Identifying the Potential for Partial Integration of Private and Public Transportation

Abstract

In this paper, we explore the potential for a partial integration of private and public transportation as provided by taxi and bus services on the level of individual trips. While these modes are typically separated for economic reasons, in the future with shared Autonomous Vehicles (AVs) providing cheap and efficient transportation services, such distinctions will blur. Consequently, optimization based on real-time data will allow exploiting parallels in demand in a dynamic way, such as the proposed approach in the current work. New operational and pricing strategies will then need to be considered, along with regulatory tools to limit congestion, encourage sharing and provide services in a more sustainable way by utilizing a dynamic landscape of urban transportation. In the current work, we evaluate existing parallels between individual bus and taxi trips in two Asian cities and show how exploiting these synergies could form the basis of achieving some of the above goals in the future.

The cost of non-coordination in urban on-demand mobility

“…Monday to Friday) as in our previous papers (e.g. 29 – 31 ) we have shown that mobility patterns can significantly vary between workdays and weekends. Moreover, in order to minimize the effects of different dataset lengths, measured in the number of days that the data was collected on, from each dataset we chose only 3 typical weeks (i.e.…”

The Integration of Shared Autonomous Vehicles in Public Transportation Services: A Systematic Review

“…Salazar et al (2018) [9] present a generic multi-commodity flow-based optimization approach to minimize the customers' travel time together with the operational costs of different transportation modes. With the hypothesis of SAVs replacing taxis in the near future, Bojic et al (2021) [31] compare bus and taxi trips and determine the overlap between the two to understand how many PT trips could be served by SAVs, and calculate a maximal weighted matching to determine an optimal assignment of passengers to SAVs maximizing the total travel time savings. Additionally, Levin et al (2019) [32] use total passenger travel time as an objective function in a linear program to optimize SAV routes, pick-up and drop-off location strategies, relocation of empty vehicles and transit headways.…”

“…Pinto et al (2019) [35] redesign the transit network together with determining SAV fleet size to optimally replace public transport routes that are inefficient in certain areas at certain times. While many authors consider multimodal trips for which public transport is used in the main trip stage, Bojic et al (2021) [31] allow the whole public transportation segment to be replaced with a shared trip in an automated vehicle, and Levin et al (2019) [32] consider transit to be used instead of or to complement SAVs only when total travel time is reduced. The integration problem between SAVs and PT has also been studied from a competitive perspective by Mo et al (2021) [20], where the private enterprises which operate the two services are under government regulation but profit-oriented.…”

Spatiotemporal Variation in Bicycle Road Crashes and Traffic Volume in Berlin: Implications for Future Research, Planning, and Network Design

“…Passengers on certain transport modes or socio-demographic cohorts may not yet be exploring (or even aware of) the urban mobility services and applications that are available through smartphones [33], and certain digital data sensors do not capture common variables used in transport research, such as transport mode or trip origin/destination [31]. For the most part, urban transport services and modes are operated and planned separately, and studies at the city scale are scarce [34].…”