Urban Simulation Tech Models Effects of Shared Mobility in Reducing Congestion
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Shared public space in the urban built environment is a scarce commodity. Increasing the capacity of motorway infrastructure is actually increasing vehicle traffic congestion. As it goes in the Fordist city allegory, more highways means more private car ownership, since supply of the infrastructure drives the demand for cars. Future proof cities are largely not replicating traditional mobility infrastructure. The sustainable methodologies of city planning require less car dependent traffic, and emphasis on allocating space for human scale activities, like walking and biking. Next generation cities share an interest in shared on-demand mobility alternatives for reducing congestion and increasing the quality of life in the dense build environments.
There are many factors that people take into consideration when deciding whether to use their private car or shared mobility services.
- Health conditions
- Purchasing power
- Transfer options of the service
- Wait time and delays
- Vehicle capacity
- Public policies regulate parking
- Congestion fees
On top of these, there are future decision points to consider, such as how far from home one might be at the end of the day, which further incentivize private vehicle use.
Planning for new, shared modes of transit that will rival private vehicles in access and convenience requires a paradigm shift in the planning process. Rather than using traditional methods, we need to capture individual behavior while interacting with the systems in questions. An increasing number of studies show that combining agent-based simulation with activity-based travel demand modeling is a good approach. This approach creates a digital twin of the population of the city, with similar characteristics as their real-world counterparts. These synthetic individuals have activities to perform through the course of the day, and need to make mobility decisions to travel between activity locations. The entire transportation infrastructure of the city is replicated on a virtual platform that simulates real life scenarios. If individual behavior and the governing laws of the digital reality are accurately reproduced, large-scale mobility demand emerges from the bottom-up, reflecting the real-world incidences.
The planner now has a sandbox for experimentation; a shared service can be made available and tailored to agents’ needs, and real-world operational problems can be anticipated long before any purchasing decision is made. More importantly, the model can capture emergent, large-scale phenomena arising out of interactions between the agents that are impossible to intuit ahead of time.
This approach makes it possible to evaluate shared-mobility systems in terms of the various considerations of the traveler (transport cost, satisfaction, and service quality), the provider (operational cost, incomes, and fleet configuration), and the city (energy consumption, emissions, transfer points, network demand satisfaction, and congestion).
At GeoTwin, our team of researchers and developers specializing in agent-based simulation and shared mobility systems has developed a platform that will allow mobility, city planning, energy, infrastructure and built environment industries to perform better project feasibility studies.
GeoTwin has completed multiple shared mobility projects in the Paris Region. In 2020 we conducted a feasibility study of an autonomous and on-demand carpooling service for Paris Olympics Games 2024. Besides the operational efficiency decision-making enablers, GeoTwin provided operational efficiency analysis, as well as some what-if analyses to help local authorities better understand the carpooling service’s impact on congestion and on CO2 emissions.
In August 2020, before joining GeoTwin the following month, Dr. Pieter Fourie concluded a 3 year study for Singaporean authorities to investigate the feasibility of large-scale deployment of shared autonomous vehicles as ‘transit-on-demand’, as a complementary mode to scheduled transit services (note acknowledgments and disclaimer). Then affiliated with ETH Zurich Future Cities Lab, his project group collaborated with researchers from MIT SMART-FM and NUS. The study was conducted for a future population of the entire city island, based on travel surveys, data on points of interest, and a survey on potential public uptake of the new mode. Their study provided exploratory insights to inform future policy planning and transit investment.
This study provides a glimpse of how flexible services can provide transit accessibility deep inside car-dominant neighborhoods. Eliminating the need for a private car for the first trip of the day removes that vehicle and the congestion it creates for the whole day. The agent-based model has shown that leaving your car at home in the morning means that you won’t use it later in the day, since you’d have to return home to get it. Importantly, commuters generally travel to areas of high transit accessibility, like the city centre, which decreases the need for a car for discretionary trips throughout the day. Consequently, the entire transportation system sees an increase in travel efficiency, both for car owners and non-owners (Figure 1).
The study investigated several practical aspects of deploying these services, which will ultimately determine its feasibility at scale. These include optimizing fleet size and deployment, understanding fleet mix, parking strategy, and drop-off bay size, in order to understand the system from both user and operator perspectives.
Simulation reveals the trade-offs and changes that planners can make at an operational level to adapt shared services to nearly any context. Fill larger on-demand vehicles by incentivizing riders to use certain modes and locations. For instance, in high density areas, make the on-demand transit service cheaper from a subset of transit stops, thus, incentivizing common origins and destinations. In low density neighborhoods, allow pre-booking for regular commutes times at lower cost. This helps bring people to that critical first decision to abandon the car for the day. Pre-booking these predictable trips allows time to optimize the allocation of vehicles, guaranteeing good user experience. They also allow the operator to place the same individuals together every day across the entire trip chain, a crucial step in minimizing contact network size during the pandemic.
Integrating on-demand and scheduled services produces more positive domino effects. An on-demand bus makes every bus stop equally accessible from every other stop, eliminating detours to transfer, and long walks for commuters to more distant stops to make their required connection. Eliminating a transfer from a transit trip makes that ride available to someone else, while reducing the delay time of the bus because one less commuter has to board and alight. On-demand transit can choose less crowded stops for transfers to or from scheduled services, thus re-balancing the whole system. Care has to be taken that this kind of service does not cannibalize rail, as commuters may opt for the new, higher surface-level access of this type system.
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