Multi-Agent Simulation of a Demand-Responsive Transit System Operated by Autonomous Vehicles

Abstract

With the ongoing urbanization and continuous growth of the world's megacities, the demand for mobility increases. Urban transportation is becoming a significant challenge for the future. In order to reduce road congestion and commuting times as well as improve customer service, the focus is shifting toward new technologies and mobility concepts (1). Autonomous electric vehicles will transport customers efficiently and effectively in a connected environment (2). To further reduce the number of vehicles, car-and ridesharing approaches are being pursued, and mobility is becoming more and more of a service than an individual good.While traditional public transit typically has fixed routes and schedules, demand-responsive transit (DRT) operates more flexibly (3). Schedules in a dial-a-ride application result from immediate or advance bookings (4). Route planning is done dynamically based on given requests. Until now, DRT has been focused on areas with low demand where fixed-route and fixed-schedule bus services face profitability issues. Common applications offer specialized transportation services for customers, feeder services to fixed-route systems, and flexible-route segment or route-deviation services (5). In the United States, DRT systems are mostly used as a paratransit service for people with limited mobility (6). Origin-destination patterns vary between many-to-many, many-to-few, many-toone, few-to-many, and few-to-few relationships (5).

Shared autonomous vehicle services: A comprehensive review

“…Dia & Javanshour (2017) assume that 25% of the conventional private car demand will be served by private autonomous vehicle and the remaining 75% by shared autonomous vehicles. Jäger et al (2018) consider replacing existing bus system and Shen et al (2018) consider replacing bus system in low-demand routes.…”

“…In the simple case, fixed travel times between nodes are assumed. Fixed travel time commonly found in the literature is 1) the free flow travel time multiplied by a factor to represent congestion (Jäger et al, 2018), 2) average travel time of off-peak and peak hour Fagnant & Kockelman, 2014;Zhang et al, 2015) and 3) an hour-based value, either extracted from Google Maps (Bauer et al, 2018) or obtained from the transport department of the city where the analysis is carried out (Kondor et al, 2018). Ma et al (2017) uses the travel time mentioned in the taxi dataset along with a correction factor, when designing a reservation-based system.…”

“…Results related to energy consumption are mainly connected to the anticipated use of Electric Vehicles (EV) in SAV services, and show a decrease in consumption (Arbib & Seba, 2017;Bauer et al, 2018;Fagnant & Kockelman, 2018;Fulton et al, 2017;Jäger et al, 2018;Moorthy et al, 2017;Ross & Guhathakurta, 2017). Bauer et al (2018) state that, unless there is a dramatic drop in oil prices or conventional car prices, electric vehicles will become cheaper.…”

Optimizing Centralized Dispatching of Flexible Feeder Transit considering Transfer Coordination with Regular Public Transit

“…Constraint (13) guarantees that each demand is satisfied by a single bus run serial number of the feeder transit to transfer to one bus run serial number of an express line. Constraint (14) guarantees that each bus run serial number of the feeder transit corresponds to a single bus run serial number of an express line (M is a large positive number). Constraint (15) guarantees that the difference value between the actual transfer time and expected transfer time is within a limit.…”

“…Moreover, Molenbruch et al [9] and Oscar et al [10] studied DART services for special travel demands, e.g., medical users with wheelchairs. Problems related to actual dispatching and new technology have been analyzed and considered in many studies, e.g., the time-varying speed of vehicles on the road network (Schilde et al [11], Wei et al [12]), flexing service schedules for demand-adaptive hybrid transit (Frei et al [13]), autonomous vehicles (Jager et al [14]), and roundtrip car-sharing systems (Jorge et al [15]).…”