SMATA

Taxi vehicles are particularly suitable for electrification due to their potentially long waiting periods at taxi stops. During their waiting time, electric taxis can be optimally charged. Currently, however, taxi drivers are not sufficiently aware of the availability of charging possibilities in urban areas.In addition, there is no platform that makes intelligent driving recommendations possible considering urban driving restrictions and battery capacities.

The efficient electrification of taxi fleets can only be achieved by networking all stakeholders in taxi and charging operations via such a platform. Through this networking, other value-added functions can be realized as well. Examples include, the computation of the optimal loading place and time, the billing of the charging process or the realization of intelligent driving mode recommendation for plug-in hybrid vehicles.

Goal

The study examines the feasibility of developing a smart platform for the data-driven networking of taxi and charging operations with the involvement of relevant stakeholders in the city area. The following sub-goals are to be achieved for this purpose: The identification and validation of the use cases and the functions to be mapped as well as the cost estimation and risk analysis for the realization of the platform.

Implementation

The work of the project is split into several work packages. The first work package defines the use of electric taxi traffic for the platform with clear system boundaries involving all stakeholders. The second work package defines the specifications for the platform to be developed. For this, the necessary data sets and possible interfaces for the scope of functions must be identified in a first step. In the third work package, the costs for implementation are estimated, risks for the project realization are identified and measures for risk reduction are identified. Finally, the findings are integrated into a basic platform specification.

Responsibilities

Within the project, the Networked Embedded Systems group is responsible for analyzing the data availability as well as identifying and defining the necessary interfaces to realize the platform functions. In addition, the group is analyzing GPS traces of several taxi vehicles in order to identify driving patterns and estimate basic parameters such as typical driving distances and waiting times.