A government agency worked with MOSIMTEC to deal with people flow management challenges in its security screening facility. They used AnyLogic simulation software to improve efficiency, reduce costs, and enhance security, while at the same time removing the need for planned investment.
The launch of passenger trains on Moscow Ring Railway is expected to improve the road traffic situation and congestion on public transportation of the city. Many of the newly built stations will be connected to subway and commuter rails by huge transfer hubs. Passenger flows, at one of the future hubs, were simulated using AnyLogic software by the ITS Consulting Company for JSC Moscow Ring Railway.
The largest CIS rail car building facility, Tver Carriage Works, needed to choose the best passenger rail car concept for a city train. The plant needed to compare passenger entrance/exit times during peak loads for concepts that had different door amounts and widths, and then choose the best variant. They contracted ITS Consulting to perform this task. The consultants found pedestrian simulation modeling to be the best possible instrument to provide a solution.
NIIAS is specialized in railway traffic and infrastructure management. Using AnyLogic NIIAS modeled 5 railway stations for Adler – Alpika Service railway, which is being constructed for Sochi 2014 Winter Olympic Games.
When planning the subway renovations, the engineers wanted to be sure that the design solutions they chose would comply with current safety regulations and ridership. For instance, at Kiyevskaya station, one of the Moscow’s largest metro hubs, with ridership of 30,000 passengers an hour, the engineers decided to enlarge the existing antechamber to increase the passenger flow, replace the escalator belts, and add an extra escalator in the rundown corridor to the Arbat-Pokrovskaya line. To evaluate the project design, they contracted the specialists of the Institute for Development of...
The 2018 FIFA World Cup is in Russia, and in preparation for the event, the extensive rail network of the enormous country had to prepare for increased traffic. A railway operator in Russia, conducted an event preparedness assessment involving 31 railway stations across the eleven host cities of: Moscow, St. Petersburg, Kazan, Sochi, Saransk, Samara, Rostov-on-Don, Volgograd, Kaliningrad, Nizhny Novgorod, and Yekaterinburg. The project was contracted out to IRTS, a company specializing in the development of transport systems.
This case study explores how crowd management technology with real-time data, artificial intelligence (AI), and predictive analytics enhanced the efficiency, safety, and service reliability of urban transportation. An innovative example of a digital twin demonstrated how to improve metro station management and passenger flow.