# Data4UrbanMobility: Towards Holistic Data Analytics for Mobility Applications in Urban Regions

## TL;DR

This paper introduces the Data4UrbanMobility platform and tools for integrated urban mobility data analytics, aiming to support holistic understanding and planning of multi-modal transportation in cities.

## Contribution

It presents a novel platform and a citizen science app for integrating diverse mobility data sources and analyzing intermodal journeys in urban environments.

## Key findings

- Development of the D4UM platform for holistic data analytics
- Introduction of the MiC app for citizen-driven intermodal mobility data collection
- Demonstration of use cases showing potential for improved urban mobility insights

## Abstract

With the increasing availability of mobility-related data, such as GPS-traces, Web queries and climate conditions, there is a growing demand to utilize this data to better understand and support urban mobility needs. However, data available from the individual actors, such as providers of information, navigation and transportation systems, is mostly restricted to isolated mobility modes, whereas holistic data analytics over integrated data sources is not sufficiently supported. In this paper we present our ongoing research in the context of holistic data analytics to support urban mobility applications in the Data4UrbanMobility (D4UM) project. First, we discuss challenges in urban mobility analytics and present the D4UM platform we are currently developing to facilitate holistic urban data analytics over integrated heterogeneous data sources along with the available data sources. Second, we present the MiC app - a tool we developed to complement available datasets with intermodal mobility data (i.e. data about journeys that involve more than one mode of mobility) using a citizen science approach. Finally, we present selected use cases and discuss our future work.