In a recent discussion at the MIT’s Mobility Initiative forum on reducing greenhouse gas emissions from transportation the question came up on why we tend to only look at the supply side, i.e. how to serve a transport need with low emissions end of pipe – instead of looking a the demand side and address the transport need as the root cause for these emissions.
This blog post is about potential means to “fix the root cause”, i.e. manage the need for passenger transportation and how this could reduce traffic, benefit physical distancing during the COVID-19 pandemic, reduce congestion, increase comfort in public transport and save costs for private and public transport providers at once.
Managing Demand for Transportation
The COVID-19 pandemic has demonstrated the magnitude of possible changes in mobility behavior. Despite its terrible impact on many lives and the global economy, it is also a one-time chance to break with some bad habits and establish more meaningful behaviors. The pandemic requires physical distancing or avoidance of crowding, which could be beneficial even after the pandemic. If we could manage to constantly utilize transit vehicles to their full capacity of seats – but not pack them much more than that, they could still operate profitably but would be much more comfortable and safe at once. Airlines are experts in this “yield management” to fill up their available seats to the max as there is no such thing as standing capacity in airplanes. If it works for airlines, it should also be possible for ground transport.
In ground transport a related approach to manage the need for transportation is Transportation Demand Management (TDM) – or a related form: Mobility Management. There is no unique definition to what TDM or mobility management means although most approaches see traffic from a systematic point of view and aim to reduce or to redistribute travel demand it in space or time in order to optimize the system. Two flavors stand out:
- A long term approach focuses on the means of urban planning and land use. In a nutshell it aims to have people living close to work, school and recreation and so reduce the need for transportation while keeping up at least the same level of accessibility. Even this might be the most powerful approach, its planning horizon with an Avoid-Shift-Improve approach is rather decades than years.
- A short term approach with operational means tries to influence user behavior directly. It aims to reduce costs, maximize accessibility and utilization of vehicles and infrastructure as well as improve user health and employee motivation. It is often applied by public transport authorities, providers or companies for their employees. We’ll have a closer look at some of its measures:
- Information aims to empower passengers to take informed decisions under the assumption that most travelers want to avoid congestion and packed vehicles – with crowding being a proxy for transit’s perceived safety. This information can range from congestion and occupancy information up to a very detailed level where platforms indicate where passengers can enter the least crowded wagons of a train. Providing information might not be sufficient in order to change long established behaviors. In order to be effective it might also require some nudging in order to be aknowledged and tested. The pandemic is a great opportunity to allow for some testing.
- Empowerment is essential for information to be effective. Even if passengers have the relevant information, they might not have the choice to change their schedules. So passengers would also need to be empowered to be flexible in their schedules. This empowerment could entail the ability to start earlier or later in the day or even work from home and not to commute at all if the nature of the work allows for it. If more people would work from home, the reduction in commutes would likely lead to a sustaining decrease in vehicle miles traveled (VTM).
A recent project has illustrated why information in combination with empowerment might work better than policy enforcement: In an advanced simulation the city of Chicago has found out that they could significantly reduce number of required school busses by shifting the start times of some schools. When they tried to implement this change they faced resistance from the pupil’s parents and had to pull back. Their simulation did not consider the many interdependencies within the families’ schedules – a complexity likely no simulation could handle. So any deterministic top-down approach will likely fail. Self-regulating systems working with empowering informed decisions are more likely to solve for the dynamics and complexity of such a system.
- Incentives can range from offering certain benefits for desired behavior (e.g. high occupancy vehicle lanes or dedicated parking for carpools or electric vehicles) to monetary incentives such as discounted tickets during off-peak times or higher “surge” prices during peak teams. A common practice in the airline and hospitality industry to manage yield is to increase prices with occupancy. In public transport this would require tariff flexibility. Surge pricing can quickly become an equality issue as often the lower income passengers don’t have that flexibility and have noch choice other than buying the more expensive tickets.
- Restrictions are the ultima ratio. They are common and accepted in the airline industry with their limited seats but can be problematic in case of public transport when there is no alternative and passengers are not empowered to change their schedules or reserve seats upfront. Introducing manadatory reservations even for commuter trains and busses is currently in discussion in several areas. In case restrictions get applied, they need to need to consider equality aspects and be transparent well in advance in order to allow passengers to adjust to it.
During the COVID-19 pandemic some bus companies asked their drivers not to let new passengers board the bus when they got overcrowded. So passengers had to wait for a random and unpredictable time until they could board a lesser crowded bus. This unpredictability made these bus routes a very unfavorable option and led to a modal shift towards cars. Once a former public transport passenger has invested in a car they will likely stick to it due to the lock-in costs – a lost customer.
Regardless of the approach, a good first step prior to defining any measures would be to get a thorough understanding of the reasons why peak traffic or other unintended patterns occur, being it due to school schedules, factory shifts or simply entrenched habits. Without a prior root cause analysis measures will unlikely be effective.
Flatten the Curve
No, this paragraph is not about flattening the coronavirus infection curve. It is about the traffic distribution over daytime, the traffic peaks, the rush hours and the benefits of equalizing the demand over the hours of operation.
