Intelligent Mobility – the New Way to Travel
Intelligent mobility is the future of transportation. This is enabled by digital communication via intelligent interfaces. Traffic crosslinks on all levels and the possibility to combine different transport systems with one another creates mobility as a service. The primary focus is the customer’s transport request not the transport vehicle.
Efficient Traffic Control Systems Using ICTS
The central computer systems of the different modes of transportation are interconnected through an Intermodal Transport Control System (ICTS). This increasingly communicative exchange of data and information allows for more efficient traffic control systems. These optimize the entire flow of traffic using remote traffic control and interactive traffic management. Only the effective interplay of different modes of travel makes this new mobility appeal to the passenger.
Find out more about the use of ITCS here.
New Mobility: E-Bike Sharing and Carsharing
Innovative mobility solutions such as E-Bike sharing, carsharing or E-Scooters make for a travel experience meeting modern requirements by combining different modes of transportation. The goal is not only efficient transport, but also sustainability. Traffic should conserve resources and be environmentally friendly.
This is the starting point of a sharing economy: The individuals no longer buy their own E-Bikes or cars, but rather share them according to need. This reduces long periods of downtime as well as the demand for parking space. Apps for peer-to-peer ridesharing facilitate carpooling and thus reduce the per capita environmental impact of road traffic. In the future, even autonomously driving cars may be integrated into the intermodal passenger services.
Find out more about innovative mobility solutions here.
Data Analytics for Traffic Systems Optimization
The basis of this innovation are modern IT systems and their communication and data technology. Firstly an interactive traffic management requires a steady exchange amongst the traffic participants, but secondly also an appropriate traffic design.
In order to identify strengths and weaknesses of traffic systems, experts today use data analytics for traffic. They employ techniques from artificial intelligence (AI), big data and data mining. Traffic with its multitude of different participants supplies proportionately large and ever growing amounts of data.
This data is saved as big data and made available in specialized datacenters. Utilizing AI the passengers can then be navigated according to the current traffic situation or their personal preferences. Data mining by contrast strives to gain new insights from the collected data.
Find out more about Data Analytics here.
Intelligent traffic management thanks to ITCS
Traffic management is the foundation of intelligent traffic. A central traffic control coordinates and optimizes traffic flow using computer-aided automatic vehicle location (AVL). To that end the system collects comprehensive traffic data to analyze the central traffic guidance. This data allows for an interactive traffic management. According to the different transport routes the different traffic guidance systems have to be made compatible. They comprise
- road traffic management,
- rail traffic management,
- air traffic management,
- ship traffic management.
Live Data Collected via ITCS
The computer-aided automatic vehicle location (AVL) was initially a self-contained system. Within a transport company the system would, for instance, take care of vehicle tracking and served as navigation system. In order to achieve efficient distribution of traffic over the different modes of travel, today’s automatic vehicle location systems are connected. Hence one no longer speaks of AVL, but rather of Intermodal Transport Control Systems (ITCS).
The connected communication of these systems provides the transport companies and traffic planners with entirely new application scenarios: They can exchange live traffic data. In case of a large event for instance, a special train and special busses for passenger transport can be planned. If the train operator shares his traffic data with the bus operator, the latter can coordinate his vehicles in accordance with the train vehicle tracking. In that fashion an excessive occupation of the station’s bus stop can be minimized.
Traffic Data Collection Enables Alternative City Navigation
With modern vehicle communication systems, Intermodal Transport Control Systems (ITCS) also enable a remote traffic control. The following scenario is possible: If traffic data collection reports an accident on a frequented bus route, the system can react with the provision of an alternative route. The ITCS then ensures that the traffic light phasing on the devised alternative route is adjusted for the increased traffic. Efficient control of the traffic light phasing improves the traffic flow, for instance by ensuring green phases along the route. The control of traffic lights is used for
- road traffic at junctions and T-junctions,
- and thereby also separately for public busses and tramways,
- bike traffic,
- pedestrian traffic,
- rail traffic.
Traffic security is absolutely essential in all applications. For subway rail traffic the ITCS determines for instance, the clearance of track sections and hence also coordinates the safety distance between the individual trains. This requires an absolutely reliable vehicle tracking. When optimizing the operating procedure, complying with safety requirements is always the highest priority.
