FIGURE 5. Heat maps used for taxi traffic management, Dubai, UAE26 Impact and takeaways The integration of the AI system within the taxi network has yielded remarkable results, notably culminating in a substantial 17% increase in taxi reservation efficiency and 40% reduction in unproductive mileage. Additionally, the system has significantly mitigated unproductive mileage by approximately 40%, consequently fostering a positive impact on both the environmental and economic landscapes. The streamlined implementation of the AI infrastructure has led to a 14% increase in the volume of bookings received, highlighting the system’s pivotal role in bolstering operational efficacy and service accessibility. 27 Dynamic heat maps have helped identify specific locations with high congestion and alleviate the pressure on the road network. These visual representations of real-time and historical data generated from the maps could play a vital role in aiding city planners and traffic management authorities to target areas where interventions are required to remove congestion. The use of heat maps has also facilitated optimized deployment of vehicles to high-demand areas, empowering drivers with a vital resource for passenger acquisition, while concurrently curbing fuel consumption and enabling reduced emissions. Additionally, precise demand indicators displayed on the heat maps result in improved customer experience by providing more information on journey times and helping to reduce waiting times for services. Overall, making use of cognitive technology solutions such as dynamic heat maps holds the potential to make shared mobility services a key means of reducing congestion and improving first- and last-mile journeys in cities. Area does not require more vehicles Demand is neutral Area requires more vehicles Combatting Congestion: How Cities and Companies are Innovating First- and Last-Mile Transport 11

Combatting Congestion 2025