Discover all of our resources available to support your sustainable urban mobility projects and urban shared mobility integration with public transport.
What SUM achieved: SUM delivered and published a scientifically robust method for dynamically steering mixed purpose on demand fleets combining passenger and parcel services. The method improves match…
What SUM achieved: SUM generated one of the clearest research to deployment transitions in the project. The demand and fleet availability forecasting logic developed in Work Package 2 was translated b…
What SUM achieved: SUM extended FleetPy to model the integration of ridepooling with fixed line public transport and used it in Jerusalem, Geneva and Munich. In these cases, FleetPy generated design e…
What SUM achieved: SUM extended and integrated MaaSSim, ExMAS and FleetPy based logic into a framework for ride pooling design, pricing and regulatory scenario analysis. Applied in the Rotterdam Hague…
Perceived_safety_choices is a GitHub repository, while Psafechoices is a python open-source package developed by NTUA. The package includes tools to investigate the overall impact of perceived safety…
What SUM achieved: SUM turned perceived safety from a soft qualitative concern into an exploitable planning asset. The tool produces safety informed routing and accessibility insights and was applied…
What SUM achieved: SUM advanced a bilevel optimisation framework for traveller centric pricing and curated trip recommendation in integrated PT and shared mobility. Using Geneva Living Lab data, the m…
We focus on the problem of defining optimal bike-sharing service regions that maximize demand coverage under the constraints of construction and operational budgets. Strategically located shared-bike…
In the case of modes like bike-sharing and scooter-sharing, fleet size decisions depend on rebalancing decisions. Smart and efficient fleet rebalancing algorithms can reduce not only operating costs,…
We consider the integration of ride-pooling fleets and public transport (PT) by developing a framework that restricts some ride-pooling vehicles to arrive at a PT station shortly before the arrival of…
We propose a preference-based optimization tool for multimodal trip planning. This tool, developed by TU Delft, integrates public transport, ride-pooling services, and shared micro-mobility options (e…
This tool assumes that fleet sizes for New Shared Modes are also subject to optimization. In this case, a full social welfare function is presented, in which users’ preferences are taken into ac…
Timetable synchronization seeks to optimize the timing of bus arrivals at transfer nodes, ensuring minimal waiting times for passengers transferring between routes. This is particularly important in m…
The SUM Simulation-Optimization Framework was developed by researchers from Tel Aviv University (TAU) and has been implemented in the Jerusalem Living Lab, in order to find promising New Shared Modes…
The dataset originates from a common revealed preferences survey conducted in 2023 across 9 European cities: Munich (Germany), Geneva (Switzerland), Jerusalem (Israel), Athens (Greece), Rotterdam (Net…
SUM Mobility Survey The objective of this survey is to investigate the travel preferences of the citizens of each Living Lab (LL). These travel preferences are collected before and after the implement…
Pre-built Excel template with formulas and data validation to help you organize raw data and calculate KPI values. The template includes examples and instructions. Relates to KPI SIEF Framework & Form…
This is the scientific baseline to understand each Key Performance Indicator. The document is a comprehensive documentation explaining each KPI, calculation methodologies, data requirements, and scien…
Sample survey questionnaire designed to collect citizen feedback and usage data for relevant KPIs. This survey should be adapted and translated for your city's specific context, according to the polic…
The information presented is based on data provided directly by the participating living labs. While the platform makes this data accessible and comparable, each living lab remains responsible for the accuracy, completeness, and interpretation of the data it reports.
The information presented is based on data provided directly by the participating living labs. While the platform makes this data accessible and comparable, each living lab remains responsible for the accuracy, completeness, and interpretation of the data it reports.