Urban transport networks are diverse and complex ecosystems.

In most cities, there are several different types of transport to choose from, such as metros, buses, trams and main line rail. In addition to this, the number of new 'on-demand' modes – from ride-hailing to e-scooters – increases every day. 

But what is missing is overall coordination of the transport offer. While public modes of mobility are actively supervised by their operators, real-time, city-level orchestration of transport is much harder to achieve. 

Intelligent multimodal supervision is vital to solving this problem – and will deliver many extra benefits as well.

The ability to centrally coordinate transport resources to be seamless is increasingly valuable. It holds the key to delivering low-carbon mobility with attractive public transport at the core, while stimulating economic growth.

Demand for urban transport grows in the Middle East

Along with this, coordination and centralisation of transport data also helps public transport authorities (PTAs) maintain their relationship with passengers in the face of competition from big tech players. 

Digitalising mobility

To meet the need for enhanced coordination of urban transport resources, transport authorities need to have a bird’s-eye view of the entire transport system of a city in real time.

An intelligent multimodal system would allow operators to monitor every aspect of the transport infrastructure. For example, it could provide insights into train occupancy, road congestion, travel times and delays. Big data tools could also make predictions and anticipate problems before they occur.

The rail industry in particular, is at the threshold of a data-driven revolution. Several disruptive transformation drivers are acting to create the right environment to develop a digital railway.

The ability to centrally coordinate transport resources to be seamless is increasingly valuable. It holds the key to delivering low-carbon mobility with attractive public transport at the core while stimulating economic growth.

The data-driven mobility approach taps into data that is already available, for example, data streams from existing metro, main line and tram control centres. Value can also be gained from real-time traffic information, weather reports, live video data and passenger density levels on trains and platforms, ticketing data and even anonymised mobile phone data.

In fact, there is no limit to the type of data that could be integrated into the data-driven control centre.

While connected and smart objects generate a new flood of digital information, big data analytics combined with artificial intelligence algorithms allow operators to filter information from noise and assist rail operators in decision-making based on facts.

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Cloud technology holds the key to the storage and processing of big data. The cloud environment makes it possible to deploy applications and insights simply on a shared infrastructure with the additional advantage of scalability.

Cybersecurity is the final - and possibly the most important - piece of the digital jigsaw. The huge amount of data generated by the digital railway to enable the optimisation of the network must be safeguarded. Securing all aspects of the system from cyber attacks and unsecure data transfers is more important than ever. 

These technologies and techniques are the building blocks of the digital railway. The confluence of these capabilities can be used to help rail operators to make better, data-driven decisions.