"Everything flows." If the Greek philosopher Heraclitus had experienced the road traffic of the 21st century as a motorist, he might have had doubts about his teachings. And yet our time also confirms his findings. Because it is never the same traffic jam with the same vehicles when we are stuck at traffic lights and on highways in the morning on our way to work or in after-work traffic. What's more, there are now plenty of data streams flowing parallel to the waves of vehicles during rush hour. Telematics and satellite systems record the movement of vehicles and pedestrians, given they are equipped with the appropriate technology or at least carry a cell phone. They provide precise information on the current traffic situation. In this way, they also give drivers the opportunity to select alternative routes, avoid traffic jams and reach their destination sooner. That is how these systems have a positive influence on individual traffic. However, that is still a long way from the utopia or the long-term goal of intelligent control. Just because there is no networking connection between road users, nor is it possible to exert a targeted influence. Important prerequisites for planning with a high probability of success are therefore missing.

Basis question

What is the best way to manage traffic and logistics?

Logistics service providers and freight forwarders generate their revenues on the roads. Because that's where they must keep their performance promises. They are therefore dependent on smooth progress if they want to be successful. If delays occur, their profits are quickly jeopardized. That's why service providers have long been optimizing all processes within their control and have made great strides in digitalization. They know where their trucks are via vehicle telematics and mobile apps. In conjunction with order data, they can use smart solutions to calculate when the truck is expected to arrive at the recipient or loading point (ETA), even on complex routes with multiple stops. Sophisticated systems do not only assess the current traffic situation but also the drivers' work and break schedule. This allows planners and dispatchers to design their own processes so that trucks start their tours as punctually as possible. However, currently they do not have any means of influencing the rest of the route.
The situation is only marginally different for traffic planners in government agencies at cities, municipalities, states, and the federal government. For the most part, they can only observe traffic – apart from traffic systems that regulate traffic on highways and in conurbations around traffic hubs. But they are not networked with road users either, and beyond electronically controlled traffic signs, they do not have the ability to communicate with vehicles.

So both lack crucial information that would need to be captured decentrally and shared in real time as soon as it becomes available – ideally already at the planning level:

• When will a vehicle be on the road?
• To what destination is it headed and when does it plan to arrive?
• What route will it take there?
• At what times will the driver take a break?

This information allows to calculate the capacity utilization of traffic routes and increase their efficiency. Assuming appropriate communication capabilities, drivers and truckers would then have the option of choosing alternative routes before heading in waves toward traffic junctions that can no longer handle such high traffic density.

Big Data

Data evaluation allows precise traffic control

To enable control, planners must have the most comprehensive knowledge possible and bring together many data sources. That’s the only way to see whether the desired goal can actually be achieved. Logisticians are already beginning to evaluate historical traffic data with artificial intelligence (AI) and incorporate the findings into their planning. In doing so, they benefit from the fact that mobile apps enable them to precisely log the actual route, speed, travel and idle times on their tours. Thus, they acquire a data resource carrying enormous potential for machine learning (ML). The mere analysis of this data enables them to plan future tours with a higher probability of success. However, the demand of a genuine and intelligent traffic control can only be met if as many data sources as possible can be evaluated for the calculations. Then it will not remain utopian to advise a driver that he had better leave twenty minutes earlier or - if that is not possible - to wait 37 minutes longer on the spot. Based upon the vehicles' position only, today's traffic information systems could not give such an advice.

Networking

Central data sources enable active traffic control

Currently, the limits of traffic control for freight forwarders and logistics service providers are within their own company and for private motorists within their own vehicle. What is annoying outside the business environment becomes an expensive cost risk for service providers. After all, delays at the very least squeeze margins or take away any revenue. If contractual penalties become due, the forwarder may even have to pay extra in the worst case. A situation that could only be improved with new data sources – which could be fed from existing technology. After all, planning systems in logistics and production know the transport orders, routes and destination times. During tours, vehicles also continuously report their position to a central system. Even private individuals are increasingly using their smartphones for navigation. If all the data collected in this way were to be anonymized and pooled in a central database, this would create a real lever for active traffic control, relieving city centers of traffic jams. And finally, the improved traffic flow would also reduce CO2 emissions and thus ease the burden on the climate.

It would then be possible not only to include the current vehicle position in the route suggestions, but also the further course of the journey. The vision of having the optimum time for a planned trip suggested on the basis of the results of a data analysis would no longer be a utopian dream. The prerequisites for this are the courage to network and the ability to communicate. Then it will be possible to direct traffic flows, relieve environmental zones, increase traffic safety and achieve a sustainable reduction in the emissions that cause climate change. The fact that the effects of networking are impressive has long been demonstrated by the example of Ingolstadt in Upper Bavaria. In the Travolution research project of the Technical University (TU) of Munich, the city's traffic lights have been networked since 2008 and their red phases are geared to ensuring that traffic flows as smoothly as possible. With success: on a daily average, stops at the traffic signals are reduced by 17 percent and time losses by as much as 21 percent. Extrapolated, this saves around 700,000 liters of fuel and CO2 emissions of 1,600 metric tons per year and avoids economic follow-up costs of delays amounting to one million euros.

Conclusion

Traffic control needs up-to-date data – but not new technology

The research project in Ingolstadt shows that even small advances in networking can have a major impact on traffic control. For drivers and logistics, this in turn means a high reward for having the courage to roll along the roads networked rather than as individual vehicles: Less congestion and fuel consumption, lower costs and, not least, significantly less stress. All this without a major technological revolution. Mobile apps already collect the important data anyway. All that's missing is the crucial point where they can be anonymously bundled, networked and compared with each other. Extrapolated from the Ingolstadt figures, impressive effects can then be achieved for all concerned. It is therefore to be hoped that the insights gained over many years will soon be followed by determined action. So that Heraclitus’ lesson applies more and more often even in 21st century road traffic and everything flows.