Rising transport infrastructure costs and the urgent need to reduce urban congestion and pollution require more sustainable and efficient transport interventions. Solutions with great potential are demand-driven mobility - also known as Mobility as a Service (MaaS) - and new green last-mile logistics solutions.
Demand and supply dynamics are key to understanding how to design such services and how to evaluate their impact on transportation systems, from the perspectives of service operators and city planners. In simpler terms, we need to predict the demand for such a service, and how this will affect the demand for other modes like private cars or public transport; we also need to predict how efficiently a service design, whether infrastructure or operations, satisfies this demand, and how the service design will affect congestion. The interdependency of these predictions makes any assessment approach highly complicated.
Traditional planning tools in the transport community are unable to capture such dynamics and their interdependencies for two reasons: first, modelling methods, usually adopted by cities’ existing tools, can’t predict how new multimodal, on-demand, digital and personalised mobility and freight logistics services may alter how and when people travel; second, they rely on traffic simulation solutions that can’t capture how those services are operated, how service fleet vehicles interact with the rest of the traffic and public transport services or how people make within-day decisions and react to, say, lack of availability or price surging.
How do we transition to a low-carbon era?
Urban and metropolitan planning authorities and service operators face a two-fold challenge: leading the necessary transition to a low-carbon era and, to do so, adopting and learning how to use new planning methods. The HARMONY project, funded by the H2020 European Commission programme, has the vision to address this exact need; and the primary means to achieve this is by developing a new flexible and user-friendly planning tool for new disruptive mobility and freight transport ecosystems: the Harmony Model Suite.
The underlying principle of this new tool’s development philosophy is to bring together, under one integrated and robust planning framework, tools that capture the new transport systems’ complex dynamics. Independent transport demand and supply simulation tools are extended with new methods that will enable simulating and evaluating system and service sustainability dimensions for new transport interventions.
Aimsun Ride: modelling demand responsive transport
In HARMONY, Aimsun enabled and facilitated the envisioned decision support tool’s development via the Aimsun Ride simulation solution. Aimsun Ride is designed to support the deployment of new mobility and last-mile logistic concepts. From e-hailing, car-share, bike sharing and autonomous on-demand mobility, to cargo bikes, micro-consolidation centres and crowd-shipping.
It is, practically, a simulation test bed for modelling and testing varying service deployment “what-if” scenarios in realistic traffic conditions. The evaluation of such scenarios is facilitated by several key performance indicators and visualisations that enable end users to monitor the performance of the service itself given fleet efficiency, customer satisfaction and network-wide performance metrics.
For example, given different demand levels, i.e. number of individuals travelling at a specific point in time from A to B with a given service, the Ride framework enables identification of a range of optimal decisions related to the design of the service and how the service should be optimally operated .
The goals may vary, e.g. reducing operating costs (or emissions), increase vehicle utilisation, reduce availability shortages, minimise travel and waiting times. Such decisions may range from infrastructure ones, like number of parking or charging stations with different capacities and locations, fleet sizing and composition (different vehicle numbers and types) as well as operational ones, such as optimal ways to assign and route vehicles to satisfy the demand or relocate them accordingly.
In the Harmony project’s context, Aimsun extended the functionalities of Aimsun Ride to address the project’s modelling requirements. At the core of the HARMONY team’s decision support tool and its transport modelling principles lies the “agent-based modelling paradigm”. In simple terms such models simulate, and eventually estimate, how (synthetic) individuals make daily decisions regarding travelling; what activities they do, how often, in what sequence, with which mode (or combination of modes) they travel to those activities, when they travel and where.
At the same time, the HARMONY team has developed several models that emulate how service operators manage the required operations for a variety of services, like demand-responsive transport (commonly referred to as e-hailing) and micro-consolidation centres with cargo bikes for last-mile deliveries.
Aimsun has thus provided an extended interoperable service fleet-traffic-public transport simulation framework that enables simulating individuals’ (passengers or cargo vehicles) trip chains, sequence of trips, with either a specific mobility service or combination of services (e.g., kiss and ride). Part of this framework is a communication interface that allowed partners from the Harmony team to “plug-in” their external service operation and passenger behaviour models (i.e. not endogenous components of Aimsun Ride).
The integrated simulation-based planning framework described above and including Aimsun Ride has been applied for the evaluation of two different mobility and last-mile logistic scenarios, evaluated mainly from the lens of the relevant planning authorities. The focus points for impact evaluation were metrics related to network performance and emissions drawn from potential service applications with different configurations as well the efficiency of the service themselves.
How can we plan last-mile freight logistics?
The simulation scenarios and corresponding results reported below are related to a new last-mile freight logistics scheme planning study for Rotterdam, Netherlands, conducted by the University of Wolverhampton. The investigated intervention is, specifically, the deployment of microhubs and cargo bikes for urban delivery services. This new scheme is investigated, in accordance with the Zero Emissions City Logistics roadmap for the city of Rotterdam – a roadmap put in place towards reducing all greenhouse emissions by 49% in 2030 and 95% by 2050. The model scope and service deployment area that has been defined for the simulation scenario – supported and advised by the Rotterdam City Council – includes the centre of the city.
Two simulation scenarios are designed and compared. The baseline scenario represents the case of using several consolidation centres (or depots) positioned in the Rotterdam area – outside the Rotterdam city centre - for parcels’ delivery in the Rotterdam centre by means of conventionally fuelled vans. The second is a cargo-bike scenario; it represents the case of using several micro-hubs positioned inside the city centre for parcel delivery (the same demand as with the baseline scenario) by means of cargo bikes (inside micro-hub zones only).
This use case is designed to investigate the effectiveness and efficiency of considering and using such micro hubs and cargo bikes for parcel deliveries, replacing depot to customer van trips in Rotterdam city centre with a more environmentally friendly form of transportation. For the cargo-bike scenario, 12 microhubs have been considered in potential locations indicated by the city’s planning entity.
Simulation results indicate that the number of deliveries accomplished within the Rotterdam city centre area by using micro hubs for parcel consolidation and cargo bikes for last-mile transport has the potential to represent about 30% of the total number of deliveries performed in the area by vans in the baseline scenario. Therefore, a 30% reduction of conventionally fuelled van trips and their substitution by a zero-emission active transport mode, i.e., cargo bikes indicated the potential of such a scheme to reduce emissions in Rotterdam quite significantly.
Decarbonising transportation: the way forward
Simulation-based intervention analyses and assessments can be key tools for transportation planners and service operators in their efforts to decarbonize transportation. More robust and reliable insights into behavioral and operational system and service dynamics in the presence of such new interventions can inform evidence-based policy making, speed up sustainable decision-making, and ensure large investments have positive and accurately predicted effects on transport systems and people’s quality of life.
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