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Das MobileCityGame ist ein ernsthaftes Spiel, das die urbane Mobilitätsplanung vereinfachen, sie für Laien zugänglich machen und frühzeitige Planung, Bürgerbeteiligung und Bildung unterstützen soll. Das von einem Team von Fraunhofer, dem KIT und der takomat GmbH entwickelte Spiel verfügt über ein systemdynamisches Modell und eine geografische Ausgabeschnittstelle, über die verschiedene Wege in Richtung Mobilität der Zukunft getestet werden können. Zu den Kernfunktionalitäten des Spiels gehören eine dynamische Modellstruktur, eine intuitive Benutzeroberfläche und Ergebnisse zu Klima, Lebensqualität und Finanzen. Die Maßnahmen des Spiels decken verschiedene Interventionskategorien, Modi und geografische Details ab, und die Nutzer können den Fortschritt von Modal Splits, CO2-Emissionen, Bewohnbarkeit und Finanzen während des Modelllaufs verfolgen. Die Szenarien des Spiels zeigen die Auswirkungen der Kombination von Maßnahmen und der Verzögerung von Maßnahmen, wobei die Ergebnisse zeigen, dass Timing wichtig ist und dass die Kombination von Push- und Pull-Maßnahmen die Lebensqualität und die Klimapunkte verbessern kann. Das MobileCityGame hat das Potenzial, ein allgemein akzeptiertes Instrument für die frühzeitige Planung von Plänen für nachhaltige urbane Mobilität (SUMPs) zu werden, Workshops, öffentliche Dialoge und Unterricht zu unterstützen.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
Strategic planning is of utmost importance for cities to achieve climate targets, while maintaining citizen satisfaction and meeting financial constraints. However, large assessment models are often costly and too complex for smaller cities. Thus, we designed a simplified, intuitive and dynamic transport simulation tool and serious game MobileCity to run on mobile devices for strategy processes, participation and teaching. A real transportation model running on local devices allows the free combination of different mobility interventions and shows dynamic indicators for climate, finances and livability, while offering an intuitive interface with supporting information. We currently extend the free iOS and Android demonstrator for Karlsruhe (Germany) and transfer it to European cities within the DUT project “CarGoNE-City”. In this paper, we present its core functionalities and features, before assessing scenarios and concluding on lessons learned and applications. Our main messages from applying the MobileCity-App are: the timing of interventions matters for all output indicators and a sound combination of push and pull measures helps meeting climate, livability and financial targets without going into extremes.
1 Motivation and Objectives
Cities and regions receive increasing pressure to meet climate targets by EU and national law [1, 2], while financial limits get tighter and differing expectations on good living standards impede policy decisions more and more. Well-developed planning tools like the SUMP methodology [3] or detailed transportation models are complex and expensive to run and partly hard to understand for non-experts or public administrations. At the same time, they miss out core policy indicators like citizens’ satisfaction, livability or communal finances.
In front of this point of departure, a team of Fraunhofer, KIT and takomat GmbH have contemplated the possibility of simplifying the core functions of larger transportation and sustainability models, make them dynamic, enrich them by information for non-experts and by additional outputs on social and financial issues and present them in an easily accessible form. The resulting tool should enable higher-level city planners to test and broadly assess various pathways towards future mobility without issuing expensive consultancy contracts in early stage planning. Second, the tool shall support public information and participation processes, and finally shall support teaching of future mobility and sustainability experts in universities.
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This aspiration led to the three-year research project MobileCityGame, co-funded by the German Ministry for Education and Science (BMBF). We created a freely available serious game for iOS and Android at the example of Karlsruhe (Germany, 300,000 inhabitants) with a simple but fully functional transportation and assessment model running on local devices and an intuitive user interface. In this paper, we present its core functionalities (Sect. 2) and features (Sect. 3) before assessing scenarios (Sect. 4) and concluding on lessons learned and applications (Sect. 5).
2 Method and Data - the Game Engine
2.1 The Model Structure
MobileCity follows the logic of a system dynamics model and a geographical output interface. Time steps for computation is one year from 2023 to 2050, but monthly steps allow starting or stopping measures more precisely. Modes considered are cars as driver and passenger, carshare, public transport by tram/light rail and bus, cycling and walking. Fleet models split car and transit into electric and fossil propulsion. Population by city district and age classes (0–19, 20–64 and ≥ 65 years) is assigned to trip purposes (education, work, errands and leisure). In the demonstrator for Karlsruhe, the model operates on the level of 27 city and 15 peripheral districts, which are linked by hyper-networks for car, transit and bike with main and district access links.
The model structures into seven computational and three output modules. Population (BEV), Regional Structure (REG), Infrastructure (INF) and Behavior (SOZ) determine Mode Choice (MOD), which communicates with Technology (TEC). Energy (ENG), INF and SOZ again drive TEC. These modules and their sub-modules communicate dynamically via stock and flow variables, and finally deliver data for the three assessment modules Climate (ENV), Livability (LEQ) and Finances (FIN) (see Fig. 1).
The modules are defined and calibrated using local and national statistics in combination with scenario runs of the agent-based model mobiTopp of KIT [4]. The system dynamics models ASTRA and ALADIN developed by Fraunhofer and partners [5, 6] provided further data in the fields of long-term economics, sustainability and vehicle fleets. A literature review on impact mechanisms accompanied these [e.g. 7, 8]. Results were discussed with the Karlsruhe city administration and in three stakeholder workshops in the Period January to June 2023.
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2.2 User Interface and Outputs
The results of MobileCity are of a didactic nature rather than providing exact values for indicators from now to 2050. Nevertheless, the three groups of output indicators are computed with data from the analytical modules and are assessed by reviews of impacts e.g. on emission factors (modules ENV and LEQ) and cost rates (module FIN). As MobileCity is a serious game, a scoring system was developed, summing up the annual performance in the three impact categories. This allowed e.g. to judge not only meeting the climate target in 2050, but also emission budgets on the way. Table 1 provides an overview of impact categories, sub-indicators and scoring principles.
