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Three years ago, Planung Transport Verkehr AG (PTV), a German developer of transportation engineering simulation software, released a series of simulation experiment videos demonstrating how different modes of transit deal with the same number of people in various hypothetical situations. Normally used by cities and regions to plan their transportation needs, PTV’s experimental videos demonstrate interesting considerations of transit utilization rates, traffic throughput, and space considerations across their various test environments. It’s worth examining these videos and their results from a planner’s perspective to see how they compare, but care must also be taken to consider what isn’t shown in the simulations.

The first simulation has each of the five modes of transit, cars, buses, trams, bicycles, and walking, in a single lane of traffic at typical occupancy rates per vehicle, with the objective to have all 200 people cross the starting line from a standstill. We can see early on that, even with a rather generous assumption of 1.5 people per vehicle, cars are at a considerable disadvantage not only in how long they take to cross the line, but also how much more space they take up in the queue. Pedestrians predictably do very well; their ability to all start so close to the line and high density of throughput means they clock in only slightly slower than the mass transit options, something that, combined with the low cost of quality pedestrian infrastructure compared to other transit modes, goes a long way towards explaining the strength of pedestrianism in bolstering downtown areas by virtue of sheer capacity. It’s interesting as well, how at the given utilization rates, buses actually beat trams across the line by a small amount. Given the high cost of rail lines, it would suggest that the typical light rail system needs to serve considerably more people than a bus route in order for the return on investment to be competitive.

A simulation presents a slightly different scenario, by demonstrating how wide each mode’s pathways have to be in order for them to cross at the same time. Again, cars perform very poorly here, requiring nearly eight times as wide a road as buses or trams. What sticks out about this comparison, however, is that cycling doesn’t perform particularly well either, requiring about four times more space than pedestrians, and five times more than the mass transit options. Cycling still offers considerable space advantages relative to cars, but their still-considerable space requirements suggest that caution needs to be taken to ensure that cycling infrastructure is properly planned and designed for best results, rather than simply taken for granted as a good idea. As the typical Rutgers bus commuter on College Avenue might tell you, it might not be worth it to divert the space for cycling lanes if might make the difference between having a dedicated, segregated bus lane or not, depending on the expected ridership for each mode.

A third simulation is a repeat of the first one, but with one important caveat; each vehicle is assumed to be fully occupied. This change doesn’t affect walking or cycling, but it demonstrates light rail’s considerable throughput advantages over buses as utilization rates increase. The single biggest category improvement, however, went to automobiles; while their initial cue is still the longest, they have a considerable reduction in relative queue distance, and now they actually beat bicycles in crossing the line. Getting five adults to share cars all the time naturally doesn’t seem realistic, but technologies like ride sharing services and self-driving capabilities may have similar effects in improving how efficiently automobiles are utilized, and thus continue to make cars more viable relative to other modes of transit in their throughput, especially as it’s demonstrated that self-driving technologies considerably improve the efficiency of queued vehicles starting from a standstill.

Illustration of a highly multi-modal urban avenue. Successful transportation planning depends on recognizing the design properties, strengths, and weaknesses of many different modes of transit, and according them space based on realistic design needs and goals in a rational, unbiased manner.

These simulations are naturally simplifications of what actually happens in transportation planning. They don’t consider variables like infrastructure costs, parking requirements, of how far people intend to travel by each mode. Nonetheless, they begin to paint a picture of how each mode of transit has particular strengths and weaknesses due to their unique properties, something that good planners recognize as part of designing integrated, multi-modal transportation networks to utilize each mode to its full potential. They also represent, especially through the last video demonstrating ideal utilization rates, how changes in the way some modes are used can have big effects on their properties, something that must be considered by planners as future technologies alter the way people travel from place to place. In order to solve difficult design problems, planners must be careful not to let their thinking calcify into rigid, inflexible design styles.