Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

Abstract

New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.

Bridging the Gap Between Mesoscopic Transport Planning and Microscopic Traffic Simulation: An Analytical and Numerical Analysis of Traffic Dynamics

“…This procedure, however, proved not useful as MATSim discards some information SUMO, by contrast, requires. For more information on the demand extraction and agent cloning procedure, refer to Triebke et al (1,31). For the sake of simplicity, we ignore SAEV fleets at this stage and focus entirely on the network-wide traffic dynamics.…”

“…In a 10 % run, for example, each simulated vehicle gets the weight of 10 cars, thus occupying a net-space of 75 m on the network (instead of 75 m in a 100 % scenario). To preserve traffic dynamics, the flow and storage capacities are adjusted accordingly and multiplied with 0 . 1 ( 1 , 13 ). Moreover, to allow for heterogeneous traffic modeling, MATSim’s queue sorting order can be changed from the first-in-first-out principle (FIFO) to a passing mode where all vehicles are sorted to their earliest link exit-time ( 23 ).…”

“…From here, we extracted a medium-sized inner-city area (as displayed in Figure 3) and transferred the road-bound travel demand to a corresponding SUMO model whose network was imported from OSM data. As stated in previous work ( 1 ), SUMO networks can also be imported from MATSim. This procedure, however, proved not useful as MATSim discards some information SUMO, by contrast, requires.…”

“…The overall calibration procedure is described in more detail by Ziemke et al ( 30 ). The SUMO model, by contrast, was calibrated against the above-mentioned MATSim model with regard to travel times by altering demand-, routing-, and congestion-influencing measures, as discussed in previous work ( 31 ). In doing so, we ensured that future SAEV trips from multi-modal MATSim simulation hold true in a subsequent SUMO simulation where fleet vehicles reach their appointed customers and charging stations in time.…”

“…Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving profiles are crucial for requirement engineering. As already stated in previous work ( 1 ), the driving profiles need to satisfy several prerequisites. Above all, they need to mirror the fleet vehicles’ movement throughout entire metropolitan areas for 24 h, accounting for all range and charging constraints as well as for different routing and dispatching strategies.…”