Range around a linear reduction path towards climate neutrality in 2045
Emissions per main mode of transport
LEQ
Social benefits (€ p.c.)
Range from zero to double 2023 social benefits with progressing scores towards the extreme ends.
Mobility costs, accessibility, transit quality, air pollution, noise, safety, acceptability
FIN
Annual balance (mill. €)
Range from + 50 to -10 mill. € annual balance, linear
Sub-accounts for car, bike, transit and urban development; details by budget, expense and revenue types
To quantify the acceptability of measures among the population a representative survey on the 25 largest German cities with 2660 responses was launched in summer 2023 [7]. Along the four measures Parking Fees of 20€, construction of Cycling Highways, Reallocation of Street Space and Free Public Transport people were asked to rate their consent before introduction with little information and after completion with full details including before-after-illustrations. Each measure in the MobileCity-App was labeled with acceptance values between −50 (complete opposition) to + 50 (full consent).
3 Features and Measures
In MobileCity, the player takes the role of the all-mighty mayor. As we look into pathways towards 2050, we deliberately ignore details on administrative, legal or technical processes. Measures are described by planning/building times, costs, acceptability and detailed impacts to individual district, hypernet or other parameters. The current demo version of MobileCity has 11 individual measures built-in, which cover various intervention categories, modes and geographical details, and which were discussed with Karlsruhe city authorities (Table 2). For real world applications this selections shall give an impression on MobileCity’s capabilities.
Table 2.
Measures in the current MobileCity demo version.
Category
Mode
Measure
Description
Building
Bike
Cycle highways
6 pre-defined investment projects
Car
E-charging stations
Setting new stations by district & year
Transit
Subway tunnel
Completed 2021: project in city center
Transit
E-buses
Fix 2030: replace diesel buses by e-buses
Pricing
Car
Parking fees
4 schemes, varying extents and e-car rebates
Transit
PT Tariff reforms
4 levels, monthly pass & single ticket price
Regulation
Car
Speed limits
3 levels, 30 km/h incl. / excl. Federal roads
Bike/Walk
Road redesign
Inner city/suburbs, 30%/70% removed parking
Incentives
Carshare
E-car vouchers
Acceptance through free rides via carshare
Car
Street labs for e-cars
Acceptance through showcase projects
Some, but not all of the measures are displayed geographically by respective layers (e.g. e-charging stations or parking fees). Further layers show travel times by mode between districts, population and CO2-emissions per capita. The progress of modal splits, CO2-Emissions. Livability and finances can be tracked during the model run via detailed graphs. After a model run, taking between 15 and 20 min, a final report is generated and can be shared with others.
4 Scenarios and Results
With the MobileCity App the effect of combining measures to bundles and of delaying action can be assessed quickly. To demonstrate this we have defined six bundles, each starting early (2024) or late (2030) plus the reference case. The bundles are:
Reference: only automated actions, i.e. electric charging station provision 50% compared to Norway, market entry of e-cars and full bus electrification in 2030.
Electrification: maximum of 130% charging stations each year, free parking, incentive programs for e-car use like vouchers for free rides and street labs.
Cycling city: all cycling highways, street redesign (30% less parking, all districts).
Parking & speed: parking fees of 10€ in the inner city with 50% rebate for e-cars, 75% of former free parking is charged plus 30 km/h speed limit on all streets.
Transit tariffs moderate: monthly pass 29€ with reductions in single ticket prices.
Climate package: bundles Cycling city, Parking & speed and PT tariffs combined. Emergency break: Cycling city, 70% of public parking removed, 20€ fees in center for all parking spaces and all cars, 9€ monthly PT pass; 130% e-charging-stations.
For the 13 variants, Fig. 3 shows the cumulated scores 2023–2050 and the climate emissions per capita 2050. Already in the reference case, the likely checkless market uptake of e-cars reduce GHG emissions 2023–2050 by 83% from 0.75 to 0.13 t CO2 p.c. Achieving further reductions is difficult, except for push measures like high parking fees. But: timing matters, as is indicated by the higher climate scores for bundles introduced early. Climate neutrality, however, fails in all cases as measures to curb grid emissions are missing. Livability and climate scores go hand in hand if push and pull measures are combined. Finally, high parking fees can re-fund practically free transit.
Figure 2 shows the modal splits at trips in 2050. According to the currently implemented model and measures, modal splits even towards 2050 appear to be resistant to major changes. Even the harsh measures under Emergency Break only manage car modal share to decline from 48% to 32%. Transit here evolves from 18% to 29%. Measures for expanding transit capacity, however, are missing in the current MobileCity version. Cycling and walking under these conditions rise form 35% to 41% while carshare does not play a significant role in either bundle or variant.
5 Conclusions and Discussion
The MobileCity-App allows users without any modelling experiences or analytical skills to play with the combination, intensity and timing of commonly discussed interventions for the mobility transition. Dynamic and detailed outputs for the numerous sub-indicators shall fuel discussions among policy-makers, citizens and students. Real-life tests of these potential application areas are part of the follow-up project CarGoNE-City. An extended App supporting more modes (including freight), more measures and a channel to communicate with users will be applied in participation processes.
With a number of additional functions and features added, we see the potential of MobileCity to emerge to a commonly accepted early-stage planning tool for preparing SUMPs, for workshops, public dialogues and teaching. Now we enter the stage of practice proof in particular for the diverse European cities. Features in preparation include more measures, urban logistics, shared and automated mobility services, user communication options and the largely automated transfer to other cities.
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