Traffic distributions in cities often show two rush hour peaks: one in the morning and one in the late afternoon. This is mainly caused by passengers commuting to school or work and back home. These peaks can lead to passenger volumes up to more than 200% of the average volume during time of operation. These peaks do not only trap car drivers in congestion, they also determine the maximum transport capacity of the traffic system and so also determine a major part of its entire costs. In the extreme case of an equal distribution with no peaks at all, half of the transport capacity could do the job.
The equal distribution of rides over time of operation is of course not realistic. Most people work during daytime and factories and schools require shifts and fixed schedules to work. So the interesting question is: how much peak is needed? The pandemic gave us a clue about what is possible: comparing traffic distributions before and during the pandemic, in some cities significant differences in the shape of the curves could be observed. Independent from the fact that the overall ride volume was much lower, the timely distribution of rides have changed as well.
In this example from Brussels we can see a significant reduction of the morning peak during the pandemic (blue line), which indicates that shifting commute times could significantly reduce traffic peaks and so reduce congestion and the required maximum capacity in the morning. Unfortunately the evening peak was not reduced significantly, so that the overall maximum capacity would still be needed. If this second peak could also be flattened out by shifting some of the ride volume to earlier or later times, the overall transport capacity needed would be lower.
McKinsey has found similar peak patterns in their study on restoring public transit amid COVID-19. A more equal distribution over time would reduce peak demand and equally reduce the need for peak capacity for all modes of transport. The British “smarter choices” study estimated up to 21% potential reduction in peak traffic. Given the cost for trains and busses reducing the need for peak capacity equals to billions of pounds in potential savings. If the peak capacity would only be reduced by half of the potential reduction, the remaining capacity gains would give transit riders more space and so improve safety and comfort.
To be clear: managing the transportation demand does not mean restricting mobility. In most cases passenger transport is just the means to get to do something, which cannot be done where the passenger was located before. But if the same thing can be done close by or at a more convenient time, the previous transport option would not be needed to achieve the same – or how an MIT professor phrased it in the discussion: “nobody wants to get stuck in a traffic jam if there is no need to“.
Less or shifted passenger transport does not mean less mobility as long as the access to the desired goals is not negatively impacted. On the other hand reducing or shifting passenger transport could have tangible benefits, such as:
- Reduced infection risk: a hot topic during the pandemic. Public transport providers suffer from the passenger’s infection risk perception during the COVID-19 pandemic. For many of them ticket sales are still down by 50% after eight months into the pandemic and with the second wave approaching this will unlikely improve soon. So changing this perception and signaling safety for some of them is key for their survival on the market. In a global survey on transportation safety McKinsey identified physical distancing as the second most important mean to regain passenger trust. Managing the crowdedness on their vehicles and transparently informing about it could regain trust, ridership, and revenues from ticket sales. With winter coming in the northern hemisphere it will become more important: commuters who cycled to work during summer might switch to cars and the perceived infection risk will likely increase as soon as the windows are closed in busses and trains. Solving for more physical distance in transit will also benefit after the current pandemic as it will also reduce the risk to catch other infectious diseases like the regular flu or other nasty bugs to come.
- Comfort and convenience: another benefit of physical distance will simply be that passengers will be able to read or use electronic devices. So they can make use of their commute time, which is not the case if they have to stand too close to each other. This non-monetary benefit could also be communicated when nudging passengers to shift their commute time towards low demand times.
- Cost reduction: if peak demand can be shifted towards low demand times, the peak capacity can be reduced without losing the respective revenues. The utilization of transport capacity will be higher during low demand times and depending on the tariff system and price differentiation revenues might remain the same while saving asset costs for peak time capacity. So managing the demand can give transport service providers a competitive advantage.
Demand Management and MaaS
For Mobility-as-a-Service managing the demand can be a success factor when it comes to convincing car drivers to switch to intermodal travel chains. Switching from the coziness of a private car to an overcrowded subway might not sound very appealing to someone who values privacy and distance. But if the MaaS offer can guarantee enough space in all modes of transport e.g. via reserved seats, the offer will look much more appealing and could likely create extra revenues by charging a premium.
On the cost side every inefficiency is a disadvantage. So every peak capacity, which is underutilized during off-peak times costs money – regardless if this is additional train, bus, car, scooter or bike capacity. So taxi, ride hailing and micro mobility fleet operators would also benefit from a “flattened” demand distribution curve and improved transport efficiency. For public transport providers this could reduce the need to subsidies.
MaaS providers could support demand management by testing price differentiation based on time of the day or fleet utilization. Transit providers might also test the introduction of reservations in modes where this was not common so far, e.g. a separate class restricted to reserved seats in commuter trains.
Municipalities might play a key role in empowering commuters to shift their commute times. They might influence schools to offer certain classes at the beginning and additionally at the end of the day – and so allow pupils to either start early or end their day late without missing this class. They might also convince their own administration and large employers to change shift plans or allow their employees to work from home to have more flexibility and reduce the need for transport.
What else could be done to reduce the demand for transport or flatten the demand curve? Please share your thoughts with the community in a comment to this post!
3 thoughts on “Mobility/Transportation Demand Management”
I wonder why the focus is almost only on improving transport options with autonomous or electric cars, switching modes or sharing vehicles. Why do so few initiatives challenge the increase in traffic as such? Maybe because one cannot earn money with doing less …?
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I am thankful to this blog giving unique and helpful knowledge about this topic.
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