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).
This paper presents a method for studying supply and demand in a taxi network in time and space by using the example of Munich. First, we introduce the necessary data collection that is linked to a fleet management system (FMS) operated by a local taxi agency and create a statistically sound database, which represents the mobility behavior on a trip level. Second, we derive key figures describing the city's taxi characteristics. Here both the temporal taxi supply and demand of 420 taxis over a period of 19 weeks is considered. As the taxi demand differs according to the city district, the investigated area has to be divided into various zones. We analyze their specific characteristics and describe key factors influencing taxi requests, such as weekdays, public holidays and number of point of interests within an area. Next, a model to predict a time-variant demand for individual districts is introduced. We classify the problem and choose an appropriate algorithm to forecast spatiotemporal taxi requests. As booking actions differ over time, a nonhomogeneous Poisson distribution is suitable for counting those events. In a final step, we validate the proposed model on a local and global scale. Our model helps to provide a better understanding of taxi fleet operations on a city scale. We use the suggested demand prediction as one input parameter for an agentbased fleet simulation that represents individual taxi movements.
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