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Getting out of a jam

New modeling and a targeted approach to relieve congestion.

Most congestion models look at flow
Most congestion models look at flow

Ludovic Leclercq, research director at IFSTTAR and professor of traffic flow theory at Lyon University, has a task on his hands that few would envy: developing new ways to deal with traffic congestion.


Lyon has the dubious honour of hosting one of the world’s longest recorded traffic jams. It was February 1980, and the Lyon-Paris auto route experienced a staggering 175 km tailback, as thousands ended their Alpine ski holidays and headed home towards the French capital, all on the same day.


Leclercq is among scores of scientists now applying their in-depth subject expertise to urban research projects funded by the European Research Council, one of Europe’s biggest frontier-research funders.


Most congestion models look at flow, treating the traffic as if it were water with vehicles and people as though they were indistinguishable molecules in the stream. “My idea is to focus more on trips, the individual trajectories,” Leclerq says.


He cites an MIT and UC Berkeley study that used cellphone tracking, to show that if you randomly eliminate 1 per cent of journeys, you reduce congestion by 3 per cent.


But if you’re smarter about it, cancelling or delaying trips by 1 per cent of drivers, but only in strategically chosen neighbourhoods, you can reduce congestion by a great deal more, eighteen per cent.


It’s common sense. If the route between the shops and the city hall is jammed, then the focus of traffic control should be on trips that take that route. But few cities have that kind of smart traffic management.


Leclercq hopes to speed things along, by finding general algorithms to determine which journeys cause the most congestion in a system. He cites the old 80-20 rule: focus on controlling the most troublesome 20 per cent of routes, to eliminate 80 per cent of the problem.


That’s a challenge as it’s hard to identify the type of travellers most clogging the roads, and harder still to get them to a) change their routes, or b) take public transport.


But with good data and modeling, traffic planners might “nudge” people to do the right thing for all.


Think about a traffic trade off for example, if you promise to take the boring bus to work on the days when you aren’t in a rush, then for the days when you have that can’t-be-late appointment, you will get priority in the transport system and zip ahead of others.


This may sound like wishful thinking now, but imagine how potent it could become when driverless vehicles enter the traffic mix, and their guidance systems connect to the city’s traffic controllers.


Adapted with permission from ’Wanted: The Human City’ Cities are getting bigger and smarter. Can we still live in them? published on ERC=Science².



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