99%

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

et al. 2020

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Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.

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Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets

Cited by 56 publications

References 30 publications

Shared autonomous vehicle services: A comprehensive review

Shared autonomous vehicle services: A comprehensive review

Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities

Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

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