Innovative Mobility Solutions
The increasing focus on sustainable traffic requires innovative mobility solutions. Autonomous vehicles, mobility as a service or sharing economy for vehicles determine the future of traffic demand management which adapts modes of transportation to the needs of the customer.
Peer-to-peer Ridesharing Apps for Sustainable Traffic
The common models of locomotion will be outdated in the near future. Of this traffic experts are convinced. The younger generation and more and more ecologically aware traffic participants feel that the concept of “one person alone in their own car” is no longer appropriate. Using peer-to-peer travel sharing systems, such as peer-to-peer ridesharing apps, vehicles can be filled up quickly with the positive effect of reducing per capita environmental burden. Even more lasting, in terms of environmental resources, is public local and long-distance travel.
The Future Belongs to Intermodal Passenger Services
The new understanding of mobility as a service is crucial. Not the possession of a means of transport is central, but rather the journey from start to finish is considered individually. To achieve this multimodal traffic is employed, that is the combined utilization of different modes of transportation. The intermodal passenger service combines the single modes in such a fashion that the passengers arrive at their destination comfortably and quickly. Suitable for combinations are for instance,
- local public transport,
- long-distance public travel,
- bike-sharing schemes,
- carsharing or car rental,
- electronic mobility.
Passengers who start from rural areas into a city are offered park and ride facilities to switch into local public transport. Destinations within the city limits can be reached by local public transport and ensuing bike-sharing products such as City Bikes.
Autonomous Cars as Part of Public Traffic
Innovative electronic mobility with products such as electronic bike-sharing or E-Scooters open these sustainable intermodal concepts up for people of weaker physical constitution. Also for couriers in areas of high population density, electronically motorized freight bicycles are the foundation of sustainable transportation.
Autonomous traffic promises to put the driverless mode of transportation into focus. Some subways, such as the metro in Copenhagen, are already operating without drivers and are fully automatic. If this concept can be realized on the roads in terms of an autonomous car, it is natural to consider integrating the automobile as a service into the intermodal passenger services. The individual traffic participant uses this vehicle together with other passengers who share the same destination – searching for a parking spot is not required since the vehicle will usually be requested for another journey via mobile app.
Data Analytics – Basis for Safe Mobility
Data analytics offers great potential to systematically analyze processes and procedures of transport companies. In the context of business intelligence, data is collected, evaluated and outlined. Results obtained from the past serve as a basis for future decisions in transportation.
Modern transport planners design visions of a mobile future using business analytics. They are concerned with potential future developments, ask themselves “What will be?” and search for ways to realize these goals. This also includes measures required to guarantee well-functioning passenger transportation. Using suitable models for instance, they can predict expected mechanical fatigue of vehicles and plan investments based on this information. The central concepts for data analytics in traffic are
- artificial intelligence (AI),
- big data,
- data mining.
Artificial intelligence is firstly concerned with the automation of intelligent behavior and, secondly with machine learning. A generally accepted definition already fails at the indefinite term intelligence. However, one can say that artificial intelligence seeks to realize objectives with machines, at which, at the moment, humans are still better.
Big Data – Optimizing Traffic Using Data
In today’s traffic, data is already used to navigate traffic participants on routes which not only realize the shortest paths, but also take into consideration the current traffic situation. Machine learning (artificial intelligence) can, provided suitable data exists, predict congestions at an early stage. In this way a traffic company can take appropriate countermeasures and maintain traffic flow, even before the apparent delays occur.
The concept of big data consists of the three Vs:
These involve the storage of a great deal of different data which arises in such a high frequency, that a human could not possibly comprehend them in a useful fashion.
Data Mining – Analyzing Correlations and Trends
Due to the multitude of single actors – from passenger and staff to vehicles – there is big data for traffic. Ticketing alone, which issues and validates electronic tickets, generates in combination with customer payment information a considerable flood of data. In addition there is operating data and vehicle tracking, as well as video recordings from surveillance cameras. Some traffic companies, such as the Verkehrsverbund Rhein-Ruhr, provide some of this data via an open data portal for public use free of charge.
Data mining builds on the big data foundation: Special statistical methods enable the identification of correlations within the data, which in turn allow the recognition of trends. This is the vitally important step in analysis which allows new insights to be derived from existing data.