Abstract
Traditional and innovative on-demand transport services, such as taxi, car sharing or dial-a-ride respectively, can provide a level of flexibility to the public transport with the aim to guarantee a better service and to reduce the exploitation costs. In this context, in order to point out the key-factors of on-demand services, this study focuses on traditional on-demand service (such as taxi one), and presents the results of a demand analysis and modelling, obtained processing taxi floating car data (FCD) available for the city of Rome. The GPS position of each taxi is logged every few seconds and it was possible to build a monthly database of historical GPS traces through around 27 thousands of GPS positions recorded per day (more than 750 thousands for the entire month). Further, the patterns of within-day and day-to-day service demand are investigated, considering the origin, the destination and other characteristics of the trips (e.g. travel time). The time-based requests for taxi service were obtained and used to analyse the trip distribution in space and on time. These analyses allow us to forecast trips generated/attracted by each zone within the cities according to land use characteristics and time slices. Therefore, a regression tree analysis was developed and different regressive model specifications with different set of attributes (e.g. number of subway stations, number of zonal employees, population) were tested in order to assess their contribution on describing such a service use.
Highlights
Inner urban areas are the ones that suffer more than others the negative impacts on high volume of private traffic, such as, among others, congestion, air pollution, and health risks
On-demand services significantly can contribute to reduce traffic congestion, energy consumption, and air pollution thanks the opportunity offered in improving the vehicle use and the optimization of vehicleskms
Limited research efforts have been implemented on forecasting such a demand, in most recent years mainly pushed by the real-world data unavailability, the studies on the taxi service can provide valuable insights since there exist strong similarities between the taxi and the on-demand services (Moreira-Matias et al 2013 a and b; Nuzzolo et al, 2019)
Summary
Inner urban areas are the ones that suffer more than others the negative impacts on high volume of private traffic, such as, among others, congestion, air pollution, and health risks. The first stage of the analysis is addressed to define the taxi status (i.e. driving with a customer, driving to a customer, at parking) analysing its movements in the space and on time This allows us to define time-dependent origin-destination trips (with the relative travel time and distance attributes) when customers are on board providing information for developing methods and models for forecasting on-demand service use. In relation to the taxi operation modelling, Wong et al (2001) set up a two-level model: the upper level simulates the relation between taxi and customer waiting times and the relation between customer demand and taxi supply; the lower level problem is a combined network equilibrium model (that describes simultaneous movements of vacant and occupied taxis and of the normal traffic) An extension of this model, considering multiple classes of users and taxi services, is reported in Wong et al (2008) where the mileage-based and congestion-based taxi fare charging mechanisms is modelled in a unified framework. A taxi is considered to be in a stand when two conditions happen: 1) the recorded vehicle position is close to a taxi stands in the city (in Rome, there are more than 100 taxi stands) and 2) the value of the waiting time is more than 2 minutes
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