Paper Number
ECIS2025-1551
Paper Type
CRP
Abstract
Increased traffic burdens cities with congestion, pollution, and noise. Smart mobility (SM) applications offer solutions by combining real-time traffic data with simulation-based forecasting to influence commuter behavior and optimize infrastructure use. While prior research has developed traffic simulation methods or assessed SM applications at a high level, detailed implementation guidelines and practical evaluations remain scarce. To address this, we benchmark traffic simulations, showing they scale near-linearly, are cost-effective, and require minimal computational resources. These features enable the creation of extensive scenario libraries for diverse urban contexts. Additionally, we compare traffic simulations with artificial neural network (ANN)-based forecasting, highlighting complementary strengths. Our results underline the potential of combining simulations and ANNs into integrative SM systems, offering potential insights for cities aiming to implement solutions for e-mobility, congestion reduction, and sustainable urban planning.
Recommended Citation
Siggel, Marc; Ebner, Katharina; and Anschütz, Christian, "In the Flow: Traffic Simulations in the Context of Smart Mobility Applications" (2025). ECIS 2025 Proceedings. 7.
https://aisel.aisnet.org/ecis2025/smart_gov/smart_gov/7
In the Flow: Traffic Simulations in the Context of Smart Mobility Applications
Increased traffic burdens cities with congestion, pollution, and noise. Smart mobility (SM) applications offer solutions by combining real-time traffic data with simulation-based forecasting to influence commuter behavior and optimize infrastructure use. While prior research has developed traffic simulation methods or assessed SM applications at a high level, detailed implementation guidelines and practical evaluations remain scarce. To address this, we benchmark traffic simulations, showing they scale near-linearly, are cost-effective, and require minimal computational resources. These features enable the creation of extensive scenario libraries for diverse urban contexts. Additionally, we compare traffic simulations with artificial neural network (ANN)-based forecasting, highlighting complementary strengths. Our results underline the potential of combining simulations and ANNs into integrative SM systems, offering potential insights for cities aiming to implement solutions for e-mobility, congestion reduction, and sustainable urban planning.
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