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Kuala Lumpur Smart Mobility: A Case Study of Malaysia City Brain and ITS Deployment
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Kuala Lumpur’s smart mobility program anchored by Malaysia City Brain, the Integrated Transport Information System (ITIS), and the SMART Tunnel shows measurable gains in operational performance. Using Leong’s ITS framework, we synthesize government reports and technical notes to assess four dimensions: data collection, connectivity, intelligent analytics, and responsiveness. After deployment, average commute time decreased from 35 to 27 minutes (−22.9%), the congestion index improved from 0.76 to 0.52 (−31.6%), and emergency response time shortened from 18 to 11 minutes (−38.9%). Comparative reading against Hangzhou and Singapore suggests that Kuala Lumpur’s hybrid governance (DBKL–MDEC–MIMOS) blends rapid tech deployment with risk-aware operations, particularly flood control via the SMART Tunnel. This study is limited by its qualitative, secondary-data design; nevertheless, it clarifies how institutional arrangements condition the payoffs of AI-enabled traffic management and offers policy cues for cities with similar constraints.
Title: Kuala Lumpur Smart Mobility: A Case Study of Malaysia City Brain and ITS Deployment
Description:
Kuala Lumpur’s smart mobility program anchored by Malaysia City Brain, the Integrated Transport Information System (ITIS), and the SMART Tunnel shows measurable gains in operational performance.
Using Leong’s ITS framework, we synthesize government reports and technical notes to assess four dimensions: data collection, connectivity, intelligent analytics, and responsiveness.
After deployment, average commute time decreased from 35 to 27 minutes (−22.
9%), the congestion index improved from 0.
76 to 0.
52 (−31.
6%), and emergency response time shortened from 18 to 11 minutes (−38.
9%).
Comparative reading against Hangzhou and Singapore suggests that Kuala Lumpur’s hybrid governance (DBKL–MDEC–MIMOS) blends rapid tech deployment with risk-aware operations, particularly flood control via the SMART Tunnel.
This study is limited by its qualitative, secondary-data design; nevertheless, it clarifies how institutional arrangements condition the payoffs of AI-enabled traffic management and offers policy cues for cities with similar constraints.
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